Compare commits
8 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
|
7655f11076 | ||
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6b87918441 | ||
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54f9d62c61 | ||
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22d3156881 | ||
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9bb3d33a46 | ||
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a05400e020 | ||
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10083244a3 | ||
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11144a3048 |
@@ -65,7 +65,7 @@ jobs:
|
||||
run_tests_torch_and_tf:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
RUN_PT_TF_CROSS_TESTS: yes
|
||||
@@ -81,9 +81,7 @@ jobs:
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece,torch-speech,vision]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
|
||||
- run: pip install tensorflow_probability
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -103,7 +101,7 @@ jobs:
|
||||
run_tests_torch_and_tf_all:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
- image: circleci/python:3.6
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
RUN_PT_TF_CROSS_TESTS: yes
|
||||
@@ -119,9 +117,7 @@ jobs:
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,tf-cpu,torch,testing,sentencepiece,torch-speech,vision]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
|
||||
- run: pip install tensorflow_probability
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -152,8 +148,7 @@ jobs:
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,flax,torch,testing,sentencepiece,torch-speech,vision]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -189,8 +184,7 @@ jobs:
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,flax,torch,testing,sentencepiece,torch-speech,vision]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -220,8 +214,7 @@ jobs:
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-torch-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -256,8 +249,7 @@ jobs:
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-torch-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -284,11 +276,8 @@ jobs:
|
||||
keys:
|
||||
- v0.4-tf-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]
|
||||
- run: pip install tensorflow_probability
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech]
|
||||
- save_cache:
|
||||
key: v0.4-tf-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -320,11 +309,8 @@ jobs:
|
||||
keys:
|
||||
- v0.4-tf-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]
|
||||
- run: pip install tensorflow_probability
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech]
|
||||
- save_cache:
|
||||
key: v0.4-tf-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -351,10 +337,8 @@ jobs:
|
||||
keys:
|
||||
- v0.4-flax-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[flax,testing,sentencepiece,flax-speech,vision]
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: sudo pip install .[flax,testing,sentencepiece,flax-speech,vision]
|
||||
- save_cache:
|
||||
key: v0.4-flax-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -386,10 +370,8 @@ jobs:
|
||||
keys:
|
||||
- v0.4-flax-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[flax,testing,sentencepiece,vision,flax-speech]
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: sudo pip install .[flax,testing,sentencepiece,vision,flax-speech]
|
||||
- save_cache:
|
||||
key: v0.4-flax-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -419,9 +401,8 @@ jobs:
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-torch-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -456,9 +437,8 @@ jobs:
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.10.0+cpu.html
|
||||
- run: pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
- run: pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision]
|
||||
- run: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
|
||||
- save_cache:
|
||||
key: v0.4-torch-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -488,7 +468,6 @@ jobs:
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
|
||||
- run: pip install tensorflow_probability
|
||||
- save_cache:
|
||||
key: v0.4-tf-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -523,7 +502,6 @@ jobs:
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[sklearn,tf-cpu,testing,sentencepiece]
|
||||
- run: pip install tensorflow_probability
|
||||
- save_cache:
|
||||
key: v0.4-tf-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
@@ -598,7 +576,7 @@ jobs:
|
||||
path: ~/transformers/examples_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
|
||||
run_examples_torch_all:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
@@ -629,69 +607,6 @@ jobs:
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_examples_flax:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
TRANSFORMERS_IS_CI: yes
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-flax_examples-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: sudo pip install .[flax,testing,sentencepiece]
|
||||
- run: pip install -r examples/flax/_tests_requirements.txt
|
||||
- save_cache:
|
||||
key: v0.4-flax_examples-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python utils/tests_fetcher.py --filters examples tests | tee test_preparation.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/test_preparation.txt
|
||||
- run: |
|
||||
if [ -f test_list.txt ]; then
|
||||
python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_flax ./examples/flax/ | tee tests_output.txt
|
||||
fi
|
||||
- store_artifacts:
|
||||
path: ~/transformers/flax_examples_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_examples_flax_all:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.7
|
||||
environment:
|
||||
OMP_NUM_THREADS: 1
|
||||
TRANSFORMERS_IS_CI: yes
|
||||
resource_class: xlarge
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-flax_examples-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: sudo pip install .[flax,testing,sentencepiece]
|
||||
- run: pip install -r examples/flax/_tests_requirements.txt
|
||||
- save_cache:
|
||||
key: v0.4-flax_examples-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: |
|
||||
TRANSFORMERS_IS_CI=1 python -m pytest -n 8 --dist=loadfile -s --make-reports=examples_flax ./examples/flax/ | tee examples_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/flax_examples_output.txt
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
run_tests_hub:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
@@ -824,6 +739,51 @@ jobs:
|
||||
- store_artifacts:
|
||||
path: ~/transformers/reports
|
||||
|
||||
build_doc:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
resource_class: large
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-build_doc-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install ."[docs]"
|
||||
- save_cache:
|
||||
key: v0.4-build_doc-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: cd docs && make html SPHINXOPTS="-W -j 4"
|
||||
- store_artifacts:
|
||||
path: ./docs/_build
|
||||
|
||||
deploy_doc:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
resource_class: large
|
||||
steps:
|
||||
- add_ssh_keys:
|
||||
fingerprints:
|
||||
- "5b:7a:95:18:07:8c:aa:76:4c:60:35:88:ad:60:56:71"
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-deploy_doc-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install ."[docs]"
|
||||
- save_cache:
|
||||
key: v0.4-deploy_doc-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: ./.circleci/deploy.sh
|
||||
|
||||
check_code_quality:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
@@ -839,6 +799,7 @@ jobs:
|
||||
- v0.4-code_quality-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install isort GitPython
|
||||
- run: pip install .[all,quality]
|
||||
- save_cache:
|
||||
key: v0.4-code_quality-{{ checksum "setup.py" }}
|
||||
@@ -849,27 +810,6 @@ jobs:
|
||||
- run: python utils/custom_init_isort.py --check_only
|
||||
- run: flake8 examples tests src utils
|
||||
- run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
|
||||
|
||||
check_repository_consistency:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
resource_class: large
|
||||
environment:
|
||||
TRANSFORMERS_IS_CI: yes
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- restore_cache:
|
||||
keys:
|
||||
- v0.4-repository_consistency-{{ checksum "setup.py" }}
|
||||
- v0.4-{{ checksum "setup.py" }}
|
||||
- run: pip install --upgrade pip
|
||||
- run: pip install .[all,quality]
|
||||
- save_cache:
|
||||
key: v0.4-repository_consistency-{{ checksum "setup.py" }}
|
||||
paths:
|
||||
- '~/.cache/pip'
|
||||
- run: python utils/check_copies.py
|
||||
- run: python utils/check_table.py
|
||||
- run: python utils/check_dummies.py
|
||||
@@ -878,6 +818,17 @@ jobs:
|
||||
- run: make deps_table_check_updated
|
||||
- run: python utils/tests_fetcher.py --sanity_check
|
||||
|
||||
check_repository_consistency:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
- image: circleci/python:3.6
|
||||
resource_class: small
|
||||
parallelism: 1
|
||||
steps:
|
||||
- checkout
|
||||
- run: pip install requests
|
||||
- run: python ./utils/link_tester.py
|
||||
|
||||
run_tests_layoutlmv2:
|
||||
working_directory: ~/transformers
|
||||
docker:
|
||||
@@ -959,7 +910,6 @@ workflows:
|
||||
- check_code_quality
|
||||
- check_repository_consistency
|
||||
- run_examples_torch
|
||||
- run_examples_flax
|
||||
- run_tests_custom_tokenizers
|
||||
- run_tests_torch_and_tf
|
||||
- run_tests_torch_and_flax
|
||||
@@ -970,7 +920,9 @@ workflows:
|
||||
- run_tests_pipelines_tf
|
||||
- run_tests_onnxruntime
|
||||
- run_tests_hub
|
||||
- build_doc
|
||||
- run_tests_layoutlmv2
|
||||
- deploy_doc: *workflow_filters
|
||||
nightly:
|
||||
triggers:
|
||||
- schedule:
|
||||
@@ -981,7 +933,6 @@ workflows:
|
||||
- master
|
||||
jobs:
|
||||
- run_examples_torch_all
|
||||
- run_examples_flax_all
|
||||
- run_tests_torch_and_tf_all
|
||||
- run_tests_torch_and_flax_all
|
||||
- run_tests_torch_all
|
||||
|
||||
75
.circleci/deploy.sh
Executable file
75
.circleci/deploy.sh
Executable file
@@ -0,0 +1,75 @@
|
||||
cd docs
|
||||
|
||||
function deploy_doc(){
|
||||
echo "Creating doc at commit $1 and pushing to folder $2"
|
||||
git checkout $1
|
||||
pip install -U ..
|
||||
if [ ! -z "$2" ]
|
||||
then
|
||||
if [ "$2" == "master" ]; then
|
||||
echo "Pushing master"
|
||||
make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir/$2/
|
||||
cp -r _build/html/_static .
|
||||
elif ssh -oStrictHostKeyChecking=no $doc "[ -d $dir/$2 ]"; then
|
||||
echo "Directory" $2 "already exists"
|
||||
scp -r -oStrictHostKeyChecking=no _static/* $doc:$dir/$2/_static/
|
||||
else
|
||||
echo "Pushing version" $2
|
||||
make clean && make html
|
||||
rm -rf _build/html/_static
|
||||
cp -r _static _build/html
|
||||
scp -r -oStrictHostKeyChecking=no _build/html $doc:$dir/$2
|
||||
fi
|
||||
else
|
||||
echo "Pushing stable"
|
||||
make clean && make html
|
||||
rm -rf _build/html/_static
|
||||
cp -r _static _build/html
|
||||
scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
|
||||
fi
|
||||
}
|
||||
|
||||
# You can find the commit for each tag on https://github.com/huggingface/transformers/tags
|
||||
deploy_doc "master" master
|
||||
deploy_doc "b33a385" v1.0.0
|
||||
deploy_doc "fe02e45" v1.1.0
|
||||
deploy_doc "89fd345" v1.2.0
|
||||
deploy_doc "fc9faa8" v2.0.0
|
||||
deploy_doc "3ddce1d" v2.1.1
|
||||
deploy_doc "3616209" v2.2.0
|
||||
deploy_doc "d0f8b9a" v2.3.0
|
||||
deploy_doc "6664ea9" v2.4.0
|
||||
deploy_doc "fb560dc" v2.5.0
|
||||
deploy_doc "b90745c" v2.5.1
|
||||
deploy_doc "fbc5bf1" v2.6.0
|
||||
deploy_doc "6f5a12a" v2.7.0
|
||||
deploy_doc "11c3257" v2.8.0
|
||||
deploy_doc "e7cfc1a" v2.9.0
|
||||
deploy_doc "7cb203f" v2.9.1
|
||||
deploy_doc "10d7239" v2.10.0
|
||||
deploy_doc "b42586e" v2.11.0
|
||||
deploy_doc "7fb8bdf" v3.0.2
|
||||
deploy_doc "4b3ee9c" v3.1.0
|
||||
deploy_doc "3ebb1b3" v3.2.0
|
||||
deploy_doc "0613f05" v3.3.1
|
||||
deploy_doc "eb0e0ce" v3.4.0
|
||||
deploy_doc "818878d" v3.5.1
|
||||
deploy_doc "c781171" v4.0.1
|
||||
deploy_doc "bfa4ccf" v4.1.1
|
||||
deploy_doc "7d9a9d0" v4.2.2
|
||||
deploy_doc "bae0c79" v4.3.3
|
||||
deploy_doc "c988db5" v4.4.0
|
||||
deploy_doc "c5d6a28" v4.4.1
|
||||
deploy_doc "6bc89ed" v4.4.2
|
||||
deploy_doc "4906a29" v4.5.0
|
||||
deploy_doc "4bae96e" v4.5.1
|
||||
deploy_doc "25dee4a" v4.6.0
|
||||
deploy_doc "7a6c9fa" v4.7.0
|
||||
deploy_doc "9252a51" v4.8.0
|
||||
deploy_doc "1366172" v4.8.1
|
||||
deploy_doc "96d1cfb" v4.8.2
|
||||
deploy_doc "72aee83" v4.9.0
|
||||
deploy_doc "bff1c71" v4.9.1
|
||||
deploy_doc "41981a2" v4.9.2
|
||||
deploy_doc "39cb6f5" v4.10.0
|
||||
deploy_doc "28e2787" # v4.10.1 Latest stable release
|
||||
44
.github/ISSUE_TEMPLATE/bug-report.md
vendored
44
.github/ISSUE_TEMPLATE/bug-report.md
vendored
@@ -27,39 +27,30 @@ assignees: ''
|
||||
|
||||
Models:
|
||||
|
||||
- ALBERT, BERT, XLM, DeBERTa, DeBERTa-v2, ELECTRA, MobileBert, SqueezeBert: @LysandreJik
|
||||
- T5, BART, Marian, Pegasus, EncoderDecoder: @patrickvonplaten
|
||||
- Blenderbot, MBART: @patil-suraj
|
||||
- Longformer, Reformer, TransfoXL, XLNet, FNet, BigBird: @patrickvonplaten
|
||||
- FSMT: @stas00
|
||||
- Funnel: @sgugger
|
||||
- GPT-2, GPT: @patrickvonplaten, @LysandreJik
|
||||
- RAG, DPR: @patrickvonplaten, @lhoestq
|
||||
- TensorFlow: @Rocketknight1
|
||||
- JAX/Flax: @patil-suraj
|
||||
- TAPAS, LayoutLM, LayoutLMv2, LUKE, ViT, BEiT, DEiT, DETR, CANINE: @NielsRogge
|
||||
- GPT-Neo, GPT-J, CLIP: @patil-suraj
|
||||
- Wav2Vec2, HuBERT, SpeechEncoderDecoder, UniSpeech, UniSpeechSAT, SEW, SEW-D, Speech2Text: @patrickvonplaten, @anton-l
|
||||
|
||||
If the model isn't in the list, ping @LysandreJik who will redirect you to the correct contributor.
|
||||
- albert, bert, xlm: @LysandreJik
|
||||
- blenderbot, bart, marian, pegasus, encoderdecoder, t5: @patrickvonplaten, @patil-suraj
|
||||
- longformer, reformer, transfoxl, xlnet: @patrickvonplaten
|
||||
- fsmt: @stas00
|
||||
- funnel: @sgugger
|
||||
- gpt2: @patrickvonplaten, @LysandreJik
|
||||
- rag: @patrickvonplaten, @lhoestq
|
||||
- tensorflow: @Rocketknight1
|
||||
|
||||
Library:
|
||||
|
||||
- Benchmarks: @patrickvonplaten
|
||||
- Deepspeed: @stas00
|
||||
- Ray/raytune: @richardliaw, @amogkam
|
||||
- Text generation: @patrickvonplaten @narsil
|
||||
- Tokenizers: @LysandreJik
|
||||
- Trainer: @sgugger
|
||||
- Pipelines: @Narsil
|
||||
- Speech: @patrickvonplaten, @anton-l
|
||||
- Vision: @NielsRogge, @sgugger
|
||||
- benchmarks: @patrickvonplaten
|
||||
- deepspeed: @stas00
|
||||
- ray/raytune: @richardliaw, @amogkam
|
||||
- text generation: @patrickvonplaten
|
||||
- tokenizers: @LysandreJik
|
||||
- trainer: @sgugger
|
||||
- pipelines: @LysandreJik
|
||||
|
||||
Documentation: @sgugger
|
||||
|
||||
Model hub:
|
||||
|
||||
- for issues with a model, report at https://discuss.huggingface.co/ and tag the model's creator.
|
||||
- for issues with a model report at https://discuss.huggingface.co/ and tag the model's creator.
|
||||
|
||||
HF projects:
|
||||
|
||||
@@ -69,9 +60,6 @@ HF projects:
|
||||
Examples:
|
||||
|
||||
- maintained examples (not research project or legacy): @sgugger, @patil-suraj
|
||||
|
||||
For research projetcs, please ping the contributor directly. For example, on the following projects:
|
||||
|
||||
- research_projects/bert-loses-patience: @JetRunner
|
||||
- research_projects/distillation: @VictorSanh
|
||||
|
||||
|
||||
50
.github/workflows/build_doc_test.yml
vendored
50
.github/workflows/build_doc_test.yml
vendored
@@ -1,50 +0,0 @@
|
||||
name: Documentation test build
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
paths:
|
||||
- "src/**"
|
||||
- "docs/**"
|
||||
- ".github/**"
|
||||
|
||||
jobs:
|
||||
build_and_package:
|
||||
runs-on: ubuntu-latest
|
||||
defaults:
|
||||
run:
|
||||
shell: bash -l {0}
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
|
||||
- name: Loading cache.
|
||||
uses: actions/cache@v2
|
||||
id: cache
|
||||
with:
|
||||
path: ~/.cache/pip
|
||||
key: v1-test_build_doc
|
||||
restore-keys: |
|
||||
v1-test_build_doc-${{ hashFiles('setup.py') }}
|
||||
v1-test_build_doc
|
||||
|
||||
- name: Setup environment
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
|
||||
pip install git+https://github.com/huggingface/doc-builder
|
||||
pip install .[dev]
|
||||
|
||||
export TORCH_VERSION=$(python -c "from torch import version; print(version.__version__.split('+')[0])")
|
||||
pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH_VERSION}+cpu.html
|
||||
|
||||
pip install torchvision
|
||||
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
|
||||
|
||||
sudo apt install tesseract-ocr
|
||||
pip install pytesseract
|
||||
pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
|
||||
|
||||
- name: Make documentation
|
||||
run: |
|
||||
doc-builder build transformers ./docs/source
|
||||
99
.github/workflows/build_documentation.yml
vendored
99
.github/workflows/build_documentation.yml
vendored
@@ -1,99 +0,0 @@
|
||||
name: Build documentation
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
- doc-builder*
|
||||
|
||||
jobs:
|
||||
build_and_package:
|
||||
runs-on: ubuntu-latest
|
||||
defaults:
|
||||
run:
|
||||
shell: bash -l {0}
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
with:
|
||||
repository: 'huggingface/doc-builder'
|
||||
path: doc-builder
|
||||
token: ${{ secrets.HUGGINGFACE_PUSH }}
|
||||
|
||||
- uses: actions/checkout@v2
|
||||
with:
|
||||
repository: 'huggingface/transformers'
|
||||
path: transformers
|
||||
|
||||
- uses: actions/checkout@v2
|
||||
with:
|
||||
repository: 'huggingface/notebooks'
|
||||
path: notebooks
|
||||
token: ${{ secrets.HUGGINGFACE_PUSH }}
|
||||
|
||||
- name: Loading cache.
|
||||
uses: actions/cache@v2
|
||||
id: cache
|
||||
with:
|
||||
path: ~/.cache/pip
|
||||
key: v1-test_build_doc
|
||||
restore-keys: |
|
||||
v1-test_build_doc-${{ hashFiles('setup.py') }}
|
||||
v1-test_build_doc
|
||||
|
||||
- name: Setup environment
|
||||
run: |
|
||||
sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev
|
||||
|
||||
pip install git+https://github.com/huggingface/doc-builder
|
||||
pip install git+https://github.com/huggingface/transformers#egg=transformers[dev]
|
||||
|
||||
export TORCH_VERSION=$(python -c "from torch import version; print(version.__version__.split('+')[0])")
|
||||
pip install torch-scatter -f https://data.pyg.org/whl/torch-${TORCH_VERSION}+cpu.html
|
||||
|
||||
pip install torchvision
|
||||
python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
|
||||
|
||||
sudo apt install tesseract-ocr
|
||||
pip install pytesseract
|
||||
pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
|
||||
- name: Setup git
|
||||
run: |
|
||||
git config --global user.name "Hugging Face"
|
||||
git config --global user.email transformers@huggingface.co
|
||||
|
||||
cd doc-builder
|
||||
git pull origin main
|
||||
cd ..
|
||||
|
||||
cd notebooks
|
||||
git pull origin master
|
||||
cd ..
|
||||
|
||||
- name: Make documentation
|
||||
run: |
|
||||
doc-builder build transformers transformers/docs/source --build_dir doc-builder/build --notebook_dir notebooks/transformers_doc --clean
|
||||
|
||||
- name: Push to repositories
|
||||
run: |
|
||||
cd doc-builder
|
||||
if [[ `git status --porcelain` ]]; then
|
||||
git add build
|
||||
git commit -m "Updated with commit ${{ github.sha }}"
|
||||
git push origin main
|
||||
else
|
||||
echo "No diff in the documentation."
|
||||
fi
|
||||
cd ..
|
||||
|
||||
cd notebooks
|
||||
if [[ `git status --porcelain` ]]; then
|
||||
git add transformers_doc
|
||||
git commit -m "Updated Transformer doc notebooks with commit ${{ github.sha }}"
|
||||
git push origin master
|
||||
else
|
||||
echo "No diff in the notebooks."
|
||||
fi
|
||||
cd ..
|
||||
2
.github/workflows/model-templates.yml
vendored
2
.github/workflows/model-templates.yml
vendored
@@ -36,7 +36,7 @@ jobs:
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip install --upgrade pip!=21.3
|
||||
pip install --upgrade pip
|
||||
sudo apt -y update && sudo apt install -y libsndfile1-dev
|
||||
pip install .[dev]
|
||||
- name: Create model files
|
||||
|
||||
39
.github/workflows/self-nightly-scheduled.yml
vendored
39
.github/workflows/self-nightly-scheduled.yml
vendored
@@ -21,7 +21,7 @@ jobs:
|
||||
run_all_tests_torch_gpu:
|
||||
runs-on: [self-hosted, docker-gpu, single-gpu]
|
||||
container:
|
||||
image: pytorch/pytorch:1.10.0-cuda11.3-cudnn8-runtime
|
||||
image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
|
||||
options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
|
||||
steps:
|
||||
- name: Launcher docker
|
||||
@@ -36,11 +36,14 @@ jobs:
|
||||
apt -y update && apt install -y libsndfile1-dev git
|
||||
pip install --upgrade pip
|
||||
pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
|
||||
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
|
||||
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
run: |
|
||||
@@ -87,7 +90,7 @@ jobs:
|
||||
run_all_tests_torch_multi_gpu:
|
||||
runs-on: [self-hosted, docker-gpu, multi-gpu]
|
||||
container:
|
||||
image: pytorch/pytorch:1.10.0-cuda11.3-cudnn8-runtime
|
||||
image: pytorch/pytorch:1.9.0-cuda11.1-cudnn8-runtime
|
||||
options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
|
||||
steps:
|
||||
- name: Launcher docker
|
||||
@@ -103,11 +106,14 @@ jobs:
|
||||
apt -y update && apt install -y libsndfile1-dev git
|
||||
pip install --upgrade pip
|
||||
pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
|
||||
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
|
||||
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
env:
|
||||
@@ -154,13 +160,16 @@ jobs:
|
||||
run: |
|
||||
apt -y update && apt install -y libaio-dev
|
||||
pip install --upgrade pip
|
||||
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
|
||||
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
|
||||
pip install .[testing,deepspeed]
|
||||
pip install git+https://github.com/microsoft/DeepSpeed
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
run: |
|
||||
@@ -195,19 +204,21 @@ jobs:
|
||||
run: |
|
||||
apt -y update && apt install -y libaio-dev
|
||||
pip install --upgrade pip
|
||||
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
|
||||
rm -rf ~/.cache/torch_extensions/ # shared between conflicting builds
|
||||
pip install .[testing,fairscale]
|
||||
pip install git+https://github.com/microsoft/DeepSpeed # testing bleeding edge
|
||||
pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html -U
|
||||
pip install .[testing,deepspeed,fairscale]
|
||||
pip install git+https://github.com/microsoft/DeepSpeed
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
run: |
|
||||
python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_cuda_extensions_multi_gpu tests/deepspeed tests/extended
|
||||
|
||||
|
||||
- name: Failure short reports
|
||||
if: ${{ always() }}
|
||||
run: cat reports/tests_torch_cuda_extensions_multi_gpu_failures_short.txt
|
||||
|
||||
38
.github/workflows/self-push.yml
vendored
38
.github/workflows/self-push.yml
vendored
@@ -34,7 +34,6 @@ jobs:
|
||||
apt install -y libsndfile1-dev
|
||||
pip install --upgrade pip
|
||||
pip install .[sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
|
||||
- name: Launcher docker
|
||||
uses: actions/checkout@v2
|
||||
@@ -47,8 +46,11 @@ jobs:
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Fetch the tests to run
|
||||
run: |
|
||||
python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
|
||||
@@ -88,7 +90,6 @@ jobs:
|
||||
pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
|
||||
pip install --upgrade pip
|
||||
pip install .[sklearn,testing,sentencepiece,flax,flax-speech,vision]
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
|
||||
- name: Launcher docker
|
||||
uses: actions/checkout@v2
|
||||
@@ -104,7 +105,7 @@ jobs:
|
||||
run: |
|
||||
python -c "from jax.lib import xla_bridge; print('GPU available:', xla_bridge.get_backend().platform)"
|
||||
python -c "import jax; print('Number of GPUs available:', len(jax.local_devices()))"
|
||||
|
||||
|
||||
- name: Fetch the tests to run
|
||||
run: |
|
||||
python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
|
||||
@@ -144,7 +145,6 @@ jobs:
|
||||
# apt -y update && apt install -y software-properties-common && apt -y update && add-apt-repository -y ppa:git-core/ppa && apt -y update && apt install -y git
|
||||
# pip install --upgrade pip
|
||||
# pip install .[sklearn,testing,onnxruntime,sentencepiece,tf-speech]
|
||||
# pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
#
|
||||
# - name: Launcher docker
|
||||
# uses: actions/checkout@v2
|
||||
@@ -203,7 +203,7 @@ jobs:
|
||||
apt install -y libsndfile1-dev
|
||||
pip install --upgrade pip
|
||||
pip install .[sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
|
||||
- name: Launcher docker
|
||||
uses: actions/checkout@v2
|
||||
with:
|
||||
@@ -216,7 +216,10 @@ jobs:
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Fetch the tests to run
|
||||
run: |
|
||||
@@ -259,7 +262,6 @@ jobs:
|
||||
# pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
|
||||
# pip install --upgrade pip
|
||||
# pip install .[sklearn,testing,sentencepiece,flax,flax-speech,vision]
|
||||
# pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
#
|
||||
# - name: Launcher docker
|
||||
# uses: actions/checkout@v2
|
||||
@@ -275,7 +277,7 @@ jobs:
|
||||
# run: |
|
||||
# python -c "from jax.lib import xla_bridge; print('GPU available:', xla_bridge.get_backend().platform)"
|
||||
# python -c "import jax; print('Number of GPUs available:', len(jax.local_devices()))"
|
||||
#
|
||||
#
|
||||
# - name: Fetch the tests to run
|
||||
# run: |
|
||||
# python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
|
||||
@@ -315,7 +317,6 @@ jobs:
|
||||
# apt -y update && apt install -y software-properties-common && apt -y update && add-apt-repository -y ppa:git-core/ppa && apt -y update && apt install -y git
|
||||
# pip install --upgrade pip
|
||||
# pip install .[sklearn,testing,onnxruntime,sentencepiece,tf-speech]
|
||||
# pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
#
|
||||
# - name: Launcher docker
|
||||
# uses: actions/checkout@v2
|
||||
@@ -384,12 +385,15 @@ jobs:
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Fetch the tests to run
|
||||
run: |
|
||||
python utils/tests_fetcher.py --diff_with_last_commit --filters tests/deepspeed tests/extended | tee test_preparation.txt
|
||||
|
||||
|
||||
- name: Report fetched tests
|
||||
uses: actions/upload-artifact@v2
|
||||
with:
|
||||
@@ -433,12 +437,14 @@ jobs:
|
||||
run: |
|
||||
apt -y update && apt install -y libaio-dev
|
||||
pip install --upgrade pip
|
||||
rm -rf ~/.cache/torch_extensions/ # shared between conflicting builds
|
||||
pip install .[testing,deepspeed,fairscale]
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Fetch the tests to run
|
||||
run: |
|
||||
|
||||
70
.github/workflows/self-scheduled.yml
vendored
70
.github/workflows/self-scheduled.yml
vendored
@@ -36,11 +36,13 @@ jobs:
|
||||
apt -y update && apt install -y libsndfile1-dev git
|
||||
pip install --upgrade pip
|
||||
pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
run: |
|
||||
@@ -103,7 +105,6 @@ jobs:
|
||||
pip install --upgrade pip
|
||||
pip install --upgrade "jax[cuda111]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
|
||||
pip install .[flax,integrations,sklearn,testing,sentencepiece,flax-speech,vision]
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
@@ -142,9 +143,7 @@ jobs:
|
||||
run: |
|
||||
apt -y update && apt install -y libsndfile1-dev git
|
||||
pip install --upgrade pip
|
||||
pip install .[sklearn,testing,onnx,sentencepiece,tf-speech,vision]
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
|
||||
pip install .[sklearn,testing,onnx,sentencepiece,tf-speech]
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
@@ -182,45 +181,6 @@ jobs:
|
||||
name: run_all_tests_tf_gpu_test_reports
|
||||
path: reports
|
||||
|
||||
run_all_examples_torch_xla_tpu:
|
||||
runs-on: [self-hosted, docker-tpu-test, tpu-v3-8]
|
||||
container:
|
||||
image: gcr.io/tpu-pytorch/xla:nightly_3.8_tpuvm
|
||||
options: --privileged -v "/lib/libtpu.so:/lib/libtpu.so" -v /mnt/cache/.cache/huggingface:/mnt/cache/ --shm-size 16G
|
||||
steps:
|
||||
- name: Launcher docker
|
||||
uses: actions/checkout@v2
|
||||
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
pip install --upgrade pip
|
||||
pip install .[testing]
|
||||
|
||||
- name: Are TPUs recognized by our DL frameworks
|
||||
env:
|
||||
XRT_TPU_CONFIG: localservice;0;localhost:51011
|
||||
run: |
|
||||
python -c "import torch_xla.core.xla_model as xm; print(xm.xla_device())"
|
||||
|
||||
- name: Run example tests on TPU
|
||||
env:
|
||||
XRT_TPU_CONFIG: "localservice;0;localhost:51011"
|
||||
MKL_SERVICE_FORCE_INTEL: "1" # See: https://github.com/pytorch/pytorch/issues/37377
|
||||
|
||||
run: |
|
||||
python -m pytest -n 1 -v --dist=loadfile --make-reports=tests_torch_xla_tpu examples/pytorch/test_xla_examples.py
|
||||
|
||||
- name: Failure short reports
|
||||
if: ${{ always() }}
|
||||
run: cat reports/tests_torch_xla_tpu_failures_short.txt
|
||||
|
||||
- name: Test suite reports artifacts
|
||||
if: ${{ always() }}
|
||||
uses: actions/upload-artifact@v2
|
||||
with:
|
||||
name: run_all_examples_torch_xla_tpu
|
||||
path: reports
|
||||
|
||||
run_all_tests_torch_multi_gpu:
|
||||
runs-on: [self-hosted, docker-gpu, multi-gpu]
|
||||
container:
|
||||
@@ -240,11 +200,13 @@ jobs:
|
||||
apt -y update && apt install -y libsndfile1-dev git
|
||||
pip install --upgrade pip
|
||||
pip install .[integrations,sklearn,testing,onnxruntime,sentencepiece,torch-speech,vision,timm]
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
env:
|
||||
@@ -292,8 +254,7 @@ jobs:
|
||||
run: |
|
||||
apt -y update && apt install -y libsndfile1-dev git
|
||||
pip install --upgrade pip
|
||||
pip install .[sklearn,testing,onnx,sentencepiece,tf-speech,vision]
|
||||
pip install https://github.com/kpu/kenlm/archive/master.zip
|
||||
pip install .[sklearn,testing,onnx,sentencepiece,tf-speech]
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
@@ -391,7 +352,10 @@ jobs:
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
run: |
|
||||
@@ -426,12 +390,14 @@ jobs:
|
||||
run: |
|
||||
apt -y update && apt install -y libaio-dev
|
||||
pip install --upgrade pip
|
||||
rm -rf ~/.cache/torch_extensions/ # shared between conflicting builds
|
||||
pip install .[testing,deepspeed,fairscale]
|
||||
|
||||
- name: Are GPUs recognized by our DL frameworks
|
||||
run: |
|
||||
utils/print_env_pt.py
|
||||
python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
|
||||
python -c "import torch; print('Cuda version:', torch.version.cuda)"
|
||||
python -c "import torch; print('CuDNN version:', torch.backends.cudnn.version())"
|
||||
python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"
|
||||
|
||||
- name: Run all tests on GPU
|
||||
run: |
|
||||
|
||||
36
.github/workflows/update_metdata.yml
vendored
36
.github/workflows/update_metdata.yml
vendored
@@ -1,36 +0,0 @@
|
||||
name: Update Transformers metadata
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- master
|
||||
- update_transformers_metadata
|
||||
|
||||
jobs:
|
||||
build_and_package:
|
||||
runs-on: ubuntu-latest
|
||||
defaults:
|
||||
run:
|
||||
shell: bash -l {0}
|
||||
|
||||
steps:
|
||||
- uses: actions/checkout@v2
|
||||
|
||||
- name: Loading cache.
|
||||
uses: actions/cache@v2
|
||||
id: cache
|
||||
with:
|
||||
path: ~/.cache/pip
|
||||
key: v1-metadata
|
||||
restore-keys: |
|
||||
v1-metadata-${{ hashFiles('setup.py') }}
|
||||
v1-metadata
|
||||
|
||||
- name: Setup environment
|
||||
run: |
|
||||
pip install git+https://github.com/huggingface/transformers#egg=transformers[dev]
|
||||
|
||||
- name: Update metadata
|
||||
run: |
|
||||
python utils/update_metadata.py --token ${{ secrets.SYLVAIN_HF_TOKEN }} --commit_sha ${{ github.sha }}
|
||||
|
||||
@@ -37,7 +37,7 @@ authors:
|
||||
- family-names: Rush
|
||||
given-names: "Alexander M."
|
||||
preferred-citation:
|
||||
type: conference-paper
|
||||
type: inproceedings
|
||||
authors:
|
||||
- family-names: Wolf
|
||||
given-names: Thomas
|
||||
|
||||
@@ -273,13 +273,8 @@ Follow these steps to start contributing:
|
||||
- If you are adding a new tokenizer, write tests, and make sure
|
||||
`RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
|
||||
CircleCI does not run the slow tests, but github actions does every night!
|
||||
6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_bert.py` for an
|
||||
6. All public methods must have informative docstrings that work nicely with sphinx. See `modeling_ctrl.py` for an
|
||||
example.
|
||||
7. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
|
||||
the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
|
||||
them by URL.
|
||||
|
||||
See more about the checks run on a pull request in our [PR guide](pr_checks)
|
||||
|
||||
### Tests
|
||||
|
||||
|
||||
@@ -205,7 +205,7 @@ You are not required to read the following guidelines before opening an issue. H
|
||||
|
||||
If you really tried to make a short reproducible code but couldn't figure it out, it might be that having a traceback will give the developer enough information to know what's going on. But if it is not enough and we can't reproduce the problem, we can't really solve it.
|
||||
|
||||
Do not despair if you can't figure it out from the beginning, just share what you can and perhaps someone else will be able to help you at the forums.
|
||||
Do not dispair if you can't figure it out from the begining, just share what you can and perhaps someone else will be able to help you at the forums.
|
||||
|
||||
If your setup involves any custom datasets, the best way to help us reproduce the problem is to create a [Google Colab notebook](https://colab.research.google.com/) that demonstrates the issue and once you verify that the issue still exists, include a link to that notebook in the Issue. Just make sure that you don't copy and paste the location bar url of the open notebook - as this is private and we won't be able to open it. Instead, you need to click on `Share` in the right upper corner of the notebook, select `Get Link` and then copy and paste the public link it will give to you.
|
||||
|
||||
|
||||
10
Makefile
10
Makefile
@@ -31,9 +31,9 @@ deps_table_check_updated:
|
||||
|
||||
autogenerate_code: deps_table_update
|
||||
|
||||
# Check that the repo is in a good state
|
||||
# Check that source code meets quality standards
|
||||
|
||||
repo-consistency:
|
||||
extra_quality_checks:
|
||||
python utils/check_copies.py
|
||||
python utils/check_table.py
|
||||
python utils/check_dummies.py
|
||||
@@ -42,13 +42,12 @@ repo-consistency:
|
||||
python utils/tests_fetcher.py --sanity_check
|
||||
|
||||
# this target runs checks on all files
|
||||
|
||||
quality:
|
||||
black --check $(check_dirs)
|
||||
isort --check-only $(check_dirs)
|
||||
python utils/custom_init_isort.py --check_only
|
||||
flake8 $(check_dirs)
|
||||
python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
|
||||
${MAKE} extra_quality_checks
|
||||
|
||||
# Format source code automatically and check is there are any problems left that need manual fixing
|
||||
|
||||
@@ -57,7 +56,6 @@ extra_style_checks:
|
||||
python utils/style_doc.py src/transformers docs/source --max_len 119
|
||||
|
||||
# this target runs checks on all files and potentially modifies some of them
|
||||
|
||||
style:
|
||||
black $(check_dirs)
|
||||
isort $(check_dirs)
|
||||
@@ -66,7 +64,7 @@ style:
|
||||
|
||||
# Super fast fix and check target that only works on relevant modified files since the branch was made
|
||||
|
||||
fixup: modified_only_fixup extra_style_checks autogenerate_code repo-consistency
|
||||
fixup: modified_only_fixup extra_style_checks autogenerate_code extra_quality_checks
|
||||
|
||||
# Make marked copies of snippets of codes conform to the original
|
||||
|
||||
|
||||
223
README.md
223
README.md
@@ -26,8 +26,8 @@ limitations under the License.
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
|
||||
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
|
||||
</a>
|
||||
<a href="https://huggingface.co/docs/transformers/index">
|
||||
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
|
||||
<a href="https://huggingface.co/transformers/index.html">
|
||||
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
|
||||
</a>
|
||||
<a href="https://github.com/huggingface/transformers/releases">
|
||||
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
|
||||
@@ -42,28 +42,19 @@ limitations under the License.
|
||||
<p>
|
||||
<b>English</b> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_zh-hans.md">简体中文</a> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_zh-hant.md">繁體中文</a> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_ko.md">한국어</a>
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_zh-hant.md">繁體中文</a>
|
||||
<p>
|
||||
</h4>
|
||||
|
||||
<h3 align="center">
|
||||
<p>State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow</p>
|
||||
<p>State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow</p>
|
||||
</h3>
|
||||
|
||||
<h3 align="center">
|
||||
<a href="https://hf.co/course"><img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/course_banner.png"></a>
|
||||
</h3>
|
||||
|
||||
🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
|
||||
|
||||
These models can be applied on:
|
||||
|
||||
* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.
|
||||
* 🖼️ Images, for tasks like image classification, object detection, and segmentation.
|
||||
* 🗣️ Audio, for tasks like speech recognition and audio classification.
|
||||
|
||||
Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
|
||||
🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone.
|
||||
|
||||
🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.
|
||||
|
||||
@@ -74,8 +65,6 @@ Transformer models can also perform tasks on **several modalities combined**, su
|
||||
You can test most of our models directly on their pages from the [model hub](https://huggingface.co/models). We also offer [private model hosting, versioning, & an inference API](https://huggingface.co/pricing) for public and private models.
|
||||
|
||||
Here are a few examples:
|
||||
|
||||
In Natural Language Processing:
|
||||
- [Masked word completion with BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Name Entity Recognition with Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [Text generation with GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
@@ -84,15 +73,6 @@ Here are a few examples:
|
||||
- [Question answering with DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [Translation with T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
In Computer Vision:
|
||||
- [Image classification with ViT](https://huggingface.co/google/vit-base-patch16-224)
|
||||
- [Object Detection with DETR](https://huggingface.co/facebook/detr-resnet-50)
|
||||
- [Image Segmentation with DETR](https://huggingface.co/facebook/detr-resnet-50-panoptic)
|
||||
|
||||
In Audio:
|
||||
- [Automatic Speech Recognition with Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
|
||||
- [Keyword Spotting with Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
|
||||
|
||||
**[Write With Transformer](https://transformer.huggingface.co)**, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities.
|
||||
|
||||
## If you are looking for custom support from the Hugging Face team
|
||||
@@ -103,7 +83,7 @@ In Audio:
|
||||
|
||||
## Quick tour
|
||||
|
||||
To immediately use a model on a given input (text, image, audio, ...), we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts:
|
||||
To immediately use a model on a given text, we provide the `pipeline` API. Pipelines group together a pretrained model with the preprocessing that was used during that model's training. Here is how to quickly use a pipeline to classify positive versus negative texts:
|
||||
|
||||
```python
|
||||
>>> from transformers import pipeline
|
||||
@@ -131,7 +111,7 @@ Many NLP tasks have a pre-trained `pipeline` ready to go. For example, we can ea
|
||||
|
||||
```
|
||||
|
||||
In addition to the answer, the pretrained model used here returned its confidence score, along with the start position and end position of the answer in the tokenized sentence. You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/docs/transformers/task_summary).
|
||||
In addition to the answer, the pretrained model used here returned its confidence score, along with the start position and end position of the answer in the tokenized sentence. You can learn more about the tasks supported by the `pipeline` API in [this tutorial](https://huggingface.co/transformers/task_summary.html).
|
||||
|
||||
To download and use any of the pretrained models on your given task, all it takes is three lines of code. Here is the PyTorch version:
|
||||
```python
|
||||
@@ -156,12 +136,12 @@ And here is the equivalent code for TensorFlow:
|
||||
|
||||
The tokenizer is responsible for all the preprocessing the pretrained model expects, and can be called directly on a single string (as in the above examples) or a list. It will output a dictionary that you can use in downstream code or simply directly pass to your model using the ** argument unpacking operator.
|
||||
|
||||
The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use normally. [This tutorial](https://huggingface.co/docs/transformers/training) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset.
|
||||
The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) or a [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (depending on your backend) which you can use normally. [This tutorial](https://huggingface.co/transformers/training.html) explains how to integrate such a model into a classic PyTorch or TensorFlow training loop, or how to use our `Trainer` API to quickly fine-tune on a new dataset.
|
||||
|
||||
## Why should I use transformers?
|
||||
|
||||
1. Easy-to-use state-of-the-art models:
|
||||
- High performance on natural language understanding & generation, computer vision, and audio tasks.
|
||||
- High performance on NLU and NLG tasks.
|
||||
- Low barrier to entry for educators and practitioners.
|
||||
- Few user-facing abstractions with just three classes to learn.
|
||||
- A unified API for using all our pretrained models.
|
||||
@@ -169,11 +149,11 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
|
||||
1. Lower compute costs, smaller carbon footprint:
|
||||
- Researchers can share trained models instead of always retraining.
|
||||
- Practitioners can reduce compute time and production costs.
|
||||
- Dozens of architectures with over 20,000 pretrained models, some in more than 100 languages.
|
||||
- Dozens of architectures with over 2,000 pretrained models, some in more than 100 languages.
|
||||
|
||||
1. Choose the right framework for every part of a model's lifetime:
|
||||
- Train state-of-the-art models in 3 lines of code.
|
||||
- Move a single model between TF2.0/PyTorch/JAX frameworks at will.
|
||||
- Move a single model between TF2.0/PyTorch frameworks at will.
|
||||
- Seamlessly pick the right framework for training, evaluation and production.
|
||||
|
||||
1. Easily customize a model or an example to your needs:
|
||||
@@ -206,7 +186,7 @@ When one of those backends has been installed, 🤗 Transformers can be installe
|
||||
pip install transformers
|
||||
```
|
||||
|
||||
If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/docs/transformers/installation#installing-from-source).
|
||||
If you'd like to play with the examples or need the bleeding edge of the code and can't wait for a new release, you must [install the library from source](https://huggingface.co/transformers/installation.html#installing-from-source).
|
||||
|
||||
### With conda
|
||||
|
||||
@@ -226,115 +206,102 @@ Follow the installation pages of Flax, PyTorch or TensorFlow to see how to insta
|
||||
|
||||
Current number of checkpoints: 
|
||||
|
||||
🤗 Transformers currently provides the following architectures (see [here](https://huggingface.co/docs/transformers/model_summary) for a high-level summary of each them):
|
||||
🤗 Transformers currently provides the following architectures (see [here](https://huggingface.co/transformers/model_summary.html) for a high-level summary of each them):
|
||||
|
||||
1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
|
||||
1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
|
||||
1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
|
||||
1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
|
||||
1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
|
||||
1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
|
||||
1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
|
||||
1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bertgeneration)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/bigbird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot_small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
|
||||
1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
|
||||
1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
|
||||
1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
|
||||
1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
|
||||
1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
|
||||
1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
|
||||
1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
|
||||
1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta_v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
|
||||
1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
|
||||
1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
|
||||
1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation) and a German version of DistilBERT.
|
||||
1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval
|
||||
1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
|
||||
1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
|
||||
1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
|
||||
1. **[BEiT](https://huggingface.co/transformers/model_doc/beit.html)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
|
||||
1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
|
||||
1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[BigBird-RoBERTa](https://huggingface.co/transformers/model_doc/bigbird.html)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[BigBird-Pegasus](https://huggingface.co/transformers/model_doc/bigbird_pegasus.html)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BlenderbotSmall](https://huggingface.co/transformers/model_doc/blenderbot_small.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BORT](https://huggingface.co/transformers/model_doc/bort.html)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
|
||||
1. **[ByT5](https://huggingface.co/transformers/model_doc/byt5.html)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
|
||||
1. **[CamemBERT](https://huggingface.co/transformers/model_doc/camembert.html)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
|
||||
1. **[CANINE](https://huggingface.co/transformers/model_doc/canine.html)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
|
||||
1. **[CLIP](https://huggingface.co/transformers/model_doc/clip.html)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
|
||||
1. **[ConvBERT](https://huggingface.co/transformers/model_doc/convbert.html)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
|
||||
1. **[CPM](https://huggingface.co/transformers/model_doc/cpm.html)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
|
||||
1. **[CTRL](https://huggingface.co/transformers/model_doc/ctrl.html)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
|
||||
1. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeBERTa-v2](https://huggingface.co/transformers/model_doc/deberta_v2.html)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeiT](https://huggingface.co/transformers/model_doc/deit.html)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
|
||||
1. **[DETR](https://huggingface.co/transformers/model_doc/detr.html)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
|
||||
1. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
|
||||
1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
|
||||
1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (from Facebook) released with the paper [Dense Passage Retrieval
|
||||
for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon
|
||||
Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoderdecoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
|
||||
1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
|
||||
1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
|
||||
1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
||||
1. **[GPT](https://huggingface.co/docs/transformers/model_doc/gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
|
||||
1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
|
||||
1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
|
||||
1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
|
||||
1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
|
||||
1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
|
||||
1. **[ImageGPT](https://huggingface.co/docs/transformers/master/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
|
||||
1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
|
||||
1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
|
||||
1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
|
||||
1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
|
||||
1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
|
||||
1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
|
||||
1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
|
||||
1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
|
||||
1. **[MBart](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
|
||||
1. **[MBart-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
|
||||
1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron_bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
|
||||
1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
|
||||
1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
|
||||
1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
|
||||
1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
|
||||
1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
|
||||
1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
|
||||
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
|
||||
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
|
||||
1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
|
||||
1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
|
||||
1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
|
||||
1. **[SqueezeBert](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
|
||||
1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transformerxl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
|
||||
1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
|
||||
1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
|
||||
1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER
|
||||
AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
|
||||
1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
|
||||
1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
|
||||
1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
|
||||
1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
|
||||
1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlmroberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
|
||||
1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
|
||||
1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
|
||||
1. **[EncoderDecoder](https://huggingface.co/transformers/model_doc/encoderdecoder.html)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
|
||||
1. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
|
||||
1. **[FNet](https://huggingface.co/transformers/model_doc/fnet.html)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
|
||||
1. **[Funnel Transformer](https://huggingface.co/transformers/model_doc/funnel.html)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
||||
1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
|
||||
1. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
|
||||
1. **[GPT-J](https://huggingface.co/transformers/model_doc/gptj.html)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
|
||||
1. **[GPT Neo](https://huggingface.co/transformers/model_doc/gpt_neo.html)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
|
||||
1. **[Hubert](https://huggingface.co/transformers/model_doc/hubert.html)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
|
||||
1. **[I-BERT](https://huggingface.co/transformers/model_doc/ibert.html)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
|
||||
1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
|
||||
1. **[LayoutLMv2](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
|
||||
1. **[LayoutXLM](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
|
||||
1. **[LED](https://huggingface.co/transformers/model_doc/led.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[LUKE](https://huggingface.co/transformers/model_doc/luke.html)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
|
||||
1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
|
||||
1. **[M2M100](https://huggingface.co/transformers/model_doc/m2m_100.html)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
|
||||
1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
|
||||
1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
|
||||
1. **[MBart-50](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
|
||||
1. **[Megatron-BERT](https://huggingface.co/transformers/model_doc/megatron_bert.html)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[Megatron-GPT2](https://huggingface.co/transformers/model_doc/megatron_gpt2.html)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
|
||||
1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
|
||||
1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
1. **[RemBERT](https://huggingface.co/transformers/model_doc/rembert.html)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
|
||||
1. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
|
||||
1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
|
||||
1. **[SpeechEncoderDecoder](https://huggingface.co/transformers/model_doc/speechencoderdecoder.html)**
|
||||
1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
|
||||
1. **[SpeechToTextTransformer2](https://huggingface.co/transformers/model_doc/speech_to_text_2.html)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
|
||||
1. **[Splinter](https://huggingface.co/transformers/model_doc/splinter.html)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
|
||||
1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
|
||||
1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[T5v1.1](https://huggingface.co/transformers/model_doc/t5v1.1.html)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[TAPAS](https://huggingface.co/transformers/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
|
||||
1. **[Vision Transformer (ViT)](https://huggingface.co/transformers/model_doc/vit.html)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
|
||||
1. **[VisualBERT](https://huggingface.co/transformers/model_doc/visual_bert.html)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
|
||||
1. **[Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
|
||||
1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
|
||||
1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
|
||||
1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
|
||||
1. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
|
||||
1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
|
||||
|
||||
To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks).
|
||||
To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#supported-frameworks).
|
||||
|
||||
These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://huggingface.co/docs/transformers/examples).
|
||||
These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://huggingface.co/transformers/examples.html).
|
||||
|
||||
|
||||
## Learn more
|
||||
|
||||
| Section | Description |
|
||||
|-|-|
|
||||
| [Documentation](https://huggingface.co/docs/transformers/) | Full API documentation and tutorials |
|
||||
| [Task summary](https://huggingface.co/docs/transformers/task_summary) | Tasks supported by 🤗 Transformers |
|
||||
| [Preprocessing tutorial](https://huggingface.co/docstransformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models |
|
||||
| [Training and fine-tuning](https://huggingface.co/docs/transformers/training) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API |
|
||||
| [Documentation](https://huggingface.co/transformers/) | Full API documentation and tutorials |
|
||||
| [Task summary](https://huggingface.co/transformers/task_summary.html) | Tasks supported by 🤗 Transformers |
|
||||
| [Preprocessing tutorial](https://huggingface.co/transformers/preprocessing.html) | Using the `Tokenizer` class to prepare data for the models |
|
||||
| [Training and fine-tuning](https://huggingface.co/transformers/training.html) | Using the models provided by 🤗 Transformers in a PyTorch/TensorFlow training loop and the `Trainer` API |
|
||||
| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/master/examples) | Example scripts for fine-tuning models on a wide range of tasks |
|
||||
| [Model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing) | Upload and share your fine-tuned models with the community |
|
||||
| [Migration](https://huggingface.co/docs/transformers/migration) | Migrate to 🤗 Transformers from `pytorch-transformers` or `pytorch-pretrained-bert` |
|
||||
| [Model sharing and uploading](https://huggingface.co/transformers/model_sharing.html) | Upload and share your fine-tuned models with the community |
|
||||
| [Migration](https://huggingface.co/transformers/migration.html) | Migrate to 🤗 Transformers from `pytorch-transformers` or `pytorch-pretrained-bert` |
|
||||
|
||||
## Citation
|
||||
|
||||
|
||||
331
README_ko.md
331
README_ko.md
@@ -1,331 +0,0 @@
|
||||
<!---
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
-->
|
||||
|
||||
<p align="center">
|
||||
<br>
|
||||
<img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
|
||||
<br>
|
||||
<p>
|
||||
<p align="center">
|
||||
<a href="https://circleci.com/gh/huggingface/transformers">
|
||||
<img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
|
||||
</a>
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
|
||||
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
|
||||
</a>
|
||||
<a href="https://huggingface.co/docs/transformers/index">
|
||||
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
|
||||
</a>
|
||||
<a href="https://github.com/huggingface/transformers/releases">
|
||||
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
|
||||
</a>
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/CODE_OF_CONDUCT.md">
|
||||
<img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
|
||||
</a>
|
||||
<a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
|
||||
</p>
|
||||
|
||||
<h4 align="center">
|
||||
<p>
|
||||
<a href="https://github.com/huggingface/transformers/">English</a> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_zh-hans.md">简体中文</a> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_zh-hant.md">繁體中文</a> |
|
||||
<b>한국어</b>
|
||||
<p>
|
||||
</h4>
|
||||
|
||||
<h3 align="center">
|
||||
<p> Jax, Pytorch, TensorFlow를 위한 최첨단 자연어처리</p>
|
||||
</h3>
|
||||
|
||||
<h3 align="center">
|
||||
<a href="https://hf.co/course"><img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/course_banner.png"></a>
|
||||
</h3>
|
||||
|
||||
🤗 Transformers는 분류, 정보 추출, 질문 답변, 요약, 번역, 문장 생성 등을 100개 이상의 언어로 수행할 수 있는 수천개의 사전학습된 모델을 제공합니다. 우리의 목표는 모두가 최첨단의 NLP 기술을 쉽게 사용하는 것입니다.
|
||||
|
||||
🤗 Transformers는 이러한 사전학습 모델을 빠르게 다운로드해 특정 텍스트에 사용하고, 원하는 데이터로 fine-tuning해 커뮤니티나 우리의 [모델 허브](https://huggingface.co/models)에 공유할 수 있도록 API를 제공합니다. 또한, 모델 구조를 정의하는 각 파이썬 모듈은 완전히 독립적이여서 연구 실험을 위해 손쉽게 수정할 수 있습니다.
|
||||
|
||||
🤗 Transformers는 가장 유명한 3개의 딥러닝 라이브러리를 지원합니다. 이들은 서로 완벽히 연동됩니다 — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/). 간단하게 이 라이브러리 중 하나로 모델을 학습하고, 또 다른 라이브러리로 추론을 위해 모델을 불러올 수 있습니다.
|
||||
|
||||
## 온라인 데모
|
||||
|
||||
대부분의 모델을 [모델 허브](https://huggingface.co/models) 페이지에서 바로 테스트해볼 수 있습니다. 공개 및 비공개 모델을 위한 [비공개 모델 호스팅, 버전 관리, 추론 API](https://huggingface.co/pricing)도 제공합니다.
|
||||
|
||||
예시:
|
||||
- [BERT로 마스킹된 단어 완성하기](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
|
||||
- [Electra를 이용한 개체명 인식](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
|
||||
- [GPT-2로 텍스트 생성하기](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
|
||||
- [RoBERTa로 자연어 추론하기](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
|
||||
- [BART를 이용한 요약](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
|
||||
- [DistilBERT를 이용한 질문 답변](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
|
||||
- [T5로 번역하기](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
|
||||
|
||||
**[Transformer와 글쓰기](https://transformer.huggingface.co)** 는 이 저장소의 텍스트 생성 능력에 관한 Hugging Face 팀의 공식 데모입니다.
|
||||
|
||||
## Hugging Face 팀의 커스텀 지원을 원한다면
|
||||
|
||||
<a target="_blank" href="https://huggingface.co/support">
|
||||
<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
|
||||
</a><br>
|
||||
|
||||
## 퀵 투어
|
||||
|
||||
원하는 텍스트에 바로 모델을 사용할 수 있도록, 우리는 `pipeline` API를 제공합니다. Pipeline은 사전학습 모델과 그 모델을 학습할 때 적용한 전처리 방식을 하나로 합칩니다. 다음은 긍정적인 텍스트와 부정적인 텍스트를 분류하기 위해 pipeline을 사용한 간단한 예시입니다:
|
||||
|
||||
```python
|
||||
>>> from transformers import pipeline
|
||||
|
||||
# Allocate a pipeline for sentiment-analysis
|
||||
>>> classifier = pipeline('sentiment-analysis')
|
||||
>>> classifier('We are very happy to introduce pipeline to the transformers repository.')
|
||||
[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
|
||||
```
|
||||
|
||||
코드의 두번째 줄은 pipeline이 사용하는 사전학습 모델을 다운로드하고 캐시로 저장합니다. 세번째 줄에선 그 모델이 주어진 텍스트를 평가합니다. 여기서 모델은 99.97%의 확률로 텍스트가 긍정적이라고 평가했습니다.
|
||||
|
||||
많은 NLP 과제들을 `pipeline`으로 바로 수행할 수 있습니다. 예를 들어, 질문과 문맥이 주어지면 손쉽게 답변을 추출할 수 있습니다:
|
||||
|
||||
``` python
|
||||
>>> from transformers import pipeline
|
||||
|
||||
# Allocate a pipeline for question-answering
|
||||
>>> question_answerer = pipeline('question-answering')
|
||||
>>> question_answerer({
|
||||
... 'question': 'What is the name of the repository ?',
|
||||
... 'context': 'Pipeline has been included in the huggingface/transformers repository'
|
||||
... })
|
||||
{'score': 0.30970096588134766, 'start': 34, 'end': 58, 'answer': 'huggingface/transformers'}
|
||||
|
||||
```
|
||||
|
||||
답변뿐만 아니라, 여기에 사용된 사전학습 모델은 확신도와 토크나이즈된 문장 속 답변의 시작점, 끝점까지 반환합니다. [이 튜토리얼](https://huggingface.co/docs/transformers/task_summary)에서 `pipeline` API가 지원하는 다양한 과제를 확인할 수 있습니다.
|
||||
|
||||
코드 3줄로 원하는 과제에 맞게 사전학습 모델을 다운로드 받고 사용할 수 있습니다. 다음은 PyTorch 버전입니다:
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = AutoModel.from_pretrained("bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
```
|
||||
다음은 TensorFlow 버전입니다:
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, TFAutoModel
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
|
||||
>>> model = TFAutoModel.from_pretrained("bert-base-uncased")
|
||||
|
||||
>>> inputs = tokenizer("Hello world!", return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
```
|
||||
|
||||
토크나이저는 사전학습 모델의 모든 전처리를 책임집니다. 그리고 (위의 예시처럼) 1개의 스트링이나 리스트도 처리할 수 있습니다. 토크나이저는 딕셔너리를 반환하는데, 이는 다운스트림 코드에 사용하거나 언패킹 연산자 ** 를 이용해 모델에 바로 전달할 수도 있습니다.
|
||||
|
||||
모델 자체는 일반적으로 사용되는 [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)나 [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model)입니다. [이 튜토리얼](https://huggingface.co/transformers/training.html)은 이러한 모델을 표준적인 PyTorch나 TensorFlow 학습 과정에서 사용하는 방법, 또는 새로운 데이터로 fine-tune하기 위해 `Trainer` API를 사용하는 방법을 설명해줍니다.
|
||||
|
||||
## 왜 transformers를 사용해야 할까요?
|
||||
|
||||
1. 손쉽게 사용할 수 있는 최첨단 모델:
|
||||
- NLU와 NLG 과제에서 뛰어난 성능을 보입니다.
|
||||
- 교육자 실무자에게 진입 장벽이 낮습니다.
|
||||
- 3개의 클래스만 배우면 바로 사용할 수 있습니다.
|
||||
- 하나의 API로 모든 사전학습 모델을 사용할 수 있습니다.
|
||||
|
||||
1. 더 적은 계산 비용, 더 적은 탄소 발자국:
|
||||
- 연구자들은 모델을 계속 다시 학습시키는 대신 학습된 모델을 공유할 수 있습니다.
|
||||
- 실무자들은 학습에 필요한 시간과 비용을 절약할 수 있습니다.
|
||||
- 수십개의 모델 구조, 2,000개 이상의 사전학습 모델, 100개 이상의 언어로 학습된 모델 등.
|
||||
|
||||
1. 모델의 각 생애주기에 적합한 프레임워크:
|
||||
- 코드 3줄로 최첨단 모델을 학습하세요.
|
||||
- 자유롭게 모델을 TF2.0나 PyTorch 프레임워크로 변환하세요.
|
||||
- 학습, 평가, 공개 등 각 단계에 맞는 프레임워크를 원하는대로 선택하세요.
|
||||
|
||||
1. 필요한 대로 모델이나 예시를 커스터마이즈하세요:
|
||||
- 우리는 저자가 공개한 결과를 재현하기 위해 각 모델 구조의 예시를 제공합니다.
|
||||
- 모델 내부 구조는 가능한 일관적으로 공개되어 있습니다.
|
||||
- 빠른 실험을 위해 모델 파일은 라이브러리와 독립적으로 사용될 수 있습니다.
|
||||
|
||||
## 왜 transformers를 사용하지 말아야 할까요?
|
||||
|
||||
- 이 라이브러리는 신경망 블록을 만들기 위한 모듈이 아닙니다. 연구자들이 여러 파일을 살펴보지 않고 바로 각 모델을 사용할 수 있도록, 모델 파일 코드의 추상화 수준을 적정하게 유지했습니다.
|
||||
- 학습 API는 모든 모델에 적용할 수 있도록 만들어지진 않았지만, 라이브러리가 제공하는 모델들에 적용할 수 있도록 최적화되었습니다. 일반적인 머신 러닝을 위해선, 다른 라이브러리를 사용하세요.
|
||||
- 가능한 많은 사용 예시를 보여드리고 싶어서, [예시 폴더](https://github.com/huggingface/transformers/tree/master/examples)의 스크립트를 준비했습니다. 이 스크립트들을 수정 없이 특정한 문제에 바로 적용하지 못할 수 있습니다. 필요에 맞게 일부 코드를 수정해야 할 수 있습니다.
|
||||
|
||||
## 설치
|
||||
|
||||
### pip로 설치하기
|
||||
|
||||
이 저장소는 Python 3.6+, Flax 0.3.2+, PyTorch 1.3.1+, TensorFlow 2.3+에서 테스트 되었습니다.
|
||||
|
||||
[가상 환경](https://docs.python.org/3/library/venv.html)에 🤗 Transformers를 설치하세요. Python 가상 환경에 익숙하지 않다면, [사용자 가이드](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)를 확인하세요.
|
||||
|
||||
우선, 사용할 Python 버전으로 가상 환경을 만들고 실행하세요.
|
||||
|
||||
그 다음, Flax, PyTorch, TensorFlow 중 적어도 하나는 설치해야 합니다.
|
||||
플랫폼에 맞는 설치 명령어를 확인하기 위해 [TensorFlow 설치 페이지](https://www.tensorflow.org/install/), [PyTorch 설치 페이지](https://pytorch.org/get-started/locally/#start-locally), [Flax 설치 페이지](https://github.com/google/flax#quick-install)를 확인하세요.
|
||||
|
||||
이들 중 적어도 하나가 설치되었다면, 🤗 Transformers는 다음과 같이 pip을 이용해 설치할 수 있습니다:
|
||||
|
||||
```bash
|
||||
pip install transformers
|
||||
```
|
||||
|
||||
예시들을 체험해보고 싶거나, 최최최첨단 코드를 원하거나, 새로운 버전이 나올 때까지 기다릴 수 없다면 [라이브러리를 소스에서 바로 설치](https://huggingface.co/docs/transformers/installation#installing-from-source)하셔야 합니다.
|
||||
|
||||
### conda로 설치하기
|
||||
|
||||
Transformers 버전 v4.0.0부터, conda 채널이 생겼습니다: `huggingface`.
|
||||
|
||||
🤗 Transformers는 다음과 같이 conda로 설치할 수 있습니다:
|
||||
|
||||
```shell script
|
||||
conda install -c huggingface transformers
|
||||
```
|
||||
|
||||
Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는 방법을 확인하세요.
|
||||
|
||||
## 모델 구조
|
||||
|
||||
**🤗 Transformers가 제공하는 [모든 모델 체크포인트](https://huggingface.co/models)** 는 huggingface.co [모델 허브](https://huggingface.co)에 완벽히 연동되어 있습니다. [개인](https://huggingface.co/users)과 [기관](https://huggingface.co/organizations)이 모델 허브에 직접 업로드할 수 있습니다.
|
||||
|
||||
현재 사용 가능한 모델 체크포인트의 개수: 
|
||||
|
||||
🤗 Transformers는 다음 모델들을 제공합니다 (각 모델의 요약은 [여기](https://huggingface.co/docs/transformers/model_summary)서 확인하세요):
|
||||
|
||||
1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
|
||||
1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
|
||||
1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
|
||||
1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
|
||||
1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
|
||||
1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
|
||||
1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bertgeneration)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
|
||||
1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/bigbird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot_small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
|
||||
1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
|
||||
1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
|
||||
1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
|
||||
1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
|
||||
1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
|
||||
1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
|
||||
1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
|
||||
1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta_v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
|
||||
1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
|
||||
1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
|
||||
1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
|
||||
1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
|
||||
1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoderdecoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
|
||||
1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
|
||||
1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
||||
1. **[GPT](https://huggingface.co/docs/transformers/model_doc/gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
|
||||
1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
|
||||
1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
|
||||
1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
|
||||
1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
|
||||
1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
|
||||
1. **[ImageGPT](https://huggingface.co/docs/transformers/master/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
|
||||
1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
|
||||
1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
|
||||
1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
|
||||
1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
|
||||
1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
|
||||
1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
|
||||
1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
|
||||
1. **[MBart](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
|
||||
1. **[MBart-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
|
||||
1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron_bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
|
||||
1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
|
||||
1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
|
||||
1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
|
||||
1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
|
||||
1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
|
||||
1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
|
||||
1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
|
||||
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
|
||||
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
|
||||
1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
|
||||
1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
|
||||
1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
|
||||
1. **[SqueezeBert](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
|
||||
1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transformerxl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
|
||||
1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
|
||||
1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
|
||||
1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
|
||||
1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
|
||||
1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
|
||||
1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
|
||||
1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
|
||||
1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlmroberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
|
||||
1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
|
||||
1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
|
||||
1. 새로운 모델을 올리고 싶나요? 우리가 **상세한 가이드와 템플릿** 으로 새로운 모델을 올리도록 도와드릴게요. 가이드와 템플릿은 이 저장소의 [`templates`](./templates) 폴더에서 확인하실 수 있습니다. [컨트리뷰션 가이드라인](./CONTRIBUTING.md)을 꼭 확인해주시고, PR을 올리기 전에 메인테이너에게 연락하거나 이슈를 오픈해 피드백을 받으시길 바랍니다.
|
||||
|
||||
각 모델이 Flax, PyTorch, TensorFlow으로 구현되었는지 또는 🤗 Tokenizers 라이브러리가 지원하는 토크나이저를 사용하는지 확인하려면, [이 표](https://huggingface.co/docs/transformers/index#supported-frameworks)를 확인하세요.
|
||||
|
||||
이 구현은 여러 데이터로 검증되었고 (예시 스크립트를 참고하세요) 오리지널 구현의 성능과 같아야 합니다. [도큐먼트](https://huggingface.co/docs/transformers/examples)의 Examples 섹션에서 성능에 대한 자세한 설명을 확인할 수 있습니다.
|
||||
|
||||
## 더 알아보기
|
||||
|
||||
| 섹션 | 설명 |
|
||||
|-|-|
|
||||
| [도큐먼트](https://huggingface.co/transformers/) | 전체 API 도큐먼트와 튜토리얼 |
|
||||
| [과제 요약](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers가 지원하는 과제들 |
|
||||
| [전처리 튜토리얼](https://huggingface.co/docs/transformers/preprocessing) | `Tokenizer` 클래스를 이용해 모델을 위한 데이터 준비하기 |
|
||||
| [학습과 fine-tuning](https://huggingface.co/docs/transformers/training) | 🤗 Transformers가 제공하는 모델 PyTorch/TensorFlow 학습 과정과 `Trainer` API에서 사용하기 |
|
||||
| [퀵 투어: Fine-tuning/사용 스크립트](https://github.com/huggingface/transformers/tree/master/examples) | 다양한 과제에서 모델 fine-tuning하는 예시 스크립트 |
|
||||
| [모델 공유 및 업로드](https://huggingface.co/docs/transformers/model_sharing) | 커뮤니티에 fine-tune된 모델을 업로드 및 공유하기 |
|
||||
| [마이그레이션](https://huggingface.co/docs/transformers/migration) | `pytorch-transformers`나 `pytorch-pretrained-bert`에서 🤗 Transformers로 이동하기|
|
||||
|
||||
## 인용
|
||||
|
||||
🤗 Transformers 라이브러리를 인용하고 싶다면, 이 [논문](https://www.aclweb.org/anthology/2020.emnlp-demos.6/)을 인용해 주세요:
|
||||
```bibtex
|
||||
@inproceedings{wolf-etal-2020-transformers,
|
||||
title = "Transformers: State-of-the-Art Natural Language Processing",
|
||||
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
|
||||
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
|
||||
month = oct,
|
||||
year = "2020",
|
||||
address = "Online",
|
||||
publisher = "Association for Computational Linguistics",
|
||||
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
|
||||
pages = "38--45"
|
||||
}
|
||||
```
|
||||
@@ -51,8 +51,8 @@ checkpoint: 检查点
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
|
||||
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
|
||||
</a>
|
||||
<a href="https://huggingface.co/docs/transformers/index">
|
||||
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
|
||||
<a href="https://huggingface.co/transformers/index.html">
|
||||
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
|
||||
</a>
|
||||
<a href="https://github.com/huggingface/transformers/releases">
|
||||
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
|
||||
@@ -67,8 +67,7 @@ checkpoint: 检查点
|
||||
<p>
|
||||
<a href="https://github.com/huggingface/transformers/">English</a> |
|
||||
<b>简体中文</b> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_zh-hant.md">繁體中文</a> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_ko.md">한국어</a>
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_zh-hant.md">繁體中文</a>
|
||||
<p>
|
||||
</h4>
|
||||
|
||||
@@ -137,7 +136,7 @@ checkpoint: 检查点
|
||||
|
||||
```
|
||||
|
||||
除了给出答案，预训练模型还给出了对应的置信度分数、答案在词符化 (tokenized) 后的文本中开始和结束的位置。你可以从[这个教程](https://huggingface.co/docs/transformers/task_summary)了解更多流水线API支持的任务。
|
||||
除了给出答案，预训练模型还给出了对应的置信度分数、答案在词符化 (tokenized) 后的文本中开始和结束的位置。你可以从[这个教程](https://huggingface.co/transformers/task_summary.html)了解更多流水线API支持的任务。
|
||||
|
||||
要在你的任务上下载和使用任意预训练模型也很简单，只需三行代码。这里是 PyTorch 版的示例：
|
||||
```python
|
||||
@@ -211,7 +210,7 @@ checkpoint: 检查点
|
||||
pip install transformers
|
||||
```
|
||||
|
||||
如果你想要试试用例或者想在正式发布前使用最新的开发中代码，你得[从源代码安装](https://huggingface.co/docs/transformers/installation#installing-from-source)。
|
||||
如果你想要试试用例或者想在正式发布前使用最新的开发中代码，你得[从源代码安装](https://huggingface.co/transformers/installation.html#installing-from-source)。
|
||||
|
||||
### 使用 conda
|
||||
|
||||
@@ -231,99 +230,87 @@ conda install -c huggingface transformers
|
||||
|
||||
目前的检查点数量： 
|
||||
|
||||
🤗 Transformers 目前支持如下的架构（模型概述请阅[这里](https://huggingface.co/docs/transformers/model_summary)）：
|
||||
🤗 Transformers 目前支持如下的架构（模型概述请阅[这里](https://huggingface.co/transformers/model_summary.html)）：
|
||||
|
||||
1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (来自 Google Research and the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。
|
||||
1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (来自 Facebook) 伴随论文 [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) 由 Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer 发布。
|
||||
1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (来自 École polytechnique) 伴随论文 [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) 由 Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis 发布。
|
||||
1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (来自 VinAI Research) 伴随论文 [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) 由 Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen 发布。
|
||||
1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (来自 Microsoft) 伴随论文 [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) 由 Hangbo Bao, Li Dong, Furu Wei 发布。
|
||||
1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (来自 Google) 伴随论文 [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) 由 Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova 发布。
|
||||
1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bertgeneration)** (来自 Google) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
|
||||
1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (来自 VinAI Research) 伴随论文 [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) 由 Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen 发布。
|
||||
1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (来自 Google Research) 伴随论文 [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) 由 Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed 发布。
|
||||
1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/bigbird)** (来自 Google Research) 伴随论文 [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) 由 Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed 发布。
|
||||
1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston 发布。
|
||||
1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot_small)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston 发布。
|
||||
1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (来自 Alexa) 伴随论文 [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) 由 Adrian de Wynter and Daniel J. Perry 发布。
|
||||
1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (来自 Google Research) 伴随论文 [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) 由 Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel 发布。
|
||||
1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (来自 Inria/Facebook/Sorbonne) 伴随论文 [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) 由 Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot 发布。
|
||||
1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (来自 Google Research) 伴随论文 [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) 由 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 发布。
|
||||
1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (来自 OpenAI) 伴随论文 [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) 由 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever 发布。
|
||||
1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (来自 YituTech) 伴随论文 [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) 由 Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan 发布。
|
||||
1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (来自 Tsinghua University) 伴随论文 [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) 由 Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun 发布。
|
||||
1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (来自 Salesforce) 伴随论文 [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) 由 Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher 发布。
|
||||
1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
|
||||
1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta_v2)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
|
||||
1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (来自 Facebook) 伴随论文 [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) 由 Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou 发布。
|
||||
1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (来自 Facebook) 伴随论文 [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) 由 Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko 发布。
|
||||
1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (来自 Microsoft Research) 伴随论文 [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) 由 Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan 发布。
|
||||
1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (来自 HuggingFace), 伴随论文 [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 同样的方法也应用于压缩 GPT-2 到 [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa 到 [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT 到 [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) 和德语版 DistilBERT。
|
||||
1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (来自 Facebook) 伴随论文 [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) 由 Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih 发布。
|
||||
1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (来自 Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning 发布。
|
||||
1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoderdecoder)** (来自 Google Research) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
|
||||
1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (来自 CNRS) 伴随论文 [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) 由 Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab 发布。
|
||||
1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (来自 Google Research) 伴随论文 [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) 由 James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon 发布。
|
||||
1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (来自 CMU/Google Brain) 伴随论文 [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) 由 Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le 发布。
|
||||
1. **[GPT](https://huggingface.co/docs/transformers/model_doc/gpt)** (来自 OpenAI) 伴随论文 [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) 由 Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever 发布。
|
||||
1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (来自 EleutherAI) 随仓库 [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) 发布。作者为 Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy 发布。
|
||||
1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (来自 OpenAI) 伴随论文 [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) 由 Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever** 发布。
|
||||
1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (来自 EleutherAI) 伴随论文 [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) 由 Ben Wang and Aran Komatsuzaki 发布。
|
||||
1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (来自 Facebook) 伴随论文 [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 由 Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 发布。
|
||||
1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (来自 Berkeley) 伴随论文 [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) 由 Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer 发布。
|
||||
1. **[ImageGPT](https://huggingface.co/docs/transformers/master/model_doc/imagegpt)** (来自 OpenAI) 伴随论文 [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) 由 Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever 发布。
|
||||
1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (来自 Microsoft Research Asia) 伴随论文 [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) 由 Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou 发布。
|
||||
1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (来自 Microsoft Research Asia) 伴随论文 [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) 由 Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou 发布。
|
||||
1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (来自 Microsoft Research Asia) 伴随论文 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) 由 Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei 发布。
|
||||
1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
|
||||
1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
|
||||
1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (来自 Studio Ousia) 伴随论文 [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) 由 Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto 发布。
|
||||
1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (来自 UNC Chapel Hill) 伴随论文 [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) 由 Hao Tan and Mohit Bansal 发布。
|
||||
1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (来自 Facebook) 伴随论文 [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) 由 Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin 发布。
|
||||
1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** 用 [OPUS](http://opus.nlpl.eu/) 数据训练的机器翻译模型由 Jörg Tiedemann 发布。[Marian Framework](https://marian-nmt.github.io/) 由微软翻译团队开发。
|
||||
1. **[MBart](https://huggingface.co/docs/transformers/model_doc/mbart)** (来自 Facebook) 伴随论文 [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) 由 Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer 发布。
|
||||
1. **[MBart-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (来自 Facebook) 伴随论文 [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) 由 Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan 发布。
|
||||
1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron_bert)** (来自 NVIDIA) 伴随论文 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。
|
||||
1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (来自 NVIDIA) 伴随论文 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。
|
||||
1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (来自 Studio Ousia) 伴随论文 [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) 由 Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka 发布。
|
||||
1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (来自 Microsoft Research) 伴随论文 [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) 由 Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu 发布。
|
||||
1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (来自 Google AI) 伴随论文 [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) 由 Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel 发布。
|
||||
1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (来自 Google) 伴随论文 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 由 Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 发布。
|
||||
1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (来自 Deepmind) 伴随论文 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 由 Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 发布。
|
||||
1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (来自 VinAI Research) 伴随论文 [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) 由 Dat Quoc Nguyen and Anh Tuan Nguyen 发布。
|
||||
1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
|
||||
1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (来自 NVIDIA) 伴随论文 [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) 由 Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius 发布。
|
||||
1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (来自 Google Research) 伴随论文 [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) 由 Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya 发布。
|
||||
1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (来自 Google Research) 伴随论文 [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) 由 Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder 发布。
|
||||
1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (来自 Facebook), 伴随论文 [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) 由 Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov 发布。
|
||||
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (来自 ZhuiyiTechnology), 伴随论文 [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) 由 Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu 发布。
|
||||
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (来自 NVIDIA) 伴随论文 [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) 由 Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo 发布。
|
||||
1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
|
||||
1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (来自 ASAPP) 伴随论文 [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) 由 Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi 发布。
|
||||
1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (来自 Facebook), 伴随论文 [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) 由 Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino 发布。
|
||||
1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (来自 Facebook) 伴随论文 [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) 由 Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau 发布。
|
||||
1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (来自 Tel Aviv University) 伴随论文 [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) 由 Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy 发布。
|
||||
1. **[SqueezeBert](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (来自 Berkeley) 伴随论文 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 由 Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 发布。
|
||||
1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (来自 Google AI) 伴随论文 [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) 由 Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu 发布。
|
||||
1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (来自 Google AI) 伴随论文 [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 由 Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu 发布。
|
||||
1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (来自 Google AI) 伴随论文 [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) 由 Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos 发布。
|
||||
1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transformerxl)** (来自 Google/CMU) 伴随论文 [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) 由 Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov 发布。
|
||||
1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (来自 Microsoft) 伴随论文 [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) 由 Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei 发布。
|
||||
1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (来自 Microsoft Research) 伴随论文 [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) 由 Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang 发布。
|
||||
1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech_sat)** (来自 Microsoft Research) 伴随论文 [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) 由 Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu 发布。
|
||||
1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (来自 Google AI) 伴随论文 [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) 由 Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby 发布。
|
||||
1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (来自 UCLA NLP) 伴随论文 [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) 由 Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang 发布。
|
||||
1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (来自 Facebook AI) 伴随论文 [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) 由 Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli 发布。
|
||||
1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (来自 Facebook) 伴随论文 [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) 由 Guillaume Lample and Alexis Conneau 发布。
|
||||
1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
|
||||
1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlmroberta)** (来自 Facebook AI), 伴随论文 [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) 由 Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov 发布。
|
||||
1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (来自 Google/CMU) 伴随论文 [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) 由 Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le 发布。
|
||||
1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (来自 Facebook AI) 伴随论文 [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) 由 Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli 发布。
|
||||
1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。
|
||||
1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (来自 Facebook) 伴随论文 [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) 由 Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer 发布。
|
||||
1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (来自 École polytechnique) 伴随论文 [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) 由 Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis 发布。
|
||||
1. **[BEiT](https://huggingface.co/transformers/model_doc/beit.html)** (来自 Microsoft) 伴随论文 [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) 由 Hangbo Bao, Li Dong, Furu Wei 发布。
|
||||
1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (来自 Google) 伴随论文 [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) 由 Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova 发布。
|
||||
1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (来自 Google) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
|
||||
1. **[BigBird-Pegasus](https://huggingface.co/transformers/model_doc/bigbird_pegasus.html)** (来自 Google Research) 伴随论文 [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) 由 Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed 发布。
|
||||
1. **[BigBird-RoBERTa](https://huggingface.co/transformers/model_doc/bigbird.html)** (来自 Google Research) 伴随论文 [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) 由 Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed 发布。
|
||||
1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston 发布。
|
||||
1. **[BlenderbotSmall](https://huggingface.co/transformers/model_doc/blenderbot_small.html)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston 发布。
|
||||
1. **[BORT](https://huggingface.co/transformers/model_doc/bort.html)** (来自 Alexa) 伴随论文 [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) 由 Adrian de Wynter and Daniel J. Perry 发布。
|
||||
1. **[ByT5](https://huggingface.co/transformers/model_doc/byt5.html)** (来自 Google Research) 伴随论文 [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) 由 Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel 发布。
|
||||
1. **[CamemBERT](https://huggingface.co/transformers/model_doc/camembert.html)** (来自 Inria/Facebook/Sorbonne) 伴随论文 [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) 由 Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot 发布。
|
||||
1. **[CANINE](https://huggingface.co/transformers/model_doc/canine.html)** (来自 Google Research) 伴随论文 [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) 由 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 发布。
|
||||
1. **[CLIP](https://huggingface.co/transformers/model_doc/clip.html)** (来自 OpenAI) 伴随论文 [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) 由 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever 发布。
|
||||
1. **[ConvBERT](https://huggingface.co/transformers/model_doc/convbert.html)** (来自 YituTech) 伴随论文 [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) 由 Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan 发布。
|
||||
1. **[CPM](https://huggingface.co/transformers/model_doc/cpm.html)** (来自 Tsinghua University) 伴随论文 [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) 由 Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun 发布。
|
||||
1. **[CTRL](https://huggingface.co/transformers/model_doc/ctrl.html)** (来自 Salesforce) 伴随论文 [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) 由 Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher 发布。
|
||||
1. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
|
||||
1. **[DeBERTa-v2](https://huggingface.co/transformers/model_doc/deberta_v2.html)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
|
||||
1. **[DeiT](https://huggingface.co/transformers/model_doc/deit.html)** (来自 Facebook) 伴随论文 [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) 由 Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou 发布。
|
||||
1. **[DETR](https://huggingface.co/transformers/model_doc/detr.html)** (来自 Facebook) 伴随论文 [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) 由 Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko 发布。
|
||||
1. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (来自 Microsoft Research) 伴随论文 [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) 由 Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan 发布。
|
||||
1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace), 伴随论文 [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 同样的方法也应用于压缩 GPT-2 到 [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa 到 [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT 到 [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) 和德语版 DistilBERT。
|
||||
1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (来自 Facebook) 伴随论文 [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) 由 Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih 发布。
|
||||
1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自 Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning 发布。
|
||||
1. **[EncoderDecoder](https://huggingface.co/transformers/model_doc/encoderdecoder.html)** (来自 Google Research) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
|
||||
1. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (来自 CNRS) 伴随论文 [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) 由 Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab 发布。
|
||||
1. **[FNet](https://huggingface.co/transformers/master/model_doc/fnet.html)** (来自 Google Research) 伴随论文 [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) 由 James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon 发布。
|
||||
1. **[Funnel Transformer](https://huggingface.co/transformers/model_doc/funnel.html)** (来自 CMU/Google Brain) 伴随论文 [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) 由 Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le 发布。
|
||||
1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (来自 OpenAI) 伴随论文 [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) 由 Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever 发布。
|
||||
1. **[GPT Neo](https://huggingface.co/transformers/model_doc/gpt_neo.html)** (来自 EleutherAI) 随仓库 [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) 发布。作者为 Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy 发布。
|
||||
1. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (来自 OpenAI) 伴随论文 [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) 由 Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever** 发布。
|
||||
1. **[GPT-J](https://huggingface.co/transformers/model_doc/gptj.html)** (来自 EleutherAI) 伴随论文 [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) 由 Ben Wang and Aran Komatsuzaki 发布。
|
||||
1. **[Hubert](https://huggingface.co/transformers/model_doc/hubert.html)** (来自 Facebook) 伴随论文 [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) 由 Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed 发布。
|
||||
1. **[I-BERT](https://huggingface.co/transformers/model_doc/ibert.html)** (来自 Berkeley) 伴随论文 [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) 由 Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer 发布。
|
||||
1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (来自 Microsoft Research Asia) 伴随论文 [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) 由 Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou 发布。
|
||||
1. **[LayoutLMv2](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (来自 Microsoft Research Asia) 伴随论文 [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) 由 Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou 发布。
|
||||
1. **[LayoutXLM](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (来自 Microsoft Research Asia) 伴随论文 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) 由 Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei 发布。
|
||||
1. **[LED](https://huggingface.co/transformers/model_doc/led.html)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
|
||||
1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
|
||||
1. **[LUKE](https://huggingface.co/transformers/model_doc/luke.html)** (来自 Studio Ousia) 伴随论文 [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) 由 Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto 发布。
|
||||
1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (来自 UNC Chapel Hill) 伴随论文 [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) 由 Hao Tan and Mohit Bansal 发布。
|
||||
1. **[M2M100](https://huggingface.co/transformers/model_doc/m2m_100.html)** (来自 Facebook) 伴随论文 [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) 由 Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin 发布。
|
||||
1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** 用 [OPUS](http://opus.nlpl.eu/) 数据训练的机器翻译模型由 Jörg Tiedemann 发布。[Marian Framework](https://marian-nmt.github.io/) 由微软翻译团队开发。
|
||||
1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (来自 Facebook) 伴随论文 [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) 由 Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer 发布。
|
||||
1. **[MBart-50](https://huggingface.co/transformers/model_doc/mbart.html)** (来自 Facebook) 伴随论文 [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) 由 Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan 发布。
|
||||
1. **[Megatron-BERT](https://huggingface.co/transformers/model_doc/megatron_bert.html)** (来自 NVIDIA) 伴随论文 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。
|
||||
1. **[Megatron-GPT2](https://huggingface.co/transformers/model_doc/megatron_gpt2.html)** (来自 NVIDIA) 伴随论文 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。
|
||||
1. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (来自 Microsoft Research) 伴随论文 [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) 由 Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu 发布。
|
||||
1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (来自 Google AI) 伴随论文 [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) 由 Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel 发布。
|
||||
1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (来自 Google) 伴随论文 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 由 Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 发布。
|
||||
1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
|
||||
1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (来自 Google Research) 伴随论文 [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) 由 Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya 发布。
|
||||
1. **[RemBERT](https://huggingface.co/transformers/model_doc/rembert.html)** (来自 Google Research) 伴随论文 [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) 由 Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder 发布。
|
||||
1. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (来自 Facebook), 伴随论文 [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) 由 Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov 发布。
|
||||
1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (来自 ZhuiyiTechnology), 伴随论文 [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) 由 Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu 发布。
|
||||
1. **[SpeechEncoderDecoder](https://huggingface.co/transformers/master/model_doc/speechencoderdecoder.html)**
|
||||
1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (来自 Facebook), 伴随论文 [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) 由 Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino 发布。
|
||||
1. **[SpeechToTextTransformer2](https://huggingface.co/transformers/master/model_doc/speech_to_text_2.html)** (来自 Facebook) 伴随论文 [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) 由 Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau 发布。
|
||||
1. **[Splinter](https://huggingface.co/transformers/model_doc/splinter.html)** (来自 Tel Aviv University) 伴随论文 [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) 由 Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy 发布。
|
||||
1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** (来自 Berkeley) 伴随论文 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 由 Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 发布。
|
||||
1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (来自 Google AI) 伴随论文 [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) 由 Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu 发布。
|
||||
1. **[T5v1.1](https://huggingface.co/transformers/model_doc/t5v1.1.html)** (来自 Google AI) 伴随论文 [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 由 Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu 发布。
|
||||
1. **[TAPAS](https://huggingface.co/transformers/model_doc/tapas.html)** (来自 Google AI) 伴随论文 [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) 由 Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos 发布。
|
||||
1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (来自 Google/CMU) 伴随论文 [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) 由 Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov 发布。
|
||||
1. **[Vision Transformer (ViT)](https://huggingface.co/transformers/model_doc/vit.html)** (来自 Google AI) 伴随论文 [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) 由 Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby 发布。
|
||||
1. **[VisualBERT](https://huggingface.co/transformers/model_doc/visual_bert.html)** (来自 UCLA NLP) 伴随论文 [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) 由 Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang 发布。
|
||||
1. **[Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html)** (来自 Facebook AI) 伴随论文 [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) 由 Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli 发布。
|
||||
1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (来自 Facebook) 伴随论文 [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) 由 Guillaume Lample and Alexis Conneau 发布。
|
||||
1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
|
||||
1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (来自 Facebook AI), 伴随论文 [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) 由 Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov 发布。
|
||||
1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (来自 Google/CMU) 伴随论文 [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) 由 Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le 发布。
|
||||
1. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (来自 Facebook AI) 伴随论文 [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) 由 Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli 发布。
|
||||
1. 想要贡献新的模型？我们这里有一份**详细指引和模板**来引导你添加新的模型。你可以在 [`templates`](./templates) 目录中找到他们。记得查看 [贡献指南](./CONTRIBUTING.md) 并在开始写 PR 前联系维护人员或开一个新的 issue 来获得反馈。
|
||||
|
||||
要检查某个模型是否已有 Flax、PyTorch 或 TensorFlow 的实现，或其是否在 🤗 Tokenizers 库中有对应词符化器（tokenizer），敬请参阅[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。
|
||||
要检查某个模型是否已有 Flax、PyTorch 或 TensorFlow 的实现，或其是否在 🤗 Tokenizers 库中有对应词符化器（tokenizer），敬请参阅[此表](https://huggingface.co/transformers/index.html#supported-frameworks)。
|
||||
|
||||
这些实现均已于多个数据集测试（请参看用例脚本）并应于原版实现表现相当。你可以在用例文档的[此节](https://huggingface.co/docs/transformers/examples)中了解表现的细节。
|
||||
这些实现均已于多个数据集测试（请参看用例脚本）并应于原版实现表现相当。你可以在用例文档的[此节](https://huggingface.co/transformers/examples.html)中了解表现的细节。
|
||||
|
||||
|
||||
## 了解更多
|
||||
@@ -331,12 +318,12 @@ conda install -c huggingface transformers
|
||||
| 章节 | 描述 |
|
||||
|-|-|
|
||||
| [文档](https://huggingface.co/transformers/) | 完整的 API 文档和教程 |
|
||||
| [任务总结](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支持的任务 |
|
||||
| [预处理教程](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 来为模型准备数据 |
|
||||
| [训练和微调](https://huggingface.co/docstransformers/training) | 在 PyTorch/TensorFlow 的训练循环或 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
|
||||
| [任务总结](https://huggingface.co/transformers/task_summary.html) | 🤗 Transformers 支持的任务 |
|
||||
| [预处理教程](https://huggingface.co/transformers/preprocessing.html) | 使用 `Tokenizer` 来为模型准备数据 |
|
||||
| [训练和微调](https://huggingface.co/transformers/training.html) | 在 PyTorch/TensorFlow 的训练循环或 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
|
||||
| [快速上手：微调和用例脚本](https://github.com/huggingface/transformers/tree/master/examples) | 为各种任务提供的用例脚本 |
|
||||
| [模型分享和上传](https://huggingface.co/docs/transformers/model_sharing) | 和社区上传和分享你微调的模型 |
|
||||
| [迁移](https://huggingface.co/docs/transformers/migration) | 从 `pytorch-transformers` 或 `pytorch-pretrained-bert` 迁移到 🤗 Transformers |
|
||||
| [模型分享和上传](https://huggingface.co/transformers/model_sharing.html) | 和社区上传和分享你微调的模型 |
|
||||
| [迁移](https://huggingface.co/transformers/migration.html) | 从 `pytorch-transformers` 或 `pytorch-pretrained-bert` 迁移到 🤗 Transformers |
|
||||
|
||||
## 引用
|
||||
|
||||
|
||||
@@ -63,8 +63,8 @@ user: 使用者
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
|
||||
<img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
|
||||
</a>
|
||||
<a href="https://huggingface.co/docs/transformers/index">
|
||||
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
|
||||
<a href="https://huggingface.co/transformers/index.html">
|
||||
<img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
|
||||
</a>
|
||||
<a href="https://github.com/huggingface/transformers/releases">
|
||||
<img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
|
||||
@@ -79,8 +79,7 @@ user: 使用者
|
||||
<p>
|
||||
<a href="https://github.com/huggingface/transformers/">English</a> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_zh-hans.md">简体中文</a> |
|
||||
<b>繁體中文</b> |
|
||||
<a href="https://github.com/huggingface/transformers/blob/master/README_ko.md">한국어</a>
|
||||
<b>繁體中文</b>
|
||||
<p>
|
||||
</h4>
|
||||
|
||||
@@ -149,7 +148,7 @@ user: 使用者
|
||||
|
||||
```
|
||||
|
||||
除了提供問題解答，預訓練模型還提供了對應的信賴度分數以及解答在 tokenized 後的文本中開始和結束的位置。你可以從[這個教學](https://huggingface.co/docs/transformers/task_summary)了解更多 `pipeline` API支援的任務。
|
||||
除了提供問題解答，預訓練模型還提供了對應的信賴度分數以及解答在 tokenized 後的文本中開始和結束的位置。你可以從[這個教學](https://huggingface.co/transformers/task_summary.html)了解更多 `pipeline` API支援的任務。
|
||||
|
||||
要在你的任務中下載和使用任何預訓練模型很簡單，只需三行程式碼。這裡是 PyTorch 版的範例：
|
||||
```python
|
||||
@@ -223,7 +222,7 @@ Tokenizer 為所有的預訓練模型提供了預處理，並可以直接轉換
|
||||
pip install transformers
|
||||
```
|
||||
|
||||
如果你想要試試範例或者想在正式發布前使用最新開發中的程式碼，你必須[從原始碼安裝](https://huggingface.co/docs/transformers/installation#installing-from-source)。
|
||||
如果你想要試試範例或者想在正式發布前使用最新開發中的程式碼，你必須[從原始碼安裝](https://huggingface.co/transformers/installation.html#installing-from-source)。
|
||||
|
||||
### 使用 conda
|
||||
|
||||
@@ -243,99 +242,87 @@ conda install -c huggingface transformers
|
||||
|
||||
目前的檢查點數量： 
|
||||
|
||||
🤗 Transformers 目前支援以下的架構（模型概覽請參閱[這裡](https://huggingface.co/docs/transformers/model_summary)）：
|
||||
🤗 Transformers 目前支援以下的架構（模型概覽請參閱[這裡](https://huggingface.co/transformers/model_summary.html)）：
|
||||
|
||||
1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
|
||||
1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
|
||||
1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
|
||||
1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
|
||||
1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
|
||||
1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
|
||||
1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bertgeneration)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
|
||||
1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/bigbird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot_small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
|
||||
1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
|
||||
1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
|
||||
1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
|
||||
1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
|
||||
1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
|
||||
1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
|
||||
1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
|
||||
1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta_v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
|
||||
1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
|
||||
1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
|
||||
1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
|
||||
1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
|
||||
1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoderdecoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
|
||||
1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
|
||||
1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
||||
1. **[GPT](https://huggingface.co/docs/transformers/model_doc/gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
|
||||
1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
|
||||
1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
|
||||
1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released with the paper [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
|
||||
1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
|
||||
1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
|
||||
1. **[ImageGPT](https://huggingface.co/docs/transformers/master/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
|
||||
1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
|
||||
1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
|
||||
1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
|
||||
1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
|
||||
1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
|
||||
1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
|
||||
1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
|
||||
1. **[MBart](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
|
||||
1. **[MBart-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
|
||||
1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron_bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
|
||||
1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
|
||||
1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
|
||||
1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
|
||||
1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
|
||||
1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
|
||||
1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
|
||||
1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
|
||||
1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
|
||||
1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
|
||||
1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
|
||||
1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook) released with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
|
||||
1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University) released with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
|
||||
1. **[SqueezeBert](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
|
||||
1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released with the paper [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transformerxl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
|
||||
1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft) released with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
|
||||
1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
|
||||
1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
|
||||
1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
|
||||
1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
|
||||
1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
|
||||
1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
|
||||
1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlmroberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
|
||||
1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
|
||||
1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
|
||||
1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
|
||||
1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
|
||||
1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
|
||||
1. **[BEiT](https://huggingface.co/transformers/model_doc/beit.html)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
|
||||
1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
|
||||
1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[BigBird-Pegasus](https://huggingface.co/transformers/model_doc/bigbird_pegasus.html)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[BigBird-RoBERTa](https://huggingface.co/transformers/model_doc/bigbird.html)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BlenderbotSmall](https://huggingface.co/transformers/model_doc/blenderbot_small.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BORT](https://huggingface.co/transformers/model_doc/bort.html)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
|
||||
1. **[ByT5](https://huggingface.co/transformers/model_doc/byt5.html)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
|
||||
1. **[CamemBERT](https://huggingface.co/transformers/model_doc/camembert.html)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
|
||||
1. **[CANINE](https://huggingface.co/transformers/model_doc/canine.html)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
|
||||
1. **[CLIP](https://huggingface.co/transformers/model_doc/clip.html)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
|
||||
1. **[ConvBERT](https://huggingface.co/transformers/model_doc/convbert.html)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
|
||||
1. **[CPM](https://huggingface.co/transformers/model_doc/cpm.html)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
|
||||
1. **[CTRL](https://huggingface.co/transformers/model_doc/ctrl.html)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
|
||||
1. **[DeBERTa](https://huggingface.co/transformers/model_doc/deberta.html)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeBERTa-v2](https://huggingface.co/transformers/model_doc/deberta_v2.html)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeiT](https://huggingface.co/transformers/model_doc/deit.html)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
|
||||
1. **[DETR](https://huggingface.co/transformers/model_doc/detr.html)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
|
||||
1. **[DialoGPT](https://huggingface.co/transformers/model_doc/dialogpt.html)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
|
||||
1. **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/distillation) and a German version of DistilBERT.
|
||||
1. **[DPR](https://huggingface.co/transformers/model_doc/dpr.html)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
|
||||
1. **[EncoderDecoder](https://huggingface.co/transformers/model_doc/encoderdecoder.html)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[FlauBERT](https://huggingface.co/transformers/model_doc/flaubert.html)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
|
||||
1. **[FNet](https://huggingface.co/transformers/master/model_doc/fnet.html)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
|
||||
1. **[Funnel Transformer](https://huggingface.co/transformers/model_doc/funnel.html)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
||||
1. **[GPT](https://huggingface.co/transformers/model_doc/gpt.html)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
|
||||
1. **[GPT Neo](https://huggingface.co/transformers/model_doc/gpt_neo.html)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
|
||||
1. **[GPT-2](https://huggingface.co/transformers/model_doc/gpt2.html)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
|
||||
1. **[GPT-J](https://huggingface.co/transformers/model_doc/gptj.html)** (from EleutherAI) released with the paper [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
|
||||
1. **[Hubert](https://huggingface.co/transformers/model_doc/hubert.html)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
|
||||
1. **[I-BERT](https://huggingface.co/transformers/model_doc/ibert.html)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
|
||||
1. **[LayoutLM](https://huggingface.co/transformers/model_doc/layoutlm.html)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
|
||||
1. **[LayoutLMv2](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
|
||||
1. **[LayoutXLM](https://huggingface.co/transformers/model_doc/layoutlmv2.html)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
|
||||
1. **[LED](https://huggingface.co/transformers/model_doc/led.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[Longformer](https://huggingface.co/transformers/model_doc/longformer.html)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[LUKE](https://huggingface.co/transformers/model_doc/luke.html)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
|
||||
1. **[LXMERT](https://huggingface.co/transformers/model_doc/lxmert.html)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
|
||||
1. **[M2M100](https://huggingface.co/transformers/model_doc/m2m_100.html)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
|
||||
1. **[MarianMT](https://huggingface.co/transformers/model_doc/marian.html)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
|
||||
1. **[MBart](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
|
||||
1. **[MBart-50](https://huggingface.co/transformers/model_doc/mbart.html)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
|
||||
1. **[Megatron-BERT](https://huggingface.co/transformers/model_doc/megatron_bert.html)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[Megatron-GPT2](https://huggingface.co/transformers/model_doc/megatron_gpt2.html)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[MPNet](https://huggingface.co/transformers/model_doc/mpnet.html)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
|
||||
1. **[MT5](https://huggingface.co/transformers/model_doc/mt5.html)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
1. **[Pegasus](https://huggingface.co/transformers/model_doc/pegasus.html)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
|
||||
1. **[ProphetNet](https://huggingface.co/transformers/model_doc/prophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[Reformer](https://huggingface.co/transformers/model_doc/reformer.html)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
1. **[RemBERT](https://huggingface.co/transformers/model_doc/rembert.html)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
|
||||
1. **[RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
|
||||
1. **[RoFormer](https://huggingface.co/transformers/model_doc/roformer.html)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
|
||||
1. **[SpeechEncoderDecoder](https://huggingface.co/transformers/master/model_doc/speechencoderdecoder.html)**
|
||||
1. **[SpeechToTextTransformer](https://huggingface.co/transformers/model_doc/speech_to_text.html)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
|
||||
1. **[SpeechToTextTransformer2](https://huggingface.co/transformers/master/model_doc/speech_to_text_2.html)** (from Facebook) released with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
|
||||
1. **[Splinter](https://huggingface.co/transformers/model_doc/splinter.html)** (from Tel Aviv University) released with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
|
||||
1. **[SqueezeBert](https://huggingface.co/transformers/model_doc/squeezebert.html)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
|
||||
1. **[T5](https://huggingface.co/transformers/model_doc/t5.html)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[T5v1.1](https://huggingface.co/transformers/model_doc/t5v1.1.html)** (from Google AI) released with the paper [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[TAPAS](https://huggingface.co/transformers/model_doc/tapas.html)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
1. **[Transformer-XL](https://huggingface.co/transformers/model_doc/transformerxl.html)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
|
||||
1. **[Vision Transformer (ViT)](https://huggingface.co/transformers/model_doc/vit.html)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
|
||||
1. **[VisualBERT](https://huggingface.co/transformers/model_doc/visual_bert.html)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
|
||||
1. **[Wav2Vec2](https://huggingface.co/transformers/model_doc/wav2vec2.html)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
|
||||
1. **[XLM](https://huggingface.co/transformers/model_doc/xlm.html)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
|
||||
1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
|
||||
1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
|
||||
1. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
|
||||
1. 想要貢獻新的模型？我們這裡有一份**詳細指引和模板**來引導你加入新的模型。你可以在 [`templates`](./templates) 目錄中找到它們。記得查看[貢獻指引](./CONTRIBUTING.md)並在開始寫 PR 前聯繫維護人員或開一個新的 issue 來獲得 feedbacks。
|
||||
|
||||
要檢查某個模型是否已有 Flax、PyTorch 或 TensorFlow 的實作，或其是否在🤗 Tokenizers 函式庫中有對應的 tokenizer，敬請參閱[此表](https://huggingface.co/docs/transformers/index#supported-frameworks)。
|
||||
要檢查某個模型是否已有 Flax、PyTorch 或 TensorFlow 的實作，或其是否在🤗 Tokenizers 函式庫中有對應的 tokenizer，敬請參閱[此表](https://huggingface.co/transformers/index.html#supported-frameworks)。
|
||||
|
||||
這些實作均已於多個資料集測試（請參閱範例腳本）並應與原版實作表現相當。你可以在範例文件的[此節](https://huggingface.co/docs/transformers/examples)中了解實作的細節。
|
||||
這些實作均已於多個資料集測試（請參閱範例腳本）並應與原版實作表現相當。你可以在範例文件的[此節](https://huggingface.co/transformers/examples.html)中了解實作的細節。
|
||||
|
||||
|
||||
## 了解更多
|
||||
@@ -343,12 +330,12 @@ conda install -c huggingface transformers
|
||||
| 章節 | 描述 |
|
||||
|-|-|
|
||||
| [文件](https://huggingface.co/transformers/) | 完整的 API 文件和教學 |
|
||||
| [任務概覽](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支援的任務 |
|
||||
| [預處理教學](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 來為模型準備資料 |
|
||||
| [訓練和微調](https://huggingface.co/docs/transformers/training) | 使用 PyTorch/TensorFlow 的內建的訓練方式或於 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
|
||||
| [任務概覽](https://huggingface.co/transformers/task_summary.html) | 🤗 Transformers 支援的任務 |
|
||||
| [預處理教學](https://huggingface.co/transformers/preprocessing.html) | 使用 `Tokenizer` 來為模型準備資料 |
|
||||
| [訓練和微調](https://huggingface.co/transformers/training.html) | 使用 PyTorch/TensorFlow 的內建的訓練方式或於 `Trainer` API 中使用 🤗 Transformers 提供的模型 |
|
||||
| [快速上手：微調和範例腳本](https://github.com/huggingface/transformers/tree/master/examples) | 為各種任務提供的範例腳本 |
|
||||
| [模型分享和上傳](https://huggingface.co/docs/transformers/model_sharing) | 上傳並與社群分享你微調的模型 |
|
||||
| [遷移](https://huggingface.co/docs/transformers/migration) | 從 `pytorch-transformers` 或 `pytorch-pretrained-bert` 遷移到 🤗 Transformers |
|
||||
| [模型分享和上傳](https://huggingface.co/transformers/model_sharing.html) | 上傳並與社群分享你微調的模型 |
|
||||
| [遷移](https://huggingface.co/transformers/migration.html) | 從 `pytorch-transformers` 或 `pytorch-pretrained-bert` 遷移到 🤗 Transformers |
|
||||
|
||||
## 引用
|
||||
|
||||
|
||||
@@ -166,7 +166,7 @@ Values that should be put in `code` should either be surrounded by double backti
|
||||
an object using the :obj: syntax: :obj:\`like so\`. Note that argument names and objects like True, None or any strings
|
||||
should usually be put in `code`.
|
||||
|
||||
When mentioning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
|
||||
When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
|
||||
linked by Sphinx: :class:\`~transformers.XXXClass\`
|
||||
|
||||
When mentioning a function, it is recommended to use the :func: syntax as the mentioned function will be automatically
|
||||
|
||||
@@ -1,9 +0,0 @@
|
||||
# docstyle-ignore
|
||||
INSTALL_CONTENT = """
|
||||
# Transformers installation
|
||||
! pip install transformers datasets
|
||||
# To install from source instead of the last release, comment the command above and uncomment the following one.
|
||||
# ! pip install git+https://github.com/huggingface/transformers.git
|
||||
"""
|
||||
|
||||
notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]
|
||||
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@@ -0,0 +1,16 @@
|
||||
|
||||
.highlight .c1, .highlight .sd{
|
||||
color: #999
|
||||
}
|
||||
|
||||
.highlight .nn, .highlight .k, .highlight .s1, .highlight .nb, .highlight .bp, .highlight .kc {
|
||||
color: #FB8D68;
|
||||
}
|
||||
|
||||
.highlight .kn, .highlight .nv, .highlight .s2, .highlight .ow {
|
||||
color: #6670FF;
|
||||
}
|
||||
|
||||
.highlight .gp {
|
||||
color: #FB8D68;
|
||||
}
|
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350
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326
docs/source/_static/js/custom.js
Normal file
326
docs/source/_static/js/custom.js
Normal file
File diff suppressed because one or more lines are too long
1
docs/source/_static/js/huggingface_logo.svg
Normal file
1
docs/source/_static/js/huggingface_logo.svg
Normal file
File diff suppressed because one or more lines are too long
|
After Width: | Height: | Size: 7.6 KiB |
@@ -1,312 +0,0 @@
|
||||
- sections:
|
||||
- local: index
|
||||
title: 🤗 Transformers
|
||||
- local: quicktour
|
||||
title: Quick tour
|
||||
- local: installation
|
||||
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|
||||
- local: philosophy
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
- local: preprocessing
|
||||
title: Preprocessing data
|
||||
- local: training
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
- local: multilingual
|
||||
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|
||||
title: "Using 🤗 Transformers"
|
||||
- sections:
|
||||
- local: examples
|
||||
title: Examples
|
||||
- local: troubleshooting
|
||||
title: Troubleshooting
|
||||
- local: custom_datasets
|
||||
title: Fine-tuning with custom datasets
|
||||
- local: notebooks
|
||||
title: "🤗 Transformers Notebooks"
|
||||
- local: sagemaker
|
||||
title: Run training on Amazon SageMaker
|
||||
- local: community
|
||||
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|
||||
- local: converting_tensorflow_models
|
||||
title: Converting Tensorflow Checkpoints
|
||||
- local: migration
|
||||
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|
||||
- local: contributing
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
- local: fast_tokenizers
|
||||
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|
||||
- local: performance
|
||||
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|
||||
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|
||||
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|
||||
- local: testing
|
||||
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|
||||
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|
||||
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|
||||
- local: serialization
|
||||
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|
||||
- local: pr_checks
|
||||
title: Checks on a Pull Request
|
||||
title: Advanced guides
|
||||
- sections:
|
||||
- local: bertology
|
||||
title: BERTology
|
||||
- local: perplexity
|
||||
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|
||||
- local: benchmarks
|
||||
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|
||||
title: Research
|
||||
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|
||||
- sections:
|
||||
- local: main_classes/callback
|
||||
title: Callbacks
|
||||
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|
||||
title: Configuration
|
||||
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|
||||
title: Data Collator
|
||||
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|
||||
title: Keras callbacks
|
||||
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|
||||
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|
||||
- local: main_classes/model
|
||||
title: Models
|
||||
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|
||||
title: Optimization
|
||||
- local: main_classes/output
|
||||
title: Model outputs
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
- local: model_doc/albert
|
||||
title: ALBERT
|
||||
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|
||||
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|
||||
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|
||||
title: BART
|
||||
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|
||||
title: BARThez
|
||||
- local: model_doc/bartpho
|
||||
title: BARTpho
|
||||
- local: model_doc/beit
|
||||
title: BEiT
|
||||
- local: model_doc/bert
|
||||
title: BERT
|
||||
- local: model_doc/bertweet
|
||||
title: Bertweet
|
||||
- local: model_doc/bertgeneration
|
||||
title: BertGeneration
|
||||
- local: model_doc/bert_japanese
|
||||
title: BertJapanese
|
||||
- local: model_doc/bigbird
|
||||
title: BigBird
|
||||
- local: model_doc/bigbird_pegasus
|
||||
title: BigBirdPegasus
|
||||
- local: model_doc/blenderbot
|
||||
title: Blenderbot
|
||||
- local: model_doc/blenderbot_small
|
||||
title: Blenderbot Small
|
||||
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|
||||
title: BORT
|
||||
- local: model_doc/byt5
|
||||
title: ByT5
|
||||
- local: model_doc/camembert
|
||||
title: CamemBERT
|
||||
- local: model_doc/canine
|
||||
title: CANINE
|
||||
- local: model_doc/clip
|
||||
title: CLIP
|
||||
- local: model_doc/convbert
|
||||
title: ConvBERT
|
||||
- local: model_doc/cpm
|
||||
title: CPM
|
||||
- local: model_doc/ctrl
|
||||
title: CTRL
|
||||
- local: model_doc/deberta
|
||||
title: DeBERTa
|
||||
- local: model_doc/deberta_v2
|
||||
title: DeBERTa-v2
|
||||
- local: model_doc/deit
|
||||
title: DeiT
|
||||
- local: model_doc/detr
|
||||
title: DETR
|
||||
- local: model_doc/dialogpt
|
||||
title: DialoGPT
|
||||
- local: model_doc/distilbert
|
||||
title: DistilBERT
|
||||
- local: model_doc/dpr
|
||||
title: DPR
|
||||
- local: model_doc/electra
|
||||
title: ELECTRA
|
||||
- local: model_doc/encoderdecoder
|
||||
title: Encoder Decoder Models
|
||||
- local: model_doc/flaubert
|
||||
title: FlauBERT
|
||||
- local: model_doc/fnet
|
||||
title: FNet
|
||||
- local: model_doc/fsmt
|
||||
title: FSMT
|
||||
- local: model_doc/funnel
|
||||
title: Funnel Transformer
|
||||
- local: model_doc/herbert
|
||||
title: herBERT
|
||||
- local: model_doc/ibert
|
||||
title: I-BERT
|
||||
- local: model_doc/imagegpt
|
||||
title: ImageGPT
|
||||
- local: model_doc/layoutlm
|
||||
title: LayoutLM
|
||||
- local: model_doc/layoutlmv2
|
||||
title: LayoutLMV2
|
||||
- local: model_doc/layoutxlm
|
||||
title: LayoutXLM
|
||||
- local: model_doc/led
|
||||
title: LED
|
||||
- local: model_doc/longformer
|
||||
title: Longformer
|
||||
- local: model_doc/luke
|
||||
title: LUKE
|
||||
- local: model_doc/lxmert
|
||||
title: LXMERT
|
||||
- local: model_doc/marian
|
||||
title: MarianMT
|
||||
- local: model_doc/m2m_100
|
||||
title: M2M100
|
||||
- local: model_doc/mbart
|
||||
title: MBart and MBart-50
|
||||
- local: model_doc/megatron_bert
|
||||
title: MegatronBERT
|
||||
- local: model_doc/megatron_gpt2
|
||||
title: MegatronGPT2
|
||||
- local: model_doc/mobilebert
|
||||
title: MobileBERT
|
||||
- local: model_doc/mluke
|
||||
title: mLUKE
|
||||
- local: model_doc/mpnet
|
||||
title: MPNet
|
||||
- local: model_doc/mt5
|
||||
title: MT5
|
||||
- local: model_doc/gpt
|
||||
title: OpenAI GPT
|
||||
- local: model_doc/gpt2
|
||||
title: OpenAI GPT2
|
||||
- local: model_doc/gptj
|
||||
title: GPT-J
|
||||
- local: model_doc/gpt_neo
|
||||
title: GPT Neo
|
||||
- local: model_doc/hubert
|
||||
title: Hubert
|
||||
- local: model_doc/perceiver
|
||||
title: Perceiver
|
||||
- local: model_doc/pegasus
|
||||
title: Pegasus
|
||||
- local: model_doc/phobert
|
||||
title: PhoBERT
|
||||
- local: model_doc/prophetnet
|
||||
title: ProphetNet
|
||||
- local: model_doc/qdqbert
|
||||
title: QDQBert
|
||||
- local: model_doc/rag
|
||||
title: RAG
|
||||
- local: model_doc/reformer
|
||||
title: Reformer
|
||||
- local: model_doc/rembert
|
||||
title: RemBERT
|
||||
- local: model_doc/retribert
|
||||
title: RetriBERT
|
||||
- local: model_doc/roberta
|
||||
title: RoBERTa
|
||||
- local: model_doc/roformer
|
||||
title: RoFormer
|
||||
- local: model_doc/segformer
|
||||
title: SegFormer
|
||||
- local: model_doc/sew
|
||||
title: SEW
|
||||
- local: model_doc/sew_d
|
||||
title: SEW-D
|
||||
- local: model_doc/speechencoderdecoder
|
||||
title: Speech Encoder Decoder Models
|
||||
- local: model_doc/speech_to_text
|
||||
title: Speech2Text
|
||||
- local: model_doc/speech_to_text_2
|
||||
title: Speech2Text2
|
||||
- local: model_doc/splinter
|
||||
title: Splinter
|
||||
- local: model_doc/squeezebert
|
||||
title: SqueezeBERT
|
||||
- local: model_doc/t5
|
||||
title: T5
|
||||
- local: model_doc/t5v1.1
|
||||
title: T5v1.1
|
||||
- local: model_doc/tapas
|
||||
title: TAPAS
|
||||
- local: model_doc/transformerxl
|
||||
title: Transformer XL
|
||||
- local: model_doc/trocr
|
||||
title: TrOCR
|
||||
- local: model_doc/unispeech
|
||||
title: UniSpeech
|
||||
- local: model_doc/unispeech_sat
|
||||
title: UniSpeech-SAT
|
||||
- local: model_doc/visionencoderdecoder
|
||||
title: Vision Encoder Decoder Models
|
||||
- local: model_doc/vision_text_dual_encoder
|
||||
title: Vision Text Dual Encoder
|
||||
- local: model_doc/vit
|
||||
title: Vision Transformer (ViT)
|
||||
- local: model_doc/visual_bert
|
||||
title: VisualBERT
|
||||
- local: model_doc/wav2vec2
|
||||
title: Wav2Vec2
|
||||
- local: model_doc/xlm
|
||||
title: XLM
|
||||
- local: model_doc/xlmprophetnet
|
||||
title: XLM-ProphetNet
|
||||
- local: model_doc/xlmroberta
|
||||
title: XLM-RoBERTa
|
||||
- local: model_doc/xlnet
|
||||
title: XLNet
|
||||
- local: model_doc/xlsr_wav2vec2
|
||||
title: XLSR-Wav2Vec2
|
||||
title: Models
|
||||
- sections:
|
||||
- local: internal/modeling_utils
|
||||
title: Custom Layers and Utilities
|
||||
- local: internal/pipelines_utils
|
||||
title: Utilities for pipelines
|
||||
- local: internal/tokenization_utils
|
||||
title: Utilities for Tokenizers
|
||||
- local: internal/trainer_utils
|
||||
title: Utilities for Trainer
|
||||
- local: internal/generation_utils
|
||||
title: Utilities for Generation
|
||||
- local: internal/file_utils
|
||||
title: General Utilities
|
||||
title: Internal Helpers
|
||||
title: API
|
||||
@@ -72,11 +72,11 @@ call the model to be added to 🤗 Transformers ``BrandNewBert``.
|
||||
|
||||
Let's take a look:
|
||||
|
||||
.. image:: /imgs/transformers_overview.png
|
||||
.. image:: ./imgs/transformers_overview.png
|
||||
|
||||
As you can see, we do make use of inheritance in 🤗 Transformers, but we keep the level of abstraction to an absolute
|
||||
minimum. There are never more than two levels of abstraction for any model in the library. :obj:`BrandNewBertModel`
|
||||
inherits from :obj:`BrandNewBertPreTrainedModel` which in turn inherits from :class:`~transformers.PreTrainedModel` and
|
||||
inherits from :obj:`BrandNewBertPreTrainedModel` which in turn inherits from :class:`~transformres.PreTrainedModel` and
|
||||
that's it. As a general rule, we want to make sure that a new model only depends on
|
||||
:class:`~transformers.PreTrainedModel`. The important functionalities that are automatically provided to every new
|
||||
model are :meth:`~transformers.PreTrainedModel.from_pretrained` and
|
||||
@@ -271,7 +271,7 @@ logical components from one another and to have faster debugging cycles as inter
|
||||
notebooks are often easier to share with other contributors, which might be very helpful if you want to ask the Hugging
|
||||
Face team for help. If you are familiar with Jupiter notebooks, we strongly recommend you to work with them.
|
||||
|
||||
The obvious disadvantage of Jupyter notebooks is that if you are not used to working with them you will have to spend
|
||||
The obvious disadvantage of Jupyther notebooks is that if you are not used to working with them you will have to spend
|
||||
some time adjusting to the new programming environment and that you might not be able to use your known debugging tools
|
||||
anymore, like ``ipdb``.
|
||||
|
||||
@@ -674,7 +674,7 @@ the ``input_ids`` (usually the word embeddings) are identical. And then work you
|
||||
network. At some point, you will notice a difference between the two implementations, which should point you to the bug
|
||||
in the 🤗 Transformers implementation. From our experience, a simple and efficient way is to add many print statements
|
||||
in both the original implementation and 🤗 Transformers implementation, at the same positions in the network
|
||||
respectively, and to successively remove print statements showing the same values for intermediate presentations.
|
||||
respectively, and to successively remove print statements showing the same values for intermediate presentions.
|
||||
|
||||
When you're confident that both implementations yield the same output, verifying the outputs with
|
||||
``torch.allclose(original_output, output, atol=1e-3)``, you're done with the most difficult part! Congratulations - the
|
||||
|
||||
@@ -13,9 +13,9 @@ How to add a pipeline to 🤗 Transformers?
|
||||
=======================================================================================================================
|
||||
|
||||
First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes,
|
||||
dictionaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as possible
|
||||
as it makes compatibility easier (even through other languages via JSON). Those will be the :obj:`inputs` of the
|
||||
pipeline (:obj:`preprocess`).
|
||||
dictionnaries or whatever seems to be the most likely desired input. Try to keep these inputs as pure Python as
|
||||
possible as it makes compatibility easier (even through other languages via JSON). Those will be the :obj:`inputs` of
|
||||
the pipeline (:obj:`preprocess`).
|
||||
|
||||
Then define the :obj:`outputs`. Same policy as the :obj:`inputs`. The simpler, the better. Those will be the outputs of
|
||||
:obj:`postprocess` method.
|
||||
@@ -29,36 +29,36 @@ Start by inheriting the base class :obj:`Pipeline`. with the 4 methods needed to
|
||||
from transformers import Pipeline
|
||||
|
||||
class MyPipeline(Pipeline):
|
||||
def _sanitize_parameters(self, **kwargs):
|
||||
def _sanitize_parameters(self, **kwargs)
|
||||
preprocess_kwargs = {}
|
||||
if "maybe_arg" in kwargs:
|
||||
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
|
||||
return preprocess_kwargs, {}, {}
|
||||
|
||||
def preprocess(self, inputs, maybe_arg=2):
|
||||
def preprocess(self, inputs, maybe_arg=2)
|
||||
model_input = Tensor(....)
|
||||
return {"model_input": model_input}
|
||||
|
||||
def _forward(self, model_inputs):
|
||||
def _forward(self, model_inputs)
|
||||
# model_inputs == {"model_input": model_input}
|
||||
outputs = self.model(**model_inputs)
|
||||
oututs = self.model(**model_inputs)
|
||||
# Maybe {"logits": Tensor(...)}
|
||||
return outputs
|
||||
|
||||
def postprocess(self, model_outputs):
|
||||
def postprocess(self, model_outputs)
|
||||
best_class = model_outputs["logits"].softmax(-1)
|
||||
return best_class
|
||||
|
||||
|
||||
The structure of this breakdown is to support relatively seamless support for CPU/GPU, while supporting doing
|
||||
The structure of this breakdown is to support relatively seemless support for CPU/GPU, while supporting doing
|
||||
pre/postprocessing on the CPU on different threads
|
||||
|
||||
:obj:`preprocess` will take the originally defined inputs, and turn them into something feedable to the model. It might
|
||||
contain more information and is usually a :obj:`Dict`.
|
||||
:obj:`preprocess` will take the original defined inputs, and turn them something feedable to the model. It might
|
||||
contain more information and is usally a :obj:`Dict`.
|
||||
|
||||
:obj:`_forward` is the implementation detail and is not meant to be called directly. :obj:`forward` is the preferred
|
||||
:obj:`_forward` is the implementation detail and is not meant to be called directly :obj:`forward` is the preferred
|
||||
called method as it contains safeguards to make sure everything is working on the expected device. If anything is
|
||||
linked to a real model it belongs in the :obj:`_forward` method, anything else is in the preprocess/postprocess.
|
||||
linked to a real model it belongs in the :obj:`_forward` method, anything else is in the preprocess/postrocess.
|
||||
|
||||
:obj:`postprocess` methods will take the output of :obj:`_forward` and turn it into the final output that were decided
|
||||
earlier.
|
||||
@@ -89,12 +89,12 @@ In order to achieve that, we'll update our :obj:`postprocess` method with a defa
|
||||
.. code-block::
|
||||
|
||||
|
||||
def postprocess(self, model_outputs, top_k=5):
|
||||
def postprocess(self, model_outputs, top_k=5)
|
||||
best_class = model_outputs["logits"].softmax(-1)
|
||||
# Add logic to handle top_k
|
||||
return best_class
|
||||
|
||||
def _sanitize_parameters(self, **kwargs):
|
||||
def _sanitize_parameters(self, **kwargs)
|
||||
preprocess_kwargs = {}
|
||||
if "maybe_arg" in kwargs:
|
||||
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
|
||||
@@ -124,7 +124,7 @@ Create a new file ``tests/test_pipelines_MY_PIPELINE.py`` with example with the
|
||||
The :obj:`run_pipeline_test` function will be very generic and run on small random models on every possible
|
||||
architecture as defined by :obj:`model_mapping` and :obj:`tf_model_mapping`.
|
||||
|
||||
This is very important to test future compatibility, meaning if someone adds a new model for
|
||||
This is very important to test future compatibilty, meaning if someone adds a new model for
|
||||
:obj:`XXXForQuestionAnswering` then the pipeline test will attempt to run on it. Because the models are random it's
|
||||
impossible to check for actual values, that's why There is a helper :obj:`ANY` that will simply attempt to match the
|
||||
output of the pipeline TYPE.
|
||||
|
||||
@@ -1,347 +0,0 @@
|
||||
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
-->
|
||||
|
||||
# Benchmarks
|
||||
|
||||
[[open-in-colab]]
|
||||
|
||||
Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
|
||||
|
||||
A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found [here](https://github.com/huggingface/transformers/tree/master/notebooks/05-benchmark.ipynb).
|
||||
|
||||
## How to benchmark 🤗 Transformer models
|
||||
|
||||
The classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] allow to flexibly benchmark 🤗 Transformer models. The benchmark classes allow us to measure the _peak memory usage_ and _required time_ for both _inference_ and _training_.
|
||||
|
||||
<Tip>
|
||||
|
||||
Hereby, _inference_ is defined by a single forward pass, and _training_ is defined by a single forward pass and
|
||||
backward pass.
|
||||
|
||||
</Tip>
|
||||
|
||||
The benchmark classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] expect an object of type [`PyTorchBenchmarkArguments`] and
|
||||
[`TensorFlowBenchmarkArguments`], respectively, for instantiation. [`PyTorchBenchmarkArguments`] and [`TensorFlowBenchmarkArguments`] are data classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it is shown how a BERT model of type _bert-base-cased_ can be benchmarked.
|
||||
|
||||
```py
|
||||
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
|
||||
|
||||
>>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> benchmark = PyTorchBenchmark(args)
|
||||
|
||||
===PT-TF-SPLIT===
|
||||
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
|
||||
|
||||
>>> args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> benchmark = TensorFlowBenchmark(args)
|
||||
```
|
||||
|
||||
Here, three arguments are given to the benchmark argument data classes, namely `models`, `batch_sizes`, and
|
||||
`sequence_lengths`. The argument `models` is required and expects a `list` of model identifiers from the
|
||||
[model hub](https://huggingface.co/models) The `list` arguments `batch_sizes` and `sequence_lengths` define
|
||||
the size of the `input_ids` on which the model is benchmarked. There are many more parameters that can be configured
|
||||
via the benchmark argument data classes. For more detail on these one can either directly consult the files
|
||||
`src/transformers/benchmark/benchmark_args_utils.py`, `src/transformers/benchmark/benchmark_args.py` (for PyTorch)
|
||||
and `src/transformers/benchmark/benchmark_args_tf.py` (for Tensorflow). Alternatively, running the following shell
|
||||
commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
|
||||
respectively.
|
||||
|
||||
```bash
|
||||
python examples/pytorch/benchmarking/run_benchmark.py --help
|
||||
|
||||
===PT-TF-SPLIT===
|
||||
python examples/tensorflow/benchmarking/run_benchmark_tf.py --help
|
||||
```
|
||||
|
||||
An instantiated benchmark object can then simply be run by calling `benchmark.run()`.
|
||||
|
||||
```py
|
||||
>>> results = benchmark.run()
|
||||
>>> print(results)
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 0.006
|
||||
bert-base-uncased 8 32 0.006
|
||||
bert-base-uncased 8 128 0.018
|
||||
bert-base-uncased 8 512 0.088
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 1227
|
||||
bert-base-uncased 8 32 1281
|
||||
bert-base-uncased 8 128 1307
|
||||
bert-base-uncased 8 512 1539
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
|
||||
- transformers_version: 2.11.0
|
||||
- framework: PyTorch
|
||||
- use_torchscript: False
|
||||
- framework_version: 1.4.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 08:58:43.371351
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
===PT-TF-SPLIT===
|
||||
>>> results = benchmark.run()
|
||||
>>> print(results)
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 0.005
|
||||
bert-base-uncased 8 32 0.008
|
||||
bert-base-uncased 8 128 0.022
|
||||
bert-base-uncased 8 512 0.105
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 1330
|
||||
bert-base-uncased 8 32 1330
|
||||
bert-base-uncased 8 128 1330
|
||||
bert-base-uncased 8 512 1770
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
|
||||
- transformers_version: 2.11.0
|
||||
- framework: Tensorflow
|
||||
- use_xla: False
|
||||
- framework_version: 2.2.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:26:35.617317
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
```
|
||||
|
||||
By default, the _time_ and the _required memory_ for _inference_ are benchmarked. In the example output above the first
|
||||
two sections show the result corresponding to _inference time_ and _inference memory_. In addition, all relevant
|
||||
information about the computing environment, _e.g._ the GPU type, the system, the library versions, etc... are printed
|
||||
out in the third section under _ENVIRONMENT INFORMATION_. This information can optionally be saved in a _.csv_ file
|
||||
when adding the argument `save_to_csv=True` to [`PyTorchBenchmarkArguments`] and
|
||||
[`TensorFlowBenchmarkArguments`] respectively. In this case, every section is saved in a separate
|
||||
_.csv_ file. The path to each _.csv_ file can optionally be defined via the argument data classes.
|
||||
|
||||
Instead of benchmarking pre-trained models via their model identifier, _e.g._ `bert-base-uncased`, the user can
|
||||
alternatively benchmark an arbitrary configuration of any available model class. In this case, a `list` of
|
||||
configurations must be inserted with the benchmark args as follows.
|
||||
|
||||
```py
|
||||
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
|
||||
|
||||
>>> args = PyTorchBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> config_base = BertConfig()
|
||||
>>> config_384_hid = BertConfig(hidden_size=384)
|
||||
>>> config_6_lay = BertConfig(num_hidden_layers=6)
|
||||
|
||||
>>> benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
|
||||
>>> benchmark.run()
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 128 0.006
|
||||
bert-base 8 512 0.006
|
||||
bert-base 8 128 0.018
|
||||
bert-base 8 512 0.088
|
||||
bert-384-hid 8 8 0.006
|
||||
bert-384-hid 8 32 0.006
|
||||
bert-384-hid 8 128 0.011
|
||||
bert-384-hid 8 512 0.054
|
||||
bert-6-lay 8 8 0.003
|
||||
bert-6-lay 8 32 0.004
|
||||
bert-6-lay 8 128 0.009
|
||||
bert-6-lay 8 512 0.044
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 1277
|
||||
bert-base 8 32 1281
|
||||
bert-base 8 128 1307
|
||||
bert-base 8 512 1539
|
||||
bert-384-hid 8 8 1005
|
||||
bert-384-hid 8 32 1027
|
||||
bert-384-hid 8 128 1035
|
||||
bert-384-hid 8 512 1255
|
||||
bert-6-lay 8 8 1097
|
||||
bert-6-lay 8 32 1101
|
||||
bert-6-lay 8 128 1127
|
||||
bert-6-lay 8 512 1359
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
|
||||
- transformers_version: 2.11.0
|
||||
- framework: PyTorch
|
||||
- use_torchscript: False
|
||||
- framework_version: 1.4.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:35:25.143267
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
===PT-TF-SPLIT===
|
||||
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig
|
||||
|
||||
>>> args = TensorFlowBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> config_base = BertConfig()
|
||||
>>> config_384_hid = BertConfig(hidden_size=384)
|
||||
>>> config_6_lay = BertConfig(num_hidden_layers=6)
|
||||
|
||||
>>> benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
|
||||
>>> benchmark.run()
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 0.005
|
||||
bert-base 8 32 0.008
|
||||
bert-base 8 128 0.022
|
||||
bert-base 8 512 0.106
|
||||
bert-384-hid 8 8 0.005
|
||||
bert-384-hid 8 32 0.007
|
||||
bert-384-hid 8 128 0.018
|
||||
bert-384-hid 8 512 0.064
|
||||
bert-6-lay 8 8 0.002
|
||||
bert-6-lay 8 32 0.003
|
||||
bert-6-lay 8 128 0.0011
|
||||
bert-6-lay 8 512 0.074
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 1330
|
||||
bert-base 8 32 1330
|
||||
bert-base 8 128 1330
|
||||
bert-base 8 512 1770
|
||||
bert-384-hid 8 8 1330
|
||||
bert-384-hid 8 32 1330
|
||||
bert-384-hid 8 128 1330
|
||||
bert-384-hid 8 512 1540
|
||||
bert-6-lay 8 8 1330
|
||||
bert-6-lay 8 32 1330
|
||||
bert-6-lay 8 128 1330
|
||||
bert-6-lay 8 512 1540
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
|
||||
- transformers_version: 2.11.0
|
||||
- framework: Tensorflow
|
||||
- use_xla: False
|
||||
- framework_version: 2.2.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:38:15.487125
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
```
|
||||
|
||||
Again, _inference time_ and _required memory_ for _inference_ are measured, but this time for customized configurations
|
||||
of the `BertModel` class. This feature can especially be helpful when deciding for which configuration the model
|
||||
should be trained.
|
||||
|
||||
|
||||
## Benchmark best practices
|
||||
|
||||
This section lists a couple of best practices one should be aware of when benchmarking a model.
|
||||
|
||||
- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
|
||||
specifies on which device the code should be run by setting the `CUDA_VISIBLE_DEVICES` environment variable in the
|
||||
shell, _e.g._ `export CUDA_VISIBLE_DEVICES=0` before running the code.
|
||||
- The option `no_multi_processing` should only be set to `True` for testing and debugging. To ensure accurate
|
||||
memory measurement it is recommended to run each memory benchmark in a separate process by making sure
|
||||
`no_multi_processing` is set to `True`.
|
||||
- One should always state the environment information when sharing the results of a model benchmark. Results can vary
|
||||
heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
|
||||
useful for the community.
|
||||
|
||||
|
||||
## Sharing your benchmark
|
||||
|
||||
Previously all available core models (10 at the time) have been benchmarked for _inference time_, across many different
|
||||
settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
|
||||
done across CPUs (except for TensorFlow XLA) and GPUs.
|
||||
|
||||
The approach is detailed in the [following blogpost](https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2) and the results are
|
||||
available [here](https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing).
|
||||
|
||||
With the new _benchmark_ tools, it is easier than ever to share your benchmark results with the community
|
||||
|
||||
- [PyTorch Benchmarking Results](https://github.com/huggingface/transformers/tree/master/examples/pytorch/benchmarking/README.md).
|
||||
- [TensorFlow Benchmarking Results](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/benchmarking/README.md).
|
||||
363
docs/source/benchmarks.rst
Normal file
363
docs/source/benchmarks.rst
Normal file
@@ -0,0 +1,363 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Benchmarks
|
||||
=======================================================================================================================
|
||||
|
||||
Let's take a look at how 🤗 Transformer models can be benchmarked, best practices, and already available benchmarks.
|
||||
|
||||
A notebook explaining in more detail how to benchmark 🤗 Transformer models can be found :prefix_link:`here
|
||||
<notebooks/05-benchmark.ipynb>`.
|
||||
|
||||
How to benchmark 🤗 Transformer models
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` allow to flexibly
|
||||
benchmark 🤗 Transformer models. The benchmark classes allow us to measure the `peak memory usage` and `required time`
|
||||
for both `inference` and `training`.
|
||||
|
||||
.. note::
|
||||
|
||||
Hereby, `inference` is defined by a single forward pass, and `training` is defined by a single forward pass and
|
||||
backward pass.
|
||||
|
||||
The benchmark classes :class:`~transformers.PyTorchBenchmark` and :class:`~transformers.TensorFlowBenchmark` expect an
|
||||
object of type :class:`~transformers.PyTorchBenchmarkArguments` and
|
||||
:class:`~transformers.TensorFlowBenchmarkArguments`, respectively, for instantiation.
|
||||
:class:`~transformers.PyTorchBenchmarkArguments` and :class:`~transformers.TensorFlowBenchmarkArguments` are data
|
||||
classes and contain all relevant configurations for their corresponding benchmark class. In the following example, it
|
||||
is shown how a BERT model of type `bert-base-cased` can be benchmarked.
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> ## PYTORCH CODE
|
||||
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
|
||||
|
||||
>>> args = PyTorchBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> benchmark = PyTorchBenchmark(args)
|
||||
|
||||
>>> ## TENSORFLOW CODE
|
||||
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
|
||||
|
||||
>>> args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> benchmark = TensorFlowBenchmark(args)
|
||||
|
||||
|
||||
Here, three arguments are given to the benchmark argument data classes, namely ``models``, ``batch_sizes``, and
|
||||
``sequence_lengths``. The argument ``models`` is required and expects a :obj:`list` of model identifiers from the
|
||||
`model hub <https://huggingface.co/models>`__ The :obj:`list` arguments ``batch_sizes`` and ``sequence_lengths`` define
|
||||
the size of the ``input_ids`` on which the model is benchmarked. There are many more parameters that can be configured
|
||||
via the benchmark argument data classes. For more detail on these one can either directly consult the files
|
||||
``src/transformers/benchmark/benchmark_args_utils.py``, ``src/transformers/benchmark/benchmark_args.py`` (for PyTorch)
|
||||
and ``src/transformers/benchmark/benchmark_args_tf.py`` (for Tensorflow). Alternatively, running the following shell
|
||||
commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
|
||||
respectively.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
## PYTORCH CODE
|
||||
python examples/pytorch/benchmarking/run_benchmark.py --help
|
||||
|
||||
## TENSORFLOW CODE
|
||||
python examples/tensorflow/benchmarking/run_benchmark_tf.py --help
|
||||
|
||||
|
||||
An instantiated benchmark object can then simply be run by calling ``benchmark.run()``.
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> ## PYTORCH CODE
|
||||
>>> results = benchmark.run()
|
||||
>>> print(results)
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 0.006
|
||||
bert-base-uncased 8 32 0.006
|
||||
bert-base-uncased 8 128 0.018
|
||||
bert-base-uncased 8 512 0.088
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 1227
|
||||
bert-base-uncased 8 32 1281
|
||||
bert-base-uncased 8 128 1307
|
||||
bert-base-uncased 8 512 1539
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
|
||||
- transformers_version: 2.11.0
|
||||
- framework: PyTorch
|
||||
- use_torchscript: False
|
||||
- framework_version: 1.4.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 08:58:43.371351
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
>>> ## TENSORFLOW CODE
|
||||
>>> results = benchmark.run()
|
||||
>>> print(results)
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 0.005
|
||||
bert-base-uncased 8 32 0.008
|
||||
bert-base-uncased 8 128 0.022
|
||||
bert-base-uncased 8 512 0.105
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base-uncased 8 8 1330
|
||||
bert-base-uncased 8 32 1330
|
||||
bert-base-uncased 8 128 1330
|
||||
bert-base-uncased 8 512 1770
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
|
||||
- transformers_version: 2.11.0
|
||||
- framework: Tensorflow
|
||||
- use_xla: False
|
||||
- framework_version: 2.2.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:26:35.617317
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
By default, the `time` and the `required memory` for `inference` are benchmarked. In the example output above the first
|
||||
two sections show the result corresponding to `inference time` and `inference memory`. In addition, all relevant
|
||||
information about the computing environment, `e.g.` the GPU type, the system, the library versions, etc... are printed
|
||||
out in the third section under `ENVIRONMENT INFORMATION`. This information can optionally be saved in a `.csv` file
|
||||
when adding the argument :obj:`save_to_csv=True` to :class:`~transformers.PyTorchBenchmarkArguments` and
|
||||
:class:`~transformers.TensorFlowBenchmarkArguments` respectively. In this case, every section is saved in a separate
|
||||
`.csv` file. The path to each `.csv` file can optionally be defined via the argument data classes.
|
||||
|
||||
Instead of benchmarking pre-trained models via their model identifier, `e.g.` `bert-base-uncased`, the user can
|
||||
alternatively benchmark an arbitrary configuration of any available model class. In this case, a :obj:`list` of
|
||||
configurations must be inserted with the benchmark args as follows.
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> ## PYTORCH CODE
|
||||
>>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
|
||||
|
||||
>>> args = PyTorchBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> config_base = BertConfig()
|
||||
>>> config_384_hid = BertConfig(hidden_size=384)
|
||||
>>> config_6_lay = BertConfig(num_hidden_layers=6)
|
||||
|
||||
>>> benchmark = PyTorchBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
|
||||
>>> benchmark.run()
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 128 0.006
|
||||
bert-base 8 512 0.006
|
||||
bert-base 8 128 0.018
|
||||
bert-base 8 512 0.088
|
||||
bert-384-hid 8 8 0.006
|
||||
bert-384-hid 8 32 0.006
|
||||
bert-384-hid 8 128 0.011
|
||||
bert-384-hid 8 512 0.054
|
||||
bert-6-lay 8 8 0.003
|
||||
bert-6-lay 8 32 0.004
|
||||
bert-6-lay 8 128 0.009
|
||||
bert-6-lay 8 512 0.044
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 1277
|
||||
bert-base 8 32 1281
|
||||
bert-base 8 128 1307
|
||||
bert-base 8 512 1539
|
||||
bert-384-hid 8 8 1005
|
||||
bert-384-hid 8 32 1027
|
||||
bert-384-hid 8 128 1035
|
||||
bert-384-hid 8 512 1255
|
||||
bert-6-lay 8 8 1097
|
||||
bert-6-lay 8 32 1101
|
||||
bert-6-lay 8 128 1127
|
||||
bert-6-lay 8 512 1359
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
|
||||
- transformers_version: 2.11.0
|
||||
- framework: PyTorch
|
||||
- use_torchscript: False
|
||||
- framework_version: 1.4.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:35:25.143267
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
>>> ## TENSORFLOW CODE
|
||||
>>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig
|
||||
|
||||
>>> args = TensorFlowBenchmarkArguments(models=["bert-base", "bert-384-hid", "bert-6-lay"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
|
||||
>>> config_base = BertConfig()
|
||||
>>> config_384_hid = BertConfig(hidden_size=384)
|
||||
>>> config_6_lay = BertConfig(num_hidden_layers=6)
|
||||
|
||||
>>> benchmark = TensorFlowBenchmark(args, configs=[config_base, config_384_hid, config_6_lay])
|
||||
>>> benchmark.run()
|
||||
==================== INFERENCE - SPEED - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Time in s
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 0.005
|
||||
bert-base 8 32 0.008
|
||||
bert-base 8 128 0.022
|
||||
bert-base 8 512 0.106
|
||||
bert-384-hid 8 8 0.005
|
||||
bert-384-hid 8 32 0.007
|
||||
bert-384-hid 8 128 0.018
|
||||
bert-384-hid 8 512 0.064
|
||||
bert-6-lay 8 8 0.002
|
||||
bert-6-lay 8 32 0.003
|
||||
bert-6-lay 8 128 0.0011
|
||||
bert-6-lay 8 512 0.074
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== INFERENCE - MEMORY - RESULT ====================
|
||||
--------------------------------------------------------------------------------
|
||||
Model Name Batch Size Seq Length Memory in MB
|
||||
--------------------------------------------------------------------------------
|
||||
bert-base 8 8 1330
|
||||
bert-base 8 32 1330
|
||||
bert-base 8 128 1330
|
||||
bert-base 8 512 1770
|
||||
bert-384-hid 8 8 1330
|
||||
bert-384-hid 8 32 1330
|
||||
bert-384-hid 8 128 1330
|
||||
bert-384-hid 8 512 1540
|
||||
bert-6-lay 8 8 1330
|
||||
bert-6-lay 8 32 1330
|
||||
bert-6-lay 8 128 1330
|
||||
bert-6-lay 8 512 1540
|
||||
--------------------------------------------------------------------------------
|
||||
|
||||
==================== ENVIRONMENT INFORMATION ====================
|
||||
|
||||
- transformers_version: 2.11.0
|
||||
- framework: Tensorflow
|
||||
- use_xla: False
|
||||
- framework_version: 2.2.0
|
||||
- python_version: 3.6.10
|
||||
- system: Linux
|
||||
- cpu: x86_64
|
||||
- architecture: 64bit
|
||||
- date: 2020-06-29
|
||||
- time: 09:38:15.487125
|
||||
- fp16: False
|
||||
- use_multiprocessing: True
|
||||
- only_pretrain_model: False
|
||||
- cpu_ram_mb: 32088
|
||||
- use_gpu: True
|
||||
- num_gpus: 1
|
||||
- gpu: TITAN RTX
|
||||
- gpu_ram_mb: 24217
|
||||
- gpu_power_watts: 280.0
|
||||
- gpu_performance_state: 2
|
||||
- use_tpu: False
|
||||
|
||||
|
||||
Again, `inference time` and `required memory` for `inference` are measured, but this time for customized configurations
|
||||
of the :obj:`BertModel` class. This feature can especially be helpful when deciding for which configuration the model
|
||||
should be trained.
|
||||
|
||||
|
||||
Benchmark best practices
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This section lists a couple of best practices one should be aware of when benchmarking a model.
|
||||
|
||||
- Currently, only single device benchmarking is supported. When benchmarking on GPU, it is recommended that the user
|
||||
specifies on which device the code should be run by setting the ``CUDA_VISIBLE_DEVICES`` environment variable in the
|
||||
shell, `e.g.` ``export CUDA_VISIBLE_DEVICES=0`` before running the code.
|
||||
- The option :obj:`no_multi_processing` should only be set to :obj:`True` for testing and debugging. To ensure accurate
|
||||
memory measurement it is recommended to run each memory benchmark in a separate process by making sure
|
||||
:obj:`no_multi_processing` is set to :obj:`True`.
|
||||
- One should always state the environment information when sharing the results of a model benchmark. Results can vary
|
||||
heavily between different GPU devices, library versions, etc., so that benchmark results on their own are not very
|
||||
useful for the community.
|
||||
|
||||
|
||||
Sharing your benchmark
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Previously all available core models (10 at the time) have been benchmarked for `inference time`, across many different
|
||||
settings: using PyTorch, with and without TorchScript, using TensorFlow, with and without XLA. All of those tests were
|
||||
done across CPUs (except for TensorFlow XLA) and GPUs.
|
||||
|
||||
The approach is detailed in the `following blogpost
|
||||
<https://medium.com/huggingface/benchmarking-transformers-pytorch-and-tensorflow-e2917fb891c2>`__ and the results are
|
||||
available `here
|
||||
<https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnxu5EAQkaohzrJbd5HdQ_w/edit?usp=sharing>`__.
|
||||
|
||||
With the new `benchmark` tools, it is easier than ever to share your benchmark results with the community
|
||||
|
||||
- :prefix_link:`PyTorch Benchmarking Results<examples/pytorch/benchmarking/README.md>`.
|
||||
- :prefix_link:`TensorFlow Benchmarking Results<examples/tensorflow/benchmarking/README.md>`.
|
||||
@@ -6,7 +6,7 @@ This page regroups resources around 🤗 Transformers developed by the community
|
||||
|
||||
| Resource | Description | Author |
|
||||
|:----------|:-------------|------:|
|
||||
| [Hugging Face Transformers Glossary Flashcards](https://www.darigovresearch.com/huggingface-transformers-glossary-flashcards) | A set of flashcards based on the [Transformers Docs Glossary](glossary) that has been put into a form which can be easily learnt/revised using [Anki ](https://apps.ankiweb.net/) an open source, cross platform app specifically designed for long term knowledge retention. See this [Introductory video on how to use the flashcards](https://www.youtube.com/watch?v=Dji_h7PILrw). | [Darigov Research](https://www.darigovresearch.com/) |
|
||||
| [Hugging Face Transformers Glossary Flashcards](https://www.darigovresearch.com/huggingface-transformers-glossary-flashcards) | A set of flashcards based on the [Transformers Docs Glossary](https://huggingface.co/transformers/master/glossary.html) that has been put into a form which can be easily learnt/revised using [Anki ](https://apps.ankiweb.net/) an open source, cross platform app specifically designed for long term knowledge retention. See this [Introductory video on how to use the flashcards](https://www.youtube.com/watch?v=Dji_h7PILrw). | [Darigov Research](https://www.darigovresearch.com/) |
|
||||
|
||||
## Community notebooks:
|
||||
|
||||
@@ -36,7 +36,7 @@ This page regroups resources around 🤗 Transformers developed by the community
|
||||
|[fine-tune a non-English GPT-2 Model with Trainer class](https://github.com/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb) | How to fine-tune a non-English GPT-2 Model with Trainer class | [Philipp Schmid](https://www.philschmid.de) | [](https://colab.research.google.com/github/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb)|
|
||||
|[Fine-tune a DistilBERT Model for Multi Label Classification task](https://github.com/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb) | How to fine-tune a DistilBERT Model for Multi Label Classification task | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [](https://colab.research.google.com/github/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb)|
|
||||
|[Fine-tune ALBERT for sentence-pair classification](https://github.com/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb) | How to fine-tune an ALBERT model or another BERT-based model for the sentence-pair classification task | [Nadir El Manouzi](https://github.com/NadirEM) | [](https://colab.research.google.com/github/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb)|
|
||||
|[Fine-tune Roberta for sentiment analysis](https://github.com/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb) | How to fine-tune a Roberta model for sentiment analysis | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [](https://colab.research.google.com/github/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb)|
|
||||
|[Fine-tune Roberta for sentiment analysis](https://github.com/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb) | How to fine-tune an Roberta model for sentiment analysis | [Dhaval Taunk](https://github.com/DhavalTaunk08) | [](https://colab.research.google.com/github/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb)|
|
||||
|[Evaluating Question Generation Models](https://github.com/flexudy-pipe/qugeev) | How accurate are the answers to questions generated by your seq2seq transformer model? | [Pascal Zoleko](https://github.com/zolekode) | [](https://colab.research.google.com/drive/1bpsSqCQU-iw_5nNoRm_crPq6FRuJthq_?usp=sharing)|
|
||||
|[Classify text with DistilBERT and Tensorflow](https://github.com/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb) | How to fine-tune DistilBERT for text classification in TensorFlow | [Peter Bayerle](https://github.com/peterbayerle) | [](https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb)|
|
||||
|[Leverage BERT for Encoder-Decoder Summarization on CNN/Dailymail](https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb) | How to warm-start a *EncoderDecoderModel* with a *bert-base-uncased* checkpoint for summarization on CNN/Dailymail | [Patrick von Platen](https://github.com/patrickvonplaten) | [](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb)|
|
||||
|
||||
226
docs/source/conf.py
Normal file
226
docs/source/conf.py
Normal file
@@ -0,0 +1,226 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Configuration file for the Sphinx documentation builder.
|
||||
#
|
||||
# This file does only contain a selection of the most common options. For a
|
||||
# full list see the documentation:
|
||||
# http://www.sphinx-doc.org/en/master/config
|
||||
|
||||
# -- Path setup --------------------------------------------------------------
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
#
|
||||
import os
|
||||
import sys
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../../src"))
|
||||
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = "transformers"
|
||||
copyright = "2020, The Hugging Face Team, Licenced under the Apache License, Version 2.0"
|
||||
author = "huggingface"
|
||||
|
||||
# The short X.Y version
|
||||
version = ""
|
||||
# The full version, including alpha/beta/rc tags
|
||||
release = "4.11.2"
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
# Prefix link to point to master, comment this during version release and uncomment below line
|
||||
extlinks = {"prefix_link": ("https://github.com/huggingface/transformers/blob/master/%s", "")}
|
||||
# Prefix link to always point to corresponding version, uncomment this during version release
|
||||
# extlinks = {'prefix_link': ('https://github.com/huggingface/transformers/blob/v'+ release + '/%s', '')}
|
||||
|
||||
# -- General configuration ---------------------------------------------------
|
||||
|
||||
# If your documentation needs a minimal Sphinx version, state it here.
|
||||
#
|
||||
# needs_sphinx = '1.0'
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = [
|
||||
"sphinx.ext.autodoc",
|
||||
"sphinx.ext.extlinks",
|
||||
"sphinx.ext.coverage",
|
||||
"sphinx.ext.napoleon",
|
||||
"recommonmark",
|
||||
"sphinx.ext.viewcode",
|
||||
"sphinx_markdown_tables",
|
||||
"sphinxext.opengraph",
|
||||
"sphinx_copybutton",
|
||||
]
|
||||
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ["_templates"]
|
||||
|
||||
# The suffix(es) of source filenames.
|
||||
# You can specify multiple suffix as a list of string:
|
||||
#
|
||||
source_suffix = [".rst", ".md"]
|
||||
# source_suffix = '.rst'
|
||||
|
||||
# The master toctree document.
|
||||
master_doc = "index"
|
||||
|
||||
# The language for content autogenerated by Sphinx. Refer to documentation
|
||||
# for a list of supported languages.
|
||||
#
|
||||
# This is also used if you do content translation via gettext catalogs.
|
||||
# Usually you set "language" from the command line for these cases.
|
||||
language = None
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This pattern also affects html_static_path and html_extra_path.
|
||||
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
|
||||
|
||||
# The name of the Pygments (syntax highlighting) style to use.
|
||||
pygments_style = None
|
||||
|
||||
# Remove the prompt when copying examples
|
||||
copybutton_prompt_text = r">>> |\.\.\. "
|
||||
copybutton_prompt_is_regexp = True
|
||||
|
||||
# -- Options for HTML output -------------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
#
|
||||
html_theme = "sphinx_rtd_theme"
|
||||
|
||||
# Theme options are theme-specific and customize the look and feel of a theme
|
||||
# further. For a list of options available for each theme, see the
|
||||
# documentation.
|
||||
#
|
||||
html_theme_options = {"analytics_id": "UA-83738774-2", "navigation_with_keys": True}
|
||||
|
||||
# Configuration for OpenGraph and Twitter Card Tags.
|
||||
# These are responsible for creating nice shareable social images https://ahrefs.com/blog/open-graph-meta-tags/
|
||||
# https://ogp.me/#type_website
|
||||
ogp_image = "https://huggingface.co/front/thumbnails/transformers.png"
|
||||
ogp_description = "State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2.0. Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. Its aim is to make cutting-edge NLP easier to use for everyone"
|
||||
ogp_description_length = 160
|
||||
|
||||
ogp_custom_meta_tags = [
|
||||
f'<meta name="twitter:image" content="{ogp_image}">',
|
||||
f'<meta name="twitter:description" content="{ogp_description}">',
|
||||
]
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ["_static"]
|
||||
|
||||
# Custom sidebar templates, must be a dictionary that maps document names
|
||||
# to template names.
|
||||
#
|
||||
# The default sidebars (for documents that don't match any pattern) are
|
||||
# defined by theme itself. Builtin themes are using these templates by
|
||||
# default: ``['localtoc.html', 'relations.html', 'sourcelink.html',
|
||||
# 'searchbox.html']``.
|
||||
#
|
||||
# html_sidebars = {}
|
||||
|
||||
# This must be the name of an image file (path relative to the configuration
|
||||
# directory) that is the favicon of the docs. Modern browsers use this as
|
||||
# the icon for tabs, windows and bookmarks. It should be a Windows-style
|
||||
# icon file (.ico).
|
||||
html_favicon = "favicon.ico"
|
||||
|
||||
|
||||
# -- Options for HTMLHelp output ---------------------------------------------
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = "transformersdoc"
|
||||
|
||||
|
||||
# -- Options for LaTeX output ------------------------------------------------
|
||||
|
||||
latex_elements = {
|
||||
# The paper size ('letterpaper' or 'a4paper').
|
||||
#
|
||||
# 'papersize': 'letterpaper',
|
||||
# The font size ('10pt', '11pt' or '12pt').
|
||||
#
|
||||
# 'pointsize': '10pt',
|
||||
# Additional stuff for the LaTeX preamble.
|
||||
#
|
||||
# 'preamble': '',
|
||||
# Latex figure (float) alignment
|
||||
#
|
||||
# 'figure_align': 'htbp',
|
||||
}
|
||||
|
||||
# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(master_doc, "transformers.tex", "transformers Documentation", "huggingface", "manual"),
|
||||
]
|
||||
|
||||
|
||||
# -- Options for manual page output ------------------------------------------
|
||||
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [(master_doc, "transformers", "transformers Documentation", [author], 1)]
|
||||
|
||||
|
||||
# -- Options for Texinfo output ----------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(
|
||||
master_doc,
|
||||
"transformers",
|
||||
"transformers Documentation",
|
||||
author,
|
||||
"transformers",
|
||||
"One line description of project.",
|
||||
"Miscellaneous",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
# -- Options for Epub output -------------------------------------------------
|
||||
|
||||
# Bibliographic Dublin Core info.
|
||||
epub_title = project
|
||||
|
||||
# The unique identifier of the text. This can be a ISBN number
|
||||
# or the project homepage.
|
||||
#
|
||||
# epub_identifier = ''
|
||||
|
||||
# A unique identification for the text.
|
||||
#
|
||||
# epub_uid = ''
|
||||
|
||||
# A list of files that should not be packed into the epub file.
|
||||
epub_exclude_files = ["search.html"]
|
||||
|
||||
# Localization
|
||||
locale_dirs = ['locale/']
|
||||
gettext_compact = False
|
||||
|
||||
def setup(app):
|
||||
app.add_css_file("css/huggingface.css")
|
||||
app.add_css_file("css/code-snippets.css")
|
||||
app.add_js_file("js/custom.js")
|
||||
|
||||
|
||||
# -- Extension configuration -------------------------------------------------
|
||||
@@ -13,8 +13,8 @@
|
||||
Converting Tensorflow Checkpoints
|
||||
=======================================================================================================================
|
||||
|
||||
A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints to models
|
||||
that can be loaded using the ``from_pretrained`` methods of the library.
|
||||
A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints in models
|
||||
than be loaded using the ``from_pretrained`` methods of the library.
|
||||
|
||||
.. note::
|
||||
Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**) available in any
|
||||
@@ -26,22 +26,22 @@ BERT
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google
|
||||
<https://github.com/google-research/bert#pre-trained-models>`_) in a PyTorch save file by using the
|
||||
<https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the
|
||||
:prefix_link:`convert_bert_original_tf_checkpoint_to_pytorch.py
|
||||
<src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py>` script.
|
||||
|
||||
This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``) and the associated
|
||||
configuration file (``bert_config.json``), and creates a PyTorch model for this configuration, loads the weights from
|
||||
the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can
|
||||
be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , :prefix_link:`run_glue.py
|
||||
<examples/pytorch/text-classification/run_glue.py>` ).
|
||||
This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated
|
||||
configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights
|
||||
from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that
|
||||
can be imported using ``from_pretrained()`` (see example in :doc:`quicktour` , :prefix_link:`run_glue.py
|
||||
<examples/pytorch/text-classification/run_glue.py>` \ ).
|
||||
|
||||
You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
|
||||
checkpoint (the three files starting with ``bert_model.ckpt``) but be sure to keep the configuration file (\
|
||||
``bert_config.json``) and the vocabulary file (``vocab.txt``) as these are needed for the PyTorch model too.
|
||||
checkpoint (the three files starting with ``bert_model.ckpt``\ ) but be sure to keep the configuration file (\
|
||||
``bert_config.json``\ ) and the vocabulary file (\ ``vocab.txt``\ ) as these are needed for the PyTorch model too.
|
||||
|
||||
To run this specific conversion script you will need to have TensorFlow and PyTorch installed (``pip install
|
||||
tensorflow``). The rest of the repository only requires PyTorch.
|
||||
To run this specific conversion script you will need to have TensorFlow and PyTorch installed (\ ``pip install
|
||||
tensorflow``\ ). The rest of the repository only requires PyTorch.
|
||||
|
||||
Here is an example of the conversion process for a pre-trained ``BERT-Base Uncased`` model:
|
||||
|
||||
@@ -64,9 +64,9 @@ Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
|
||||
:prefix_link:`convert_albert_original_tf_checkpoint_to_pytorch.py
|
||||
<src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py>` script.
|
||||
|
||||
The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``) and the accompanying
|
||||
configuration file (``albert_config.json``), then creates and saves a PyTorch model. To run this conversion you will
|
||||
need to have TensorFlow and PyTorch installed.
|
||||
The CLI takes as input a TensorFlow checkpoint (three files starting with ``model.ckpt-best``\ ) and the accompanying
|
||||
configuration file (\ ``albert_config.json``\ ), then creates and saves a PyTorch model. To run this conversion you
|
||||
will need to have TensorFlow and PyTorch installed.
|
||||
|
||||
Here is an example of the conversion process for the pre-trained ``ALBERT Base`` model:
|
||||
|
||||
@@ -104,7 +104,7 @@ OpenAI GPT-2
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see `here
|
||||
<https://github.com/openai/gpt-2>`__)
|
||||
<https://github.com/openai/gpt-2>`__\ )
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
@@ -120,7 +120,7 @@ Transformer-XL
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
Here is an example of the conversion process for a pre-trained Transformer-XL model (see `here
|
||||
<https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__)
|
||||
<https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models>`__\ )
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
|
||||
@@ -1,681 +0,0 @@
|
||||
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
-->
|
||||
|
||||
# How to fine-tune a model for common downstream tasks
|
||||
|
||||
[[open-in-colab]]
|
||||
|
||||
This guide will show you how to fine-tune 🤗 Transformers models for common downstream tasks. You will use the 🤗
|
||||
Datasets library to quickly load and preprocess the datasets, getting them ready for training with PyTorch and
|
||||
TensorFlow.
|
||||
|
||||
Before you begin, make sure you have the 🤗 Datasets library installed. For more detailed installation instructions,
|
||||
refer to the 🤗 Datasets [installation page](https://huggingface.co/docs/datasets/installation.html). All of the
|
||||
examples in this guide will use 🤗 Datasets to load and preprocess a dataset.
|
||||
|
||||
```bash
|
||||
pip install datasets
|
||||
```
|
||||
|
||||
Learn how to fine-tune a model for:
|
||||
|
||||
- [seq_imdb](#seq_imdb)
|
||||
- [tok_ner](#tok_ner)
|
||||
- [qa_squad](#qa_squad)
|
||||
|
||||
<a id='seq_imdb'></a>
|
||||
|
||||
## Sequence classification with IMDb reviews
|
||||
|
||||
Sequence classification refers to the task of classifying sequences of text according to a given number of classes. In
|
||||
this example, learn how to fine-tune a model on the [IMDb dataset](https://huggingface.co/datasets/imdb) to determine
|
||||
whether a review is positive or negative.
|
||||
|
||||
<Tip>
|
||||
|
||||
For a more in-depth example of how to fine-tune a model for text classification, take a look at the corresponding
|
||||
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb)
|
||||
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification-tf.ipynb).
|
||||
|
||||
</Tip>
|
||||
|
||||
### Load IMDb dataset
|
||||
|
||||
The 🤗 Datasets library makes it simple to load a dataset:
|
||||
|
||||
```python
|
||||
from datasets import load_dataset
|
||||
imdb = load_dataset("imdb")
|
||||
```
|
||||
|
||||
This loads a `DatasetDict` object which you can index into to view an example:
|
||||
|
||||
```python
|
||||
imdb["train"][0]
|
||||
{'label': 1,
|
||||
'text': 'Bromwell High is a cartoon comedy. It ran at the same time as some other programs about school life, such as "Teachers". My 35 years in the teaching profession lead me to believe that Bromwell High\'s satire is much closer to reality than is "Teachers". The scramble to survive financially, the insightful students who can see right through their pathetic teachers\' pomp, the pettiness of the whole situation, all remind me of the schools I knew and their students. When I saw the episode in which a student repeatedly tried to burn down the school, I immediately recalled ......... at .......... High. A classic line: INSPECTOR: I\'m here to sack one of your teachers. STUDENT: Welcome to Bromwell High. I expect that many adults of my age think that Bromwell High is far fetched. What a pity that it isn\'t!'
|
||||
}
|
||||
```
|
||||
|
||||
### Preprocess
|
||||
|
||||
The next step is to tokenize the text into a readable format by the model. It is important to load the same tokenizer a
|
||||
model was trained with to ensure appropriately tokenized words. Load the DistilBERT tokenizer with the
|
||||
[`AutoTokenizer`] because we will eventually train a classifier using a pretrained [DistilBERT](https://huggingface.co/distilbert-base-uncased) model:
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Now that you have instantiated a tokenizer, create a function that will tokenize the text. You should also truncate
|
||||
longer sequences in the text to be no longer than the model's maximum input length:
|
||||
|
||||
```python
|
||||
def preprocess_function(examples):
|
||||
return tokenizer(examples["text"], truncation=True)
|
||||
```
|
||||
|
||||
Use 🤗 Datasets `map` function to apply the preprocessing function to the entire dataset. You can also set
|
||||
`batched=True` to apply the preprocessing function to multiple elements of the dataset at once for faster
|
||||
preprocessing:
|
||||
|
||||
```python
|
||||
tokenized_imdb = imdb.map(preprocess_function, batched=True)
|
||||
```
|
||||
|
||||
Lastly, pad your text so they are a uniform length. While it is possible to pad your text in the `tokenizer` function
|
||||
by setting `padding=True`, it is more efficient to only pad the text to the length of the longest element in its
|
||||
batch. This is known as **dynamic padding**. You can do this with the `DataCollatorWithPadding` function:
|
||||
|
||||
```python
|
||||
from transformers import DataCollatorWithPadding
|
||||
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
|
||||
```
|
||||
|
||||
### Fine-tune with the Trainer API
|
||||
|
||||
Now load your model with the [`AutoModelForSequenceClassification`] class along with the number of expected labels:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForSequenceClassification
|
||||
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
|
||||
```
|
||||
|
||||
At this point, only three steps remain:
|
||||
|
||||
1. Define your training hyperparameters in [`TrainingArguments`].
|
||||
2. Pass the training arguments to a [`Trainer`] along with the model, dataset, tokenizer, and data collator.
|
||||
3. Call [`Trainer.train()`] to fine-tune your model.
|
||||
|
||||
```python
|
||||
from transformers import TrainingArguments, Trainer
|
||||
|
||||
training_args = TrainingArguments(
|
||||
output_dir='./results',
|
||||
learning_rate=2e-5,
|
||||
per_device_train_batch_size=16,
|
||||
per_device_eval_batch_size=16,
|
||||
num_train_epochs=5,
|
||||
weight_decay=0.01,
|
||||
)
|
||||
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=tokenized_imdb["train"],
|
||||
eval_dataset=tokenized_imdb["test"],
|
||||
tokenizer=tokenizer,
|
||||
data_collator=data_collator,
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
```
|
||||
|
||||
### Fine-tune with TensorFlow
|
||||
|
||||
Fine-tuning with TensorFlow is just as easy, with only a few differences.
|
||||
|
||||
Start by batching the processed examples together with dynamic padding using the [`DataCollatorWithPadding`] function.
|
||||
Make sure you set `return_tensors="tf"` to return `tf.Tensor` outputs instead of PyTorch tensors!
|
||||
|
||||
```python
|
||||
from transformers import DataCollatorWithPadding
|
||||
data_collator = DataCollatorWithPadding(tokenizer, return_tensors="tf")
|
||||
```
|
||||
|
||||
Next, convert your datasets to the `tf.data.Dataset` format with `to_tf_dataset`. Specify inputs and labels in the
|
||||
`columns` argument:
|
||||
|
||||
```python
|
||||
tf_train_dataset = tokenized_imdb["train"].to_tf_dataset(
|
||||
columns=['attention_mask', 'input_ids', 'label'],
|
||||
shuffle=True,
|
||||
batch_size=16,
|
||||
collate_fn=data_collator,
|
||||
)
|
||||
|
||||
tf_validation_dataset = tokenized_imdb["train"].to_tf_dataset(
|
||||
columns=['attention_mask', 'input_ids', 'label'],
|
||||
shuffle=False,
|
||||
batch_size=16,
|
||||
collate_fn=data_collator,
|
||||
)
|
||||
```
|
||||
|
||||
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
|
||||
|
||||
```python
|
||||
from transformers import create_optimizer
|
||||
import tensorflow as tf
|
||||
|
||||
batch_size = 16
|
||||
num_epochs = 5
|
||||
batches_per_epoch = len(tokenized_imdb["train"]) // batch_size
|
||||
total_train_steps = int(batches_per_epoch * num_epochs)
|
||||
optimizer, schedule = create_optimizer(
|
||||
init_lr=2e-5,
|
||||
num_warmup_steps=0,
|
||||
num_train_steps=total_train_steps
|
||||
)
|
||||
```
|
||||
|
||||
Load your model with the [`TFAutoModelForSequenceClassification`] class along with the number of expected labels:
|
||||
|
||||
```python
|
||||
from transformers import TFAutoModelForSequenceClassification
|
||||
model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
|
||||
```
|
||||
|
||||
Compile the model:
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
model.compile(optimizer=optimizer)
|
||||
```
|
||||
|
||||
Finally, fine-tune the model by calling `model.fit`:
|
||||
|
||||
```python
|
||||
model.fit(
|
||||
tf_train_set,
|
||||
validation_data=tf_validation_set,
|
||||
epochs=num_train_epochs,
|
||||
)
|
||||
```
|
||||
|
||||
<a id='tok_ner'></a>
|
||||
|
||||
## Token classification with WNUT emerging entities
|
||||
|
||||
Token classification refers to the task of classifying individual tokens in a sentence. One of the most common token
|
||||
classification tasks is Named Entity Recognition (NER). NER attempts to find a label for each entity in a sentence,
|
||||
such as a person, location, or organization. In this example, learn how to fine-tune a model on the [WNUT 17](https://huggingface.co/datasets/wnut_17) dataset to detect new entities.
|
||||
|
||||
<Tip>
|
||||
|
||||
For a more in-depth example of how to fine-tune a model for token classification, take a look at the corresponding
|
||||
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)
|
||||
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification-tf.ipynb).
|
||||
|
||||
</Tip>
|
||||
|
||||
### Load WNUT 17 dataset
|
||||
|
||||
Load the WNUT 17 dataset from the 🤗 Datasets library:
|
||||
|
||||
```python
|
||||
from datasets import load_dataset
|
||||
wnut = load_dataset("wnut_17")
|
||||
```
|
||||
|
||||
A quick look at the dataset shows the labels associated with each word in the sentence:
|
||||
|
||||
```python
|
||||
wnut["train"][0]
|
||||
{'id': '0',
|
||||
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
|
||||
'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']
|
||||
}
|
||||
```
|
||||
|
||||
View the specific NER tags by:
|
||||
|
||||
```python
|
||||
label_list = wnut["train"].features[f"ner_tags"].feature.names
|
||||
label_list
|
||||
['O',
|
||||
'B-corporation',
|
||||
'I-corporation',
|
||||
'B-creative-work',
|
||||
'I-creative-work',
|
||||
'B-group',
|
||||
'I-group',
|
||||
'B-location',
|
||||
'I-location',
|
||||
'B-person',
|
||||
'I-person',
|
||||
'B-product',
|
||||
'I-product'
|
||||
]
|
||||
```
|
||||
|
||||
A letter prefixes each NER tag which can mean:
|
||||
|
||||
- `B-` indicates the beginning of an entity.
|
||||
- `I-` indicates a token is contained inside the same entity (e.g., the `State` token is a part of an entity like
|
||||
`Empire State Building`).
|
||||
- `0` indicates the token doesn't correspond to any entity.
|
||||
|
||||
### Preprocess
|
||||
|
||||
Now you need to tokenize the text. Load the DistilBERT tokenizer with an [`AutoTokenizer`]:
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Since the input has already been split into words, set `is_split_into_words=True` to tokenize the words into
|
||||
subwords:
|
||||
|
||||
```python
|
||||
tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
|
||||
tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
|
||||
tokens
|
||||
['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']
|
||||
```
|
||||
|
||||
The addition of the special tokens `[CLS]` and `[SEP]` and subword tokenization creates a mismatch between the
|
||||
input and labels. Realign the labels and tokens by:
|
||||
|
||||
1. Mapping all tokens to their corresponding word with the `word_ids` method.
|
||||
2. Assigning the label `-100` to the special tokens `[CLS]` and ``[SEP]``` so the PyTorch loss function ignores
|
||||
them.
|
||||
3. Only labeling the first token of a given word. Assign `-100` to the other subtokens from the same word.
|
||||
|
||||
Here is how you can create a function that will realign the labels and tokens:
|
||||
|
||||
```python
|
||||
def tokenize_and_align_labels(examples):
|
||||
tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
|
||||
|
||||
labels = []
|
||||
for i, label in enumerate(examples[f"ner_tags"]):
|
||||
word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
|
||||
previous_word_idx = None
|
||||
label_ids = []
|
||||
for word_idx in word_ids: # Set the special tokens to -100.
|
||||
if word_idx is None:
|
||||
label_ids.append(-100)
|
||||
elif word_idx != previous_word_idx: # Only label the first token of a given word.
|
||||
label_ids.append(label[word_idx])
|
||||
|
||||
labels.append(label_ids)
|
||||
|
||||
tokenized_inputs["labels"] = labels
|
||||
return tokenized_inputs
|
||||
```
|
||||
|
||||
Now tokenize and align the labels over the entire dataset with 🤗 Datasets `map` function:
|
||||
|
||||
```python
|
||||
tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
|
||||
```
|
||||
|
||||
Finally, pad your text and labels, so they are a uniform length:
|
||||
|
||||
```python
|
||||
from transformers import DataCollatorForTokenClassification
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer)
|
||||
```
|
||||
|
||||
### Fine-tune with the Trainer API
|
||||
|
||||
Load your model with the [`AutoModelForTokenClassification`] class along with the number of expected labels:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
|
||||
model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))
|
||||
```
|
||||
|
||||
Gather your training arguments in [`TrainingArguments`]:
|
||||
|
||||
```python
|
||||
training_args = TrainingArguments(
|
||||
output_dir='./results',
|
||||
evaluation_strategy="epoch",
|
||||
learning_rate=2e-5,
|
||||
per_device_train_batch_size=16,
|
||||
per_device_eval_batch_size=16,
|
||||
num_train_epochs=3,
|
||||
weight_decay=0.01,
|
||||
)
|
||||
```
|
||||
|
||||
Collect your model, training arguments, dataset, data collator, and tokenizer in [`Trainer`]:
|
||||
|
||||
```python
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=tokenized_wnut["train"],
|
||||
eval_dataset=tokenized_wnut["test"],
|
||||
data_collator=data_collator,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
```
|
||||
|
||||
Fine-tune your model:
|
||||
|
||||
```python
|
||||
trainer.train()
|
||||
```
|
||||
|
||||
### Fine-tune with TensorFlow
|
||||
|
||||
Batch your examples together and pad your text and labels, so they are a uniform length:
|
||||
|
||||
```python
|
||||
from transformers import DataCollatorForTokenClassification
|
||||
data_collator = DataCollatorForTokenClassification(tokenizer, return_tensors="tf")
|
||||
```
|
||||
|
||||
Convert your datasets to the `tf.data.Dataset` format with `to_tf_dataset`:
|
||||
|
||||
```python
|
||||
tf_train_set = tokenized_wnut["train"].to_tf_dataset(
|
||||
columns=["attention_mask", "input_ids", "labels"],
|
||||
shuffle=True,
|
||||
batch_size=16,
|
||||
collate_fn=data_collator,
|
||||
)
|
||||
|
||||
tf_validation_set = tokenized_wnut["validation"].to_tf_dataset(
|
||||
columns=["attention_mask", "input_ids", "labels"],
|
||||
shuffle=False,
|
||||
batch_size=16,
|
||||
collate_fn=data_collator,
|
||||
)
|
||||
```
|
||||
|
||||
Load the model with the [`TFAutoModelForTokenClassification`] class along with the number of expected labels:
|
||||
|
||||
```python
|
||||
from transformers import TFAutoModelForTokenClassification
|
||||
model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased", num_labels=len(label_list))
|
||||
```
|
||||
|
||||
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
|
||||
|
||||
```python
|
||||
from transformers import create_optimizer
|
||||
|
||||
batch_size = 16
|
||||
num_train_epochs = 3
|
||||
num_train_steps = (len(tokenized_datasets["train"]) // batch_size) * num_train_epochs
|
||||
optimizer, lr_schedule = create_optimizer(
|
||||
init_lr=2e-5,
|
||||
num_train_steps=num_train_steps,
|
||||
weight_decay_rate=0.01,
|
||||
num_warmup_steps=0,
|
||||
)
|
||||
```
|
||||
|
||||
Compile the model:
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
model.compile(optimizer=optimizer)
|
||||
```
|
||||
|
||||
Call `model.fit` to fine-tune your model:
|
||||
|
||||
```python
|
||||
model.fit(
|
||||
tf_train_set,
|
||||
validation_data=tf_validation_set,
|
||||
epochs=num_train_epochs,
|
||||
)
|
||||
```
|
||||
|
||||
<a id='qa_squad'></a>
|
||||
|
||||
## Question Answering with SQuAD
|
||||
|
||||
There are many types of question answering (QA) tasks. Extractive QA focuses on identifying the answer from the text
|
||||
given a question. In this example, learn how to fine-tune a model on the [SQuAD](https://huggingface.co/datasets/squad) dataset.
|
||||
|
||||
<Tip>
|
||||
|
||||
For a more in-depth example of how to fine-tune a model for question answering, take a look at the corresponding
|
||||
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb)
|
||||
or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering-tf.ipynb).
|
||||
|
||||
</Tip>
|
||||
|
||||
### Load SQuAD dataset
|
||||
|
||||
Load the SQuAD dataset from the 🤗 Datasets library:
|
||||
|
||||
```python
|
||||
from datasets import load_dataset
|
||||
squad = load_dataset("squad")
|
||||
```
|
||||
|
||||
Take a look at an example from the dataset:
|
||||
|
||||
```python
|
||||
squad["train"][0]
|
||||
{'answers': {'answer_start': [515], 'text': ['Saint Bernadette Soubirous']},
|
||||
'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.',
|
||||
'id': '5733be284776f41900661182',
|
||||
'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
|
||||
'title': 'University_of_Notre_Dame'
|
||||
}
|
||||
```
|
||||
|
||||
### Preprocess
|
||||
|
||||
Load the DistilBERT tokenizer with an [`AutoTokenizer`]:
|
||||
|
||||
```python
|
||||
from transformers import AutoTokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
|
||||
```
|
||||
|
||||
There are a few things to be aware of when preprocessing text for question answering:
|
||||
|
||||
1. Some examples in a dataset may have a very long `context` that exceeds the maximum input length of the model. You
|
||||
can deal with this by truncating the `context` and set `truncation="only_second"`.
|
||||
2. Next, you need to map the start and end positions of the answer to the original context. Set
|
||||
`return_offset_mapping=True` to handle this.
|
||||
3. With the mapping in hand, you can find the start and end tokens of the answer. Use the `sequence_ids` method to
|
||||
find which part of the offset corresponds to the question, and which part of the offset corresponds to the context.
|
||||
|
||||
Assemble everything in a preprocessing function as shown below:
|
||||
|
||||
```python
|
||||
def preprocess_function(examples):
|
||||
questions = [q.strip() for q in examples["question"]]
|
||||
inputs = tokenizer(
|
||||
questions,
|
||||
examples["context"],
|
||||
max_length=384,
|
||||
truncation="only_second",
|
||||
return_offsets_mapping=True,
|
||||
padding="max_length",
|
||||
)
|
||||
|
||||
offset_mapping = inputs.pop("offset_mapping")
|
||||
answers = examples["answers"]
|
||||
start_positions = []
|
||||
end_positions = []
|
||||
|
||||
for i, offset in enumerate(offset_mapping):
|
||||
answer = answers[i]
|
||||
start_char = answer["answer_start"][0]
|
||||
end_char = answer["answer_start"][0] + len(answer["text"][0])
|
||||
sequence_ids = inputs.sequence_ids(i)
|
||||
|
||||
# Find the start and end of the context
|
||||
idx = 0
|
||||
while sequence_ids[idx] != 1:
|
||||
idx += 1
|
||||
context_start = idx
|
||||
while sequence_ids[idx] == 1:
|
||||
idx += 1
|
||||
context_end = idx - 1
|
||||
|
||||
# If the answer is not fully inside the context, label it (0, 0)
|
||||
if offset[context_start][0] > end_char or offset[context_end][1] < start_char:
|
||||
start_positions.append(0)
|
||||
end_positions.append(0)
|
||||
else:
|
||||
# Otherwise it's the start and end token positions
|
||||
idx = context_start
|
||||
while idx <= context_end and offset[idx][0] <= start_char:
|
||||
idx += 1
|
||||
start_positions.append(idx - 1)
|
||||
|
||||
idx = context_end
|
||||
while idx >= context_start and offset[idx][1] >= end_char:
|
||||
idx -= 1
|
||||
end_positions.append(idx + 1)
|
||||
|
||||
inputs["start_positions"] = start_positions
|
||||
inputs["end_positions"] = end_positions
|
||||
return inputs
|
||||
```
|
||||
|
||||
Apply the preprocessing function over the entire dataset with 🤗 Datasets `map` function:
|
||||
|
||||
```python
|
||||
tokenized_squad = squad.map(preprocess_function, batched=True, remove_columns=squad["train"].column_names)
|
||||
```
|
||||
|
||||
Batch the processed examples together:
|
||||
|
||||
```python
|
||||
from transformers import default_data_collator
|
||||
data_collator = default_data_collator
|
||||
```
|
||||
|
||||
### Fine-tune with the Trainer API
|
||||
|
||||
Load your model with the [`AutoModelForQuestionAnswering`] class:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForQuestionAnswering, TrainingArguments, Trainer
|
||||
model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Gather your training arguments in [`TrainingArguments`]:
|
||||
|
||||
```python
|
||||
training_args = TrainingArguments(
|
||||
output_dir='./results',
|
||||
evaluation_strategy="epoch",
|
||||
learning_rate=2e-5,
|
||||
per_device_train_batch_size=16,
|
||||
per_device_eval_batch_size=16,
|
||||
num_train_epochs=3,
|
||||
weight_decay=0.01,
|
||||
)
|
||||
```
|
||||
|
||||
Collect your model, training arguments, dataset, data collator, and tokenizer in [`Trainer`]:
|
||||
|
||||
```python
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=tokenized_squad["train"],
|
||||
eval_dataset=tokenized_squad["validation"],
|
||||
data_collator=data_collator,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
```
|
||||
|
||||
Fine-tune your model:
|
||||
|
||||
```python
|
||||
trainer.train()
|
||||
```
|
||||
|
||||
### Fine-tune with TensorFlow
|
||||
|
||||
Batch the processed examples together with a TensorFlow default data collator:
|
||||
|
||||
```python
|
||||
from transformers.data.data_collator import tf_default_collator
|
||||
data_collator = tf_default_collator
|
||||
```
|
||||
|
||||
Convert your datasets to the `tf.data.Dataset` format with the `to_tf_dataset` function:
|
||||
|
||||
```python
|
||||
tf_train_set = tokenized_squad["train"].to_tf_dataset(
|
||||
columns=["attention_mask", "input_ids", "start_positions", "end_positions"],
|
||||
dummy_labels=True,
|
||||
shuffle=True,
|
||||
batch_size=16,
|
||||
collate_fn=data_collator,
|
||||
)
|
||||
|
||||
tf_validation_set = tokenized_squad["validation"].to_tf_dataset(
|
||||
columns=["attention_mask", "input_ids", "start_positions", "end_positions"],
|
||||
dummy_labels=True,
|
||||
shuffle=False,
|
||||
batch_size=16,
|
||||
collate_fn=data_collator,
|
||||
)
|
||||
```
|
||||
|
||||
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
|
||||
|
||||
```python
|
||||
from transformers import create_optimizer
|
||||
|
||||
batch_size = 16
|
||||
num_epochs = 2
|
||||
total_train_steps = (len(tokenized_squad["train"]) // batch_size) * num_epochs
|
||||
optimizer, schedule = create_optimizer(
|
||||
init_lr=2e-5,
|
||||
num_warmup_steps=0,
|
||||
num_train_steps=total_train_steps,
|
||||
)
|
||||
```
|
||||
|
||||
Load your model with the [`TFAutoModelForQuestionAnswering`] class:
|
||||
|
||||
```python
|
||||
from transformers import TFAutoModelForQuestionAnswering
|
||||
model = TFAutoModelForQuestionAnswering("distilbert-base-uncased")
|
||||
```
|
||||
|
||||
Compile the model:
|
||||
|
||||
```python
|
||||
import tensorflow as tf
|
||||
model.compile(optimizer=optimizer)
|
||||
```
|
||||
|
||||
Call `model.fit` to fine-tune the model:
|
||||
|
||||
```python
|
||||
model.fit(
|
||||
tf_train_set,
|
||||
validation_data=tf_validation_set,
|
||||
epochs=num_train_epochs,
|
||||
)
|
||||
```
|
||||
729
docs/source/custom_datasets.rst
Normal file
729
docs/source/custom_datasets.rst
Normal file
@@ -0,0 +1,729 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Fine-tuning with custom datasets
|
||||
=======================================================================================================================
|
||||
|
||||
.. note::
|
||||
|
||||
The datasets used in this tutorial are available and can be more easily accessed using the `🤗 Datasets library
|
||||
<https://github.com/huggingface/datasets>`_. We do not use this library to access the datasets here since this
|
||||
tutorial meant to illustrate how to work with your own data. A brief of introduction can be found at the end of the
|
||||
tutorial in the section ":ref:`datasetslib`".
|
||||
|
||||
This tutorial will take you through several examples of using 🤗 Transformers models with your own datasets. The guide
|
||||
shows one of many valid workflows for using these models and is meant to be illustrative rather than definitive. We
|
||||
show examples of reading in several data formats, preprocessing the data for several types of tasks, and then preparing
|
||||
the data into PyTorch/TensorFlow ``Dataset`` objects which can easily be used either with
|
||||
:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow.
|
||||
|
||||
We include several examples, each of which demonstrates a different type of common downstream task:
|
||||
|
||||
- :ref:`seq_imdb`
|
||||
- :ref:`tok_ner`
|
||||
- :ref:`qa_squad`
|
||||
- :ref:`resources`
|
||||
|
||||
.. _seq_imdb:
|
||||
|
||||
Sequence Classification with IMDb Reviews
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
.. note::
|
||||
|
||||
This dataset can be explored in the Hugging Face model hub (`IMDb <https://huggingface.co/datasets/imdb>`_), and
|
||||
can be alternatively downloaded with the 🤗 Datasets library with ``load_dataset("imdb")``.
|
||||
|
||||
In this example, we'll show how to download, tokenize, and train a model on the IMDb reviews dataset. This task takes
|
||||
the text of a review and requires the model to predict whether the sentiment of the review is positive or negative.
|
||||
Let's start by downloading the dataset from the `Large Movie Review Dataset
|
||||
<http://ai.stanford.edu/~amaas/data/sentiment/>`_ webpage.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
|
||||
tar -xf aclImdb_v1.tar.gz
|
||||
|
||||
This data is organized into ``pos`` and ``neg`` folders with one text file per example. Let's write a function that can
|
||||
read this in.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
def read_imdb_split(split_dir):
|
||||
split_dir = Path(split_dir)
|
||||
texts = []
|
||||
labels = []
|
||||
for label_dir in ["pos", "neg"]:
|
||||
for text_file in (split_dir/label_dir).iterdir():
|
||||
texts.append(text_file.read_text())
|
||||
labels.append(0 if label_dir is "neg" else 1)
|
||||
|
||||
return texts, labels
|
||||
|
||||
train_texts, train_labels = read_imdb_split('aclImdb/train')
|
||||
test_texts, test_labels = read_imdb_split('aclImdb/test')
|
||||
|
||||
We now have a train and test dataset, but let's also also create a validation set which we can use for for evaluation
|
||||
and tuning without tainting our test set results. Sklearn has a convenient utility for creating such splits:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from sklearn.model_selection import train_test_split
|
||||
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
|
||||
|
||||
Alright, we've read in our dataset. Now let's tackle tokenization. We'll eventually train a classifier using
|
||||
pre-trained DistilBert, so let's use the DistilBert tokenizer.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import DistilBertTokenizerFast
|
||||
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
|
||||
|
||||
Now we can simply pass our texts to the tokenizer. We'll pass ``truncation=True`` and ``padding=True``, which will
|
||||
ensure that all of our sequences are padded to the same length and are truncated to be no longer model's maximum input
|
||||
length. This will allow us to feed batches of sequences into the model at the same time.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
|
||||
val_encodings = tokenizer(val_texts, truncation=True, padding=True)
|
||||
test_encodings = tokenizer(test_texts, truncation=True, padding=True)
|
||||
|
||||
Now, let's turn our labels and encodings into a Dataset object. In PyTorch, this is done by subclassing a
|
||||
``torch.utils.data.Dataset`` object and implementing ``__len__`` and ``__getitem__``. In TensorFlow, we pass our input
|
||||
encodings and labels to the ``from_tensor_slices`` constructor method. We put the data in this format so that the data
|
||||
can be easily batched such that each key in the batch encoding corresponds to a named parameter of the
|
||||
:meth:`~transformers.DistilBertForSequenceClassification.forward` method of the model we will train.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
import torch
|
||||
|
||||
class IMDbDataset(torch.utils.data.Dataset):
|
||||
def __init__(self, encodings, labels):
|
||||
self.encodings = encodings
|
||||
self.labels = labels
|
||||
|
||||
def __getitem__(self, idx):
|
||||
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
|
||||
item['labels'] = torch.tensor(self.labels[idx])
|
||||
return item
|
||||
|
||||
def __len__(self):
|
||||
return len(self.labels)
|
||||
|
||||
train_dataset = IMDbDataset(train_encodings, train_labels)
|
||||
val_dataset = IMDbDataset(val_encodings, val_labels)
|
||||
test_dataset = IMDbDataset(test_encodings, test_labels)
|
||||
## TENSORFLOW CODE
|
||||
import tensorflow as tf
|
||||
|
||||
train_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(train_encodings),
|
||||
train_labels
|
||||
))
|
||||
val_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(val_encodings),
|
||||
val_labels
|
||||
))
|
||||
test_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(test_encodings),
|
||||
test_labels
|
||||
))
|
||||
|
||||
Now that our datasets our ready, we can fine-tune a model either with the 🤗
|
||||
:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow. See :doc:`training
|
||||
<training>`.
|
||||
|
||||
.. _ft_trainer:
|
||||
|
||||
Fine-tuning with Trainer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The steps above prepared the datasets in the way that the trainer is expected. Now all we need to do is create a model
|
||||
to fine-tune, define the :class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` and
|
||||
instantiate a :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from transformers import DistilBertForSequenceClassification, Trainer, TrainingArguments
|
||||
|
||||
training_args = TrainingArguments(
|
||||
output_dir='./results', # output directory
|
||||
num_train_epochs=3, # total number of training epochs
|
||||
per_device_train_batch_size=16, # batch size per device during training
|
||||
per_device_eval_batch_size=64, # batch size for evaluation
|
||||
warmup_steps=500, # number of warmup steps for learning rate scheduler
|
||||
weight_decay=0.01, # strength of weight decay
|
||||
logging_dir='./logs', # directory for storing logs
|
||||
logging_steps=10,
|
||||
)
|
||||
|
||||
model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
|
||||
trainer = Trainer(
|
||||
model=model, # the instantiated 🤗 Transformers model to be trained
|
||||
args=training_args, # training arguments, defined above
|
||||
train_dataset=train_dataset, # training dataset
|
||||
eval_dataset=val_dataset # evaluation dataset
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
## TENSORFLOW CODE
|
||||
from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments
|
||||
|
||||
training_args = TFTrainingArguments(
|
||||
output_dir='./results', # output directory
|
||||
num_train_epochs=3, # total number of training epochs
|
||||
per_device_train_batch_size=16, # batch size per device during training
|
||||
per_device_eval_batch_size=64, # batch size for evaluation
|
||||
warmup_steps=500, # number of warmup steps for learning rate scheduler
|
||||
weight_decay=0.01, # strength of weight decay
|
||||
logging_dir='./logs', # directory for storing logs
|
||||
logging_steps=10,
|
||||
)
|
||||
|
||||
with training_args.strategy.scope():
|
||||
model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
|
||||
|
||||
trainer = TFTrainer(
|
||||
model=model, # the instantiated 🤗 Transformers model to be trained
|
||||
args=training_args, # training arguments, defined above
|
||||
train_dataset=train_dataset, # training dataset
|
||||
eval_dataset=val_dataset # evaluation dataset
|
||||
)
|
||||
|
||||
trainer.train()
|
||||
|
||||
.. _ft_native:
|
||||
|
||||
Fine-tuning with native PyTorch/TensorFlow
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
We can also train use native PyTorch or TensorFlow:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from torch.utils.data import DataLoader
|
||||
from transformers import DistilBertForSequenceClassification, AdamW
|
||||
|
||||
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
|
||||
|
||||
model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
||||
model.to(device)
|
||||
model.train()
|
||||
|
||||
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
|
||||
|
||||
optim = AdamW(model.parameters(), lr=5e-5)
|
||||
|
||||
for epoch in range(3):
|
||||
for batch in train_loader:
|
||||
optim.zero_grad()
|
||||
input_ids = batch['input_ids'].to(device)
|
||||
attention_mask = batch['attention_mask'].to(device)
|
||||
labels = batch['labels'].to(device)
|
||||
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
|
||||
loss = outputs[0]
|
||||
loss.backward()
|
||||
optim.step()
|
||||
|
||||
model.eval()
|
||||
## TENSORFLOW CODE
|
||||
from transformers import TFDistilBertForSequenceClassification
|
||||
|
||||
model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
||||
|
||||
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
|
||||
model.compile(optimizer=optimizer, loss=model.compute_loss) # can also use any keras loss fn
|
||||
model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
|
||||
|
||||
.. _tok_ner:
|
||||
|
||||
Token Classification with W-NUT Emerging Entities
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
.. note::
|
||||
|
||||
This dataset can be explored in the Hugging Face model hub (`WNUT-17 <https://huggingface.co/datasets/wnut_17>`_),
|
||||
and can be alternatively downloaded with the 🤗 Datasets library with ``load_dataset("wnut_17")``.
|
||||
|
||||
Next we will look at token classification. Rather than classifying an entire sequence, this task classifies token by
|
||||
token. We'll demonstrate how to do this with `Named Entity Recognition
|
||||
<http://nlpprogress.com/english/named_entity_recognition.html>`_, which involves identifying tokens which correspond to
|
||||
a predefined set of "entities". Specifically, we'll use the `W-NUT Emerging and Rare entities
|
||||
<http://noisy-text.github.io/2017/emerging-rare-entities.html>`_ corpus. The data is given as a collection of
|
||||
pre-tokenized documents where each token is assigned a tag.
|
||||
|
||||
Let's start by downloading the data.
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
wget http://noisy-text.github.io/2017/files/wnut17train.conll
|
||||
|
||||
In this case, we'll just download the train set, which is a single text file. Each line of the file contains either (1)
|
||||
a word and tag separated by a tab, or (2) a blank line indicating the end of a document. Let's write a function to read
|
||||
this in. We'll take in the file path and return ``token_docs`` which is a list of lists of token strings, and
|
||||
``token_tags`` which is a list of lists of tag strings.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from pathlib import Path
|
||||
import re
|
||||
|
||||
def read_wnut(file_path):
|
||||
file_path = Path(file_path)
|
||||
|
||||
raw_text = file_path.read_text().strip()
|
||||
raw_docs = re.split(r'\n\t?\n', raw_text)
|
||||
token_docs = []
|
||||
tag_docs = []
|
||||
for doc in raw_docs:
|
||||
tokens = []
|
||||
tags = []
|
||||
for line in doc.split('\n'):
|
||||
token, tag = line.split('\t')
|
||||
tokens.append(token)
|
||||
tags.append(tag)
|
||||
token_docs.append(tokens)
|
||||
tag_docs.append(tags)
|
||||
|
||||
return token_docs, tag_docs
|
||||
|
||||
texts, tags = read_wnut('wnut17train.conll')
|
||||
|
||||
Just to see what this data looks like, let's take a look at a segment of the first document.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
>>> print(texts[0][10:17], tags[0][10:17], sep='\n')
|
||||
['for', 'two', 'weeks', '.', 'Empire', 'State', 'Building']
|
||||
['O', 'O', 'O', 'O', 'B-location', 'I-location', 'I-location']
|
||||
|
||||
``location`` is an entity type, ``B-`` indicates the beginning of an entity, and ``I-`` indicates consecutive positions
|
||||
of the same entity ("Empire State Building" is considered one entity). ``O`` indicates the token does not correspond to
|
||||
any entity.
|
||||
|
||||
Now that we've read the data in, let's create a train/validation split:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from sklearn.model_selection import train_test_split
|
||||
train_texts, val_texts, train_tags, val_tags = train_test_split(texts, tags, test_size=.2)
|
||||
|
||||
Next, let's create encodings for our tokens and tags. For the tags, we can start by just create a simple mapping which
|
||||
we'll use in a moment:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
unique_tags = set(tag for doc in tags for tag in doc)
|
||||
tag2id = {tag: id for id, tag in enumerate(unique_tags)}
|
||||
id2tag = {id: tag for tag, id in tag2id.items()}
|
||||
|
||||
To encode the tokens, we'll use a pre-trained DistilBert tokenizer. We can tell the tokenizer that we're dealing with
|
||||
ready-split tokens rather than full sentence strings by passing ``is_split_into_words=True``. We'll also pass
|
||||
``padding=True`` and ``truncation=True`` to pad the sequences to be the same length. Lastly, we can tell the model to
|
||||
return information about the tokens which are split by the wordpiece tokenization process, which we will need in a
|
||||
moment.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import DistilBertTokenizerFast
|
||||
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-cased')
|
||||
train_encodings = tokenizer(train_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)
|
||||
val_encodings = tokenizer(val_texts, is_split_into_words=True, return_offsets_mapping=True, padding=True, truncation=True)
|
||||
|
||||
Great, so now our tokens are nicely encoded in the format that they need to be in to feed them into our DistilBert
|
||||
model below.
|
||||
|
||||
Now we arrive at a common obstacle with using pre-trained models for token-level classification: many of the tokens in
|
||||
the W-NUT corpus are not in DistilBert's vocabulary. Bert and many models like it use a method called WordPiece
|
||||
Tokenization, meaning that single words are split into multiple tokens such that each token is likely to be in the
|
||||
vocabulary. For example, DistilBert's tokenizer would split the Twitter handle ``@huggingface`` into the tokens ``['@',
|
||||
'hugging', '##face']``. This is a problem for us because we have exactly one tag per token. If the tokenizer splits a
|
||||
token into multiple sub-tokens, then we will end up with a mismatch between our tokens and our labels.
|
||||
|
||||
One way to handle this is to only train on the tag labels for the first subtoken of a split token. We can do this in 🤗
|
||||
Transformers by setting the labels we wish to ignore to ``-100``. In the example above, if the label for
|
||||
``@HuggingFace`` is ``3`` (indexing ``B-corporation``), we would set the labels of ``['@', 'hugging', '##face']`` to
|
||||
``[3, -100, -100]``.
|
||||
|
||||
Let's write a function to do this. This is where we will use the ``offset_mapping`` from the tokenizer as mentioned
|
||||
above. For each sub-token returned by the tokenizer, the offset mapping gives us a tuple indicating the sub-token's
|
||||
start position and end position relative to the original token it was split from. That means that if the first position
|
||||
in the tuple is anything other than ``0``, we will set its corresponding label to ``-100``. While we're at it, we can
|
||||
also set labels to ``-100`` if the second position of the offset mapping is ``0``, since this means it must be a
|
||||
special token like ``[PAD]`` or ``[CLS]``.
|
||||
|
||||
.. note::
|
||||
|
||||
Due to a recently fixed bug, -1 must be used instead of -100 when using TensorFlow in 🤗 Transformers <= 3.02.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import numpy as np
|
||||
|
||||
def encode_tags(tags, encodings):
|
||||
labels = [[tag2id[tag] for tag in doc] for doc in tags]
|
||||
encoded_labels = []
|
||||
for doc_labels, doc_offset in zip(labels, encodings.offset_mapping):
|
||||
# create an empty array of -100
|
||||
doc_enc_labels = np.ones(len(doc_offset),dtype=int) * -100
|
||||
arr_offset = np.array(doc_offset)
|
||||
|
||||
# set labels whose first offset position is 0 and the second is not 0
|
||||
doc_enc_labels[(arr_offset[:,0] == 0) & (arr_offset[:,1] != 0)] = doc_labels
|
||||
encoded_labels.append(doc_enc_labels.tolist())
|
||||
|
||||
return encoded_labels
|
||||
|
||||
train_labels = encode_tags(train_tags, train_encodings)
|
||||
val_labels = encode_tags(val_tags, val_encodings)
|
||||
|
||||
The hard part is now done. Just as in the sequence classification example above, we can create a dataset object:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
import torch
|
||||
|
||||
class WNUTDataset(torch.utils.data.Dataset):
|
||||
def __init__(self, encodings, labels):
|
||||
self.encodings = encodings
|
||||
self.labels = labels
|
||||
|
||||
def __getitem__(self, idx):
|
||||
item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
|
||||
item['labels'] = torch.tensor(self.labels[idx])
|
||||
return item
|
||||
|
||||
def __len__(self):
|
||||
return len(self.labels)
|
||||
|
||||
train_encodings.pop("offset_mapping") # we don't want to pass this to the model
|
||||
val_encodings.pop("offset_mapping")
|
||||
train_dataset = WNUTDataset(train_encodings, train_labels)
|
||||
val_dataset = WNUTDataset(val_encodings, val_labels)
|
||||
## TENSORFLOW CODE
|
||||
import tensorflow as tf
|
||||
|
||||
train_encodings.pop("offset_mapping") # we don't want to pass this to the model
|
||||
val_encodings.pop("offset_mapping")
|
||||
|
||||
train_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(train_encodings),
|
||||
train_labels
|
||||
))
|
||||
val_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
dict(val_encodings),
|
||||
val_labels
|
||||
))
|
||||
|
||||
Now load in a token classification model and specify the number of labels:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from transformers import DistilBertForTokenClassification
|
||||
model = DistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
|
||||
## TENSORFLOW CODE
|
||||
from transformers import TFDistilBertForTokenClassification
|
||||
model = TFDistilBertForTokenClassification.from_pretrained('distilbert-base-cased', num_labels=len(unique_tags))
|
||||
|
||||
The data and model are both ready to go. You can train the model either with
|
||||
:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` or with native PyTorch/TensorFlow, exactly as in the
|
||||
sequence classification example above.
|
||||
|
||||
- :ref:`ft_trainer`
|
||||
- :ref:`ft_native`
|
||||
|
||||
.. _qa_squad:
|
||||
|
||||
Question Answering with SQuAD 2.0
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
.. note::
|
||||
|
||||
This dataset can be explored in the Hugging Face model hub (`SQuAD V2
|
||||
<https://huggingface.co/datasets/squad_v2>`_), and can be alternatively downloaded with the 🤗 Datasets library with
|
||||
``load_dataset("squad_v2")``.
|
||||
|
||||
Question answering comes in many forms. In this example, we'll look at the particular type of extractive QA that
|
||||
involves answering a question about a passage by highlighting the segment of the passage that answers the question.
|
||||
This involves fine-tuning a model which predicts a start position and an end position in the passage. We will use the
|
||||
`Stanford Question Answering Dataset (SQuAD) 2.0 <https://rajpurkar.github.io/SQuAD-explorer/>`_.
|
||||
|
||||
We will start by downloading the data:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
mkdir squad
|
||||
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json -O squad/train-v2.0.json
|
||||
wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json -O squad/dev-v2.0.json
|
||||
|
||||
Each split is in a structured json file with a number of questions and answers for each passage (or context). We'll
|
||||
take this apart into parallel lists of contexts, questions, and answers (note that the contexts here are repeated since
|
||||
there are multiple questions per context):
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
|
||||
def read_squad(path):
|
||||
path = Path(path)
|
||||
with open(path, 'rb') as f:
|
||||
squad_dict = json.load(f)
|
||||
|
||||
contexts = []
|
||||
questions = []
|
||||
answers = []
|
||||
for group in squad_dict['data']:
|
||||
for passage in group['paragraphs']:
|
||||
context = passage['context']
|
||||
for qa in passage['qas']:
|
||||
question = qa['question']
|
||||
for answer in qa['answers']:
|
||||
contexts.append(context)
|
||||
questions.append(question)
|
||||
answers.append(answer)
|
||||
|
||||
return contexts, questions, answers
|
||||
|
||||
train_contexts, train_questions, train_answers = read_squad('squad/train-v2.0.json')
|
||||
val_contexts, val_questions, val_answers = read_squad('squad/dev-v2.0.json')
|
||||
|
||||
The contexts and questions are just strings. The answers are dicts containing the subsequence of the passage with the
|
||||
correct answer as well as an integer indicating the character at which the answer begins. In order to train a model on
|
||||
this data we need (1) the tokenized context/question pairs, and (2) integers indicating at which *token* positions the
|
||||
answer begins and ends.
|
||||
|
||||
First, let's get the *character* position at which the answer ends in the passage (we are given the starting position).
|
||||
Sometimes SQuAD answers are off by one or two characters, so we will also adjust for that.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def add_end_idx(answers, contexts):
|
||||
for answer, context in zip(answers, contexts):
|
||||
gold_text = answer['text']
|
||||
start_idx = answer['answer_start']
|
||||
end_idx = start_idx + len(gold_text)
|
||||
|
||||
# sometimes squad answers are off by a character or two – fix this
|
||||
if context[start_idx:end_idx] == gold_text:
|
||||
answer['answer_end'] = end_idx
|
||||
elif context[start_idx-1:end_idx-1] == gold_text:
|
||||
answer['answer_start'] = start_idx - 1
|
||||
answer['answer_end'] = end_idx - 1 # When the gold label is off by one character
|
||||
elif context[start_idx-2:end_idx-2] == gold_text:
|
||||
answer['answer_start'] = start_idx - 2
|
||||
answer['answer_end'] = end_idx - 2 # When the gold label is off by two characters
|
||||
|
||||
add_end_idx(train_answers, train_contexts)
|
||||
add_end_idx(val_answers, val_contexts)
|
||||
|
||||
Now ``train_answers`` and ``val_answers`` include the character end positions and the corrected start positions. Next,
|
||||
let's tokenize our context/question pairs. 🤗 Tokenizers can accept parallel lists of sequences and encode them together
|
||||
as sequence pairs.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from transformers import DistilBertTokenizerFast
|
||||
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
|
||||
|
||||
train_encodings = tokenizer(train_contexts, train_questions, truncation=True, padding=True)
|
||||
val_encodings = tokenizer(val_contexts, val_questions, truncation=True, padding=True)
|
||||
|
||||
Next we need to convert our character start/end positions to token start/end positions. When using 🤗 Fast Tokenizers,
|
||||
we can use the built in :func:`~transformers.BatchEncoding.char_to_token` method.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
def add_token_positions(encodings, answers):
|
||||
start_positions = []
|
||||
end_positions = []
|
||||
for i in range(len(answers)):
|
||||
start_positions.append(encodings.char_to_token(i, answers[i]['answer_start']))
|
||||
end_positions.append(encodings.char_to_token(i, answers[i]['answer_end'] - 1))
|
||||
|
||||
# if start position is None, the answer passage has been truncated
|
||||
if start_positions[-1] is None:
|
||||
start_positions[-1] = tokenizer.model_max_length
|
||||
if end_positions[-1] is None:
|
||||
end_positions[-1] = tokenizer.model_max_length
|
||||
|
||||
encodings.update({'start_positions': start_positions, 'end_positions': end_positions})
|
||||
|
||||
add_token_positions(train_encodings, train_answers)
|
||||
add_token_positions(val_encodings, val_answers)
|
||||
|
||||
Our data is ready. Let's just put it in a PyTorch/TensorFlow dataset so that we can easily use it for training. In
|
||||
PyTorch, we define a custom ``Dataset`` class. In TensorFlow, we pass a tuple of ``(inputs_dict, labels_dict)`` to the
|
||||
``from_tensor_slices`` method.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
import torch
|
||||
|
||||
class SquadDataset(torch.utils.data.Dataset):
|
||||
def __init__(self, encodings):
|
||||
self.encodings = encodings
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
|
||||
|
||||
def __len__(self):
|
||||
return len(self.encodings.input_ids)
|
||||
|
||||
train_dataset = SquadDataset(train_encodings)
|
||||
val_dataset = SquadDataset(val_encodings)
|
||||
## TENSORFLOW CODE
|
||||
import tensorflow as tf
|
||||
|
||||
train_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
{key: train_encodings[key] for key in ['input_ids', 'attention_mask']},
|
||||
{key: train_encodings[key] for key in ['start_positions', 'end_positions']}
|
||||
))
|
||||
val_dataset = tf.data.Dataset.from_tensor_slices((
|
||||
{key: val_encodings[key] for key in ['input_ids', 'attention_mask']},
|
||||
{key: val_encodings[key] for key in ['start_positions', 'end_positions']}
|
||||
))
|
||||
|
||||
Now we can use a DistilBert model with a QA head for training:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from transformers import DistilBertForQuestionAnswering
|
||||
model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
## TENSORFLOW CODE
|
||||
from transformers import TFDistilBertForQuestionAnswering
|
||||
model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
|
||||
|
||||
|
||||
The data and model are both ready to go. You can train the model with
|
||||
:class:`~transformers.Trainer`/:class:`~transformers.TFTrainer` exactly as in the sequence classification example
|
||||
above. If using native PyTorch, replace ``labels`` with ``start_positions`` and ``end_positions`` in the training
|
||||
example. If using Keras's ``fit``, we need to make a minor modification to handle this example since it involves
|
||||
multiple model outputs.
|
||||
|
||||
- :ref:`ft_trainer`
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
from torch.utils.data import DataLoader
|
||||
from transformers import AdamW
|
||||
|
||||
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
|
||||
|
||||
model.to(device)
|
||||
model.train()
|
||||
|
||||
train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
|
||||
|
||||
optim = AdamW(model.parameters(), lr=5e-5)
|
||||
|
||||
for epoch in range(3):
|
||||
for batch in train_loader:
|
||||
optim.zero_grad()
|
||||
input_ids = batch['input_ids'].to(device)
|
||||
attention_mask = batch['attention_mask'].to(device)
|
||||
start_positions = batch['start_positions'].to(device)
|
||||
end_positions = batch['end_positions'].to(device)
|
||||
outputs = model(input_ids, attention_mask=attention_mask, start_positions=start_positions, end_positions=end_positions)
|
||||
loss = outputs[0]
|
||||
loss.backward()
|
||||
optim.step()
|
||||
|
||||
model.eval()
|
||||
## TENSORFLOW CODE
|
||||
# Keras will expect a tuple when dealing with labels
|
||||
train_dataset = train_dataset.map(lambda x, y: (x, (y['start_positions'], y['end_positions'])))
|
||||
|
||||
# Keras will assign a separate loss for each output and add them together. So we'll just use the standard CE loss
|
||||
# instead of using the built-in model.compute_loss, which expects a dict of outputs and averages the two terms.
|
||||
# Note that this means the loss will be 2x of when using TFTrainer since we're adding instead of averaging them.
|
||||
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
|
||||
model.distilbert.return_dict = False # if using 🤗 Transformers >3.02, make sure outputs are tuples
|
||||
|
||||
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
|
||||
model.compile(optimizer=optimizer, loss=loss) # can also use any keras loss fn
|
||||
model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
|
||||
|
||||
.. _resources:
|
||||
|
||||
Additional Resources
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
- `How to train a new language model from scratch using Transformers and Tokenizers
|
||||
<https://huggingface.co/blog/how-to-train>`_. Blog post showing the steps to load in Esperanto data and train a
|
||||
masked language model from scratch.
|
||||
- :doc:`Preprocessing <preprocessing>`. Docs page on data preprocessing.
|
||||
- :doc:`Training <training>`. Docs page on training and fine-tuning.
|
||||
|
||||
.. _datasetslib:
|
||||
|
||||
Using the 🤗 Datasets & Metrics library
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
This tutorial demonstrates how to read in datasets from various raw text formats and prepare them for training with 🤗
|
||||
Transformers so that you can do the same thing with your own custom datasets. However, we recommend users use the `🤗
|
||||
Datasets library <https://github.com/huggingface/datasets>`_ for working with the 150+ datasets included in the `hub
|
||||
<https://huggingface.co/datasets>`_, including the three datasets used in this tutorial. As a very brief overview, we
|
||||
will show how to use the Datasets library to download and prepare the IMDb dataset from the first example,
|
||||
:ref:`seq_imdb`.
|
||||
|
||||
Start by downloading the dataset:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from datasets import load_dataset
|
||||
train = load_dataset("imdb", split="train")
|
||||
|
||||
Each dataset has multiple columns corresponding to different features. Let's see what our columns are.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
>>> print(train.column_names)
|
||||
['label', 'text']
|
||||
|
||||
Great. Now let's tokenize the text. We can do this using the ``map`` method. We'll also rename the ``label`` column to
|
||||
``labels`` to match the model's input arguments.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
train = train.map(lambda batch: tokenizer(batch["text"], truncation=True, padding=True), batched=True)
|
||||
train.rename_column_("label", "labels")
|
||||
|
||||
Lastly, we can use the ``set_format`` method to determine which columns and in what data format we want to access
|
||||
dataset elements.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
## PYTORCH CODE
|
||||
>>> train.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
|
||||
>>> {key: val.shape for key, val in train[0].items()})
|
||||
{'labels': torch.Size([]), 'input_ids': torch.Size([512]), 'attention_mask': torch.Size([512])}
|
||||
## TENSORFLOW CODE
|
||||
>>> train.set_format("tensorflow", columns=["input_ids", "attention_mask", "labels"])
|
||||
>>> {key: val.shape for key, val in train[0].items()})
|
||||
{'labels': TensorShape([]), 'input_ids': TensorShape([512]), 'attention_mask': TensorShape([512])}
|
||||
|
||||
We now have a fully-prepared dataset. Check out `the 🤗 Datasets docs
|
||||
<https://huggingface.co/docs/datasets/processing.html>`_ for a more thorough introduction.
|
||||
@@ -154,7 +154,7 @@ input elements was ``6.27e+04`` and same for the output was ``inf``.
|
||||
You can see here, that ``T5DenseGatedGeluDense.forward`` resulted in output activations, whose absolute max value was
|
||||
around 62.7K, which is very close to fp16's top limit of 64K. In the next frame we have ``Dropout`` which renormalizes
|
||||
the weights, after it zeroed some of the elements, which pushes the absolute max value to more than 64K, and we get an
|
||||
overflow (``inf``).
|
||||
overlow (``inf``).
|
||||
|
||||
As you can see it's the previous frames that we need to look into when the numbers start going into very large for fp16
|
||||
numbers.
|
||||
|
||||
BIN
docs/source/favicon.ico
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BIN
docs/source/favicon.ico
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|
After Width: | Height: | Size: 47 KiB |
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|
Before Width: | Height: | Size: 126 KiB After Width: | Height: | Size: 4.4 KiB |
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|
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|
Before Width: | Height: | Size: 32 KiB |
@@ -1,271 +0,0 @@
|
||||
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
-->
|
||||
|
||||
# 🤗 Transformers
|
||||
|
||||
State-of-the-art Machine Learning for Jax, Pytorch and TensorFlow
|
||||
|
||||
🤗 Transformers (formerly known as _pytorch-transformers_ and _pytorch-pretrained-bert_) provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
|
||||
|
||||
These models can applied on:
|
||||
|
||||
* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.
|
||||
* 🖼️ Images, for tasks like image classification, object detection, and segmentation.
|
||||
* 🗣️ Audio, for tasks like speech recognition and audio classification.
|
||||
|
||||
Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
|
||||
|
||||
🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our [model hub](https://huggingface.co/models). At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.
|
||||
|
||||
🤗 Transformers is backed by the three most popular deep learning libraries — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) — with a seamless integration between them. It's straightforward to train your models with one before loading them for inference with the other.
|
||||
|
||||
This is the documentation of our repository [transformers](https://github.com/huggingface/transformers). You can
|
||||
also follow our [online course](https://huggingface.co/course) that teaches how to use this library, as well as the
|
||||
other libraries developed by Hugging Face and the Hub.
|
||||
|
||||
## If you are looking for custom support from the Hugging Face team
|
||||
|
||||
<a target="_blank" href="https://huggingface.co/support">
|
||||
<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
|
||||
</a><br>
|
||||
|
||||
## Features
|
||||
|
||||
1. Easy-to-use state-of-the-art models:
|
||||
- High performance on natural language understanding & generation, computer vision, and audio tasks.
|
||||
- Low barrier to entry for educators and practitioners.
|
||||
- Few user-facing abstractions with just three classes to learn.
|
||||
- A unified API for using all our pretrained models.
|
||||
|
||||
1. Lower compute costs, smaller carbon footprint:
|
||||
- Researchers can share trained models instead of always retraining.
|
||||
- Practitioners can reduce compute time and production costs.
|
||||
- Dozens of architectures with over 20,000 pretrained models, some in more than 100 languages.
|
||||
|
||||
1. Choose the right framework for every part of a model's lifetime:
|
||||
- Train state-of-the-art models in 3 lines of code.
|
||||
- Move a single model between TF2.0/PyTorch/JAX frameworks at will.
|
||||
- Seamlessly pick the right framework for training, evaluation and production.
|
||||
|
||||
1. Easily customize a model or an example to your needs:
|
||||
- We provide examples for each architecture to reproduce the results published by its original authors.
|
||||
- Model internals are exposed as consistently as possible.
|
||||
- Model files can be used independently of the library for quick experiments.
|
||||
|
||||
[All the model checkpoints](https://huggingface.co/models) are seamlessly integrated from the huggingface.co [model
|
||||
hub](https://huggingface.co) where they are uploaded directly by [users](https://huggingface.co/users) and
|
||||
[organizations](https://huggingface.co/organizations).
|
||||
|
||||
Current number of checkpoints: <img src="https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen">
|
||||
|
||||
## Contents
|
||||
|
||||
The documentation is organized in five parts:
|
||||
|
||||
- **GET STARTED** contains a quick tour, the installation instructions and some useful information about our philosophy
|
||||
and a glossary.
|
||||
- **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
|
||||
- **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
|
||||
- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general research in
|
||||
transformers model
|
||||
- **API** contains the documentation of each public class and function, grouped in:
|
||||
|
||||
- **MAIN CLASSES** for the main classes exposing the important APIs of the library.
|
||||
- **MODELS** for the classes and functions related to each model implemented in the library.
|
||||
- **INTERNAL HELPERS** for the classes and functions we use internally.
|
||||
|
||||
The library currently contains Jax, PyTorch and Tensorflow implementations, pretrained model weights, usage scripts and
|
||||
conversion utilities for the following models.
|
||||
|
||||
### Supported models
|
||||
|
||||
<!--This list is updated automatically from the README with _make fix-copies_. Do not update manually! -->
|
||||
|
||||
1. **[ALBERT](model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
|
||||
1. **[BART](model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
|
||||
1. **[BARThez](model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
|
||||
1. **[BARTpho](model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
|
||||
1. **[BEiT](model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
|
||||
1. **[BERT](model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
|
||||
1. **[BERTweet](model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
|
||||
1. **[BERT For Sequence Generation](model_doc/bertgeneration)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[BigBird-RoBERTa](model_doc/bigbird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[BigBird-Pegasus](model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
1. **[Blenderbot](model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BlenderbotSmall](model_doc/blenderbot_small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
1. **[BORT](model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
|
||||
1. **[ByT5](model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
|
||||
1. **[CamemBERT](model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
|
||||
1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
|
||||
1. **[CLIP](model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
|
||||
1. **[ConvBERT](model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
|
||||
1. **[CPM](model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
|
||||
1. **[CTRL](model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
|
||||
1. **[DeBERTa](model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeBERTa-v2](model_doc/deberta_v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
|
||||
1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
|
||||
1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
|
||||
1. **[DialoGPT](model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
|
||||
1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation) and a German version of DistilBERT.
|
||||
1. **[DPR](model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
1. **[EncoderDecoder](model_doc/encoderdecoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
|
||||
1. **[ELECTRA](model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
|
||||
1. **[FlauBERT](model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
|
||||
1. **[FNet](model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
|
||||
1. **[Funnel Transformer](model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
||||
1. **[GPT](model_doc/gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
|
||||
1. **[GPT-2](model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
|
||||
1. **[GPT-J](model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
|
||||
1. **[GPT Neo](model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
|
||||
1. **[Hubert](model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
|
||||
1. **[I-BERT](model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
|
||||
1. **[ImageGPT](model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
|
||||
1. **[LayoutLM](model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
|
||||
1. **[LayoutLMv2](model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
|
||||
1. **[LayoutXLM](model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
|
||||
1. **[LED](model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[Longformer](model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
1. **[LUKE](model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
|
||||
1. **[mLUKE](model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
|
||||
1. **[LXMERT](model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
|
||||
1. **[M2M100](model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
|
||||
1. **[MarianMT](model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
|
||||
1. **[MBart](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
|
||||
1. **[MBart-50](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
|
||||
1. **[Megatron-BERT](model_doc/megatron_bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[Megatron-GPT2](model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
1. **[MPNet](model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
|
||||
1. **[MT5](model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
1. **[Pegasus](model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
|
||||
1. **[Perceiver IO](model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
|
||||
1. **[PhoBERT](model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
|
||||
1. **[ProphetNet](model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[QDQBert](model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
|
||||
1. **[Reformer](model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
1. **[RemBERT](model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
|
||||
1. **[RoBERTa](model_doc/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
|
||||
1. **[RoFormer](model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
|
||||
1. **[SegFormer](model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
|
||||
1. **[SEW](model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SEW-D](model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
1. **[SpeechToTextTransformer](model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
|
||||
1. **[SpeechToTextTransformer2](model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
|
||||
1. **[Splinter](model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
|
||||
1. **[SqueezeBert](model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
|
||||
1. **[T5](model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
1. **[TAPAS](model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
1. **[Transformer-XL](model_doc/transformerxl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
|
||||
1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
|
||||
1. **[UniSpeech](model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
|
||||
1. **[UniSpeechSat](model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
|
||||
1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
|
||||
1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
|
||||
1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
|
||||
1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
|
||||
1. **[XLM-ProphetNet](model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
1. **[XLM-RoBERTa](model_doc/xlmroberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
|
||||
1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
|
||||
1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
|
||||
|
||||
|
||||
### Supported frameworks
|
||||
|
||||
The table below represents the current support in the library for each of those models, whether they have a Python
|
||||
tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via
|
||||
Flax), PyTorch, and/or TensorFlow.
|
||||
|
||||
<!--This table is updated automatically from the auto modules with _make fix-copies_. Do not update manually!-->
|
||||
|
||||
| Model | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
|
||||
|-----------------------------|----------------|----------------|-----------------|--------------------|--------------|
|
||||
| ALBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| BART | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| BEiT | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
| BERT | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| Bert Generation | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| BigBird | ✅ | ✅ | ✅ | ❌ | ✅ |
|
||||
| BigBirdPegasus | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| Blenderbot | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| BlenderbotSmall | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| CamemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| Canine | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| CLIP | ✅ | ✅ | ✅ | ❌ | ✅ |
|
||||
| ConvBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| CTRL | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
| DeBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| DeBERTa-v2 | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
| DeiT | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| DETR | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| DistilBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| DPR | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| ELECTRA | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
|
||||
| FairSeq Machine-Translation | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| FlauBERT | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
| FNet | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
| Funnel Transformer | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| GPT Neo | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
| GPT-J | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
| Hubert | ❌ | ❌ | ✅ | ✅ | ❌ |
|
||||
| I-BERT | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| ImageGPT | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| LayoutLM | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| LayoutLMv2 | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
| LED | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| Longformer | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| LUKE | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| LXMERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| M2M100 | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| Marian | ✅ | ❌ | ✅ | ✅ | ✅ |
|
||||
| mBART | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| MegatronBert | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| MobileBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| MPNet | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| mT5 | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| OpenAI GPT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| OpenAI GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| Pegasus | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| Perceiver | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| QDQBert | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| RAG | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
| Reformer | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
| RemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| RetriBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
| RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| RoFormer | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| SegFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| SEW | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| SEW-D | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| Speech Encoder decoder | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| Speech2Text | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| Speech2Text2 | ✅ | ❌ | ❌ | ❌ | ❌ |
|
||||
| Splinter | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
| SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
| T5 | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
| TAPAS | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
| Transformer-XL | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
| TrOCR | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| UniSpeech | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| UniSpeechSat | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| Vision Encoder decoder | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
| VisionTextDualEncoder | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
| VisualBert | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
| ViT | ❌ | ❌ | ✅ | ✅ | ✅ |
|
||||
| Wav2Vec2 | ✅ | ❌ | ✅ | ✅ | ✅ |
|
||||
| XLM | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
| XLM-RoBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
| XLMProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
| XLNet | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
|
||||
<!-- End table-->
|
||||
643
docs/source/index.rst
Normal file
643
docs/source/index.rst
Normal file
@@ -0,0 +1,643 @@
|
||||
Transformers
|
||||
=======================================================================================================================
|
||||
|
||||
State-of-the-art Natural Language Processing for Jax, Pytorch and TensorFlow
|
||||
|
||||
🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides general-purpose
|
||||
architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural
|
||||
Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between Jax,
|
||||
PyTorch and TensorFlow.
|
||||
|
||||
This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`__. You can
|
||||
also follow our `online course <https://huggingface.co/course>`__ that teaches how to use this library, as well as the
|
||||
other libraries developed by Hugging Face and the Hub.
|
||||
|
||||
If you are looking for custom support from the Hugging Face team
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
.. raw:: html
|
||||
|
||||
<a target="_blank" href="https://huggingface.co/support">
|
||||
<img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
|
||||
</a><br>
|
||||
|
||||
Features
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
- High performance on NLU and NLG tasks
|
||||
- Low barrier to entry for educators and practitioners
|
||||
|
||||
State-of-the-art NLP for everyone:
|
||||
|
||||
- Deep learning researchers
|
||||
- Hands-on practitioners
|
||||
- AI/ML/NLP teachers and educators
|
||||
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Lower compute costs, smaller carbon footprint:
|
||||
|
||||
- Researchers can share trained models instead of always retraining
|
||||
- Practitioners can reduce compute time and production costs
|
||||
- 8 architectures with over 30 pretrained models, some in more than 100 languages
|
||||
|
||||
Choose the right framework for every part of a model's lifetime:
|
||||
|
||||
- Train state-of-the-art models in 3 lines of code
|
||||
- Deep interoperability between Jax, Pytorch and TensorFlow models
|
||||
- Move a single model between Jax/PyTorch/TensorFlow frameworks at will
|
||||
- Seamlessly pick the right framework for training, evaluation, production
|
||||
|
||||
The support for Jax is still experimental (with a few models right now), expect to see it grow in the coming months!
|
||||
|
||||
`All the model checkpoints <https://huggingface.co/models>`__ are seamlessly integrated from the huggingface.co `model
|
||||
hub <https://huggingface.co>`__ where they are uploaded directly by `users <https://huggingface.co/users>`__ and
|
||||
`organizations <https://huggingface.co/organizations>`__.
|
||||
|
||||
Current number of checkpoints: |checkpoints|
|
||||
|
||||
.. |checkpoints| image:: https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen
|
||||
|
||||
Contents
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
The documentation is organized in five parts:
|
||||
|
||||
- **GET STARTED** contains a quick tour, the installation instructions and some useful information about our philosophy
|
||||
and a glossary.
|
||||
- **USING 🤗 TRANSFORMERS** contains general tutorials on how to use the library.
|
||||
- **ADVANCED GUIDES** contains more advanced guides that are more specific to a given script or part of the library.
|
||||
- **RESEARCH** focuses on tutorials that have less to do with how to use the library but more about general research in
|
||||
transformers model
|
||||
- The three last section contain the documentation of each public class and function, grouped in:
|
||||
|
||||
- **MAIN CLASSES** for the main classes exposing the important APIs of the library.
|
||||
- **MODELS** for the classes and functions related to each model implemented in the library.
|
||||
- **INTERNAL HELPERS** for the classes and functions we use internally.
|
||||
|
||||
The library currently contains Jax, PyTorch and Tensorflow implementations, pretrained model weights, usage scripts and
|
||||
conversion utilities for the following models.
|
||||
|
||||
Supported models
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
..
|
||||
This list is updated automatically from the README with `make fix-copies`. Do not update manually!
|
||||
|
||||
1. :doc:`ALBERT <model_doc/albert>` (from Google Research and the Toyota Technological Institute at Chicago) released
|
||||
with the paper `ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
|
||||
<https://arxiv.org/abs/1909.11942>`__, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush
|
||||
Sharma, Radu Soricut.
|
||||
2. :doc:`BART <model_doc/bart>` (from Facebook) released with the paper `BART: Denoising Sequence-to-Sequence
|
||||
Pre-training for Natural Language Generation, Translation, and Comprehension
|
||||
<https://arxiv.org/pdf/1910.13461.pdf>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
|
||||
Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
|
||||
3. :doc:`BARThez <model_doc/barthez>` (from École polytechnique) released with the paper `BARThez: a Skilled Pretrained
|
||||
French Sequence-to-Sequence Model <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P.
|
||||
Tixier, Michalis Vazirgiannis.
|
||||
4. :doc:`BEiT <model_doc/beit>` (from Microsoft) released with the paper `BEiT: BERT Pre-Training of Image Transformers
|
||||
<https://arxiv.org/abs/2106.08254>`__ by Hangbo Bao, Li Dong, Furu Wei.
|
||||
5. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
|
||||
Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang,
|
||||
Kenton Lee and Kristina Toutanova.
|
||||
6. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
|
||||
Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
|
||||
Narayan, Aliaksei Severyn.
|
||||
7. :doc:`BigBird-RoBERTa <model_doc/bigbird>` (from Google Research) released with the paper `Big Bird: Transformers
|
||||
for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua
|
||||
Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
8. :doc:`BigBird-Pegasus <model_doc/bigbird_pegasus>` (from Google Research) released with the paper `Big Bird:
|
||||
Transformers for Longer Sequences <https://arxiv.org/abs/2007.14062>`__ by Manzil Zaheer, Guru Guruganesh, Avinava
|
||||
Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
|
||||
9. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
|
||||
open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
|
||||
Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
10. :doc:`BlenderbotSmall <model_doc/blenderbot_small>` (from Facebook) released with the paper `Recipes for building
|
||||
an open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju,
|
||||
Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
|
||||
11. :doc:`BORT <model_doc/bort>` (from Alexa) released with the paper `Optimal Subarchitecture Extraction For BERT
|
||||
<https://arxiv.org/abs/2010.10499>`__ by Adrian de Wynter and Daniel J. Perry.
|
||||
12. :doc:`ByT5 <model_doc/byt5>` (from Google Research) released with the paper `ByT5: Towards a token-free future with
|
||||
pre-trained byte-to-byte models <https://arxiv.org/abs/2105.13626>`__ by Linting Xue, Aditya Barua, Noah Constant,
|
||||
Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
|
||||
13. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
|
||||
French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
|
||||
Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
|
||||
14. :doc:`CANINE <model_doc/canine>` (from Google Research) released with the paper `CANINE: Pre-training an Efficient
|
||||
Tokenization-Free Encoder for Language Representation <https://arxiv.org/abs/2103.06874>`__ by Jonathan H. Clark,
|
||||
Dan Garrette, Iulia Turc, John Wieting.
|
||||
15. :doc:`CLIP <model_doc/clip>` (from OpenAI) released with the paper `Learning Transferable Visual Models From
|
||||
Natural Language Supervision <https://arxiv.org/abs/2103.00020>`__ by Alec Radford, Jong Wook Kim, Chris Hallacy,
|
||||
Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen
|
||||
Krueger, Ilya Sutskever.
|
||||
16. :doc:`ConvBERT <model_doc/convbert>` (from YituTech) released with the paper `ConvBERT: Improving BERT with
|
||||
Span-based Dynamic Convolution <https://arxiv.org/abs/2008.02496>`__ by Zihang Jiang, Weihao Yu, Daquan Zhou,
|
||||
Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
|
||||
17. :doc:`CPM <model_doc/cpm>` (from Tsinghua University) released with the paper `CPM: A Large-scale Generative
|
||||
Chinese Pre-trained Language Model <https://arxiv.org/abs/2012.00413>`__ by Zhengyan Zhang, Xu Han, Hao Zhou, Pei
|
||||
Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng,
|
||||
Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang,
|
||||
Juanzi Li, Xiaoyan Zhu, Maosong Sun.
|
||||
18. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
|
||||
Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
|
||||
Lav R. Varshney, Caiming Xiong and Richard Socher.
|
||||
19. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft) released with the paper `DeBERTa: Decoding-enhanced BERT with
|
||||
Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu
|
||||
Chen.
|
||||
20. :doc:`DeBERTa-v2 <model_doc/deberta_v2>` (from Microsoft) released with the paper `DeBERTa: Decoding-enhanced BERT
|
||||
with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao,
|
||||
Weizhu Chen.
|
||||
21. :doc:`DeiT <model_doc/deit>` (from Facebook) released with the paper `Training data-efficient image transformers &
|
||||
distillation through attention <https://arxiv.org/abs/2012.12877>`__ by Hugo Touvron, Matthieu Cord, Matthijs
|
||||
Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
|
||||
22. :doc:`DETR <model_doc/detr>` (from Facebook) released with the paper `End-to-End Object Detection with Transformers
|
||||
<https://arxiv.org/abs/2005.12872>`__ by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier,
|
||||
Alexander Kirillov, Sergey Zagoruyko.
|
||||
23. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
|
||||
Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe
|
||||
Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
|
||||
24. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
|
||||
distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
|
||||
Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
|
||||
<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
|
||||
<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
|
||||
`DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
|
||||
version of DistilBERT.
|
||||
25. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
|
||||
Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
|
||||
Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
|
||||
26. :doc:`EncoderDecoder <model_doc/encoderdecoder>` (from Google Research) released with the paper `Leveraging
|
||||
Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
|
||||
Narayan, Aliaksei Severyn.
|
||||
27. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
|
||||
Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
|
||||
Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
|
||||
28. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
|
||||
Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
|
||||
Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
|
||||
29. :doc:`FNet <model_doc/fnet>` (from Google Research) released with the paper `FNet: Mixing Tokens with Fourier
|
||||
Transforms <https://arxiv.org/abs/2105.03824>`__ by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago
|
||||
Ontanon.
|
||||
30. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
|
||||
Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
|
||||
Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
|
||||
31. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
|
||||
Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
|
||||
and Ilya Sutskever.
|
||||
32. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
|
||||
Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
|
||||
Luan, Dario Amodei** and Ilya Sutskever**.
|
||||
33. :doc:`GPT-J <model_doc/gptj>` (from EleutherAI) released in the repository `kingoflolz/mesh-transformer-jax
|
||||
<https://github.com/kingoflolz/mesh-transformer-jax/>`__ by Ben Wang and Aran Komatsuzaki.
|
||||
34. :doc:`GPT Neo <model_doc/gpt_neo>` (from EleutherAI) released in the repository `EleutherAI/gpt-neo
|
||||
<https://github.com/EleutherAI/gpt-neo>`__ by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
|
||||
35. :doc:`Hubert <model_doc/hubert>` (from Facebook) released with the paper `HuBERT: Self-Supervised Speech
|
||||
Representation Learning by Masked Prediction of Hidden Units <https://arxiv.org/abs/2106.07447>`__ by Wei-Ning Hsu,
|
||||
Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
|
||||
36. :doc:`I-BERT <model_doc/ibert>` (from Berkeley) released with the paper `I-BERT: Integer-only BERT Quantization
|
||||
<https://arxiv.org/abs/2101.01321>`__ by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
|
||||
37. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
|
||||
of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
|
||||
Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
|
||||
38. :doc:`LayoutLMv2 <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutLMv2:
|
||||
Multi-modal Pre-training for Visually-Rich Document Understanding <https://arxiv.org/abs/2012.14740>`__ by Yang Xu,
|
||||
Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min
|
||||
Zhang, Lidong Zhou.
|
||||
39. :doc:`LayoutXLM <model_doc/layoutlmv2>` (from Microsoft Research Asia) released with the paper `LayoutXLM:
|
||||
Multimodal Pre-training for Multilingual Visually-rich Document Understanding <https://arxiv.org/abs/2104.08836>`__
|
||||
by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
|
||||
40. :doc:`LED <model_doc/led>` (from AllenAI) released with the paper `Longformer: The Long-Document Transformer
|
||||
<https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
41. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
|
||||
Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
|
||||
42. :doc:`LUKE <model_doc/luke>` (from Studio Ousia) released with the paper `LUKE: Deep Contextualized Entity
|
||||
Representations with Entity-aware Self-attention <https://arxiv.org/abs/2010.01057>`__ by Ikuya Yamada, Akari Asai,
|
||||
Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
|
||||
43. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
|
||||
Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
|
||||
by Hao Tan and Mohit Bansal.
|
||||
44. :doc:`M2M100 <model_doc/m2m_100>` (from Facebook) released with the paper `Beyond English-Centric Multilingual
|
||||
Machine Translation <https://arxiv.org/abs/2010.11125>`__ by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma,
|
||||
Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal,
|
||||
Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
|
||||
45. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
|
||||
Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
|
||||
Translator Team.
|
||||
46. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
|
||||
Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
|
||||
Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
|
||||
47. :doc:`MBart-50 <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Translation with Extensible
|
||||
Multilingual Pretraining and Finetuning <https://arxiv.org/abs/2008.00401>`__ by Yuqing Tang, Chau Tran, Xian Li,
|
||||
Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
|
||||
48. :doc:`Megatron-BERT <model_doc/megatron_bert>` (from NVIDIA) released with the paper `Megatron-LM: Training
|
||||
Multi-Billion Parameter Language Models Using Model Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad
|
||||
Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
49. :doc:`Megatron-GPT2 <model_doc/megatron_gpt2>` (from NVIDIA) released with the paper `Megatron-LM: Training
|
||||
Multi-Billion Parameter Language Models Using Model Parallelism <https://arxiv.org/abs/1909.08053>`__ by Mohammad
|
||||
Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
|
||||
50. :doc:`MPNet <model_doc/mpnet>` (from Microsoft Research) released with the paper `MPNet: Masked and Permuted
|
||||
Pre-training for Language Understanding <https://arxiv.org/abs/2004.09297>`__ by Kaitao Song, Xu Tan, Tao Qin,
|
||||
Jianfeng Lu, Tie-Yan Liu.
|
||||
51. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
|
||||
text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
|
||||
Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
|
||||
52. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
|
||||
Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__ by Jingqing Zhang, Yao Zhao,
|
||||
Mohammad Saleh and Peter J. Liu.
|
||||
53. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
|
||||
Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
|
||||
Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
54. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
|
||||
Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
|
||||
55. :doc:`RemBERT <model_doc/rembert>` (from Google Research) released with the paper `Rethinking embedding coupling in
|
||||
pre-trained language models <https://arxiv.org/pdf/2010.12821.pdf>`__ by Hyung Won Chung, Thibault Févry, Henry
|
||||
Tsai, M. Johnson, Sebastian Ruder.
|
||||
56. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
|
||||
Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
|
||||
Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
|
||||
57. :doc:`RoFormer <model_doc/roformer>` (from ZhuiyiTechnology), released together with the paper a `RoFormer:
|
||||
Enhanced Transformer with Rotary Position Embedding <https://arxiv.org/pdf/2104.09864v1.pdf>`__ by Jianlin Su and
|
||||
Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
|
||||
58. :doc:`SpeechEncoderDecoder <model_doc/speechencoderdecoder>`
|
||||
59. :doc:`SpeechToTextTransformer <model_doc/speech_to_text>` (from Facebook), released together with the paper
|
||||
`fairseq S2T: Fast Speech-to-Text Modeling with fairseq <https://arxiv.org/abs/2010.05171>`__ by Changhan Wang, Yun
|
||||
Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
|
||||
60. :doc:`SpeechToTextTransformer2 <model_doc/speech_to_text_2>` (from Facebook), released together with the paper
|
||||
`Large-Scale Self- and Semi-Supervised Learning for Speech Translation <https://arxiv.org/abs/2104.06678>`__ by
|
||||
Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
|
||||
61. :doc:`Splinter <model_doc/splinter>` (from Tel Aviv University), released together with the paper `Few-Shot
|
||||
Question Answering by Pretraining Span Selection <https://arxiv.org/abs/2101.00438>`__ by Ori Ram, Yuval Kirstain,
|
||||
Jonathan Berant, Amir Globerson, Omer Levy.
|
||||
62. :doc:`SqueezeBert <model_doc/squeezebert>` (from Berkeley) released with the paper `SqueezeBERT: What can computer
|
||||
vision teach NLP about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola,
|
||||
Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
|
||||
63. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
|
||||
Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
|
||||
Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
|
||||
64. :doc:`T5v1.1 <model_doc/t5v1.1>` (from Google AI) released in the repository
|
||||
`google-research/text-to-text-transfer-transformer
|
||||
<https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511>`__ by
|
||||
Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi
|
||||
Zhou and Wei Li and Peter J. Liu.
|
||||
65. :doc:`TAPAS <model_doc/tapas>` (from Google AI) released with the paper `TAPAS: Weakly Supervised Table Parsing via
|
||||
Pre-training <https://arxiv.org/abs/2004.02349>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
|
||||
Francesco Piccinno and Julian Martin Eisenschlos.
|
||||
66. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
|
||||
Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
|
||||
Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
|
||||
67. :doc:`Vision Transformer (ViT) <model_doc/vit>` (from Google AI) released with the paper `An Image is Worth 16x16
|
||||
Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`__ by Alexey Dosovitskiy,
|
||||
Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias
|
||||
Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
|
||||
68. :doc:`VisualBERT <model_doc/visual_bert>` (from UCLA NLP) released with the paper `VisualBERT: A Simple and
|
||||
Performant Baseline for Vision and Language <https://arxiv.org/pdf/1908.03557>`__ by Liunian Harold Li, Mark
|
||||
Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
|
||||
69. :doc:`Wav2Vec2 <model_doc/wav2vec2>` (from Facebook AI) released with the paper `wav2vec 2.0: A Framework for
|
||||
Self-Supervised Learning of Speech Representations <https://arxiv.org/abs/2006.11477>`__ by Alexei Baevski, Henry
|
||||
Zhou, Abdelrahman Mohamed, Michael Auli.
|
||||
70. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
|
||||
Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
|
||||
71. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
|
||||
Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
|
||||
Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
|
||||
72. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
|
||||
Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
|
||||
Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
|
||||
Zettlemoyer and Veselin Stoyanov.
|
||||
73. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
|
||||
Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
|
||||
Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
|
||||
74. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
|
||||
Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis
|
||||
Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
|
||||
|
||||
|
||||
Supported frameworks
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The table below represents the current support in the library for each of those models, whether they have a Python
|
||||
tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in Jax (via
|
||||
Flax), PyTorch, and/or TensorFlow.
|
||||
|
||||
..
|
||||
This table is updated automatically from the auto modules with `make fix-copies`. Do not update manually!
|
||||
|
||||
.. rst-class:: center-aligned-table
|
||||
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Model | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
|
||||
+=============================+================+================+=================+====================+==============+
|
||||
| ALBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| BART | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| BeiT | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| BERT | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Bert Generation | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| BigBird | ✅ | ✅ | ✅ | ❌ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| BigBirdPegasus | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Blenderbot | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| BlenderbotSmall | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| CamemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Canine | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| CLIP | ✅ | ✅ | ✅ | ❌ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| ConvBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| CTRL | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| DeBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| DeBERTa-v2 | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| DeiT | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| DETR | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| DistilBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| DPR | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| ELECTRA | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Encoder decoder | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| FairSeq Machine-Translation | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| FlauBERT | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| FNet | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Funnel Transformer | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| GPT Neo | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| GPT-J | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Hubert | ❌ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| I-BERT | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| LayoutLM | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| LayoutLMv2 | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| LED | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Longformer | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| LUKE | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| LXMERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| M2M100 | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Marian | ✅ | ❌ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| mBART | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| MegatronBert | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| MobileBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| MPNet | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| mT5 | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| OpenAI GPT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| OpenAI GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Pegasus | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| RAG | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Reformer | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| RemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| RetriBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| RoFormer | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Speech Encoder decoder | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Speech2Text | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Speech2Text2 | ✅ | ❌ | ❌ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Splinter | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| T5 | ✅ | ✅ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| TAPAS | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Transformer-XL | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| VisualBert | ❌ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| ViT | ❌ | ❌ | ✅ | ❌ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| Wav2Vec2 | ✅ | ❌ | ✅ | ✅ | ✅ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| XLM | ✅ | ❌ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| XLM-RoBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| XLMProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
| XLNet | ✅ | ✅ | ✅ | ✅ | ❌ |
|
||||
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Get started
|
||||
|
||||
quicktour
|
||||
installation
|
||||
philosophy
|
||||
glossary
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Using 🤗 Transformers
|
||||
|
||||
task_summary
|
||||
model_summary
|
||||
preprocessing
|
||||
training
|
||||
model_sharing
|
||||
tokenizer_summary
|
||||
multilingual
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Advanced guides
|
||||
|
||||
pretrained_models
|
||||
examples
|
||||
troubleshooting
|
||||
custom_datasets
|
||||
notebooks
|
||||
sagemaker
|
||||
community
|
||||
converting_tensorflow_models
|
||||
migration
|
||||
contributing
|
||||
add_new_model
|
||||
add_new_pipeline
|
||||
fast_tokenizers
|
||||
performance
|
||||
parallelism
|
||||
testing
|
||||
debugging
|
||||
serialization
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Research
|
||||
|
||||
bertology
|
||||
perplexity
|
||||
benchmarks
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Main Classes
|
||||
|
||||
main_classes/callback
|
||||
main_classes/configuration
|
||||
main_classes/data_collator
|
||||
main_classes/logging
|
||||
main_classes/model
|
||||
main_classes/optimizer_schedules
|
||||
main_classes/output
|
||||
main_classes/pipelines
|
||||
main_classes/processors
|
||||
main_classes/tokenizer
|
||||
main_classes/trainer
|
||||
main_classes/deepspeed
|
||||
main_classes/feature_extractor
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Models
|
||||
|
||||
model_doc/albert
|
||||
model_doc/auto
|
||||
model_doc/bart
|
||||
model_doc/barthez
|
||||
model_doc/beit
|
||||
model_doc/bert
|
||||
model_doc/bertweet
|
||||
model_doc/bertgeneration
|
||||
model_doc/bert_japanese
|
||||
model_doc/bigbird
|
||||
model_doc/bigbird_pegasus
|
||||
model_doc/blenderbot
|
||||
model_doc/blenderbot_small
|
||||
model_doc/bort
|
||||
model_doc/byt5
|
||||
model_doc/camembert
|
||||
model_doc/canine
|
||||
model_doc/clip
|
||||
model_doc/convbert
|
||||
model_doc/cpm
|
||||
model_doc/ctrl
|
||||
model_doc/deberta
|
||||
model_doc/deberta_v2
|
||||
model_doc/deit
|
||||
model_doc/detr
|
||||
model_doc/dialogpt
|
||||
model_doc/distilbert
|
||||
model_doc/dpr
|
||||
model_doc/electra
|
||||
model_doc/encoderdecoder
|
||||
model_doc/flaubert
|
||||
model_doc/fnet
|
||||
model_doc/fsmt
|
||||
model_doc/funnel
|
||||
model_doc/herbert
|
||||
model_doc/ibert
|
||||
model_doc/layoutlm
|
||||
model_doc/layoutlmv2
|
||||
model_doc/layoutxlm
|
||||
model_doc/led
|
||||
model_doc/longformer
|
||||
model_doc/luke
|
||||
model_doc/lxmert
|
||||
model_doc/marian
|
||||
model_doc/m2m_100
|
||||
model_doc/mbart
|
||||
model_doc/megatron_bert
|
||||
model_doc/megatron_gpt2
|
||||
model_doc/mobilebert
|
||||
model_doc/mpnet
|
||||
model_doc/mt5
|
||||
model_doc/gpt
|
||||
model_doc/gpt2
|
||||
model_doc/gptj
|
||||
model_doc/gpt_neo
|
||||
model_doc/hubert
|
||||
model_doc/pegasus
|
||||
model_doc/phobert
|
||||
model_doc/prophetnet
|
||||
model_doc/rag
|
||||
model_doc/reformer
|
||||
model_doc/rembert
|
||||
model_doc/retribert
|
||||
model_doc/roberta
|
||||
model_doc/roformer
|
||||
model_doc/speechencoderdecoder
|
||||
model_doc/speech_to_text
|
||||
model_doc/speech_to_text_2
|
||||
model_doc/splinter
|
||||
model_doc/squeezebert
|
||||
model_doc/t5
|
||||
model_doc/t5v1.1
|
||||
model_doc/tapas
|
||||
model_doc/transformerxl
|
||||
model_doc/vit
|
||||
model_doc/visual_bert
|
||||
model_doc/wav2vec2
|
||||
model_doc/xlm
|
||||
model_doc/xlmprophetnet
|
||||
model_doc/xlmroberta
|
||||
model_doc/xlnet
|
||||
model_doc/xlsr_wav2vec2
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Internal Helpers
|
||||
|
||||
internal/modeling_utils
|
||||
internal/pipelines_utils
|
||||
internal/tokenization_utils
|
||||
internal/trainer_utils
|
||||
internal/generation_utils
|
||||
internal/file_utils
|
||||
@@ -79,9 +79,9 @@ Here is how to quickly install `transformers` from source:
|
||||
pip install git+https://github.com/huggingface/transformers
|
||||
```
|
||||
|
||||
Note that this will install not the latest released version, but the bleeding edge `master` version, which you may want to use in case a bug has been fixed since the last official release and a new release hasn't been yet rolled out.
|
||||
Note that this will install not the latest released version, but the bleeding edge `master` version, which you may want to use in case a bug has been fixed since the last official release and a new release hasn't been yet rolled out.
|
||||
|
||||
While we strive to keep `master` operational at all times, if you notice some issues, they usually get fixed within a few hours or a day and you're more than welcome to help us detect any problems by opening an [Issue](https://github.com/huggingface/transformers/issues) and this way, things will get fixed even sooner.
|
||||
While we strive to keep `master` operational at all times, if you notice some issues, they usually get fixed within a few hours or a day and and you're more than welcome to help us detect any problems by opening an [Issue](https://github.com/huggingface/transformers/issues) and this way, things will get fixed even sooner.
|
||||
|
||||
Again, you can run:
|
||||
|
||||
|
||||
@@ -29,13 +29,6 @@ Default data collator
|
||||
.. autofunction:: transformers.data.data_collator.default_data_collator
|
||||
|
||||
|
||||
DefaultDataCollator
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.data.data_collator.DefaultDataCollator
|
||||
:members:
|
||||
|
||||
|
||||
DataCollatorWithPadding
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -46,20 +46,6 @@ won't be possible on a single GPU.
|
||||
parts of DeepSpeed like ``zero.Init`` for ZeRO stage 3 and higher. To tap into this feature read the docs on
|
||||
:ref:`deepspeed-non-trainer-integration`.
|
||||
|
||||
What is integrated:
|
||||
|
||||
Training:
|
||||
|
||||
1. DeepSpeed ZeRO training supports the full ZeRO stages 1, 2 and 3 with ZeRO-Infinity (CPU and NVME offload).
|
||||
|
||||
Inference:
|
||||
|
||||
1. DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. It uses the same ZeRO protocol as training, but
|
||||
it doesn't use an optimizer and a lr scheduler and only stage 3 is relevant. For more details see:
|
||||
:ref:`deepspeed-zero-inference`.
|
||||
|
||||
There is also DeepSpeed Inference - this is a totally different technology which uses Tensor Parallelism instead of
|
||||
ZeRO (coming soon).
|
||||
|
||||
|
||||
|
||||
@@ -1642,47 +1628,6 @@ larger multi-dimensional shape, this means that the parameter is partitioned and
|
||||
|
||||
|
||||
|
||||
.. _deepspeed-zero-inference:
|
||||
|
||||
|
||||
ZeRO Inference
|
||||
=======================================================================================================================
|
||||
|
||||
ZeRO Inference uses the same config as ZeRO-3 Training. You just don't need the optimizer and scheduler sections. In
|
||||
fact you can leave these in the config file if you want to share the same one with the training. They will just be
|
||||
ignored.
|
||||
|
||||
Otherwise you just need to pass the usual :class:`~transformers.TrainingArguments` arguments. For example:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
deepspeed --num_gpus=2 your_program.py <normal cl args> --do_eval --deepspeed ds_config.json
|
||||
|
||||
The only important thing is that you need to use a ZeRO-3 configuration, since ZeRO-2 provides no benefit whatsoever
|
||||
for the inference as only ZeRO-3 performs sharding of parameters, whereas ZeRO-1 shards gradients and optimizer states.
|
||||
|
||||
Here is an example of running ``run_translation.py`` under DeepSpeed deploying all available GPUs:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
deepspeed examples/pytorch/translation/run_translation.py \
|
||||
--deepspeed tests/deepspeed/ds_config_zero3.json \
|
||||
--model_name_or_path t5-small --output_dir output_dir \
|
||||
--do_eval --max_eval_samples 50 --warmup_steps 50 \
|
||||
--max_source_length 128 --val_max_target_length 128 \
|
||||
--overwrite_output_dir --per_device_eval_batch_size 4 \
|
||||
--predict_with_generate --dataset_config "ro-en" --fp16 \
|
||||
--source_lang en --target_lang ro --dataset_name wmt16 \
|
||||
--source_prefix "translate English to Romanian: "
|
||||
|
||||
Since for inference there is no need for additional large memory used by the optimizer states and the gradients you
|
||||
should be able to fit much larger batches and/or sequence length onto the same hardware.
|
||||
|
||||
|
||||
Additionally DeepSpeed is currently developing a related product called Deepspeed-Inference which has no relationship
|
||||
to the ZeRO technology, but instead uses tensor parallelism to scale models that can't fit onto a single GPU. This is a
|
||||
work in progress and we will provide the integration once that product is complete.
|
||||
|
||||
|
||||
Filing Issues
|
||||
=======================================================================================================================
|
||||
|
||||
@@ -1,22 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Keras callbacks
|
||||
=======================================================================================================================
|
||||
|
||||
When training a Transformers model with Keras, there are some library-specific callbacks available to automate common
|
||||
tasks:
|
||||
|
||||
PushToHubCallback
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
.. autoclass:: transformers.keras_callbacks.PushToHubCallback
|
||||
@@ -210,13 +210,6 @@ TFBaseModelOutputWithPooling
|
||||
:members:
|
||||
|
||||
|
||||
TFBaseModelOutputWithPoolingAndCrossAttentions
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions
|
||||
:members:
|
||||
|
||||
|
||||
TFBaseModelOutputWithPast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -224,13 +217,6 @@ TFBaseModelOutputWithPast
|
||||
:members:
|
||||
|
||||
|
||||
TFBaseModelOutputWithPastAndCrossAttentions
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_outputs.TFBaseModelOutputWithPastAndCrossAttentions
|
||||
:members:
|
||||
|
||||
|
||||
TFSeq2SeqModelOutput
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -245,13 +231,6 @@ TFCausalLMOutput
|
||||
:members:
|
||||
|
||||
|
||||
TFCausalLMOutputWithCrossAttentions
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions
|
||||
:members:
|
||||
|
||||
|
||||
TFCausalLMOutputWithPast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -29,7 +29,6 @@ There are two categories of pipeline abstractions to be aware about:
|
||||
- :class:`~transformers.FeatureExtractionPipeline`
|
||||
- :class:`~transformers.FillMaskPipeline`
|
||||
- :class:`~transformers.ImageClassificationPipeline`
|
||||
- :class:`~transformers.ImageSegmentationPipeline`
|
||||
- :class:`~transformers.ObjectDetectionPipeline`
|
||||
- :class:`~transformers.QuestionAnsweringPipeline`
|
||||
- :class:`~transformers.SummarizationPipeline`
|
||||
@@ -45,7 +44,7 @@ The pipeline abstraction
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The `pipeline` abstraction is a wrapper around all the other available pipelines. It is instantiated as any other
|
||||
pipeline but can provide additional quality of life.
|
||||
pipeline but requires an additional argument which is the `task`.
|
||||
|
||||
Simple call on one item:
|
||||
|
||||
@@ -55,15 +54,6 @@ Simple call on one item:
|
||||
>>> pipe("This restaurant is awesome")
|
||||
[{'label': 'POSITIVE', 'score': 0.9998743534088135}]
|
||||
|
||||
If you want to use a specific model from the `hub <https://huggingface.co>`__ you can ignore the task if the model on
|
||||
the hub already defines it:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> pipe = pipeline(model="roberta-large-mnli")
|
||||
>>> pipe("This restaurant is awesome")
|
||||
[{'label': 'POSITIVE', 'score': 0.9998743534088135}]
|
||||
|
||||
To call a pipeline on many items, you can either call with a `list`.
|
||||
|
||||
.. code-block::
|
||||
@@ -80,11 +70,6 @@ GPU. If it doesn't don't hesitate to create an issue.
|
||||
|
||||
.. code-block::
|
||||
|
||||
import datasets
|
||||
from transformers import pipeline
|
||||
from transformers.pipelines.base import KeyDataset
|
||||
import tqdm
|
||||
|
||||
pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=0)
|
||||
dataset = datasets.load_dataset("superb", name="asr", split="test")
|
||||
|
||||
@@ -99,170 +84,6 @@ GPU. If it doesn't don't hesitate to create an issue.
|
||||
|
||||
.. autofunction:: transformers.pipeline
|
||||
|
||||
Pipeline batching
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
All pipelines (except `zero-shot-classification` and `question-answering` currently) can use batching. This will work
|
||||
whenever the pipeline uses its streaming ability (so when passing lists or :obj:`Dataset`).
|
||||
|
||||
.. code-block::
|
||||
|
||||
from transformers import pipeline
|
||||
from transformers.pipelines.base import KeyDataset
|
||||
import datasets
|
||||
import tqdm
|
||||
|
||||
dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
|
||||
pipe = pipeline("text-classification", device=0)
|
||||
for out in pipe(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"):
|
||||
print(out)
|
||||
# [{'label': 'POSITIVE', 'score': 0.9998743534088135}]
|
||||
# Exactly the same output as before, but the content are passed
|
||||
# as batches to the model
|
||||
|
||||
|
||||
.. warning::
|
||||
|
||||
However, this is not automatically a win for performance. It can be either a 10x speedup or 5x slowdown depending
|
||||
on hardware, data and the actual model being used.
|
||||
|
||||
Example where it's most a speedup:
|
||||
|
||||
|
||||
.. code-block::
|
||||
|
||||
from transformers import pipeline
|
||||
from torch.utils.data import Dataset
|
||||
import tqdm
|
||||
|
||||
|
||||
pipe = pipeline("text-classification", device=0)
|
||||
|
||||
|
||||
class MyDataset(Dataset):
|
||||
def __len__(self):
|
||||
return 5000
|
||||
|
||||
def __getitem__(self, i):
|
||||
return "This is a test"
|
||||
|
||||
|
||||
dataset = MyDataset()
|
||||
|
||||
for batch_size in [1, 8, 64, 256]:
|
||||
print("-" * 30)
|
||||
print(f"Streaming batch_size={batch_size}")
|
||||
for out in tqdm.tqdm(pipe(dataset, batch_size=batch_size), total=len(dataset)):
|
||||
pass
|
||||
|
||||
|
||||
.. code-block::
|
||||
|
||||
# On GTX 970
|
||||
------------------------------
|
||||
Streaming no batching
|
||||
100%|██████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.52it/s]
|
||||
------------------------------
|
||||
Streaming batch_size=8
|
||||
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:04<00:00, 1205.95it/s]
|
||||
------------------------------
|
||||
Streaming batch_size=64
|
||||
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:02<00:00, 2478.24it/s]
|
||||
------------------------------
|
||||
Streaming batch_size=256
|
||||
100%|█████████████████████████████████████████████████████████████████████| 5000/5000 [00:01<00:00, 2554.43it/s]
|
||||
(diminishing returns, saturated the GPU)
|
||||
|
||||
|
||||
Example where it's most a slowdown:
|
||||
|
||||
.. code-block::
|
||||
|
||||
class MyDataset(Dataset):
|
||||
def __len__(self):
|
||||
return 5000
|
||||
|
||||
def __getitem__(self, i):
|
||||
if i % 64 == 0:
|
||||
n = 100
|
||||
else:
|
||||
n = 1
|
||||
return "This is a test" * n
|
||||
|
||||
This is a occasional very long sentence compared to the other. In that case, the **whole** batch will need to be 400
|
||||
tokens long, so the whole batch will be [64, 400] instead of [64, 4], leading to the high slowdown. Even worse, on
|
||||
bigger batches, the program simply crashes.
|
||||
|
||||
|
||||
.. code-block::
|
||||
|
||||
------------------------------
|
||||
Streaming no batching
|
||||
100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 183.69it/s]
|
||||
------------------------------
|
||||
Streaming batch_size=8
|
||||
100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 265.74it/s]
|
||||
------------------------------
|
||||
Streaming batch_size=64
|
||||
100%|██████████████████████████████████████████████████████████████████████| 1000/1000 [00:26<00:00, 37.80it/s]
|
||||
------------------------------
|
||||
Streaming batch_size=256
|
||||
0%| | 0/1000 [00:00<?, ?it/s]
|
||||
Traceback (most recent call last):
|
||||
File "/home/nicolas/src/transformers/test.py", line 42, in <module>
|
||||
for out in tqdm.tqdm(pipe(dataset, batch_size=256), total=len(dataset)):
|
||||
....
|
||||
q = q / math.sqrt(dim_per_head) # (bs, n_heads, q_length, dim_per_head)
|
||||
RuntimeError: CUDA out of memory. Tried to allocate 376.00 MiB (GPU 0; 3.95 GiB total capacity; 1.72 GiB already allocated; 354.88 MiB free; 2.46 GiB reserved in total by PyTorch)
|
||||
|
||||
|
||||
There are no good (general) solutions for this problem, and your mileage may vary depending on your use cases. Rule of
|
||||
thumb:
|
||||
|
||||
For users, a rule of thumb is:
|
||||
|
||||
- **Measure performance on your load, with your hardware. Measure, measure, and keep measuring. Real numbers are the
|
||||
only way to go.**
|
||||
- If you are latency constrained (live product doing inference), don't batch
|
||||
- If you are using CPU, don't batch.
|
||||
- If you are using throughput (you want to run your model on a bunch of static data), on GPU, then:
|
||||
|
||||
- If you have no clue about the size of the sequence_length ("natural" data), by default don't batch, measure and
|
||||
try tentatively to add it, add OOM checks to recover when it will fail (and it will at some point if you don't
|
||||
control the sequence_length.)
|
||||
- If your sequence_length is super regular, then batching is more likely to be VERY interesting, measure and push
|
||||
it until you get OOMs.
|
||||
- The larger the GPU the more likely batching is going to be more interesting
|
||||
- As soon as you enable batching, make sure you can handle OOMs nicely.
|
||||
|
||||
Pipeline custom code
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
If you want to override a specific pipeline.
|
||||
|
||||
Don't hesitate to create an issue for your task at hand, the goal of the pipeline is to be easy to use and support most
|
||||
cases, so :obj:`transformers` could maybe support your use case.
|
||||
|
||||
|
||||
If you want to try simply you can:
|
||||
|
||||
- Subclass your pipeline of choice
|
||||
|
||||
.. code-block::
|
||||
|
||||
class MyPipeline(TextClassificationPipeline):
|
||||
def postprocess(...):
|
||||
...
|
||||
scores = scores * 100
|
||||
...
|
||||
|
||||
my_pipeline = MyPipeline(model=model, tokenizer=tokenizer, ...)
|
||||
# or if you use `pipeline` function, then:
|
||||
my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline)
|
||||
|
||||
That should enable you to do all the custom code you want.
|
||||
|
||||
|
||||
Implementing a pipeline
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -316,13 +137,6 @@ ImageClassificationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
ImageSegmentationPipeline
|
||||
=======================================================================================================================
|
||||
|
||||
.. autoclass:: transformers.ImageSegmentationPipeline
|
||||
:special-members: __call__
|
||||
:members:
|
||||
|
||||
NerPipeline
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
@@ -20,7 +20,7 @@ Rust library `tokenizers <https://github.com/huggingface/tokenizers>`__. The "Fa
|
||||
1. a significant speed-up in particular when doing batched tokenization and
|
||||
2. additional methods to map between the original string (character and words) and the token space (e.g. getting the
|
||||
index of the token comprising a given character or the span of characters corresponding to a given token). Currently
|
||||
no "Fast" implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLM-RoBERTa
|
||||
no "Fast" implementation is available for the SentencePiece-based tokenizers (for T5, ALBERT, CamemBERT, XLMRoBERTa
|
||||
and XLNet models).
|
||||
|
||||
The base classes :class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast`
|
||||
@@ -39,8 +39,7 @@ methods for using all the tokenizers:
|
||||
- Managing special tokens (like mask, beginning-of-sentence, etc.): adding them, assigning them to attributes in the
|
||||
tokenizer for easy access and making sure they are not split during tokenization.
|
||||
|
||||
:class:`~transformers.BatchEncoding` holds the output of the
|
||||
:class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase`'s encoding methods (``__call__``,
|
||||
:class:`~transformers.BatchEncoding` holds the output of the tokenizer's encoding methods (``__call__``,
|
||||
``encode_plus`` and ``batch_encode_plus``) and is derived from a Python dictionary. When the tokenizer is a pure python
|
||||
tokenizer, this class behaves just like a standard python dictionary and holds the various model inputs computed by
|
||||
these methods (``input_ids``, ``attention_mask``...). When the tokenizer is a "Fast" tokenizer (i.e., backed by
|
||||
|
||||
@@ -1,550 +0,0 @@
|
||||
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
-->
|
||||
|
||||
# Trainer
|
||||
|
||||
The [`Trainer`] class provides an API for feature-complete training in PyTorch for most standard use cases. It's used in most of the [example scripts](../examples).
|
||||
|
||||
Before instantiating your [`Trainer`], create a [`TrainingArguments`] to access all the points of customization during training.
|
||||
|
||||
The API supports distributed training on multiple GPUs/TPUs, mixed precision through [NVIDIA Apex](https://github.com/NVIDIA/apex) and Native AMP for PyTorch.
|
||||
|
||||
The [`Trainer`] contains the basic training loop which supports the above features. To inject custom behavior you can subclass them and override the following methods:
|
||||
|
||||
- **get_train_dataloader** -- Creates the training DataLoader.
|
||||
- **get_eval_dataloader** -- Creates the evaluation DataLoader.
|
||||
- **get_test_dataloader** -- Creates the test DataLoader.
|
||||
- **log** -- Logs information on the various objects watching training.
|
||||
- **create_optimizer_and_scheduler** -- Sets up the optimizer and learning rate scheduler if they were not passed at
|
||||
init. Note, that you can also subclass or override the `create_optimizer` and `create_scheduler` methods
|
||||
separately.
|
||||
- **create_optimizer** -- Sets up the optimizer if it wasn't passed at init.
|
||||
- **create_scheduler** -- Sets up the learning rate scheduler if it wasn't passed at init.
|
||||
- **compute_loss** - Computes the loss on a batch of training inputs.
|
||||
- **training_step** -- Performs a training step.
|
||||
- **prediction_step** -- Performs an evaluation/test step.
|
||||
- **evaluate** -- Runs an evaluation loop and returns metrics.
|
||||
- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
|
||||
|
||||
<Tip warning={true}>
|
||||
|
||||
The [`Trainer`] class is optimized for 🤗 Transformers models and can have surprising behaviors
|
||||
when you use it on other models. When using it on your own model, make sure:
|
||||
|
||||
- your model always return tuples or subclasses of [`~file_utils.ModelOutput`].
|
||||
- your model can compute the loss if a `labels` argument is provided and that loss is returned as the first
|
||||
element of the tuple (if your model returns tuples)
|
||||
- your model can accept multiple label arguments (use the `label_names` in your [`TrainingArguments`] to indicate their name to the [`Trainer`]) but none of them should be named `"label"`.
|
||||
|
||||
</Tip>
|
||||
|
||||
Here is an example of how to customize [`Trainer`] using a custom loss function for multi-label classification:
|
||||
|
||||
```python
|
||||
from torch import nn
|
||||
from transformers import Trainer
|
||||
|
||||
class MultilabelTrainer(Trainer):
|
||||
def compute_loss(self, model, inputs, return_outputs=False):
|
||||
labels = inputs.get("labels")
|
||||
outputs = model(**inputs)
|
||||
logits = outputs.get('logits')
|
||||
loss_fct = nn.BCEWithLogitsLoss()
|
||||
loss = loss_fct(logits.view(-1, self.model.config.num_labels),
|
||||
labels.float().view(-1, self.model.config.num_labels))
|
||||
return (loss, outputs) if return_outputs else loss
|
||||
```
|
||||
|
||||
Another way to customize the training loop behavior for the PyTorch [`Trainer`] is to use [callbacks](callback) that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early stopping).
|
||||
|
||||
|
||||
## Trainer
|
||||
|
||||
[[autodoc]] Trainer
|
||||
- all
|
||||
|
||||
## Seq2SeqTrainer
|
||||
|
||||
[[autodoc]] Seq2SeqTrainer
|
||||
- evaluate
|
||||
- predict
|
||||
|
||||
## TrainingArguments
|
||||
|
||||
[[autodoc]] TrainingArguments
|
||||
- all
|
||||
|
||||
## Seq2SeqTrainingArguments
|
||||
|
||||
[[autodoc]] Seq2SeqTrainingArguments
|
||||
- all
|
||||
|
||||
## Checkpoints
|
||||
|
||||
By default, [`Trainer`] will save all checkpoints in the `output_dir` you set in the
|
||||
[`TrainingArguments`] you are using. Those will go in subfolder named `checkpoint-xxx` with xxx
|
||||
being the step at which the training was at.
|
||||
|
||||
Resuming training from a checkpoint can be done when calling [`Trainer.train`] with either:
|
||||
|
||||
- `resume_from_checkpoint=True` which will resume training from the latest checkpoint
|
||||
- `resume_from_checkpoint=checkpoint_dir` which will resume training from the specific checkpoint in the directory
|
||||
passed.
|
||||
|
||||
In addition, you can easily save your checkpoints on the Model Hub when using `push_to_hub=True`. By default, all
|
||||
the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
|
||||
the `hub-strategy` value of your [`TrainingArguments`] to either:
|
||||
|
||||
- `"checkpoint"`: the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
|
||||
resume training easily with `trainer.train(resume_from_checkpoint="output_dir/last-checkpoint")`.
|
||||
- `"all_checkpoints"`: all checkpoints are pushed like they appear in the output folder (so you will get one
|
||||
checkpoint folder per folder in your final repository)
|
||||
|
||||
|
||||
## Logging
|
||||
|
||||
By default [`Trainer`] will use `logging.INFO` for the main process and `logging.WARNING` for the replicas if any.
|
||||
|
||||
These defaults can be overridden to use any of the 5 `logging` levels with [`TrainingArguments`]'s
|
||||
arguments:
|
||||
|
||||
- `log_level` - for the main process
|
||||
- `log_level_replica` - for the replicas
|
||||
|
||||
Further, if [`TrainingArguments`]'s `log_on_each_node` is set to `False` only the main node will
|
||||
use the log level settings for its main process, all other nodes will use the log level settings for replicas.
|
||||
|
||||
Note that [`Trainer`] is going to set `transformers`'s log level separately for each node in its
|
||||
[`Trainer.__init__`]. So you may want to set this sooner (see the next example) if you tap into other
|
||||
`transformers` functionality before creating the [`Trainer`] object.
|
||||
|
||||
Here is an example of how this can be used in an application:
|
||||
|
||||
```python
|
||||
[...]
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
handlers=[logging.StreamHandler(sys.stdout)],
|
||||
)
|
||||
|
||||
# set the main code and the modules it uses to the same log-level according to the node
|
||||
log_level = training_args.get_process_log_level()
|
||||
logger.setLevel(log_level)
|
||||
datasets.utils.logging.set_verbosity(log_level)
|
||||
transformers.utils.logging.set_verbosity(log_level)
|
||||
|
||||
trainer = Trainer(...)
|
||||
```
|
||||
|
||||
And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated
|
||||
warnings you could run it as:
|
||||
|
||||
```bash
|
||||
my_app.py ... --log_level warning --log_level_replica error
|
||||
```
|
||||
|
||||
In the multi-node environment if you also don't want the logs to repeat for each node's main process, you will want to
|
||||
change the above to:
|
||||
|
||||
```bash
|
||||
my_app.py ... --log_level warning --log_level_replica error --log_on_each_node 0
|
||||
```
|
||||
|
||||
and then only the main process of the first node will log at the "warning" level, and all other processes on the main
|
||||
node and all processes on other nodes will log at the "error" level.
|
||||
|
||||
If you need your application to be as quiet as possible you could do:
|
||||
|
||||
```bash
|
||||
my_app.py ... --log_level error --log_level_replica error --log_on_each_node 0
|
||||
```
|
||||
|
||||
(add `--log_on_each_node 0` if on multi-node environment)
|
||||
|
||||
|
||||
## Randomness
|
||||
|
||||
When resuming from a checkpoint generated by [`Trainer`] all efforts are made to restore the
|
||||
_python_, _numpy_ and _pytorch_ RNG states to the same states as they were at the moment of saving that checkpoint,
|
||||
which should make the "stop and resume" style of training as close as possible to non-stop training.
|
||||
|
||||
However, due to various default non-deterministic pytorch settings this might not fully work. If you want full
|
||||
determinism please refer to [Controlling sources of randomness](https://pytorch.org/docs/stable/notes/randomness). As explained in the document, that some of those settings
|
||||
that make things deterministic (.e.g., `torch.backends.cudnn.deterministic`) may slow things down, therefore this
|
||||
can't be done by default, but you can enable those yourself if needed.
|
||||
|
||||
|
||||
## Trainer Integrations
|
||||
|
||||
The [`Trainer`] has been extended to support libraries that may dramatically improve your training
|
||||
time and fit much bigger models.
|
||||
|
||||
Currently it supports third party solutions, [DeepSpeed](https://github.com/microsoft/DeepSpeed) and [FairScale](https://github.com/facebookresearch/fairscale/), which implement parts of the paper [ZeRO: Memory Optimizations
|
||||
Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He](https://arxiv.org/abs/1910.02054).
|
||||
|
||||
This provided support is new and experimental as of this writing.
|
||||
|
||||
<a id='zero-install-notes'></a>
|
||||
|
||||
### CUDA Extension Installation Notes
|
||||
|
||||
As of this writing, both FairScale and Deepspeed require compilation of CUDA C++ code, before they can be used.
|
||||
|
||||
While all installation issues should be dealt with through the corresponding GitHub Issues of [FairScale](https://github.com/facebookresearch/fairscale/issues) and [Deepspeed](https://github.com/microsoft/DeepSpeed/issues), there are a few common issues that one may encounter while building
|
||||
any PyTorch extension that needs to build CUDA extensions.
|
||||
|
||||
Therefore, if you encounter a CUDA-related build issue while doing one of the following or both:
|
||||
|
||||
```bash
|
||||
pip install fairscale
|
||||
pip install deepspeed
|
||||
```
|
||||
|
||||
please, read the following notes first.
|
||||
|
||||
In these notes we give examples for what to do when `pytorch` has been built with CUDA `10.2`. If your situation is
|
||||
different remember to adjust the version number to the one you are after.
|
||||
|
||||
#### Possible problem #1
|
||||
|
||||
While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA
|
||||
installed system-wide.
|
||||
|
||||
For example, if you installed `pytorch` with `cudatoolkit==10.2` in the Python environment, you also need to have
|
||||
CUDA `10.2` installed system-wide.
|
||||
|
||||
The exact location may vary from system to system, but `/usr/local/cuda-10.2` is the most common location on many
|
||||
Unix systems. When CUDA is correctly set up and added to the `PATH` environment variable, one can find the
|
||||
installation location by doing:
|
||||
|
||||
```bash
|
||||
which nvcc
|
||||
```
|
||||
|
||||
If you don't have CUDA installed system-wide, install it first. You will find the instructions by using your favorite
|
||||
search engine. For example, if you're on Ubuntu you may want to search for: [ubuntu cuda 10.2 install](https://www.google.com/search?q=ubuntu+cuda+10.2+install).
|
||||
|
||||
#### Possible problem #2
|
||||
|
||||
Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you
|
||||
may have:
|
||||
|
||||
```bash
|
||||
/usr/local/cuda-10.2
|
||||
/usr/local/cuda-11.0
|
||||
```
|
||||
|
||||
Now, in this situation you need to make sure that your `PATH` and `LD_LIBRARY_PATH` environment variables contain
|
||||
the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the
|
||||
last version was installed. If you encounter the problem, where the package build fails because it can't find the right
|
||||
CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned
|
||||
environment variables.
|
||||
|
||||
First, you may look at their contents:
|
||||
|
||||
```bash
|
||||
echo $PATH
|
||||
echo $LD_LIBRARY_PATH
|
||||
```
|
||||
|
||||
so you get an idea of what is inside.
|
||||
|
||||
It's possible that `LD_LIBRARY_PATH` is empty.
|
||||
|
||||
`PATH` lists the locations of where executables can be found and `LD_LIBRARY_PATH` is for where shared libraries
|
||||
are to looked for. In both cases, earlier entries have priority over the later ones. `:` is used to separate multiple
|
||||
entries.
|
||||
|
||||
Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by
|
||||
doing:
|
||||
|
||||
```bash
|
||||
export PATH=/usr/local/cuda-10.2/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
|
||||
```
|
||||
|
||||
Note that we aren't overwriting the existing values, but prepending instead.
|
||||
|
||||
Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do
|
||||
exist. `lib64` sub-directory is where the various CUDA `.so` objects, like `libcudart.so` reside, it's unlikely
|
||||
that your system will have it named differently, but if it is adjust it to reflect your reality.
|
||||
|
||||
|
||||
#### Possible problem #3
|
||||
|
||||
Some older CUDA versions may refuse to build with newer compilers. For example, you my have `gcc-9` but it wants
|
||||
`gcc-7`.
|
||||
|
||||
There are various ways to go about it.
|
||||
|
||||
If you can install the latest CUDA toolkit it typically should support the newer compiler.
|
||||
|
||||
Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may
|
||||
already have it but it's not the default one, so the build system can't see it. If you have `gcc-7` installed but the
|
||||
build system complains it can't find it, the following might do the trick:
|
||||
|
||||
```bash
|
||||
sudo ln -s /usr/bin/gcc-7 /usr/local/cuda-10.2/bin/gcc
|
||||
sudo ln -s /usr/bin/g++-7 /usr/local/cuda-10.2/bin/g++
|
||||
```
|
||||
|
||||
Here, we are making a symlink to `gcc-7` from `/usr/local/cuda-10.2/bin/gcc` and since
|
||||
`/usr/local/cuda-10.2/bin/` should be in the `PATH` environment variable (see the previous problem's solution), it
|
||||
should find `gcc-7` (and `g++7`) and then the build will succeed.
|
||||
|
||||
As always make sure to edit the paths in the example to match your situation.
|
||||
|
||||
### FairScale
|
||||
|
||||
By integrating [FairScale](https://github.com/facebookresearch/fairscale/) the [`Trainer`]
|
||||
provides support for the following features from [the ZeRO paper](https://arxiv.org/abs/1910.02054):
|
||||
|
||||
1. Optimizer State Sharding
|
||||
2. Gradient Sharding
|
||||
3. Model Parameters Sharding (new and very experimental)
|
||||
4. CPU offload (new and very experimental)
|
||||
|
||||
You will need at least two GPUs to use this feature.
|
||||
|
||||
|
||||
**Installation**:
|
||||
|
||||
Install the library via pypi:
|
||||
|
||||
```bash
|
||||
pip install fairscale
|
||||
```
|
||||
|
||||
or via `transformers`' `extras`:
|
||||
|
||||
```bash
|
||||
pip install transformers[fairscale]
|
||||
```
|
||||
|
||||
(available starting from `transformers==4.6.0`) or find more details on [the FairScale's GitHub page](https://github.com/facebookresearch/fairscale/#installation).
|
||||
|
||||
If you're still struggling with the build, first make sure to read [CUDA Extension Installation Notes](#zero-install-notes).
|
||||
|
||||
If it's still not resolved the build issue, here are a few more ideas.
|
||||
|
||||
`fairscale` seems to have an issue with the recently introduced by pip build isolation feature. If you have a problem
|
||||
with it, you may want to try one of:
|
||||
|
||||
```bash
|
||||
pip install fairscale --no-build-isolation .
|
||||
```
|
||||
|
||||
or:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/facebookresearch/fairscale/
|
||||
cd fairscale
|
||||
rm -r dist build
|
||||
python setup.py bdist_wheel
|
||||
pip uninstall -y fairscale
|
||||
pip install dist/fairscale-*.whl
|
||||
```
|
||||
|
||||
`fairscale` also has issues with building against pytorch-nightly, so if you use it you may have to try one of:
|
||||
|
||||
```bash
|
||||
pip uninstall -y fairscale; pip install fairscale --pre \
|
||||
-f https://download.pytorch.org/whl/nightly/cu110/torch_nightly \
|
||||
--no-cache --no-build-isolation
|
||||
```
|
||||
|
||||
or:
|
||||
|
||||
```bash
|
||||
pip install -v --disable-pip-version-check . \
|
||||
-f https://download.pytorch.org/whl/nightly/cu110/torch_nightly --pre
|
||||
```
|
||||
|
||||
Of course, adjust the urls to match the cuda version you use.
|
||||
|
||||
If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of
|
||||
[FairScale](https://github.com/facebookresearch/fairscale/issues).
|
||||
|
||||
|
||||
|
||||
**Usage**:
|
||||
|
||||
To use the first version of Sharded data-parallelism, add `--sharded_ddp simple` to the command line arguments, and
|
||||
make sure you have added the distributed launcher `-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE` if you haven't been using it already.
|
||||
|
||||
For example here is how you could use it for `run_translation.py` with 2 GPUs:
|
||||
|
||||
```bash
|
||||
python -m torch.distributed.launch --nproc_per_node=2 examples/pytorch/translation/run_translation.py \
|
||||
--model_name_or_path t5-small --per_device_train_batch_size 1 \
|
||||
--output_dir output_dir --overwrite_output_dir \
|
||||
--do_train --max_train_samples 500 --num_train_epochs 1 \
|
||||
--dataset_name wmt16 --dataset_config "ro-en" \
|
||||
--source_lang en --target_lang ro \
|
||||
--fp16 --sharded_ddp simple
|
||||
```
|
||||
|
||||
Notes:
|
||||
|
||||
- This feature requires distributed training (so multiple GPUs).
|
||||
- It is not implemented for TPUs.
|
||||
- It works with `--fp16` too, to make things even faster.
|
||||
- One of the main benefits of enabling `--sharded_ddp simple` is that it uses a lot less GPU memory, so you should be
|
||||
able to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to
|
||||
significantly shorter training time.
|
||||
|
||||
3. To use the second version of Sharded data-parallelism, add `--sharded_ddp zero_dp_2` or `--sharded_ddp zero_dp_3` to the command line arguments, and make sure you have added the distributed launcher `-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE` if you haven't been using it already.
|
||||
|
||||
For example here is how you could use it for `run_translation.py` with 2 GPUs:
|
||||
|
||||
```bash
|
||||
python -m torch.distributed.launch --nproc_per_node=2 examples/pytorch/translation/run_translation.py \
|
||||
--model_name_or_path t5-small --per_device_train_batch_size 1 \
|
||||
--output_dir output_dir --overwrite_output_dir \
|
||||
--do_train --max_train_samples 500 --num_train_epochs 1 \
|
||||
--dataset_name wmt16 --dataset_config "ro-en" \
|
||||
--source_lang en --target_lang ro \
|
||||
--fp16 --sharded_ddp zero_dp_2
|
||||
```
|
||||
|
||||
`zero_dp_2` is an optimized version of the simple wrapper, while `zero_dp_3` fully shards model weights,
|
||||
gradients and optimizer states.
|
||||
|
||||
Both are compatible with adding `cpu_offload` to enable ZeRO-offload (activate it like this: `--sharded_ddp "zero_dp_2 cpu_offload"`).
|
||||
|
||||
Notes:
|
||||
|
||||
- This feature requires distributed training (so multiple GPUs).
|
||||
- It is not implemented for TPUs.
|
||||
- It works with `--fp16` too, to make things even faster.
|
||||
- The `cpu_offload` additional option requires `--fp16`.
|
||||
- This is an area of active development, so make sure you have a source install of fairscale to use this feature as
|
||||
some bugs you encounter may have been fixed there already.
|
||||
|
||||
Known caveats:
|
||||
|
||||
- This feature is incompatible with `--predict_with_generate` in the _run_translation.py_ script.
|
||||
- Using `--sharded_ddp zero_dp_3` requires wrapping each layer of the model in the special container
|
||||
`FullyShardedDataParallelism` of fairscale. It should be used with the option `auto_wrap` if you are not
|
||||
doing this yourself: `--sharded_ddp "zero_dp_3 auto_wrap"`.
|
||||
|
||||
|
||||
### DeepSpeed
|
||||
|
||||
|
||||
Moved to [Trainer DeepSpeed integration](deepspeed#trainer-deepspeed-integration).
|
||||
|
||||
|
||||
#### Installation
|
||||
|
||||
Moved to [Installation](deepspeed#deepspeed-installation).
|
||||
|
||||
|
||||
#### Deployment with multiple GPUs
|
||||
|
||||
Moved to [Deployment with multiple GPUs](deepspeed#deepspeed-multi-gpu).
|
||||
|
||||
|
||||
#### Deployment with one GPU
|
||||
|
||||
Moved to [Deployment with one GPU](deepspeed#deepspeed-one-gpu).
|
||||
|
||||
|
||||
#### Deployment in Notebooks
|
||||
|
||||
Moved to [Deployment in Notebooks](deepspeed#deepspeed-notebook).
|
||||
|
||||
|
||||
#### Configuration
|
||||
|
||||
Moved to [Configuration](deepspeed#deepspeed-config).
|
||||
|
||||
|
||||
#### Passing Configuration
|
||||
|
||||
Moved to [Passing Configuration](deepspeed#deepspeed-config-passing).
|
||||
|
||||
|
||||
#### Shared Configuration
|
||||
|
||||
Moved to [Shared Configuration](deepspeed#deepspeed-config-shared).
|
||||
|
||||
#### ZeRO
|
||||
|
||||
Moved to [ZeRO](deepspeed#deepspeed-zero).
|
||||
|
||||
##### ZeRO-2 Config
|
||||
|
||||
Moved to [ZeRO-2 Config](deepspeed#deepspeed-zero2-config).
|
||||
|
||||
##### ZeRO-3 Config
|
||||
|
||||
Moved to [ZeRO-3 Config](deepspeed#deepspeed-zero3-config).
|
||||
|
||||
|
||||
#### NVMe Support
|
||||
|
||||
Moved to [NVMe Support](deepspeed#deepspeed-nvme).
|
||||
|
||||
##### ZeRO-2 vs ZeRO-3 Performance
|
||||
|
||||
Moved to [ZeRO-2 vs ZeRO-3 Performance](deepspeed#deepspeed-zero2-zero3-performance).
|
||||
|
||||
##### ZeRO-2 Example
|
||||
|
||||
Moved to [ZeRO-2 Example](deepspeed#deepspeed-zero2-example).
|
||||
|
||||
##### ZeRO-3 Example
|
||||
|
||||
Moved to [ZeRO-3 Example](deepspeed#deepspeed-zero3-example).
|
||||
|
||||
|
||||
#### Optimizer and Scheduler
|
||||
|
||||
##### Optimizer
|
||||
|
||||
Moved to [Optimizer](deepspeed#deepspeed-optimizer).
|
||||
|
||||
|
||||
##### Scheduler
|
||||
|
||||
Moved to [Scheduler](deepspeed#deepspeed-scheduler).
|
||||
|
||||
#### fp32 Precision
|
||||
|
||||
Moved to [fp32 Precision](deepspeed#deepspeed-fp32).
|
||||
|
||||
#### Automatic Mixed Precision
|
||||
|
||||
Moved to [Automatic Mixed Precision](deepspeed#deepspeed-amp).
|
||||
|
||||
#### Batch Size
|
||||
|
||||
Moved to [Batch Size](deepspeed#deepspeed-bs).
|
||||
|
||||
#### Gradient Accumulation
|
||||
|
||||
Moved to [Gradient Accumulation](deepspeed#deepspeed-grad-acc).
|
||||
|
||||
|
||||
#### Gradient Clipping
|
||||
|
||||
Moved to [Gradient Clipping](deepspeed#deepspeed-grad-clip).
|
||||
|
||||
|
||||
#### Getting The Model Weights Out
|
||||
|
||||
Moved to [Getting The Model Weights Out](deepspeed#deepspeed-weight-extraction).
|
||||
632
docs/source/main_classes/trainer.rst
Normal file
632
docs/source/main_classes/trainer.rst
Normal file
@@ -0,0 +1,632 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Trainer
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
The :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` classes provide an API for feature-complete
|
||||
training in most standard use cases. It's used in most of the :doc:`example scripts <../examples>`.
|
||||
|
||||
Before instantiating your :class:`~transformers.Trainer`/:class:`~transformers.TFTrainer`, create a
|
||||
:class:`~transformers.TrainingArguments`/:class:`~transformers.TFTrainingArguments` to access all the points of
|
||||
customization during training.
|
||||
|
||||
The API supports distributed training on multiple GPUs/TPUs, mixed precision through `NVIDIA Apex
|
||||
<https://github.com/NVIDIA/apex>`__ and Native AMP for PyTorch and :obj:`tf.keras.mixed_precision` for TensorFlow.
|
||||
|
||||
Both :class:`~transformers.Trainer` and :class:`~transformers.TFTrainer` contain the basic training loop which supports
|
||||
the above features. To inject custom behavior you can subclass them and override the following methods:
|
||||
|
||||
- **get_train_dataloader**/**get_train_tfdataset** -- Creates the training DataLoader (PyTorch) or TF Dataset.
|
||||
- **get_eval_dataloader**/**get_eval_tfdataset** -- Creates the evaluation DataLoader (PyTorch) or TF Dataset.
|
||||
- **get_test_dataloader**/**get_test_tfdataset** -- Creates the test DataLoader (PyTorch) or TF Dataset.
|
||||
- **log** -- Logs information on the various objects watching training.
|
||||
- **create_optimizer_and_scheduler** -- Sets up the optimizer and learning rate scheduler if they were not passed at
|
||||
init. Note, that you can also subclass or override the ``create_optimizer`` and ``create_scheduler`` methods
|
||||
separately.
|
||||
- **create_optimizer** -- Sets up the optimizer if it wasn't passed at init.
|
||||
- **create_scheduler** -- Sets up the learning rate scheduler if it wasn't passed at init.
|
||||
- **compute_loss** - Computes the loss on a batch of training inputs.
|
||||
- **training_step** -- Performs a training step.
|
||||
- **prediction_step** -- Performs an evaluation/test step.
|
||||
- **run_model** (TensorFlow only) -- Basic pass through the model.
|
||||
- **evaluate** -- Runs an evaluation loop and returns metrics.
|
||||
- **predict** -- Returns predictions (with metrics if labels are available) on a test set.
|
||||
|
||||
.. warning::
|
||||
|
||||
The :class:`~transformers.Trainer` class is optimized for 🤗 Transformers models and can have surprising behaviors
|
||||
when you use it on other models. When using it on your own model, make sure:
|
||||
|
||||
- your model always return tuples or subclasses of :class:`~transformers.file_utils.ModelOutput`.
|
||||
- your model can compute the loss if a :obj:`labels` argument is provided and that loss is returned as the first
|
||||
element of the tuple (if your model returns tuples)
|
||||
- your model can accept multiple label arguments (use the :obj:`label_names` in your
|
||||
:class:`~transformers.TrainingArguments` to indicate their name to the :class:`~transformers.Trainer`) but none
|
||||
of them should be named :obj:`"label"`.
|
||||
|
||||
Here is an example of how to customize :class:`~transformers.Trainer` using a custom loss function for multi-label
|
||||
classification:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from torch import nn
|
||||
from transformers import Trainer
|
||||
|
||||
class MultilabelTrainer(Trainer):
|
||||
def compute_loss(self, model, inputs, return_outputs=False):
|
||||
labels = inputs.get("labels")
|
||||
outputs = model(**inputs)
|
||||
logits = outputs.get('logits')
|
||||
loss_fct = nn.BCEWithLogitsLoss()
|
||||
loss = loss_fct(logits.view(-1, self.model.config.num_labels),
|
||||
labels.float().view(-1, self.model.config.num_labels))
|
||||
return (loss, outputs) if return_outputs else loss
|
||||
|
||||
Another way to customize the training loop behavior for the PyTorch :class:`~transformers.Trainer` is to use
|
||||
:doc:`callbacks <callback>` that can inspect the training loop state (for progress reporting, logging on TensorBoard or
|
||||
other ML platforms...) and take decisions (like early stopping).
|
||||
|
||||
|
||||
Trainer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.Trainer
|
||||
:members:
|
||||
|
||||
|
||||
Seq2SeqTrainer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.Seq2SeqTrainer
|
||||
:members: evaluate, predict
|
||||
|
||||
|
||||
TFTrainer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFTrainer
|
||||
:members:
|
||||
|
||||
|
||||
TrainingArguments
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TrainingArguments
|
||||
:members:
|
||||
|
||||
|
||||
Seq2SeqTrainingArguments
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.Seq2SeqTrainingArguments
|
||||
:members:
|
||||
|
||||
|
||||
TFTrainingArguments
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFTrainingArguments
|
||||
:members:
|
||||
|
||||
|
||||
Checkpoints
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
By default, :class:`~transformers.Trainer` will save all checkpoints in the :obj:`output_dir` you set in the
|
||||
:class:`~transformers.TrainingArguments` you are using. Those will go in subfolder named :obj:`checkpoint-xxx` with xxx
|
||||
being the step at which the training was at.
|
||||
|
||||
Resuming training from a checkpoint can be done when calling :meth:`~transformers.Trainer.train` with either:
|
||||
|
||||
- :obj:`resume_from_checkpoint=True` which will resume training from the latest checkpoint
|
||||
- :obj:`resume_from_checkpoint=checkpoint_dir` which will resume training from the specific checkpoint in the directory
|
||||
passed.
|
||||
|
||||
In addition, you can easily save your checkpoints on the Model Hub when using :obj:`push_to_hub=True`. By default, all
|
||||
the models saved in intermediate checkpoints are saved in different commits, but not the optimizer state. You can adapt
|
||||
the :obj:`hub-strategy` value of your :class:`~transformers.TrainingArguments` to either:
|
||||
|
||||
- :obj:`"checkpoint"`: the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to
|
||||
resume training easily with :obj:`trainer.train(resume_from_checkpoint="output_dir/last-checkpoint")`.
|
||||
- :obj:`"all_checkpoints"`: all checkpoints are pushed like they appear in the output folder (so you will get one
|
||||
checkpoint folder per folder in your final repository)
|
||||
|
||||
|
||||
Logging
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
By default :class:`~transformers.Trainer` will use ``logging.INFO`` for the main process and ``logging.WARNING`` for
|
||||
the replicas if any.
|
||||
|
||||
These defaults can be overridden to use any of the 5 ``logging`` levels with :class:`~transformers.TrainingArguments`'s
|
||||
arguments:
|
||||
|
||||
- ``log_level`` - for the main process
|
||||
- ``log_level_replica`` - for the replicas
|
||||
|
||||
Further, if :class:`~transformers.TrainingArguments`'s ``log_on_each_node`` is set to ``False`` only the main node will
|
||||
use the log level settings for its main process, all other nodes will use the log level settings for replicas.
|
||||
|
||||
Note that :class:`~transformers.Trainer` is going to set ``transformers``'s log level separately for each node in its
|
||||
:meth:`~transformers.Trainer.__init__`. So you may want to set this sooner (see the next example) if you tap into other
|
||||
``transformers`` functionality before creating the :class:`~transformers.Trainer` object.
|
||||
|
||||
Here is an example of how this can be used in an application:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
[...]
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# Setup logging
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
handlers=[logging.StreamHandler(sys.stdout)],
|
||||
)
|
||||
|
||||
# set the main code and the modules it uses to the same log-level according to the node
|
||||
log_level = training_args.get_process_log_level()
|
||||
logger.setLevel(log_level)
|
||||
datasets.utils.logging.set_verbosity(log_level)
|
||||
transformers.utils.logging.set_verbosity(log_level)
|
||||
|
||||
trainer = Trainer(...)
|
||||
|
||||
And then if you only want to see warnings on the main node and all other nodes to not print any most likely duplicated
|
||||
warnings you could run it as:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
my_app.py ... --log_level warning --log_level_replica error
|
||||
|
||||
In the multi-node environment if you also don't want the logs to repeat for each node's main process, you will want to
|
||||
change the above to:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
my_app.py ... --log_level warning --log_level_replica error --log_on_each_node 0
|
||||
|
||||
and then only the main process of the first node will log at the "warning" level, and all other processes on the main
|
||||
node and all processes on other nodes will log at the "error" level.
|
||||
|
||||
If you need your application to be as quiet as possible you could do:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
my_app.py ... --log_level error --log_level_replica error --log_on_each_node 0
|
||||
|
||||
(add ``--log_on_each_node 0`` if on multi-node environment)
|
||||
|
||||
|
||||
|
||||
Randomness
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
When resuming from a checkpoint generated by :class:`~transformers.Trainer` all efforts are made to restore the
|
||||
`python`, `numpy` and `pytorch` RNG states to the same states as they were at the moment of saving that checkpoint,
|
||||
which should make the "stop and resume" style of training as close as possible to non-stop training.
|
||||
|
||||
However, due to various default non-deterministic pytorch settings this might not fully work. If you want full
|
||||
determinism please refer to `Controlling sources of randomness
|
||||
<https://pytorch.org/docs/stable/notes/randomness.html>`__. As explained in the document, that some of those settings
|
||||
that make things deterministic (.e.g., ``torch.backends.cudnn.deterministic``) may slow things down, therefore this
|
||||
can't be done by default, but you can enable those yourself if needed.
|
||||
|
||||
|
||||
Trainer Integrations
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
|
||||
The :class:`~transformers.Trainer` has been extended to support libraries that may dramatically improve your training
|
||||
time and fit much bigger models.
|
||||
|
||||
Currently it supports third party solutions, `DeepSpeed <https://github.com/microsoft/DeepSpeed>`__ and `FairScale
|
||||
<https://github.com/facebookresearch/fairscale/>`__, which implement parts of the paper `ZeRO: Memory Optimizations
|
||||
Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He
|
||||
<https://arxiv.org/abs/1910.02054>`__.
|
||||
|
||||
This provided support is new and experimental as of this writing.
|
||||
|
||||
.. _zero-install-notes:
|
||||
|
||||
CUDA Extension Installation Notes
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
As of this writing, both FairScale and Deepspeed require compilation of CUDA C++ code, before they can be used.
|
||||
|
||||
While all installation issues should be dealt with through the corresponding GitHub Issues of `FairScale
|
||||
<https://github.com/facebookresearch/fairscale/issues>`__ and `Deepspeed
|
||||
<https://github.com/microsoft/DeepSpeed/issues>`__, there are a few common issues that one may encounter while building
|
||||
any PyTorch extension that needs to build CUDA extensions.
|
||||
|
||||
Therefore, if you encounter a CUDA-related build issue while doing one of the following or both:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install fairscale
|
||||
pip install deepspeed
|
||||
|
||||
please, read the following notes first.
|
||||
|
||||
In these notes we give examples for what to do when ``pytorch`` has been built with CUDA ``10.2``. If your situation is
|
||||
different remember to adjust the version number to the one you are after.
|
||||
|
||||
Possible problem #1
|
||||
=======================================================================================================================
|
||||
|
||||
While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA
|
||||
installed system-wide.
|
||||
|
||||
For example, if you installed ``pytorch`` with ``cudatoolkit==10.2`` in the Python environment, you also need to have
|
||||
CUDA ``10.2`` installed system-wide.
|
||||
|
||||
The exact location may vary from system to system, but ``/usr/local/cuda-10.2`` is the most common location on many
|
||||
Unix systems. When CUDA is correctly set up and added to the ``PATH`` environment variable, one can find the
|
||||
installation location by doing:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
which nvcc
|
||||
|
||||
If you don't have CUDA installed system-wide, install it first. You will find the instructions by using your favorite
|
||||
search engine. For example, if you're on Ubuntu you may want to search for: `ubuntu cuda 10.2 install
|
||||
<https://www.google.com/search?q=ubuntu+cuda+10.2+install>`__.
|
||||
|
||||
Possible problem #2
|
||||
=======================================================================================================================
|
||||
|
||||
Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you
|
||||
may have:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
/usr/local/cuda-10.2
|
||||
/usr/local/cuda-11.0
|
||||
|
||||
Now, in this situation you need to make sure that your ``PATH`` and ``LD_LIBRARY_PATH`` environment variables contain
|
||||
the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the
|
||||
last version was installed. If you encounter the problem, where the package build fails because it can't find the right
|
||||
CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned
|
||||
environment variables.
|
||||
|
||||
First, you may look at their contents:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
echo $PATH
|
||||
echo $LD_LIBRARY_PATH
|
||||
|
||||
so you get an idea of what is inside.
|
||||
|
||||
It's possible that ``LD_LIBRARY_PATH`` is empty.
|
||||
|
||||
``PATH`` lists the locations of where executables can be found and ``LD_LIBRARY_PATH`` is for where shared libraries
|
||||
are to looked for. In both cases, earlier entries have priority over the later ones. ``:`` is used to separate multiple
|
||||
entries.
|
||||
|
||||
Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by
|
||||
doing:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
export PATH=/usr/local/cuda-10.2/bin:$PATH
|
||||
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
|
||||
|
||||
Note that we aren't overwriting the existing values, but prepending instead.
|
||||
|
||||
Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do
|
||||
exist. ``lib64`` sub-directory is where the various CUDA ``.so`` objects, like ``libcudart.so`` reside, it's unlikely
|
||||
that your system will have it named differently, but if it is adjust it to reflect your reality.
|
||||
|
||||
|
||||
Possible problem #3
|
||||
=======================================================================================================================
|
||||
|
||||
Some older CUDA versions may refuse to build with newer compilers. For example, you my have ``gcc-9`` but it wants
|
||||
``gcc-7``.
|
||||
|
||||
There are various ways to go about it.
|
||||
|
||||
If you can install the latest CUDA toolkit it typically should support the newer compiler.
|
||||
|
||||
Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may
|
||||
already have it but it's not the default one, so the build system can't see it. If you have ``gcc-7`` installed but the
|
||||
build system complains it can't find it, the following might do the trick:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
sudo ln -s /usr/bin/gcc-7 /usr/local/cuda-10.2/bin/gcc
|
||||
sudo ln -s /usr/bin/g++-7 /usr/local/cuda-10.2/bin/g++
|
||||
|
||||
|
||||
Here, we are making a symlink to ``gcc-7`` from ``/usr/local/cuda-10.2/bin/gcc`` and since
|
||||
``/usr/local/cuda-10.2/bin/`` should be in the ``PATH`` environment variable (see the previous problem's solution), it
|
||||
should find ``gcc-7`` (and ``g++7``) and then the build will succeed.
|
||||
|
||||
As always make sure to edit the paths in the example to match your situation.
|
||||
|
||||
FairScale
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
By integrating `FairScale <https://github.com/facebookresearch/fairscale/>`__ the :class:`~transformers.Trainer`
|
||||
provides support for the following features from `the ZeRO paper <https://arxiv.org/abs/1910.02054>`__:
|
||||
|
||||
1. Optimizer State Sharding
|
||||
2. Gradient Sharding
|
||||
3. Model Parameters Sharding (new and very experimental)
|
||||
4. CPU offload (new and very experimental)
|
||||
|
||||
You will need at least two GPUs to use this feature.
|
||||
|
||||
|
||||
**Installation**:
|
||||
|
||||
Install the library via pypi:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install fairscale
|
||||
|
||||
or via ``transformers``' ``extras``:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install transformers[fairscale]
|
||||
|
||||
(will become available starting from ``transformers==4.6.0``)
|
||||
|
||||
or find more details on `the FairScale's GitHub page <https://github.com/facebookresearch/fairscale/#installation>`__.
|
||||
|
||||
If you're still struggling with the build, first make sure to read :ref:`zero-install-notes`.
|
||||
|
||||
If it's still not resolved the build issue, here are a few more ideas.
|
||||
|
||||
``fairscale`` seems to have an issue with the recently introduced by pip build isolation feature. If you have a problem
|
||||
with it, you may want to try one of:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install fairscale --no-build-isolation .
|
||||
|
||||
or:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
git clone https://github.com/facebookresearch/fairscale/
|
||||
cd fairscale
|
||||
rm -r dist build
|
||||
python setup.py bdist_wheel
|
||||
pip uninstall -y fairscale
|
||||
pip install dist/fairscale-*.whl
|
||||
|
||||
``fairscale`` also has issues with building against pytorch-nightly, so if you use it you may have to try one of:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip uninstall -y fairscale; pip install fairscale --pre \
|
||||
-f https://download.pytorch.org/whl/nightly/cu110/torch_nightly.html \
|
||||
--no-cache --no-build-isolation
|
||||
|
||||
or:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install -v --disable-pip-version-check . \
|
||||
-f https://download.pytorch.org/whl/nightly/cu110/torch_nightly.html --pre
|
||||
|
||||
Of course, adjust the urls to match the cuda version you use.
|
||||
|
||||
If after trying everything suggested you still encounter build issues, please, proceed with the GitHub Issue of
|
||||
`FairScale <https://github.com/facebookresearch/fairscale/issues>`__.
|
||||
|
||||
|
||||
|
||||
**Usage**:
|
||||
|
||||
To use the first version of Sharded data-parallelism, add ``--sharded_ddp simple`` to the command line arguments, and
|
||||
make sure you have added the distributed launcher ``-m torch.distributed.launch
|
||||
--nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE`` if you haven't been using it already.
|
||||
|
||||
For example here is how you could use it for ``run_translation.py`` with 2 GPUs:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node=2 examples/pytorch/translation/run_translation.py \
|
||||
--model_name_or_path t5-small --per_device_train_batch_size 1 \
|
||||
--output_dir output_dir --overwrite_output_dir \
|
||||
--do_train --max_train_samples 500 --num_train_epochs 1 \
|
||||
--dataset_name wmt16 --dataset_config "ro-en" \
|
||||
--source_lang en --target_lang ro \
|
||||
--fp16 --sharded_ddp simple
|
||||
|
||||
Notes:
|
||||
|
||||
- This feature requires distributed training (so multiple GPUs).
|
||||
- It is not implemented for TPUs.
|
||||
- It works with ``--fp16`` too, to make things even faster.
|
||||
- One of the main benefits of enabling ``--sharded_ddp simple`` is that it uses a lot less GPU memory, so you should be
|
||||
able to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to
|
||||
significantly shorter training time.
|
||||
|
||||
3. To use the second version of Sharded data-parallelism, add ``--sharded_ddp zero_dp_2`` or ``--sharded_ddp
|
||||
zero_dp_3`` to the command line arguments, and make sure you have added the distributed launcher ``-m
|
||||
torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE`` if you haven't been using it already.
|
||||
|
||||
For example here is how you could use it for ``run_translation.py`` with 2 GPUs:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
python -m torch.distributed.launch --nproc_per_node=2 examples/pytorch/translation/run_translation.py \
|
||||
--model_name_or_path t5-small --per_device_train_batch_size 1 \
|
||||
--output_dir output_dir --overwrite_output_dir \
|
||||
--do_train --max_train_samples 500 --num_train_epochs 1 \
|
||||
--dataset_name wmt16 --dataset_config "ro-en" \
|
||||
--source_lang en --target_lang ro \
|
||||
--fp16 --sharded_ddp zero_dp_2
|
||||
|
||||
:obj:`zero_dp_2` is an optimized version of the simple wrapper, while :obj:`zero_dp_3` fully shards model weights,
|
||||
gradients and optimizer states.
|
||||
|
||||
Both are compatible with adding :obj:`cpu_offload` to enable ZeRO-offload (activate it like this: :obj:`--sharded_ddp
|
||||
"zero_dp_2 cpu_offload"`).
|
||||
|
||||
Notes:
|
||||
|
||||
- This feature requires distributed training (so multiple GPUs).
|
||||
- It is not implemented for TPUs.
|
||||
- It works with ``--fp16`` too, to make things even faster.
|
||||
- The ``cpu_offload`` additional option requires ``--fp16``.
|
||||
- This is an area of active development, so make sure you have a source install of fairscale to use this feature as
|
||||
some bugs you encounter may have been fixed there already.
|
||||
|
||||
Known caveats:
|
||||
|
||||
- This feature is incompatible with :obj:`--predict_with_generate` in the `run_translation.py` script.
|
||||
- Using :obj:`--sharded_ddp zero_dp_3` requires wrapping each layer of the model in the special container
|
||||
:obj:`FullyShardedDataParallelism` of fairscale. It should be used with the option :obj:`auto_wrap` if you are not
|
||||
doing this yourself: :obj:`--sharded_ddp "zero_dp_3 auto_wrap"`.
|
||||
|
||||
|
||||
DeepSpeed
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
|
||||
Moved to :ref:`deepspeed-trainer-integration`.
|
||||
|
||||
|
||||
Installation
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-installation`.
|
||||
|
||||
|
||||
Deployment with multiple GPUs
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-multi-gpu`.
|
||||
|
||||
|
||||
Deployment with one GPU
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-one-gpu`.
|
||||
|
||||
|
||||
Deployment in Notebooks
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-notebook`.
|
||||
|
||||
|
||||
Configuration
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-config`.
|
||||
|
||||
|
||||
Passing Configuration
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-config-passing`.
|
||||
|
||||
|
||||
Shared Configuration
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-config-shared`.
|
||||
|
||||
ZeRO
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-zero`.
|
||||
|
||||
ZeRO-2 Config
|
||||
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
|
||||
Moved to :ref:`deepspeed-zero2-config`.
|
||||
|
||||
ZeRO-3 Config
|
||||
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
|
||||
Moved to :ref:`deepspeed-zero3-config`.
|
||||
|
||||
|
||||
NVMe Support
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-nvme`.
|
||||
|
||||
ZeRO-2 vs ZeRO-3 Performance
|
||||
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
|
||||
Moved to :ref:`deepspeed-zero2-zero3-performance`.
|
||||
|
||||
ZeRO-2 Example
|
||||
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
|
||||
Moved to :ref:`deepspeed-zero2-example`.
|
||||
|
||||
ZeRO-3 Example
|
||||
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
|
||||
Moved to :ref:`deepspeed-zero3-example`.
|
||||
|
||||
Optimizer and Scheduler
|
||||
=======================================================================================================================
|
||||
|
||||
|
||||
|
||||
Optimizer
|
||||
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
|
||||
Moved to :ref:`deepspeed-optimizer`.
|
||||
|
||||
|
||||
Scheduler
|
||||
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
|
||||
|
||||
Moved to :ref:`deepspeed-scheduler`.
|
||||
|
||||
fp32 Precision
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-fp32`.
|
||||
|
||||
Automatic Mixed Precision
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-amp`.
|
||||
|
||||
Batch Size
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-bs`.
|
||||
|
||||
Gradient Accumulation
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-grad-acc`.
|
||||
|
||||
|
||||
Gradient Clipping
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-grad-clip`.
|
||||
|
||||
|
||||
Getting The Model Weights Out
|
||||
=======================================================================================================================
|
||||
|
||||
Moved to :ref:`deepspeed-weight-extraction`.
|
||||
@@ -31,7 +31,7 @@ This introduces two breaking changes:
|
||||
|
||||
##### How to obtain the same behavior as v3.x in v4.x
|
||||
|
||||
- The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](main_classes/pipelines#transformers.TokenClassificationPipeline).
|
||||
- The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](https://huggingface.co/transformers/main_classes/pipelines.html?highlight=textclassification#tokenclassificationpipeline).
|
||||
- The auto-tokenizers now return rust tokenizers. In order to obtain the python tokenizers instead, the user may use the `use_fast` flag by setting it to `False`:
|
||||
|
||||
In version `v3.x`:
|
||||
@@ -98,7 +98,7 @@ from transformers.models.bert.modeling_bert import BertLayer
|
||||
|
||||
#### 4. Switching the `return_dict` argument to `True` by default
|
||||
|
||||
The [`return_dict` argument](main_classes/output) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice.
|
||||
The [`return_dict` argument](https://huggingface.co/transformers/main_classes/output.html) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice.
|
||||
|
||||
This is a breaking change as the limitation of that tuple is that it cannot be unpacked: `value0, value1 = outputs` will not work.
|
||||
|
||||
|
||||
@@ -27,32 +27,7 @@ Instantiating one of :class:`~transformers.AutoConfig`, :class:`~transformers.Au
|
||||
|
||||
will create a model that is an instance of :class:`~transformers.BertModel`.
|
||||
|
||||
There is one class of :obj:`AutoModel` for each task, and for each backend (PyTorch, TensorFlow, or Flax).
|
||||
|
||||
Extending the Auto Classes
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
|
||||
custom class of model :obj:`NewModel`, make sure you have a :obj:`NewModelConfig` then you can add those to the auto
|
||||
classes like this:
|
||||
|
||||
.. code-block::
|
||||
|
||||
from transformers import AutoConfig, AutoModel
|
||||
|
||||
AutoConfig.register("new-model", NewModelConfig)
|
||||
AutoModel.register(NewModelConfig, NewModel)
|
||||
|
||||
You will then be able to use the auto classes like you would usually do!
|
||||
|
||||
.. warning::
|
||||
|
||||
If your :obj:`NewModelConfig` is a subclass of :class:`~transformer.PretrainedConfig`, make sure its
|
||||
:obj:`model_type` attribute is set to the same key you use when registering the config (here :obj:`"new-model"`).
|
||||
|
||||
Likewise, if your :obj:`NewModel` is a subclass of :class:`~transformers.PreTrainedModel`, make sure its
|
||||
:obj:`config_class` attribute is set to the same class you use when registering the model (here
|
||||
:obj:`NewModelConfig`).
|
||||
There is one class of :obj:`AutoModel` for each task, and for each backend (PyTorch or TensorFlow).
|
||||
|
||||
|
||||
AutoConfig
|
||||
@@ -76,13 +51,6 @@ AutoFeatureExtractor
|
||||
:members:
|
||||
|
||||
|
||||
AutoProcessor
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoProcessor
|
||||
:members:
|
||||
|
||||
|
||||
AutoModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -167,13 +135,6 @@ AutoModelForImageClassification
|
||||
:members:
|
||||
|
||||
|
||||
AutoModelForVision2Seq
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoModelForVision2Seq
|
||||
:members:
|
||||
|
||||
|
||||
AutoModelForAudioClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -202,13 +163,6 @@ AutoModelForObjectDetection
|
||||
:members:
|
||||
|
||||
|
||||
AutoModelForImageSegmentation
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.AutoModelForImageSegmentation
|
||||
:members:
|
||||
|
||||
|
||||
TFAutoModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -230,13 +184,6 @@ TFAutoModelForCausalLM
|
||||
:members:
|
||||
|
||||
|
||||
TFAutoModelForImageClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAutoModelForImageClassification
|
||||
:members:
|
||||
|
||||
|
||||
TFAutoModelForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -265,13 +212,6 @@ TFAutoModelForMultipleChoice
|
||||
:members:
|
||||
|
||||
|
||||
TFAutoModelForTableQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFAutoModelForTableQuestionAnswering
|
||||
:members:
|
||||
|
||||
|
||||
TFAutoModelForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -361,10 +301,3 @@ FlaxAutoModelForImageClassification
|
||||
|
||||
.. autoclass:: transformers.FlaxAutoModelForImageClassification
|
||||
:members:
|
||||
|
||||
|
||||
FlaxAutoModelForVision2Seq
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxAutoModelForVision2Seq
|
||||
:members:
|
||||
|
||||
@@ -74,7 +74,7 @@ The :obj:`facebook/bart-base` and :obj:`facebook/bart-large` checkpoints can be
|
||||
.. code-block::
|
||||
|
||||
from transformers import BartForConditionalGeneration, BartTokenizer
|
||||
model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", forced_bos_token_id=0)
|
||||
model = BartForConditionalGeneration.from_pretrained("facebook/bart-large", force_bos_token_to_be_generated=True)
|
||||
tok = BartTokenizer.from_pretrained("facebook/bart-large")
|
||||
example_english_phrase = "UN Chief Says There Is No <mask> in Syria"
|
||||
batch = tok(example_english_phrase, return_tensors='pt')
|
||||
|
||||
@@ -1,86 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
BARTpho
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The BARTpho model was proposed in `BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
|
||||
<https://arxiv.org/abs/2109.09701>`__ by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*We present BARTpho with two versions -- BARTpho_word and BARTpho_syllable -- the first public large-scale monolingual
|
||||
sequence-to-sequence models pre-trained for Vietnamese. Our BARTpho uses the "large" architecture and pre-training
|
||||
scheme of the sequence-to-sequence denoising model BART, thus especially suitable for generative NLP tasks. Experiments
|
||||
on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, our BARTpho
|
||||
outperforms the strong baseline mBART and improves the state-of-the-art. We release BARTpho to facilitate future
|
||||
research and applications of generative Vietnamese NLP tasks.*
|
||||
|
||||
Example of use:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> import torch
|
||||
>>> from transformers import AutoModel, AutoTokenizer
|
||||
|
||||
>>> bartpho = AutoModel.from_pretrained("vinai/bartpho-syllable")
|
||||
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained("vinai/bartpho-syllable")
|
||||
|
||||
>>> line = "Chúng tôi là những nghiên cứu viên."
|
||||
|
||||
>>> input_ids = tokenizer(line, return_tensors="pt")
|
||||
|
||||
>>> with torch.no_grad():
|
||||
... features = bartpho(**input_ids) # Models outputs are now tuples
|
||||
|
||||
>>> # With TensorFlow 2.0+:
|
||||
>>> from transformers import TFAutoModel
|
||||
>>> bartpho = TFAutoModel.from_pretrained("vinai/bartpho-syllable")
|
||||
>>> input_ids = tokenizer(line, return_tensors="tf")
|
||||
>>> features = bartpho(**input_ids)
|
||||
|
||||
Tips:
|
||||
|
||||
- Following mBART, BARTpho uses the "large" architecture of BART with an additional layer-normalization layer on top of
|
||||
both the encoder and decoder. Thus, usage examples in the :doc:`documentation of BART <bart>`, when adapting to use
|
||||
with BARTpho, should be adjusted by replacing the BART-specialized classes with the mBART-specialized counterparts.
|
||||
For example:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import MBartForConditionalGeneration
|
||||
>>> bartpho = MBartForConditionalGeneration.from_pretrained("vinai/bartpho-syllable")
|
||||
>>> TXT = 'Chúng tôi là <mask> nghiên cứu viên.'
|
||||
>>> input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
|
||||
>>> logits = bartpho(input_ids).logits
|
||||
>>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item()
|
||||
>>> probs = logits[0, masked_index].softmax(dim=0)
|
||||
>>> values, predictions = probs.topk(5)
|
||||
>>> print(tokenizer.decode(predictions).split())
|
||||
|
||||
- This implementation is only for tokenization: "monolingual_vocab_file" consists of Vietnamese-specialized types
|
||||
extracted from the pre-trained SentencePiece model "vocab_file" that is available from the multilingual XLM-RoBERTa.
|
||||
Other languages, if employing this pre-trained multilingual SentencePiece model "vocab_file" for subword
|
||||
segmentation, can reuse BartphoTokenizer with their own language-specialized "monolingual_vocab_file".
|
||||
|
||||
This model was contributed by `dqnguyen <https://huggingface.co/dqnguyen>`__. The original code can be found `here
|
||||
<https://github.com/VinAIResearch/BARTpho>`__.
|
||||
|
||||
BartphoTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BartphoTokenizer
|
||||
:members:
|
||||
@@ -40,15 +40,8 @@ significantly outperforming from-scratch DeiT training (81.8%) with the same set
|
||||
Tips:
|
||||
|
||||
- BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They
|
||||
outperform both the :doc:`original model (ViT) <vit>` as well as :doc:`Data-efficient Image Transformers (DeiT)
|
||||
<deit>` when fine-tuned on ImageNet-1K and CIFAR-100. You can check out demo notebooks regarding inference as well as
|
||||
fine-tuning on custom data `here
|
||||
<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer>`__ (you can just replace
|
||||
:class:`~transformers.ViTFeatureExtractor` by :class:`~transformers.BeitFeatureExtractor` and
|
||||
:class:`~transformers.ViTForImageClassification` by :class:`~transformers.BeitForImageClassification`).
|
||||
- There's also a demo notebook available which showcases how to combine DALL-E's image tokenizer with BEiT for
|
||||
performing masked image modeling. You can find it `here
|
||||
<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/BEiT>`__.
|
||||
outperform both the original model (ViT) as well as Data-efficient Image Transformers (DeiT) when fine-tuned on
|
||||
ImageNet-1K and CIFAR-100.
|
||||
- As the BEiT models expect each image to be of the same size (resolution), one can use
|
||||
:class:`~transformers.BeitFeatureExtractor` to resize (or rescale) and normalize images for the model.
|
||||
- Both the patch resolution and image resolution used during pre-training or fine-tuning are reflected in the name of
|
||||
@@ -70,17 +63,6 @@ This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The JA
|
||||
contributed by `kamalkraj <https://huggingface.co/kamalkraj>`__. The original code can be found `here
|
||||
<https://github.com/microsoft/unilm/tree/master/beit>`__.
|
||||
|
||||
|
||||
BEiT specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.models.beit.modeling_beit.BeitModelOutputWithPooling
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.models.beit.modeling_flax_beit.FlaxBeitModelOutputWithPooling
|
||||
:members:
|
||||
|
||||
|
||||
BeitConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -116,13 +98,6 @@ BeitForImageClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
BeitForSemanticSegmentation
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BeitForSemanticSegmentation
|
||||
:members: forward
|
||||
|
||||
|
||||
FlaxBeitModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -10,7 +10,7 @@
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
BERTweet
|
||||
Bertweet
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
|
||||
@@ -47,7 +47,7 @@ Implementation Notes
|
||||
- Available checkpoints can be found in the `model hub <https://huggingface.co/models?search=blenderbot>`__.
|
||||
- This is the `default` Blenderbot model class. However, some smaller checkpoints, such as
|
||||
``facebook/blenderbot_small_90M``, have a different architecture and consequently should be used with
|
||||
`BlenderbotSmall <blenderbot_small>`__.
|
||||
`BlenderbotSmall <https://huggingface.co/transformers/master/model_doc/blenderbot_small.html>`__.
|
||||
|
||||
|
||||
Usage
|
||||
@@ -81,13 +81,6 @@ BlenderbotTokenizer
|
||||
:members: build_inputs_with_special_tokens
|
||||
|
||||
|
||||
BlenderbotTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.BlenderbotTokenizerFast
|
||||
:members: build_inputs_with_special_tokens
|
||||
|
||||
|
||||
BlenderbotModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -125,17 +118,3 @@ TFBlenderbotForConditionalGeneration
|
||||
|
||||
.. autoclass:: transformers.TFBlenderbotForConditionalGeneration
|
||||
:members: call
|
||||
|
||||
|
||||
FlaxBlenderbotModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxBlenderbotModel
|
||||
:members: __call__, encode, decode
|
||||
|
||||
|
||||
FlaxBlenderbotForConditionalGeneration
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxBlenderbotForConditionalGeneration
|
||||
:members: __call__, encode, decode
|
||||
|
||||
@@ -97,17 +97,3 @@ TFBlenderbotSmallForConditionalGeneration
|
||||
|
||||
.. autoclass:: transformers.TFBlenderbotSmallForConditionalGeneration
|
||||
:members: call
|
||||
|
||||
|
||||
FlaxBlenderbotSmallModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxBlenderbotSmallModel
|
||||
:members: __call__, encode, decode
|
||||
|
||||
|
||||
FlaxBlenderbotForConditionalGeneration
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxBlenderbotSmallForConditionalGeneration
|
||||
:members: __call__, encode, decode
|
||||
|
||||
@@ -25,12 +25,12 @@ Overview
|
||||
|
||||
The DeiT model was proposed in `Training data-efficient image transformers & distillation through attention
|
||||
<https://arxiv.org/abs/2012.12877>`__ by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre
|
||||
Sablayrolles, Hervé Jégou. The `Vision Transformer (ViT) <vit>`__ introduced in `Dosovitskiy et al., 2020
|
||||
<https://arxiv.org/abs/2010.11929>`__ has shown that one can match or even outperform existing convolutional neural
|
||||
networks using a Transformer encoder (BERT-like). However, the ViT models introduced in that paper required training on
|
||||
expensive infrastructure for multiple weeks, using external data. DeiT (data-efficient image transformers) are more
|
||||
efficiently trained transformers for image classification, requiring far less data and far less computing resources
|
||||
compared to the original ViT models.
|
||||
Sablayrolles, Hervé Jégou. The `Vision Transformer (ViT) <https://huggingface.co/transformers/model_doc/vit.html>`__
|
||||
introduced in `Dosovitskiy et al., 2020 <https://arxiv.org/abs/2010.11929>`__ has shown that one can match or even
|
||||
outperform existing convolutional neural networks using a Transformer encoder (BERT-like). However, the ViT models
|
||||
introduced in that paper required training on expensive infrastructure for multiple weeks, using external data. DeiT
|
||||
(data-efficient image transformers) are more efficiently trained transformers for image classification, requiring far
|
||||
less data and far less computing resources compared to the original ViT models.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
|
||||
@@ -1,169 +0,0 @@
|
||||
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
-->
|
||||
|
||||
# DETR
|
||||
|
||||
## Overview
|
||||
|
||||
The DETR model was proposed in [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by
|
||||
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR
|
||||
consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for
|
||||
object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use
|
||||
things like region proposals, non-maximum suppression procedure and anchor generation. Moreover, DETR can also be
|
||||
naturally extended to perform panoptic segmentation, by simply adding a mask head on top of the decoder outputs.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the
|
||||
detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression
|
||||
procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the
|
||||
new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via
|
||||
bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries,
|
||||
DETR reasons about the relations of the objects and the global image context to directly output the final set of
|
||||
predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many
|
||||
other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and
|
||||
highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily
|
||||
generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive
|
||||
baselines.*
|
||||
|
||||
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/facebookresearch/detr).
|
||||
|
||||
The quickest way to get started with DETR is by checking the [example notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR) (which showcase both inference and
|
||||
fine-tuning on custom data).
|
||||
|
||||
Here's a TLDR explaining how [`~transformers.DetrForObjectDetection`] works:
|
||||
|
||||
First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
|
||||
ResNet-50/ResNet-101). Let's assume we also add a batch dimension. This means that the input to the backbone is a
|
||||
tensor of shape `(batch_size, 3, height, width)`, assuming the image has 3 color channels (RGB). The CNN backbone
|
||||
outputs a new lower-resolution feature map, typically of shape `(batch_size, 2048, height/32, width/32)`. This is
|
||||
then projected to match the hidden dimension of the Transformer of DETR, which is `256` by default, using a
|
||||
`nn.Conv2D` layer. So now, we have a tensor of shape `(batch_size, 256, height/32, width/32).` Next, the
|
||||
feature map is flattened and transposed to obtain a tensor of shape `(batch_size, seq_len, d_model)` =
|
||||
`(batch_size, width/32*height/32, 256)`. So a difference with NLP models is that the sequence length is actually
|
||||
longer than usual, but with a smaller `d_model` (which in NLP is typically 768 or higher).
|
||||
|
||||
Next, this is sent through the encoder, outputting `encoder_hidden_states` of the same shape (you can consider
|
||||
these as image features). Next, so-called **object queries** are sent through the decoder. This is a tensor of shape
|
||||
`(batch_size, num_queries, d_model)`, with `num_queries` typically set to 100 and initialized with zeros.
|
||||
These input embeddings are learnt positional encodings that the authors refer to as object queries, and similarly to
|
||||
the encoder, they are added to the input of each attention layer. Each object query will look for a particular object
|
||||
in the image. The decoder updates these embeddings through multiple self-attention and encoder-decoder attention layers
|
||||
to output `decoder_hidden_states` of the same shape: `(batch_size, num_queries, d_model)`. Next, two heads
|
||||
are added on top for object detection: a linear layer for classifying each object query into one of the objects or "no
|
||||
object", and a MLP to predict bounding boxes for each query.
|
||||
|
||||
The model is trained using a **bipartite matching loss**: so what we actually do is compare the predicted classes +
|
||||
bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N
|
||||
(so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as
|
||||
bounding box). The [Hungarian matching algorithm](https://en.wikipedia.org/wiki/Hungarian_algorithm) is used to find
|
||||
an optimal one-to-one mapping of each of the N queries to each of the N annotations. Next, standard cross-entropy (for
|
||||
the classes) and a linear combination of the L1 and [generalized IoU loss](https://giou.stanford.edu/) (for the
|
||||
bounding boxes) are used to optimize the parameters of the model.
|
||||
|
||||
DETR can be naturally extended to perform panoptic segmentation (which unifies semantic segmentation and instance
|
||||
segmentation). [`~transformers.DetrForSegmentation`] adds a segmentation mask head on top of
|
||||
[`~transformers.DetrForObjectDetection`]. The mask head can be trained either jointly, or in a two steps process,
|
||||
where one first trains a [`~transformers.DetrForObjectDetection`] model to detect bounding boxes around both
|
||||
"things" (instances) and "stuff" (background things like trees, roads, sky), then freeze all the weights and train only
|
||||
the mask head for 25 epochs. Experimentally, these two approaches give similar results. Note that predicting boxes is
|
||||
required for the training to be possible, since the Hungarian matching is computed using distances between boxes.
|
||||
|
||||
Tips:
|
||||
|
||||
- DETR uses so-called **object queries** to detect objects in an image. The number of queries determines the maximum
|
||||
number of objects that can be detected in a single image, and is set to 100 by default (see parameter
|
||||
`num_queries` of [`~transformers.DetrConfig`]). Note that it's good to have some slack (in COCO, the
|
||||
authors used 100, while the maximum number of objects in a COCO image is ~70).
|
||||
- The decoder of DETR updates the query embeddings in parallel. This is different from language models like GPT-2,
|
||||
which use autoregressive decoding instead of parallel. Hence, no causal attention mask is used.
|
||||
- DETR adds position embeddings to the hidden states at each self-attention and cross-attention layer before projecting
|
||||
to queries and keys. For the position embeddings of the image, one can choose between fixed sinusoidal or learned
|
||||
absolute position embeddings. By default, the parameter `position_embedding_type` of
|
||||
[`~transformers.DetrConfig`] is set to `"sine"`.
|
||||
- During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help
|
||||
the model output the correct number of objects of each class. If you set the parameter `auxiliary_loss` of
|
||||
[`~transformers.DetrConfig`] to `True`, then prediction feedforward neural networks and Hungarian losses
|
||||
are added after each decoder layer (with the FFNs sharing parameters).
|
||||
- If you want to train the model in a distributed environment across multiple nodes, then one should update the
|
||||
_num_boxes_ variable in the _DetrLoss_ class of _modeling_detr.py_. When training on multiple nodes, this should be
|
||||
set to the average number of target boxes across all nodes, as can be seen in the original implementation [here](https://github.com/facebookresearch/detr/blob/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5/models/detr.py#L227-L232).
|
||||
- [`~transformers.DetrForObjectDetection`] and [`~transformers.DetrForSegmentation`] can be initialized with
|
||||
any convolutional backbone available in the [timm library](https://github.com/rwightman/pytorch-image-models).
|
||||
Initializing with a MobileNet backbone for example can be done by setting the `backbone` attribute of
|
||||
[`~transformers.DetrConfig`] to `"tf_mobilenetv3_small_075"`, and then initializing the model with that
|
||||
config.
|
||||
- DETR resizes the input images such that the shortest side is at least a certain amount of pixels while the longest is
|
||||
at most 1333 pixels. At training time, scale augmentation is used such that the shortest side is randomly set to at
|
||||
least 480 and at most 800 pixels. At inference time, the shortest side is set to 800. One can use
|
||||
[`~transformers.DetrFeatureExtractor`] to prepare images (and optional annotations in COCO format) for the
|
||||
model. Due to this resizing, images in a batch can have different sizes. DETR solves this by padding images up to the
|
||||
largest size in a batch, and by creating a pixel mask that indicates which pixels are real/which are padding.
|
||||
Alternatively, one can also define a custom `collate_fn` in order to batch images together, using
|
||||
[`~transformers.DetrFeatureExtractor.pad_and_create_pixel_mask`].
|
||||
- The size of the images will determine the amount of memory being used, and will thus determine the `batch_size`.
|
||||
It is advised to use a batch size of 2 per GPU. See [this Github thread](https://github.com/facebookresearch/detr/issues/150) for more info.
|
||||
|
||||
As a summary, consider the following table:
|
||||
|
||||
| Task | Object detection | Instance segmentation | Panoptic segmentation |
|
||||
|------|------------------|-----------------------|-----------------------|
|
||||
| **Description** | Predicting bounding boxes and class labels around objects in an image | Predicting masks around objects (i.e. instances) in an image | Predicting masks around both objects (i.e. instances) as well as "stuff" (i.e. background things like trees and roads) in an image |
|
||||
| **Model** | [`~transformers.DetrForObjectDetection`] | [`~transformers.DetrForSegmentation`] | [`~transformers.DetrForSegmentation`] |
|
||||
| **Example dataset** | COCO detection | COCO detection, COCO panoptic | COCO panoptic | |
|
||||
| **Format of annotations to provide to** [`~transformers.DetrFeatureExtractor`] | {'image_id': `int`, 'annotations': `List[Dict]`} each Dict being a COCO object annotation | {'image_id': `int`, 'annotations': `List[Dict]`} (in case of COCO detection) or {'file_name': `str`, 'image_id': `int`, 'segments_info': `List[Dict]`} (in case of COCO panoptic) | {'file_name': `str`, 'image_id': `int`, 'segments_info': `List[Dict]`} and masks_path (path to directory containing PNG files of the masks) |
|
||||
| **Postprocessing** (i.e. converting the output of the model to COCO API) | [`~transformers.DetrFeatureExtractor.post_process`] | [`~transformers.DetrFeatureExtractor.post_process_segmentation`] | [`~transformers.DetrFeatureExtractor.post_process_segmentation`], [`~transformers.DetrFeatureExtractor.post_process_panoptic`] |
|
||||
| **evaluators** | `CocoEvaluator` with `iou_types="bbox"` | `CocoEvaluator` with `iou_types="bbox"` or `"segm"` | `CocoEvaluator` with `iou_tupes="bbox"` or `"segm"`, `PanopticEvaluator` |
|
||||
|
||||
In short, one should prepare the data either in COCO detection or COCO panoptic format, then use
|
||||
[`~transformers.DetrFeatureExtractor`] to create `pixel_values`, `pixel_mask` and optional
|
||||
`labels`, which can then be used to train (or fine-tune) a model. For evaluation, one should first convert the
|
||||
outputs of the model using one of the postprocessing methods of [`~transformers.DetrFeatureExtractor`]. These can
|
||||
be be provided to either `CocoEvaluator` or `PanopticEvaluator`, which allow you to calculate metrics like
|
||||
mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the [original repository](https://github.com/facebookresearch/detr). See the [example notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR) for more info regarding evaluation.
|
||||
|
||||
|
||||
## DETR specific outputs
|
||||
|
||||
[[autodoc]] models.detr.modeling_detr.DetrModelOutput
|
||||
|
||||
[[autodoc]] models.detr.modeling_detr.DetrObjectDetectionOutput
|
||||
|
||||
[[autodoc]] models.detr.modeling_detr.DetrSegmentationOutput
|
||||
|
||||
## DetrConfig
|
||||
|
||||
[[autodoc]] DetrConfig
|
||||
|
||||
## DetrFeatureExtractor
|
||||
|
||||
[[autodoc]] DetrFeatureExtractor
|
||||
- __call__
|
||||
- pad_and_create_pixel_mask
|
||||
- post_process
|
||||
- post_process_segmentation
|
||||
- post_process_panoptic
|
||||
|
||||
## DetrModel
|
||||
|
||||
[[autodoc]] DetrModel
|
||||
- forward
|
||||
|
||||
## DetrForObjectDetection
|
||||
|
||||
[[autodoc]] DetrForObjectDetection
|
||||
- forward
|
||||
|
||||
## DetrForSegmentation
|
||||
|
||||
[[autodoc]] DetrForSegmentation
|
||||
- forward
|
||||
207
docs/source/model_doc/detr.rst
Normal file
207
docs/source/model_doc/detr.rst
Normal file
@@ -0,0 +1,207 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
DETR
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The DETR model was proposed in `End-to-End Object Detection with Transformers <https://arxiv.org/abs/2005.12872>`__ by
|
||||
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR
|
||||
consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for
|
||||
object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use
|
||||
things like region proposals, non-maximum suppression procedure and anchor generation. Moreover, DETR can also be
|
||||
naturally extended to perform panoptic segmentation, by simply adding a mask head on top of the decoder outputs.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the
|
||||
detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression
|
||||
procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the
|
||||
new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via
|
||||
bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries,
|
||||
DETR reasons about the relations of the objects and the global image context to directly output the final set of
|
||||
predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many
|
||||
other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and
|
||||
highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily
|
||||
generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive
|
||||
baselines.*
|
||||
|
||||
This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
|
||||
<https://github.com/facebookresearch/detr>`__.
|
||||
|
||||
The quickest way to get started with DETR is by checking the `example notebooks
|
||||
<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR>`__ (which showcase both inference and
|
||||
fine-tuning on custom data).
|
||||
|
||||
Here's a TLDR explaining how :class:`~transformers.DetrForObjectDetection` works:
|
||||
|
||||
First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
|
||||
ResNet-50/ResNet-101). Let's assume we also add a batch dimension. This means that the input to the backbone is a
|
||||
tensor of shape :obj:`(batch_size, 3, height, width)`, assuming the image has 3 color channels (RGB). The CNN backbone
|
||||
outputs a new lower-resolution feature map, typically of shape :obj:`(batch_size, 2048, height/32, width/32)`. This is
|
||||
then projected to match the hidden dimension of the Transformer of DETR, which is :obj:`256` by default, using a
|
||||
:obj:`nn.Conv2D` layer. So now, we have a tensor of shape :obj:`(batch_size, 256, height/32, width/32).` Next, the
|
||||
feature map is flattened and transposed to obtain a tensor of shape :obj:`(batch_size, seq_len, d_model)` =
|
||||
:obj:`(batch_size, width/32*height/32, 256)`. So a difference with NLP models is that the sequence length is actually
|
||||
longer than usual, but with a smaller :obj:`d_model` (which in NLP is typically 768 or higher).
|
||||
|
||||
Next, this is sent through the encoder, outputting :obj:`encoder_hidden_states` of the same shape (you can consider
|
||||
these as image features). Next, so-called **object queries** are sent through the decoder. This is a tensor of shape
|
||||
:obj:`(batch_size, num_queries, d_model)`, with :obj:`num_queries` typically set to 100 and initialized with zeros.
|
||||
These input embeddings are learnt positional encodings that the authors refer to as object queries, and similarly to
|
||||
the encoder, they are added to the input of each attention layer. Each object query will look for a particular object
|
||||
in the image. The decoder updates these embeddings through multiple self-attention and encoder-decoder attention layers
|
||||
to output :obj:`decoder_hidden_states` of the same shape: :obj:`(batch_size, num_queries, d_model)`. Next, two heads
|
||||
are added on top for object detection: a linear layer for classifying each object query into one of the objects or "no
|
||||
object", and a MLP to predict bounding boxes for each query.
|
||||
|
||||
The model is trained using a **bipartite matching loss**: so what we actually do is compare the predicted classes +
|
||||
bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N
|
||||
(so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as
|
||||
bounding box). The `Hungarian matching algorithm <https://en.wikipedia.org/wiki/Hungarian_algorithm>`__ is used to find
|
||||
an optimal one-to-one mapping of each of the N queries to each of the N annotations. Next, standard cross-entropy (for
|
||||
the classes) and a linear combination of the L1 and `generalized IoU loss <https://giou.stanford.edu/>`__ (for the
|
||||
bounding boxes) are used to optimize the parameters of the model.
|
||||
|
||||
DETR can be naturally extended to perform panoptic segmentation (which unifies semantic segmentation and instance
|
||||
segmentation). :class:`~transformers.DetrForSegmentation` adds a segmentation mask head on top of
|
||||
:class:`~transformers.DetrForObjectDetection`. The mask head can be trained either jointly, or in a two steps process,
|
||||
where one first trains a :class:`~transformers.DetrForObjectDetection` model to detect bounding boxes around both
|
||||
"things" (instances) and "stuff" (background things like trees, roads, sky), then freeze all the weights and train only
|
||||
the mask head for 25 epochs. Experimentally, these two approaches give similar results. Note that predicting boxes is
|
||||
required for the training to be possible, since the Hungarian matching is computed using distances between boxes.
|
||||
|
||||
Tips:
|
||||
|
||||
- DETR uses so-called **object queries** to detect objects in an image. The number of queries determines the maximum
|
||||
number of objects that can be detected in a single image, and is set to 100 by default (see parameter
|
||||
:obj:`num_queries` of :class:`~transformers.DetrConfig`). Note that it's good to have some slack (in COCO, the
|
||||
authors used 100, while the maximum number of objects in a COCO image is ~70).
|
||||
- The decoder of DETR updates the query embeddings in parallel. This is different from language models like GPT-2,
|
||||
which use autoregressive decoding instead of parallel. Hence, no causal attention mask is used.
|
||||
- DETR adds position embeddings to the hidden states at each self-attention and cross-attention layer before projecting
|
||||
to queries and keys. For the position embeddings of the image, one can choose between fixed sinusoidal or learned
|
||||
absolute position embeddings. By default, the parameter :obj:`position_embedding_type` of
|
||||
:class:`~transformers.DetrConfig` is set to :obj:`"sine"`.
|
||||
- During training, the authors of DETR did find it helpful to use auxiliary losses in the decoder, especially to help
|
||||
the model output the correct number of objects of each class. If you set the parameter :obj:`auxiliary_loss` of
|
||||
:class:`~transformers.DetrConfig` to :obj:`True`, then prediction feedforward neural networks and Hungarian losses
|
||||
are added after each decoder layer (with the FFNs sharing parameters).
|
||||
- If you want to train the model in a distributed environment across multiple nodes, then one should update the
|
||||
`num_boxes` variable in the `DetrLoss` class of `modeling_detr.py`. When training on multiple nodes, this should be
|
||||
set to the average number of target boxes across all nodes, as can be seen in the original implementation `here
|
||||
<https://github.com/facebookresearch/detr/blob/a54b77800eb8e64e3ad0d8237789fcbf2f8350c5/models/detr.py#L227-L232>`__.
|
||||
- :class:`~transformers.DetrForObjectDetection` and :class:`~transformers.DetrForSegmentation` can be initialized with
|
||||
any convolutional backbone available in the `timm library <https://github.com/rwightman/pytorch-image-models>`__.
|
||||
Initializing with a MobileNet backbone for example can be done by setting the :obj:`backbone` attribute of
|
||||
:class:`~transformers.DetrConfig` to :obj:`"tf_mobilenetv3_small_075"`, and then initializing the model with that
|
||||
config.
|
||||
- DETR resizes the input images such that the shortest side is at least a certain amount of pixels while the longest is
|
||||
at most 1333 pixels. At training time, scale augmentation is used such that the shortest side is randomly set to at
|
||||
least 480 and at most 800 pixels. At inference time, the shortest side is set to 800. One can use
|
||||
:class:`~transformers.DetrFeatureExtractor` to prepare images (and optional annotations in COCO format) for the
|
||||
model. Due to this resizing, images in a batch can have different sizes. DETR solves this by padding images up to the
|
||||
largest size in a batch, and by creating a pixel mask that indicates which pixels are real/which are padding.
|
||||
Alternatively, one can also define a custom :obj:`collate_fn` in order to batch images together, using
|
||||
:meth:`~transformers.DetrFeatureExtractor.pad_and_create_pixel_mask`.
|
||||
- The size of the images will determine the amount of memory being used, and will thus determine the :obj:`batch_size`.
|
||||
It is advised to use a batch size of 2 per GPU. See `this Github thread
|
||||
<https://github.com/facebookresearch/detr/issues/150>`__ for more info.
|
||||
|
||||
As a summary, consider the following table:
|
||||
|
||||
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
|
||||
| **Task** | **Object detection** | **Instance segmentation** | **Panoptic segmentation** |
|
||||
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
|
||||
| **Description** | Predicting bounding boxes and class labels around | Predicting masks around objects (i.e. instances) in an image | Predicting masks around both objects (i.e. instances) as well as |
|
||||
| | objects in an image | | "stuff" (i.e. background things like trees and roads) in an image |
|
||||
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
|
||||
| **Model** | :class:`~transformers.DetrForObjectDetection` | :class:`~transformers.DetrForSegmentation` | :class:`~transformers.DetrForSegmentation` |
|
||||
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
|
||||
| **Example dataset** | COCO detection | COCO detection, | COCO panoptic |
|
||||
| | | COCO panoptic | |
|
||||
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
|
||||
| **Format of annotations to provide to** | {‘image_id’: int, | {‘image_id’: int, | {‘file_name: str, |
|
||||
| :class:`~transformers.DetrFeatureExtractor` | ‘annotations’: List[Dict]}, each Dict being a COCO | ‘annotations’: [List[Dict]] } (in case of COCO detection) | ‘image_id: int, |
|
||||
| | object annotation | | ‘segments_info’: List[Dict] } |
|
||||
| | | or | |
|
||||
| | | | and masks_path (path to directory containing PNG files of the masks) |
|
||||
| | | {‘file_name’: str, | |
|
||||
| | | ‘image_id’: int, | |
|
||||
| | | ‘segments_info’: List[Dict]} (in case of COCO panoptic) | |
|
||||
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
|
||||
| **Postprocessing** (i.e. converting the | :meth:`~transformers.DetrFeatureExtractor.post_process` | :meth:`~transformers.DetrFeatureExtractor.post_process_segmentation` | :meth:`~transformers.DetrFeatureExtractor.post_process_segmentation`, |
|
||||
| output of the model to COCO API) | | | :meth:`~transformers.DetrFeatureExtractor.post_process_panoptic` |
|
||||
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
|
||||
| **evaluators** | :obj:`CocoEvaluator` with iou_types = “bbox” | :obj:`CocoEvaluator` with iou_types = “bbox”, “segm” | :obj:`CocoEvaluator` with iou_tupes = “bbox, “segm” |
|
||||
| | | | |
|
||||
| | | | :obj:`PanopticEvaluator` |
|
||||
+---------------------------------------------+---------------------------------------------------------+----------------------------------------------------------------------+------------------------------------------------------------------------+
|
||||
|
||||
In short, one should prepare the data either in COCO detection or COCO panoptic format, then use
|
||||
:class:`~transformers.DetrFeatureExtractor` to create :obj:`pixel_values`, :obj:`pixel_mask` and optional
|
||||
:obj:`labels`, which can then be used to train (or fine-tune) a model. For evaluation, one should first convert the
|
||||
outputs of the model using one of the postprocessing methods of :class:`~transformers.DetrFeatureExtractor`. These can
|
||||
be be provided to either :obj:`CocoEvaluator` or :obj:`PanopticEvaluator`, which allow you to calculate metrics like
|
||||
mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are implemented in the `original repository
|
||||
<https://github.com/facebookresearch/detr>`__. See the `example notebooks
|
||||
<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR>`__ for more info regarding evaluation.
|
||||
|
||||
|
||||
DETR specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.models.detr.modeling_detr.DetrModelOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.models.detr.modeling_detr.DetrObjectDetectionOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.models.detr.modeling_detr.DetrSegmentationOutput
|
||||
:members:
|
||||
|
||||
|
||||
DetrConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DetrConfig
|
||||
:members:
|
||||
|
||||
|
||||
DetrFeatureExtractor
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DetrFeatureExtractor
|
||||
:members: __call__, pad_and_create_pixel_mask, post_process, post_process_segmentation, post_process_panoptic
|
||||
|
||||
|
||||
DetrModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DetrModel
|
||||
:members: forward
|
||||
|
||||
|
||||
DetrForObjectDetection
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DetrForObjectDetection
|
||||
:members: forward
|
||||
|
||||
|
||||
DetrForSegmentation
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.DetrForSegmentation
|
||||
:members: forward
|
||||
@@ -27,25 +27,6 @@ An application of this architecture could be to leverage two pretrained :class:`
|
||||
and decoder for a summarization model as was shown in: `Text Summarization with Pretrained Encoders
|
||||
<https://arxiv.org/abs/1908.08345>`__ by Yang Liu and Mirella Lapata.
|
||||
|
||||
The :meth:`~transformers.TFEncoderDecoderModel.from_pretrained` currently doesn't support initializing the model from a
|
||||
pytorch checkpoint. Passing ``from_pt=True`` to this method will throw an exception. If there are only pytorch
|
||||
checkpoints for a particular encoder-decoder model, a workaround is:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> # a workaround to load from pytorch checkpoint
|
||||
>>> _model = EncoderDecoderModel.from_pretrained("patrickvonplaten/bert2bert-cnn_dailymail-fp16")
|
||||
>>> _model.encoder.save_pretrained("./encoder")
|
||||
>>> _model.decoder.save_pretrained("./decoder")
|
||||
>>> model = TFEncoderDecoderModel.from_encoder_decoder_pretrained(
|
||||
... "./encoder", "./decoder", encoder_from_pt=True, decoder_from_pt=True
|
||||
... )
|
||||
>>> # This is only for copying some specific attributes of this particular model.
|
||||
>>> model.config = _model.config
|
||||
|
||||
This model was contributed by `thomwolf <https://github.com/thomwolf>`__. This model's TensorFlow and Flax versions
|
||||
were contributed by `ydshieh <https://github.com/ydshieh>`__.
|
||||
|
||||
|
||||
EncoderDecoderConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
@@ -61,13 +42,6 @@ EncoderDecoderModel
|
||||
:members: forward, from_encoder_decoder_pretrained
|
||||
|
||||
|
||||
TFEncoderDecoderModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFEncoderDecoderModel
|
||||
:members: call, from_encoder_decoder_pretrained
|
||||
|
||||
|
||||
FlaxEncoderDecoderModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -41,8 +41,6 @@ Tips:
|
||||
pre-computed values in the context of text generation. For PyTorch, see `past_key_values` argument of the
|
||||
:meth:`~transformers.GPT2Model.forward` method, or for TF the `past` argument of the
|
||||
:meth:`~transformers.TFGPT2Model.call` method for more information on its usage.
|
||||
- Enabling the `scale_attn_by_inverse_layer_idx` and `reorder_and_upcast_attn` flags will apply the training stability
|
||||
improvements from `Mistral <https://github.com/stanford-crfm/mistral/>`__ (for PyTorch only).
|
||||
|
||||
`Write With Transformer <https://transformer.huggingface.co/doc/gpt2-large>`__ is a webapp created and hosted by
|
||||
Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
|
||||
|
||||
@@ -24,33 +24,19 @@ This model was contributed by `Stella Biderman <https://huggingface.co/stellaath
|
||||
|
||||
Tips:
|
||||
|
||||
- To load `GPT-J <https://huggingface.co/EleutherAI/gpt-j-6B>`__ in float32 one would need at least 2x model size CPU
|
||||
RAM: 1x for initial weights and another 1x to load the checkpoint. So for GPT-J it would take at least 48GB of CPU
|
||||
RAM to just load the model. To reduce the CPU RAM usage there are a few options. The ``torch_dtype`` argument can be
|
||||
used to initialize the model in half-precision. And the ``low_cpu_mem_usage`` argument can be used to keep the RAM
|
||||
usage to 1x. There is also a `fp16 branch <https://huggingface.co/EleutherAI/gpt-j-6B/tree/float16>`__ which stores
|
||||
the fp16 weights, which could be used to further minimize the RAM usage. Combining all this it should take roughly
|
||||
12.1GB of CPU RAM to load the model.
|
||||
- Running [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) in float32 precision on GPU requires at least 24 GB of
|
||||
RAM. On GPUs with less than 24 GB RAM, one should therefore load the model in half-precision:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import GPTJForCausalLM
|
||||
>>> import torch
|
||||
|
||||
>>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", revision="float16", torch_dtype=torch.float16, low_cpu_mem_usage=True)
|
||||
|
||||
|
||||
- The model should fit on 16GB GPU for inference. For training/fine-tuning it would take much more GPU RAM. Adam
|
||||
optimizer for example makes four copies of the model: model, gradients, average and squared average of the gradients.
|
||||
So it would need at least 4x model size GPU memory, even with mixed precision as gradient updates are in fp32. This
|
||||
is not including the activations and data batches, which would again require some more GPU RAM. So one should explore
|
||||
solutions such as DeepSpeed, to train/fine-tune the model. Another option is to use the original codebase to
|
||||
train/fine-tune the model on TPU and then convert the model to Transformers format for inference. Instructions for
|
||||
that could be found `here <https://github.com/kingoflolz/mesh-transformer-jax/blob/master/howto_finetune.md>`__
|
||||
>>> model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.float16)
|
||||
|
||||
- Although the embedding matrix has a size of 50400, only 50257 entries are used by the GPT-2 tokenizer. These extra
|
||||
tokens are added for the sake of efficiency on TPUs. To avoid the mis-match between embedding matrix size and vocab
|
||||
size, the tokenizer for `GPT-J <https://huggingface.co/EleutherAI/gpt-j-6B>`__ contains 143 extra tokens
|
||||
size, the tokenizer for [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) contains 143 extra tokens
|
||||
``<|extratoken_1|>... <|extratoken_143|>``, so the ``vocab_size`` of tokenizer also becomes 50400.
|
||||
|
||||
Generation
|
||||
@@ -119,24 +105,3 @@ GPTJForSequenceClassification
|
||||
|
||||
.. autoclass:: transformers.GPTJForSequenceClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
GPTJForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.GPTJForQuestionAnswering
|
||||
:members: forward
|
||||
|
||||
|
||||
FlaxGPTJModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxGPTJModel
|
||||
:members: __call__
|
||||
|
||||
|
||||
FlaxGPTJForCausalLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxGPTJForCausalLM
|
||||
:members: __call__
|
||||
|
||||
@@ -10,13 +10,13 @@
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
HerBERT
|
||||
herBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The HerBERT model was proposed in `KLEJ: Comprehensive Benchmark for Polish Language Understanding
|
||||
The herBERT model was proposed in `KLEJ: Comprehensive Benchmark for Polish Language Understanding
|
||||
<https://www.aclweb.org/anthology/2020.acl-main.111.pdf>`__ by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and
|
||||
Ireneusz Gawlik. It is a BERT-based Language Model trained on Polish Corpora using only MLM objective with dynamic
|
||||
masking of whole words.
|
||||
|
||||
@@ -1,100 +0,0 @@
|
||||
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
|
||||
License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
|
||||
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License. -->
|
||||
|
||||
# ImageGPT
|
||||
|
||||
## Overview
|
||||
|
||||
The ImageGPT model was proposed in [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt) by Mark
|
||||
Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever. ImageGPT (iGPT) is a GPT-2-like
|
||||
model trained to predict the next pixel value, allowing for both unconditional and conditional image generation.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Inspired by progress in unsupervised representation learning for natural language, we examine whether similar models
|
||||
can learn useful representations for images. We train a sequence Transformer to auto-regressively predict pixels,
|
||||
without incorporating knowledge of the 2D input structure. Despite training on low-resolution ImageNet without labels,
|
||||
we find that a GPT-2 scale model learns strong image representations as measured by linear probing, fine-tuning, and
|
||||
low-data classification. On CIFAR-10, we achieve 96.3% accuracy with a linear probe, outperforming a supervised Wide
|
||||
ResNet, and 99.0% accuracy with full fine-tuning, matching the top supervised pre-trained models. We are also
|
||||
competitive with self-supervised benchmarks on ImageNet when substituting pixels for a VQVAE encoding, achieving 69.0%
|
||||
top-1 accuracy on a linear probe of our features.*
|
||||
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/imagegpt_architecture.png"
|
||||
alt="drawing" width="600"/>
|
||||
|
||||
<small> Summary of the approach. Taken from the [original paper](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf). </small>
|
||||
|
||||
This model was contributed by [nielsr](https://huggingface.co/nielsr), based on [this issue](https://github.com/openai/image-gpt/issues/7). The original code can be found
|
||||
[here](https://github.com/openai/image-gpt).
|
||||
|
||||
Tips:
|
||||
|
||||
- Demo notebooks for ImageGPT can be found
|
||||
[here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/ImageGPT).
|
||||
- ImageGPT is almost exactly the same as [GPT-2](./model_doc/gpt2), with the exception that a different activation
|
||||
function is used (namely "quick gelu"), and the layer normalization layers don't mean center the inputs. ImageGPT
|
||||
also doesn't have tied input- and output embeddings.
|
||||
- As the time- and memory requirements of the attention mechanism of Transformers scales quadratically in the sequence
|
||||
length, the authors pre-trained ImageGPT on smaller input resolutions, such as 32x32 and 64x64. However, feeding a
|
||||
sequence of 32x32x3=3072 tokens from 0..255 into a Transformer is still prohibitively large. Therefore, the authors
|
||||
applied k-means clustering to the (R,G,B) pixel values with k=512. This way, we only have a 32*32 = 1024-long
|
||||
sequence, but now of integers in the range 0..511. So we are shrinking the sequence length at the cost of a bigger
|
||||
embedding matrix. In other words, the vocabulary size of ImageGPT is 512, + 1 for a special "start of sentence" (SOS)
|
||||
token, used at the beginning of every sequence. One can use [`ImageGPTFeatureExtractor`] to prepare
|
||||
images for the model.
|
||||
- Despite being pre-trained entirely unsupervised (i.e. without the use of any labels), ImageGPT produces fairly
|
||||
performant image features useful for downstream tasks, such as image classification. The authors showed that the
|
||||
features in the middle of the network are the most performant, and can be used as-is to train a linear model (such as
|
||||
a sklearn logistic regression model for example). This is also referred to as "linear probing". Features can be
|
||||
easily obtained by first forwarding the image through the model, then specifying `output_hidden_states=True`, and
|
||||
then average-pool the hidden states at whatever layer you like.
|
||||
- Alternatively, one can further fine-tune the entire model on a downstream dataset, similar to BERT. For this, you can
|
||||
use [`ImageGPTForImageClassification`].
|
||||
- ImageGPT comes in different sizes: there's ImageGPT-small, ImageGPT-medium and ImageGPT-large. The authors did also
|
||||
train an XL variant, which they didn't release. The differences in size are summarized in the following table:
|
||||
|
||||
| **Model variant** | **Depths** | **Hidden sizes** | **Decoder hidden size** | **Params (M)** | **ImageNet-1k Top 1** |
|
||||
|---|---|---|---|---|---|
|
||||
| MiT-b0 | [2, 2, 2, 2] | [32, 64, 160, 256] | 256 | 3.7 | 70.5 |
|
||||
| MiT-b1 | [2, 2, 2, 2] | [64, 128, 320, 512] | 256 | 14.0 | 78.7 |
|
||||
| MiT-b2 | [3, 4, 6, 3] | [64, 128, 320, 512] | 768 | 25.4 | 81.6 |
|
||||
| MiT-b3 | [3, 4, 18, 3] | [64, 128, 320, 512] | 768 | 45.2 | 83.1 |
|
||||
| MiT-b4 | [3, 8, 27, 3] | [64, 128, 320, 512] | 768 | 62.6 | 83.6 |
|
||||
| MiT-b5 | [3, 6, 40, 3] | [64, 128, 320, 512] | 768 | 82.0 | 83.8 |
|
||||
|
||||
## ImageGPTConfig
|
||||
|
||||
[[autodoc]] ImageGPTConfig
|
||||
|
||||
## ImageGPTFeatureExtractor
|
||||
|
||||
[[autodoc]] ImageGPTFeatureExtractor
|
||||
|
||||
- __call__
|
||||
|
||||
## ImageGPTModel
|
||||
|
||||
[[autodoc]] ImageGPTModel
|
||||
|
||||
- forward
|
||||
|
||||
## ImageGPTForCausalImageModeling
|
||||
|
||||
[[autodoc]] ImageGPTForCausalImageModeling
|
||||
|
||||
- forward
|
||||
|
||||
## ImageGPTForImageClassification
|
||||
|
||||
[[autodoc]] ImageGPTForImageClassification
|
||||
|
||||
- forward
|
||||
@@ -18,8 +18,9 @@ Overview
|
||||
|
||||
The LayoutLMV2 model was proposed in `LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding
|
||||
<https://arxiv.org/abs/2012.14740>`__ by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu,
|
||||
Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves `LayoutLM <layoutlm>`__ to obtain
|
||||
state-of-the-art results across several document image understanding benchmarks:
|
||||
Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves `LayoutLM
|
||||
<https://huggingface.co/transformers/model_doc/layoutlm.html>`__ to obtain state-of-the-art results across several
|
||||
document image understanding benchmarks:
|
||||
|
||||
- information extraction from scanned documents: the `FUNSD <https://guillaumejaume.github.io/FUNSD/>`__ dataset (a
|
||||
collection of 199 annotated forms comprising more than 30,000 words), the `CORD <https://github.com/clovaai/cord>`__
|
||||
|
||||
@@ -40,45 +40,17 @@ One can directly plug in the weights of LayoutXLM into a LayoutLMv2 model, like
|
||||
|
||||
model = LayoutLMv2Model.from_pretrained('microsoft/layoutxlm-base')
|
||||
|
||||
Note that LayoutXLM has its own tokenizer, based on
|
||||
:class:`~transformers.LayoutXLMTokenizer`/:class:`~transformers.LayoutXLMTokenizerFast`. You can initialize it as
|
||||
follows:
|
||||
Note that LayoutXLM requires a different tokenizer, based on :class:`~transformers.XLMRobertaTokenizer`. You can
|
||||
initialize it as follows:
|
||||
|
||||
.. code-block::
|
||||
|
||||
from transformers import LayoutXLMTokenizer
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
tokenizer = LayoutXLMTokenizer.from_pretrained('microsoft/layoutxlm-base')
|
||||
|
||||
Similar to LayoutLMv2, you can use :class:`~transformers.LayoutXLMProcessor` (which internally applies
|
||||
:class:`~transformers.LayoutLMv2FeatureExtractor` and
|
||||
:class:`~transformers.LayoutXLMTokenizer`/:class:`~transformers.LayoutXLMTokenizerFast` in sequence) to prepare all
|
||||
data for the model.
|
||||
tokenizer = AutoTokenizer.from_pretrained('microsoft/layoutxlm-base')
|
||||
|
||||
As LayoutXLM's architecture is equivalent to that of LayoutLMv2, one can refer to :doc:`LayoutLMv2's documentation page
|
||||
<layoutlmv2>` for all tips, code examples and notebooks.
|
||||
|
||||
This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
|
||||
<https://github.com/microsoft/unilm>`__.
|
||||
|
||||
|
||||
LayoutXLMTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LayoutXLMTokenizer
|
||||
:members: __call__, build_inputs_with_special_tokens, get_special_tokens_mask,
|
||||
create_token_type_ids_from_sequences, save_vocabulary
|
||||
|
||||
|
||||
LayoutXLMTokenizerFast
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LayoutXLMTokenizerFast
|
||||
:members: __call__
|
||||
|
||||
|
||||
LayoutXLMProcessor
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LayoutXLMProcessor
|
||||
:members: __call__
|
||||
|
||||
@@ -74,9 +74,6 @@ Tips:
|
||||
head models by specifying ``task="entity_classification"``, ``task="entity_pair_classification"``, or
|
||||
``task="entity_span_classification"``. Please refer to the example code of each head models.
|
||||
|
||||
A demo notebook on how to fine-tune :class:`~transformers.LukeForEntityPairClassification` for relation
|
||||
classification can be found `here <https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LUKE>`__.
|
||||
|
||||
There are also 3 notebooks available, which showcase how you can reproduce the results as reported in the paper with
|
||||
the HuggingFace implementation of LUKE. They can be found `here
|
||||
<https://github.com/studio-ousia/luke/tree/master/notebooks>`__.
|
||||
@@ -140,12 +137,6 @@ LukeModel
|
||||
.. autoclass:: transformers.LukeModel
|
||||
:members: forward
|
||||
|
||||
LukeForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.LukeForMaskedLM
|
||||
:members: forward
|
||||
|
||||
|
||||
LukeForEntityClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -103,8 +103,8 @@ Here is the code to see all available pretrained models on the hub:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
from huggingface_hub import list_models
|
||||
model_list = list_models()
|
||||
from huggingface_hub.hf_api import HfApi
|
||||
model_list = HfApi().list_models()
|
||||
org = "Helsinki-NLP"
|
||||
model_ids = [x.modelId for x in model_list if x.modelId.startswith(org)]
|
||||
suffix = [x.split('/')[1] for x in model_ids]
|
||||
|
||||
@@ -1,66 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
mLUKE
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The mLUKE model was proposed in `mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
|
||||
<https://arxiv.org/abs/2110.08151>`__ by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka. It's a multilingual extension
|
||||
of the `LUKE model <https://arxiv.org/abs/2010.01057>`__ trained on the basis of XLM-RoBERTa.
|
||||
|
||||
It is based on XLM-RoBERTa and adds entity embeddings, which helps improve performance on various downstream tasks
|
||||
involving reasoning about entities such as named entity recognition, extractive question answering, relation
|
||||
classification, cloze-style knowledge completion.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Recent studies have shown that multilingual pretrained language models can be effectively improved with cross-lingual
|
||||
alignment information from Wikipedia entities. However, existing methods only exploit entity information in pretraining
|
||||
and do not explicitly use entities in downstream tasks. In this study, we explore the effectiveness of leveraging
|
||||
entity representations for downstream cross-lingual tasks. We train a multilingual language model with 24 languages
|
||||
with entity representations and show the model consistently outperforms word-based pretrained models in various
|
||||
cross-lingual transfer tasks. We also analyze the model and the key insight is that incorporating entity
|
||||
representations into the input allows us to extract more language-agnostic features. We also evaluate the model with a
|
||||
multilingual cloze prompt task with the mLAMA dataset. We show that entity-based prompt elicits correct factual
|
||||
knowledge more likely than using only word representations.*
|
||||
|
||||
One can directly plug in the weights of mLUKE into a LUKE model, like so:
|
||||
|
||||
.. code-block::
|
||||
|
||||
from transformers import LukeModel
|
||||
|
||||
model = LukeModel.from_pretrained('studio-ousia/mluke-base')
|
||||
|
||||
Note that mLUKE has its own tokenizer, :class:`~transformers.MLukeTokenizer`. You can initialize it as follows:
|
||||
|
||||
.. code-block::
|
||||
|
||||
from transformers import MLukeTokenizer
|
||||
|
||||
tokenizer = MLukeTokenizer.from_pretrained('studio-ousia/mluke-base')
|
||||
|
||||
|
||||
As mLUKE's architecture is equivalent to that of LUKE, one can refer to :doc:`LUKE's documentation page <luke>` for all
|
||||
tips, code examples and notebooks.
|
||||
|
||||
This model was contributed by `ryo0634 <https://huggingface.co/ryo0634>`__. The original code can be found `here
|
||||
<https://github.com/studio-ousia/luke>`__.
|
||||
|
||||
MLukeTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.MLukeTokenizer
|
||||
:members: __call__, save_vocabulary
|
||||
@@ -1,211 +0,0 @@
|
||||
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
-->
|
||||
|
||||
# Perceiver
|
||||
|
||||
## Overview
|
||||
|
||||
The Perceiver IO model was proposed in [Perceiver IO: A General Architecture for Structured Inputs &
|
||||
Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch,
|
||||
Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M.
|
||||
Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
|
||||
|
||||
Perceiver IO is a generalization of [Perceiver](https://arxiv.org/abs/2103.03206) to handle arbitrary outputs in
|
||||
addition to arbitrary inputs. The original Perceiver only produced a single classification label. In addition to
|
||||
classification labels, Perceiver IO can produce (for example) language, optical flow, and multimodal videos with audio.
|
||||
This is done using the same building blocks as the original Perceiver. The computational complexity of Perceiver IO is
|
||||
linear in the input and output size and the bulk of the processing occurs in the latent space, allowing us to process
|
||||
inputs and outputs that are much larger than can be handled by standard Transformers. This means, for example,
|
||||
Perceiver IO can do BERT-style masked language modeling directly using bytes instead of tokenized inputs.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*The recently-proposed Perceiver model obtains good results on several domains (images, audio, multimodal, point
|
||||
clouds) while scaling linearly in compute and memory with the input size. While the Perceiver supports many kinds of
|
||||
inputs, it can only produce very simple outputs such as class scores. Perceiver IO overcomes this limitation without
|
||||
sacrificing the original's appealing properties by learning to flexibly query the model's latent space to produce
|
||||
outputs of arbitrary size and semantics. Perceiver IO still decouples model depth from data size and still scales
|
||||
linearly with data size, but now with respect to both input and output sizes. The full Perceiver IO model achieves
|
||||
strong results on tasks with highly structured output spaces, such as natural language and visual understanding,
|
||||
StarCraft II, and multi-task and multi-modal domains. As highlights, Perceiver IO matches a Transformer-based BERT
|
||||
baseline on the GLUE language benchmark without the need for input tokenization and achieves state-of-the-art
|
||||
performance on Sintel optical flow estimation.*
|
||||
|
||||
Here's a TLDR explaining how Perceiver works:
|
||||
|
||||
The main problem with the self-attention mechanism of the Transformer is that the time and memory requirements scale
|
||||
quadratically with the sequence length. Hence, models like BERT and RoBERTa are limited to a max sequence length of 512
|
||||
tokens. Perceiver aims to solve this issue by, instead of performing self-attention on the inputs, perform it on a set
|
||||
of latent variables, and only use the inputs for cross-attention. In this way, the time and memory requirements don't
|
||||
depend on the length of the inputs anymore, as one uses a fixed amount of latent variables, like 256 or 512. These are
|
||||
randomly initialized, after which they are trained end-to-end using backpropagation.
|
||||
|
||||
Internally, [`PerceiverModel`] will create the latents, which is a tensor of shape `(batch_size, num_latents,
|
||||
d_latents)`. One must provide `inputs` (which could be text, images, audio, you name it!) to the model, which it will
|
||||
use to perform cross-attention with the latents. The output of the Perceiver encoder is a tensor of the same shape. One
|
||||
can then, similar to BERT, convert the last hidden states of the latents to classification logits by averaging along
|
||||
the sequence dimension, and placing a linear layer on top of that to project the `d_latents` to `num_labels`.
|
||||
|
||||
This was the idea of the original Perceiver paper. However, it could only output classification logits. In a follow-up
|
||||
work, PerceiverIO, they generalized it to let the model also produce outputs of arbitrary size. How, you might ask? The
|
||||
idea is actually relatively simple: one defines outputs of an arbitrary size, and then applies cross-attention with the
|
||||
last hidden states of the latents, using the outputs as queries, and the latents as keys and values.
|
||||
|
||||
So let's say one wants to perform masked language modeling (BERT-style) with the Perceiver. As the Perceiver's input
|
||||
length will not have an impact on the computation time of the self-attention layers, one can provide raw bytes,
|
||||
providing `inputs` of length 2048 to the model. If one now masks out certain of these 2048 tokens, one can define the
|
||||
`outputs` as being of shape: `(batch_size, 2048, 768)`. Next, one performs cross-attention with the final hidden states
|
||||
of the latents to update the `outputs` tensor. After cross-attention, one still has a tensor of shape `(batch_size,
|
||||
2048, 768)`. One can then place a regular language modeling head on top, to project the last dimension to the
|
||||
vocabulary size of the model, i.e. creating logits of shape `(batch_size, 2048, 262)` (as Perceiver uses a vocabulary
|
||||
size of 262 byte IDs).
|
||||
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/perceiver_architecture.jpg"
|
||||
alt="drawing" width="600"/>
|
||||
|
||||
<small> Perceiver IO architecture. Taken from the [original paper](https://arxiv.org/abs/2105.15203) </small>
|
||||
|
||||
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found
|
||||
[here](https://github.com/deepmind/deepmind-research/tree/master/perceiver).
|
||||
|
||||
Tips:
|
||||
|
||||
- The quickest way to get started with the Perceiver is by checking the [tutorial
|
||||
notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Perceiver).
|
||||
- Refer to the [blog post](https://huggingface.co/blog/perceiver) if you want to fully understand how the model works and
|
||||
is implemented in the library. Note that the models available in the library only showcase some examples of what you can do
|
||||
with the Perceiver. There are many more use cases, including question answering, named-entity recognition, object detection,
|
||||
audio classification, video classification, etc.
|
||||
|
||||
## Perceiver specific outputs
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverModelOutput
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverDecoderOutput
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverMaskedLMOutput
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverClassifierOutput
|
||||
|
||||
## PerceiverConfig
|
||||
|
||||
[[autodoc]] PerceiverConfig
|
||||
|
||||
## PerceiverTokenizer
|
||||
|
||||
[[autodoc]] PerceiverTokenizer
|
||||
- __call__
|
||||
|
||||
## PerceiverFeatureExtractor
|
||||
|
||||
[[autodoc]] PerceiverFeatureExtractor
|
||||
- __call__
|
||||
|
||||
## PerceiverTextPreprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverTextPreprocessor
|
||||
|
||||
## PerceiverImagePreprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverImagePreprocessor
|
||||
|
||||
## PerceiverOneHotPreprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverOneHotPreprocessor
|
||||
|
||||
## PerceiverAudioPreprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverAudioPreprocessor
|
||||
|
||||
## PerceiverMultimodalPreprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverMultimodalPreprocessor
|
||||
|
||||
## PerceiverProjectionDecoder
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverProjectionDecoder
|
||||
|
||||
## PerceiverBasicDecoder
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverBasicDecoder
|
||||
|
||||
## PerceiverClassificationDecoder
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverClassificationDecoder
|
||||
|
||||
## PerceiverOpticalFlowDecoder
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverOpticalFlowDecoder
|
||||
|
||||
## PerceiverBasicVideoAutoencodingDecoder
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverBasicVideoAutoencodingDecoder
|
||||
|
||||
## PerceiverMultimodalDecoder
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverMultimodalDecoder
|
||||
|
||||
## PerceiverProjectionPostprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverProjectionPostprocessor
|
||||
|
||||
## PerceiverAudioPostprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverAudioPostprocessor
|
||||
|
||||
## PerceiverClassificationPostprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverClassificationPostprocessor
|
||||
|
||||
## PerceiverMultimodalPostprocessor
|
||||
|
||||
[[autodoc]] models.perceiver.modeling_perceiver.PerceiverMultimodalPostprocessor
|
||||
|
||||
## PerceiverModel
|
||||
|
||||
[[autodoc]] PerceiverModel
|
||||
- forward
|
||||
|
||||
## PerceiverForMaskedLM
|
||||
|
||||
[[autodoc]] PerceiverForMaskedLM
|
||||
- forward
|
||||
|
||||
## PerceiverForSequenceClassification
|
||||
|
||||
[[autodoc]] PerceiverForSequenceClassification
|
||||
- forward
|
||||
|
||||
## PerceiverForImageClassificationLearned
|
||||
|
||||
[[autodoc]] PerceiverForImageClassificationLearned
|
||||
- forward
|
||||
|
||||
## PerceiverForImageClassificationFourier
|
||||
|
||||
[[autodoc]] PerceiverForImageClassificationFourier
|
||||
- forward
|
||||
|
||||
## PerceiverForImageClassificationConvProcessing
|
||||
|
||||
[[autodoc]] PerceiverForImageClassificationConvProcessing
|
||||
- forward
|
||||
|
||||
## PerceiverForOpticalFlow
|
||||
|
||||
[[autodoc]] PerceiverForOpticalFlow
|
||||
- forward
|
||||
|
||||
## PerceiverForMultimodalAutoencoding
|
||||
|
||||
[[autodoc]] PerceiverForMultimodalAutoencoding
|
||||
- forward
|
||||
@@ -50,8 +50,7 @@ Example of use:
|
||||
>>> # phobert = TFAutoModel.from_pretrained("vinai/phobert-base")
|
||||
|
||||
|
||||
This model was contributed by `dqnguyen <https://huggingface.co/dqnguyen>`__. The original code can be found `here
|
||||
<https://github.com/VinAIResearch/PhoBERT>`__.
|
||||
This model was contributed by `dqnguyen <https://huggingface.co/dqnguyen>`__. The original code can be found `here <https://github.com/VinAIResearch/PhoBERT>`__.
|
||||
|
||||
PhobertTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@@ -1,189 +0,0 @@
|
||||
..
|
||||
Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
QDQBERT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The QDQBERT model can be referenced in `Integer Quantization for Deep Learning Inference: Principles and Empirical
|
||||
Evaluation <https://arxiv.org/abs/2004.09602>`__ by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius
|
||||
Micikevicius.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by
|
||||
taking advantage of high throughput integer instructions. In this paper we review the mathematical aspects of
|
||||
quantization parameters and evaluate their choices on a wide range of neural network models for different application
|
||||
domains, including vision, speech, and language. We focus on quantization techniques that are amenable to acceleration
|
||||
by processors with high-throughput integer math pipelines. We also present a workflow for 8-bit quantization that is
|
||||
able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are
|
||||
more difficult to quantize, such as MobileNets and BERT-large.*
|
||||
|
||||
Tips:
|
||||
|
||||
- QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to (i) linear layer
|
||||
inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.
|
||||
|
||||
- QDQBERT requires the dependency of `Pytorch Quantization Toolkit
|
||||
<https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization>`__. To install ``pip install
|
||||
pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com``
|
||||
|
||||
- QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example *bert-base-uncased*), and
|
||||
perform Quantization Aware Training/Post Training Quantization.
|
||||
|
||||
- A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for
|
||||
SQUAD task can be found at `transformers/examples/research_projects/quantization-qdqbert/
|
||||
</examples/research_projects/quantization-qdqbert/>`_.
|
||||
|
||||
This model was contributed by `shangz <https://huggingface.co/shangz>`__.
|
||||
|
||||
|
||||
Set default quantizers
|
||||
_______________________________________________________________________________________________________________________
|
||||
|
||||
QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to BERT by
|
||||
:obj:`TensorQuantizer` in `Pytorch Quantization Toolkit
|
||||
<https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization>`__. :obj:`TensorQuantizer` is the module
|
||||
for quantizing tensors, with :obj:`QuantDescriptor` defining how the tensor should be quantized. Refer to `Pytorch
|
||||
Quantization Toolkit userguide
|
||||
<https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html>`__ for more details.
|
||||
|
||||
Before creating QDQBERT model, one has to set the default :obj:`QuantDescriptor` defining default tensor quantizers.
|
||||
Example:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> import pytorch_quantization.nn as quant_nn
|
||||
>>> from pytorch_quantization.tensor_quant import QuantDescriptor
|
||||
|
||||
>>> # The default tensor quantizer is set to use Max calibration method
|
||||
>>> input_desc = QuantDescriptor(num_bits=8, calib_method="max")
|
||||
>>> # The default tensor quantizer is set to be per-channel quantization for weights
|
||||
>>> weight_desc = QuantDescriptor(num_bits=8, axis=((0,)))
|
||||
>>> quant_nn.QuantLinear.set_default_quant_desc_input(input_desc)
|
||||
>>> quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc)
|
||||
|
||||
|
||||
Calibration
|
||||
_______________________________________________________________________________________________________________________
|
||||
|
||||
Calibration is the terminology of passing data samples to the quantizer and deciding the best scaling factors for
|
||||
tensors. After setting up the tensor quantizers, one can use the following example to calibrate the model:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> # Find the TensorQuantizer and enable calibration
|
||||
>>> for name, module in model.named_modules():
|
||||
>>> if name.endswith('_input_quantizer'):
|
||||
>>> module.enable_calib()
|
||||
>>> module.disable_quant() # Use full precision data to calibrate
|
||||
|
||||
>>> # Feeding data samples
|
||||
>>> model(x)
|
||||
>>> # ...
|
||||
|
||||
>>> # Finalize calibration
|
||||
>>> for name, module in model.named_modules():
|
||||
>>> if name.endswith('_input_quantizer'):
|
||||
>>> module.load_calib_amax()
|
||||
>>> module.enable_quant()
|
||||
|
||||
>>> # If running on GPU, it needs to call .cuda() again because new tensors will be created by calibration process
|
||||
>>> model.cuda()
|
||||
|
||||
>>> # Keep running the quantized model
|
||||
>>> # ...
|
||||
|
||||
|
||||
Export to ONNX
|
||||
_______________________________________________________________________________________________________________________
|
||||
|
||||
The goal of exporting to ONNX is to deploy inference by `TensorRT <https://developer.nvidia.com/tensorrt>`__. Fake
|
||||
quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. After setting static member of
|
||||
TensorQuantizer to use Pytorch’s own fake quantization functions, fake quantized model can be exported to ONNX, follow
|
||||
the instructions in `torch.onnx <https://pytorch.org/docs/stable/onnx.html>`__. Example:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from pytorch_quantization.nn import TensorQuantizer
|
||||
>>> TensorQuantizer.use_fb_fake_quant = True
|
||||
|
||||
>>> # Load the calibrated model
|
||||
>>> ...
|
||||
>>> # ONNX export
|
||||
>>> torch.onnx.export(...)
|
||||
|
||||
|
||||
QDQBertConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertConfig
|
||||
:members:
|
||||
|
||||
|
||||
QDQBertModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertModel
|
||||
:members: forward
|
||||
|
||||
|
||||
QDQBertLMHeadModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertLMHeadModel
|
||||
:members: forward
|
||||
|
||||
|
||||
QDQBertForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertForMaskedLM
|
||||
:members: forward
|
||||
|
||||
|
||||
QDQBertForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertForSequenceClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
QDQBertForNextSentencePrediction
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertForNextSentencePrediction
|
||||
:members: forward
|
||||
|
||||
|
||||
QDQBertForMultipleChoice
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertForMultipleChoice
|
||||
:members: forward
|
||||
|
||||
|
||||
QDQBertForTokenClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertForTokenClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
QDQBertForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.QDQBertForQuestionAnswering
|
||||
:members: forward
|
||||
|
||||
@@ -126,13 +126,6 @@ TFRobertaModel
|
||||
:members: call
|
||||
|
||||
|
||||
TFRobertaForCausalLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFRobertaForCausalLM
|
||||
:members: call
|
||||
|
||||
|
||||
TFRobertaForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -1,132 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
SegFormer
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The SegFormer model was proposed in `SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
|
||||
<https://arxiv.org/abs/2105.15203>`__ by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping
|
||||
Luo. The model consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great
|
||||
results on image segmentation benchmarks such as ADE20K and Cityscapes.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with
|
||||
lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel
|
||||
hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding,
|
||||
thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution
|
||||
differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from
|
||||
different layers, and thus combining both local attention and global attention to render powerful representations. We
|
||||
show that this simple and lightweight design is the key to efficient segmentation on Transformers. We scale our
|
||||
approach up to obtain a series of models from SegFormer-B0 to SegFormer-B5, reaching significantly better performance
|
||||
and efficiency than previous counterparts. For example, SegFormer-B4 achieves 50.3% mIoU on ADE20K with 64M parameters,
|
||||
being 5x smaller and 2.2% better than the previous best method. Our best model, SegFormer-B5, achieves 84.0% mIoU on
|
||||
Cityscapes validation set and shows excellent zero-shot robustness on Cityscapes-C.*
|
||||
|
||||
The figure below illustrates the architecture of SegFormer. Taken from the `original paper
|
||||
<https://arxiv.org/abs/2105.15203>`__.
|
||||
|
||||
.. image:: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/segformer_architecture.png
|
||||
:width: 600
|
||||
|
||||
This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
|
||||
<https://github.com/NVlabs/SegFormer>`__.
|
||||
|
||||
Tips:
|
||||
|
||||
- SegFormer consists of a hierarchical Transformer encoder, and a lightweight all-MLP decode head.
|
||||
:class:`~transformers.SegformerModel` is the hierarchical Transformer encoder (which in the paper is also referred to
|
||||
as Mix Transformer or MiT). :class:`~transformers.SegformerForSemanticSegmentation` adds the all-MLP decode head on
|
||||
top to perform semantic segmentation of images. In addition, there's
|
||||
:class:`~transformers.SegformerForImageClassification` which can be used to - you guessed it - classify images. The
|
||||
authors of SegFormer first pre-trained the Transformer encoder on ImageNet-1k to classify images. Next, they throw
|
||||
away the classification head, and replace it by the all-MLP decode head. Next, they fine-tune the model altogether on
|
||||
ADE20K, Cityscapes and COCO-stuff, which are important benchmarks for semantic segmentation. All checkpoints can be
|
||||
found on the `hub <https://huggingface.co/models?other=segformer>`__.
|
||||
- The quickest way to get started with SegFormer is by checking the `example notebooks
|
||||
<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SegFormer>`__ (which showcase both inference and
|
||||
fine-tuning on custom data).
|
||||
- One can use :class:`~transformers.SegformerFeatureExtractor` to prepare images and corresponding segmentation maps
|
||||
for the model. Note that this feature extractor is fairly basic and does not include all data augmentations used in
|
||||
the original paper. The original preprocessing pipelines (for the ADE20k dataset for instance) can be found `here
|
||||
<https://github.com/NVlabs/SegFormer/blob/master/local_configs/_base_/datasets/ade20k_repeat.py>`__. The most
|
||||
important preprocessing step is that images and segmentation maps are randomly cropped and padded to the same size,
|
||||
such as 512x512 or 640x640, after which they are normalized.
|
||||
- One additional thing to keep in mind is that one can initialize :class:`~transformers.SegformerFeatureExtractor` with
|
||||
:obj:`reduce_labels` set to `True` or `False`. In some datasets (like ADE20k), the 0 index is used in the annotated
|
||||
segmentation maps for background. However, ADE20k doesn't include the "background" class in its 150 labels.
|
||||
Therefore, :obj:`reduce_labels` is used to reduce all labels by 1, and to make sure no loss is computed for the
|
||||
background class (i.e. it replaces 0 in the annotated maps by 255, which is the `ignore_index` of the loss function
|
||||
used by :class:`~transformers.SegformerForSemanticSegmentation`). However, other datasets use the 0 index as
|
||||
background class and include this class as part of all labels. In that case, :obj:`reduce_labels` should be set to
|
||||
`False`, as loss should also be computed for the background class.
|
||||
- As most models, SegFormer comes in different sizes, the details of which can be found in the table below.
|
||||
|
||||
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
|
||||
| **Model variant** | **Depths** | **Hidden sizes** | **Decoder hidden size** | **Params (M)** | **ImageNet-1k Top 1** |
|
||||
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
|
||||
| MiT-b0 | [2, 2, 2, 2] | [32, 64, 160, 256] | 256 | 3.7 | 70.5 |
|
||||
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
|
||||
| MiT-b1 | [2, 2, 2, 2] | [64, 128, 320, 512] | 256 | 14.0 | 78.7 |
|
||||
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
|
||||
| MiT-b2 | [3, 4, 6, 3] | [64, 128, 320, 512] | 768 | 25.4 | 81.6 |
|
||||
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
|
||||
| MiT-b3 | [3, 4, 18, 3] | [64, 128, 320, 512] | 768 | 45.2 | 83.1 |
|
||||
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
|
||||
| MiT-b4 | [3, 8, 27, 3] | [64, 128, 320, 512] | 768 | 62.6 | 83.6 |
|
||||
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
|
||||
| MiT-b5 | [3, 6, 40, 3] | [64, 128, 320, 512] | 768 | 82.0 | 83.8 |
|
||||
+-------------------+---------------+---------------------+-------------------------+----------------+-----------------------+
|
||||
|
||||
SegformerConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SegformerConfig
|
||||
:members:
|
||||
|
||||
|
||||
SegformerFeatureExtractor
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SegformerFeatureExtractor
|
||||
:members: __call__
|
||||
|
||||
|
||||
SegformerModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SegformerModel
|
||||
:members: forward
|
||||
|
||||
|
||||
SegformerDecodeHead
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SegformerDecodeHead
|
||||
:members: forward
|
||||
|
||||
|
||||
SegformerForImageClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SegformerForImageClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
SegformerForSemanticSegmentation
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SegformerForSemanticSegmentation
|
||||
:members: forward
|
||||
@@ -1,67 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
SEW
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
SEW (Squeezed and Efficient Wav2Vec) was proposed in `Performance-Efficiency Trade-offs in Unsupervised Pre-training
|
||||
for Speech Recognition <https://arxiv.org/abs/2109.06870>`__ by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q.
|
||||
Weinberger, Yoav Artzi.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition
|
||||
(ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance
|
||||
and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a
|
||||
pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a
|
||||
variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x
|
||||
inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference
|
||||
time, SEW reduces word error rate by 25-50% across different model sizes.*
|
||||
|
||||
Tips:
|
||||
|
||||
- SEW is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
|
||||
- SEWForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded using
|
||||
:class:`~transformers.Wav2Vec2CTCTokenizer`.
|
||||
|
||||
This model was contributed by `anton-l <https://huggingface.co/anton-l>`__.
|
||||
|
||||
|
||||
SEWConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SEWConfig
|
||||
:members:
|
||||
|
||||
|
||||
SEWModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SEWModel
|
||||
:members: forward
|
||||
|
||||
|
||||
SEWForCTC
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SEWForCTC
|
||||
:members: forward
|
||||
|
||||
|
||||
SEWForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SEWForSequenceClassification
|
||||
:members: forward
|
||||
@@ -1,66 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
SEW-D
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
SEW-D (Squeezed and Efficient Wav2Vec with Disentangled attention) was proposed in `Performance-Efficiency Trade-offs
|
||||
in Unsupervised Pre-training for Speech Recognition <https://arxiv.org/abs/2109.06870>`__ by Felix Wu, Kwangyoun Kim,
|
||||
Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*This paper is a study of performance-efficiency trade-offs in pre-trained models for automatic speech recognition
|
||||
(ASR). We focus on wav2vec 2.0, and formalize several architecture designs that influence both the model performance
|
||||
and its efficiency. Putting together all our observations, we introduce SEW (Squeezed and Efficient Wav2vec), a
|
||||
pre-trained model architecture with significant improvements along both performance and efficiency dimensions across a
|
||||
variety of training setups. For example, under the 100h-960h semi-supervised setup on LibriSpeech, SEW achieves a 1.9x
|
||||
inference speedup compared to wav2vec 2.0, with a 13.5% relative reduction in word error rate. With a similar inference
|
||||
time, SEW reduces word error rate by 25-50% across different model sizes.*
|
||||
|
||||
Tips:
|
||||
|
||||
- SEW-D is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
|
||||
- SEWDForCTC is fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
|
||||
using :class:`~transformers.Wav2Vec2CTCTokenizer`.
|
||||
|
||||
This model was contributed by `anton-l <https://huggingface.co/anton-l>`__.
|
||||
|
||||
|
||||
SEWDConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SEWDConfig
|
||||
:members:
|
||||
|
||||
|
||||
SEWDModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SEWDModel
|
||||
:members: forward
|
||||
|
||||
|
||||
SEWDForCTC
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SEWDForCTC
|
||||
:members: forward
|
||||
|
||||
SEWDForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.SEWDForSequenceClassification
|
||||
:members: forward
|
||||
@@ -66,7 +66,7 @@ be installed as follows: ``apt install libsndfile1-dev``
|
||||
... batch["speech"] = speech
|
||||
... return batch
|
||||
|
||||
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
||||
>>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
|
||||
>>> ds = ds.map(map_to_array)
|
||||
|
||||
>>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
|
||||
@@ -98,7 +98,7 @@ be installed as follows: ``apt install libsndfile1-dev``
|
||||
... batch["speech"] = speech
|
||||
... return batch
|
||||
|
||||
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
||||
>>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
|
||||
>>> ds = ds.map(map_to_array)
|
||||
|
||||
>>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
|
||||
|
||||
@@ -36,7 +36,7 @@ Tips:
|
||||
- Speech2Text2 achieves state-of-the-art results on the CoVoST Speech Translation dataset. For more information, see
|
||||
the `official models <https://huggingface.co/models?other=speech2text2>`__ .
|
||||
- Speech2Text2 is always used within the :doc:`SpeechEncoderDecoder <speechencoderdecoder>` framework.
|
||||
- Speech2Text2's tokenizer is based on `fastBPE <https://github.com/glample/fastBPE>`.
|
||||
- Speech2Text2's tokenizer currently only supports inference, but not training.
|
||||
|
||||
Inference
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
@@ -68,7 +68,7 @@ predicted token ids.
|
||||
... batch["speech"] = speech
|
||||
... return batch
|
||||
|
||||
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
||||
>>> ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
|
||||
>>> ds = ds.map(map_to_array)
|
||||
|
||||
>>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
|
||||
@@ -86,7 +86,7 @@ predicted token ids.
|
||||
>>> from datasets import load_dataset
|
||||
>>> from transformers import pipeline
|
||||
|
||||
>>> librispeech_en = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
||||
>>> librispeech_en = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation")
|
||||
>>> asr = pipeline("automatic-speech-recognition", model="facebook/s2t-wav2vec2-large-en-de", feature_extractor="facebook/s2t-wav2vec2-large-en-de")
|
||||
|
||||
>>> translation_de = asr(librispeech_en[0]["file"])
|
||||
|
||||
@@ -191,8 +191,10 @@ language modeling head on top of the decoder.
|
||||
labels = target_encoding.input_ids
|
||||
|
||||
# replace padding token id's of the labels by -100
|
||||
labels = [
|
||||
[(label if label != tokenizer.pad_token_id else -100) for label in labels_example] for labels_example in labels
|
||||
]
|
||||
labels = torch.tensor(labels)
|
||||
labels[labels == tokenizer.pad_token_id] = -100
|
||||
|
||||
# forward pass
|
||||
loss = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels).loss
|
||||
|
||||
@@ -1,511 +0,0 @@
|
||||
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
-->
|
||||
|
||||
# TAPAS
|
||||
|
||||
## Overview
|
||||
|
||||
The TAPAS model was proposed in [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://www.aclweb.org/anthology/2020.acl-main.398)
|
||||
by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically
|
||||
designed (and pre-trained) for answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7
|
||||
token types that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset comprising
|
||||
millions of tables from English Wikipedia and corresponding texts.
|
||||
|
||||
For question answering, TAPAS has 2 heads on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or summing) among selected cells. TAPAS has been fine-tuned on several datasets:
|
||||
- [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253) (Sequential Question Answering by Microsoft)
|
||||
- [WTQ](https://github.com/ppasupat/WikiTableQuestions) (Wiki Table Questions by Stanford University)
|
||||
- [WikiSQL](https://github.com/salesforce/WikiSQL) (by Salesforce).
|
||||
|
||||
It achieves state-of-the-art on both SQA and WTQ, while having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.*
|
||||
|
||||
In addition, the authors have further pre-trained TAPAS to recognize **table entailment**, by creating a balanced dataset of millions of automatically created training examples which are learned in an intermediate step prior to fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on [TabFact](https://github.com/wenhuchen/Table-Fact-Checking), a large-scale dataset with 16k Wikipedia tables for table entailment (a binary classification task). For more details, see their follow-up paper: [Understanding tables with intermediate pre-training](https://www.aclweb.org/anthology/2020.findings-emnlp.27/) by Julian Martin Eisenschlos, Syrine Krichene and Thomas Müller.
|
||||
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/tapas_architecture.png"
|
||||
alt="drawing" width="600"/>
|
||||
|
||||
<small> TAPAS architecture. Taken from the [official blog post](https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html). </small>
|
||||
|
||||
This model was contributed by [nielsr](https://huggingface.co/nielsr). The Tensorflow version of this model was contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/google-research/tapas).
|
||||
|
||||
Tips:
|
||||
|
||||
- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell of the table). Note that this is something that was added after the publication of the original TAPAS paper. According to the authors, this usually results in a slightly better performance, and allows you to encode longer sequences without running out of embeddings. This is reflected in the `reset_position_index_per_cell` parameter of [`TapasConfig`], which is set to `True` by default. The default versions of the models available on the [hub](https://huggingface.co/models?search=tapas) all use relative position embeddings. You can still use the ones with absolute position embeddings by passing in an additional argument `revision="no_reset"` when calling the `from_pretrained()` method. Note that it's usually advised to pad the inputs on the right rather than the left.
|
||||
- TAPAS is based on BERT, so `TAPAS-base` for example corresponds to a `BERT-base` architecture. Of course, `TAPAS-large` will result in the best performance (the results reported in the paper are from `TAPAS-large`). Results of the various sized models are shown on the [original Github repository](https://github.com/google-research/tapas>).
|
||||
- TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a conversational set-up. This means that you can ask follow-up questions such as "what is his age?" related to the previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that case, you have to feed every table-question pair one by one to the model, such that the `prev_labels` token type ids can be overwritten by the predicted `labels` of the model to the previous question. See "Usage" section for more info.
|
||||
- TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained with a causal language modeling (CLM) objective are better in that regard. Note that TAPAS can be used as an encoder in the EncoderDecoderModel framework, to combine it with an autoregressive text decoder such as GPT-2.
|
||||
|
||||
## Usage: fine-tuning
|
||||
|
||||
Here we explain how you can fine-tune [`TapasForQuestionAnswering`] on your own dataset.
|
||||
|
||||
**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
|
||||
|
||||
Basically, there are 3 different ways in which one can fine-tune [`TapasForQuestionAnswering`], corresponding to the different datasets on which Tapas was fine-tuned:
|
||||
|
||||
1. SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
|
||||
2. WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or averaging cell values. You can then for example ask "what's the total number of goals Cristiano Ronaldo made in his career?". This case is also called **weak supervision**, since the model itself must learn the appropriate aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
|
||||
3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation operator during training. This is also called **strong supervision**. Here, learning the appropriate aggregation operator is much easier.
|
||||
|
||||
To summarize:
|
||||
|
||||
| **Task** | **Example dataset** | **Description** |
|
||||
|-------------------------------------|---------------------|---------------------------------------------------------------------------------------------------------|
|
||||
| Conversational | SQA | Conversational, only cell selection questions |
|
||||
| Weak supervision for aggregation | WTQ | Questions might involve aggregation, and the model must learn this given only the answer as supervision |
|
||||
| Strong supervision for aggregation | WikiSQL-supervised | Questions might involve aggregation, and the model must learn this given the gold aggregation operator |
|
||||
|
||||
Initializing a model with a pre-trained base and randomly initialized classification heads from the hub can be done as shown below. Be sure to have installed the
|
||||
[torch-scatter](https://github.com/rusty1s/pytorch_scatter) dependency for your environment in case you're using PyTorch, or the [tensorflow_probability](https://github.com/tensorflow/probability)
|
||||
dependency in case you're using Tensorflow:
|
||||
|
||||
```py
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering
|
||||
|
||||
>>> # for example, the base sized model with default SQA configuration
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base')
|
||||
|
||||
>>> # or, the base sized model with WTQ configuration
|
||||
>>> config = TapasConfig.from_pretrained('google/tapas-base-finetuned-wtq')
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
|
||||
>>> # or, the base sized model with WikiSQL configuration
|
||||
>>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
===PT-TF-SPLIT===
|
||||
>>> from transformers import TapasConfig, TFTapasForQuestionAnswering
|
||||
|
||||
>>> # for example, the base sized model with default SQA configuration
|
||||
>>> model = TFTapasForQuestionAnswering.from_pretrained('google/tapas-base')
|
||||
|
||||
>>> # or, the base sized model with WTQ configuration
|
||||
>>> config = TapasConfig.from_pretrained('google/tapas-base-finetuned-wtq')
|
||||
>>> model = TFTapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
|
||||
>>> # or, the base sized model with WikiSQL configuration
|
||||
>>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
|
||||
>>> model = TFTapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
```
|
||||
|
||||
Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also experiment by defining any hyperparameters you want when initializing [`TapasConfig`], and then create a [`TapasForQuestionAnswering`] based on that configuration. For example, if you have a dataset that has both conversational questions and questions that might involve aggregation, then you can do it this way. Here's an example:
|
||||
|
||||
```py
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering
|
||||
|
||||
>>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
|
||||
>>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
|
||||
>>> # initializing the pre-trained base sized model with our custom classification heads
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
===PT-TF-SPLIT===
|
||||
>>> from transformers import TapasConfig, TFTapasForQuestionAnswering
|
||||
|
||||
>>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
|
||||
>>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True)
|
||||
>>> # initializing the pre-trained base sized model with our custom classification heads
|
||||
>>> model = TFTapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
```
|
||||
|
||||
What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See [here](https://github.com/google-research/tapas/issues/91#issuecomment-735719340) for more info.
|
||||
|
||||
For a list of all pre-trained and fine-tuned TAPAS checkpoints available on HuggingFace's hub, see [here](https://huggingface.co/models?search=tapas).
|
||||
|
||||
**STEP 2: Prepare your data in the SQA format**
|
||||
|
||||
Second, no matter what you picked above, you should prepare your dataset in the [SQA](https://www.microsoft.com/en-us/download/details.aspx?id=54253) format. This format is a TSV/CSV file with the following columns:
|
||||
|
||||
- `id`: optional, id of the table-question pair, for bookkeeping purposes.
|
||||
- `annotator`: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
|
||||
- `position`: integer indicating if the question is the first, second, third,... related to the table. Only required in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
|
||||
- `question`: string
|
||||
- `table_file`: string, name of a csv file containing the tabular data
|
||||
- `answer_coordinates`: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is part of the answer)
|
||||
- `answer_text`: list of one or more strings (each string being a cell value that is part of the answer)
|
||||
- `aggregation_label`: index of the aggregation operator. Only required in case of strong supervision for aggregation (the WikiSQL-supervised case)
|
||||
- `float_answer`: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of weak supervision for aggregation (such as WTQ and WikiSQL)
|
||||
|
||||
The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the SQA format. The author explains this [here](https://github.com/google-research/tapas/issues/50#issuecomment-705465960). A conversion of this script that works with HuggingFace's implementation can be found [here](https://github.com/NielsRogge/tapas_utils). Interestingly, these conversion scripts are not perfect (the `answer_coordinates` and `float_answer` fields are populated based on the `answer_text`), meaning that WTQ and WikiSQL results could actually be improved.
|
||||
|
||||
**STEP 3: Convert your data into PyTorch/TensorFlow tensors using TapasTokenizer**
|
||||
|
||||
Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular data), you can then use [`TapasTokenizer`] to convert table-question pairs into `input_ids`, `attention_mask`, `token_type_ids` and so on. Again, based on which of the three cases you picked above, [`TapasForQuestionAnswering`]/[`TFTapasForQuestionAnswering`] requires different
|
||||
inputs to be fine-tuned:
|
||||
|
||||
| **Task** | **Required inputs** |
|
||||
|------------------------------------|---------------------------------------------------------------------------------------------------------------------|
|
||||
| Conversational | `input_ids`, `attention_mask`, `token_type_ids`, `labels` |
|
||||
| Weak supervision for aggregation | `input_ids`, `attention_mask`, `token_type_ids`, `labels`, `numeric_values`, `numeric_values_scale`, `float_answer` |
|
||||
| Strong supervision for aggregation | `input ids`, `attention mask`, `token type ids`, `labels`, `aggregation_labels` |
|
||||
|
||||
[`TapasTokenizer`] creates the `labels`, `numeric_values` and `numeric_values_scale` based on the `answer_coordinates` and `answer_text` columns of the TSV file. The `float_answer` and `aggregation_labels` are already in the TSV file of step 2. Here's an example:
|
||||
|
||||
```py
|
||||
>>> from transformers import TapasTokenizer
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> model_name = 'google/tapas-base'
|
||||
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
|
||||
|
||||
>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
|
||||
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
|
||||
>>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
|
||||
>>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
|
||||
>>> table = pd.DataFrame.from_dict(data)
|
||||
>>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='pt')
|
||||
>>> inputs
|
||||
{'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
|
||||
'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
|
||||
===PT-TF-SPLIT===
|
||||
>>> from transformers import TapasTokenizer
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> model_name = 'google/tapas-base'
|
||||
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
|
||||
|
||||
>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
|
||||
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
|
||||
>>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
|
||||
>>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
|
||||
>>> table = pd.DataFrame.from_dict(data)
|
||||
>>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='tf')
|
||||
>>> inputs
|
||||
{'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
|
||||
'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
|
||||
```
|
||||
|
||||
Note that [`TapasTokenizer`] expects the data of the table to be **text-only**. You can use `.astype(str)` on a dataframe to turn it into text-only data.
|
||||
Of course, this only shows how to encode a single training example. It is advised to create a dataloader to iterate over batches:
|
||||
|
||||
```py
|
||||
>>> import torch
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> tsv_path = "your_path_to_the_tsv_file"
|
||||
>>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
|
||||
|
||||
>>> class TableDataset(torch.utils.data.Dataset):
|
||||
... def __init__(self, data, tokenizer):
|
||||
... self.data = data
|
||||
... self.tokenizer = tokenizer
|
||||
...
|
||||
... def __getitem__(self, idx):
|
||||
... item = data.iloc[idx]
|
||||
... table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
|
||||
... encoding = self.tokenizer(table=table,
|
||||
... queries=item.question,
|
||||
... answer_coordinates=item.answer_coordinates,
|
||||
... answer_text=item.answer_text,
|
||||
... truncation=True,
|
||||
... padding="max_length",
|
||||
... return_tensors="pt"
|
||||
... )
|
||||
... # remove the batch dimension which the tokenizer adds by default
|
||||
... encoding = {key: val.squeeze(0) for key, val in encoding.items()}
|
||||
... # add the float_answer which is also required (weak supervision for aggregation case)
|
||||
... encoding["float_answer"] = torch.tensor(item.float_answer)
|
||||
... return encoding
|
||||
...
|
||||
... def __len__(self):
|
||||
... return len(self.data)
|
||||
|
||||
>>> data = pd.read_csv(tsv_path, sep='\t')
|
||||
>>> train_dataset = TableDataset(data, tokenizer)
|
||||
>>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
|
||||
===PT-TF-SPLIT===
|
||||
>>> import tensorflow as tf
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> tsv_path = "your_path_to_the_tsv_file"
|
||||
>>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
|
||||
|
||||
>>> class TableDataset:
|
||||
... def __init__(self, data, tokenizer):
|
||||
... self.data = data
|
||||
... self.tokenizer = tokenizer
|
||||
...
|
||||
... def __iter__(self):
|
||||
... for idx in range(self.__len__()):
|
||||
... item = self.data.iloc[idx]
|
||||
... table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
|
||||
... encoding = self.tokenizer(table=table,
|
||||
... queries=item.question,
|
||||
... answer_coordinates=item.answer_coordinates,
|
||||
... answer_text=item.answer_text,
|
||||
... truncation=True,
|
||||
... padding="max_length",
|
||||
... return_tensors="tf"
|
||||
... )
|
||||
... # remove the batch dimension which the tokenizer adds by default
|
||||
... encoding = {key: tf.squeeze(val,0) for key, val in encoding.items()}
|
||||
... # add the float_answer which is also required (weak supervision for aggregation case)
|
||||
... encoding["float_answer"] = tf.convert_to_tensor(item.float_answer,dtype=tf.float32)
|
||||
... yield encoding['input_ids'], encoding['attention_mask'], encoding['numeric_values'], \
|
||||
... encoding['numeric_values_scale'], encoding['token_type_ids'], encoding['labels'], \
|
||||
... encoding['float_answer']
|
||||
...
|
||||
... def __len__(self):
|
||||
... return len(self.data)
|
||||
|
||||
>>> data = pd.read_csv(tsv_path, sep='\t')
|
||||
>>> train_dataset = TableDataset(data, tokenizer)
|
||||
>>> output_signature = (
|
||||
... tf.TensorSpec(shape=(512,), dtype=tf.int32),
|
||||
... tf.TensorSpec(shape=(512,), dtype=tf.int32),
|
||||
... tf.TensorSpec(shape=(512,), dtype=tf.float32),
|
||||
... tf.TensorSpec(shape=(512,), dtype=tf.float32),
|
||||
... tf.TensorSpec(shape=(512,7), dtype=tf.int32),
|
||||
... tf.TensorSpec(shape=(512,), dtype=tf.int32),
|
||||
... tf.TensorSpec(shape=(512,), dtype=tf.float32))
|
||||
>>> train_dataloader = tf.data.Dataset.from_generator(train_dataset, output_signature=output_signature).batch(32)
|
||||
```
|
||||
|
||||
Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group together the `queries`, `answer_coordinates` and `answer_text` per table (in the order of their `position`
|
||||
index) and batch encode each table with its questions. This will make sure that the `prev_labels` token types (see docs of [`TapasTokenizer`]) are set correctly. See [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) for more info. See [this notebook](https://github.com/kamalkraj/Tapas-Tutorial/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) for more info regarding using the TensorFlow model.
|
||||
|
||||
**STEP 4: Train (fine-tune) TapasForQuestionAnswering/TFTapasForQuestionAnswering**
|
||||
|
||||
You can then fine-tune [`TapasForQuestionAnswering`] or [`TFTapasForQuestionAnswering`] as follows (shown here for the weak supervision for aggregation case):
|
||||
|
||||
```py
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
|
||||
|
||||
>>> # this is the default WTQ configuration
|
||||
>>> config = TapasConfig(
|
||||
... num_aggregation_labels = 4,
|
||||
... use_answer_as_supervision = True,
|
||||
... answer_loss_cutoff = 0.664694,
|
||||
... cell_selection_preference = 0.207951,
|
||||
... huber_loss_delta = 0.121194,
|
||||
... init_cell_selection_weights_to_zero = True,
|
||||
... select_one_column = True,
|
||||
... allow_empty_column_selection = False,
|
||||
... temperature = 0.0352513,
|
||||
... )
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
|
||||
|
||||
>>> optimizer = AdamW(model.parameters(), lr=5e-5)
|
||||
|
||||
>>> model.train()
|
||||
>>> for epoch in range(2): # loop over the dataset multiple times
|
||||
... for batch in train_dataloader:
|
||||
... # get the inputs;
|
||||
... input_ids = batch["input_ids"]
|
||||
... attention_mask = batch["attention_mask"]
|
||||
... token_type_ids = batch["token_type_ids"]
|
||||
... labels = batch["labels"]
|
||||
... numeric_values = batch["numeric_values"]
|
||||
... numeric_values_scale = batch["numeric_values_scale"]
|
||||
... float_answer = batch["float_answer"]
|
||||
|
||||
... # zero the parameter gradients
|
||||
... optimizer.zero_grad()
|
||||
|
||||
... # forward + backward + optimize
|
||||
... outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids,
|
||||
... labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale,
|
||||
... float_answer=float_answer)
|
||||
... loss = outputs.loss
|
||||
... loss.backward()
|
||||
... optimizer.step()
|
||||
===PT-TF-SPLIT===
|
||||
>>> import tensorflow as tf
|
||||
>>> from transformers import TapasConfig, TFTapasForQuestionAnswering
|
||||
|
||||
>>> # this is the default WTQ configuration
|
||||
>>> config = TapasConfig(
|
||||
... num_aggregation_labels = 4,
|
||||
... use_answer_as_supervision = True,
|
||||
... answer_loss_cutoff = 0.664694,
|
||||
... cell_selection_preference = 0.207951,
|
||||
... huber_loss_delta = 0.121194,
|
||||
... init_cell_selection_weights_to_zero = True,
|
||||
... select_one_column = True,
|
||||
... allow_empty_column_selection = False,
|
||||
... temperature = 0.0352513,
|
||||
... )
|
||||
>>> model = TFTapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
|
||||
|
||||
>>> optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
|
||||
|
||||
>>> for epoch in range(2): # loop over the dataset multiple times
|
||||
... for batch in train_dataloader:
|
||||
... # get the inputs;
|
||||
... input_ids = batch[0]
|
||||
... attention_mask = batch[1]
|
||||
... token_type_ids = batch[4]
|
||||
... labels = batch[-1]
|
||||
... numeric_values = batch[2]
|
||||
... numeric_values_scale = batch[3]
|
||||
... float_answer = batch[6]
|
||||
|
||||
... # forward + backward + optimize
|
||||
... with tf.GradientTape() as tape:
|
||||
... outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids,
|
||||
... labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale,
|
||||
... float_answer=float_answer )
|
||||
... grads = tape.gradient(outputs.loss, model.trainable_weights)
|
||||
... optimizer.apply_gradients(zip(grads, model.trainable_weights))
|
||||
```
|
||||
|
||||
## Usage: inference
|
||||
|
||||
Here we explain how you can use [`TapasForQuestionAnswering`] or [`TFTapasForQuestionAnswering`] for inference (i.e. making predictions on new data). For inference, only `input_ids`, `attention_mask` and `token_type_ids` (which you can obtain using [`TapasTokenizer`]) have to be provided to the model to obtain the logits. Next, you can use the handy [`~models.tapas.tokenization_tapas.convert_logits_to_predictions`] method to convert these into predicted coordinates and optional aggregation indices.
|
||||
|
||||
However, note that inference is **different** depending on whether or not the setup is conversational. In a non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example of that:
|
||||
|
||||
```py
|
||||
>>> from transformers import TapasTokenizer, TapasForQuestionAnswering
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> model_name = 'google/tapas-base-finetuned-wtq'
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained(model_name)
|
||||
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
|
||||
|
||||
>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
|
||||
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
|
||||
>>> table = pd.DataFrame.from_dict(data)
|
||||
>>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
>>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
|
||||
... inputs,
|
||||
... outputs.logits.detach(),
|
||||
... outputs.logits_aggregation.detach()
|
||||
... )
|
||||
|
||||
>>> # let's print out the results:
|
||||
>>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
|
||||
>>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
|
||||
|
||||
>>> answers = []
|
||||
>>> for coordinates in predicted_answer_coordinates:
|
||||
... if len(coordinates) == 1:
|
||||
... # only a single cell:
|
||||
... answers.append(table.iat[coordinates[0]])
|
||||
... else:
|
||||
... # multiple cells
|
||||
... cell_values = []
|
||||
... for coordinate in coordinates:
|
||||
... cell_values.append(table.iat[coordinate])
|
||||
... answers.append(", ".join(cell_values))
|
||||
|
||||
>>> display(table)
|
||||
>>> print("")
|
||||
>>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
|
||||
... print(query)
|
||||
... if predicted_agg == "NONE":
|
||||
... print("Predicted answer: " + answer)
|
||||
... else:
|
||||
... print("Predicted answer: " + predicted_agg + " > " + answer)
|
||||
What is the name of the first actor?
|
||||
Predicted answer: Brad Pitt
|
||||
How many movies has George Clooney played in?
|
||||
Predicted answer: COUNT > 69
|
||||
What is the total number of movies?
|
||||
Predicted answer: SUM > 87, 53, 69
|
||||
===PT-TF-SPLIT===
|
||||
>>> from transformers import TapasTokenizer, TFTapasForQuestionAnswering
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> model_name = 'google/tapas-base-finetuned-wtq'
|
||||
>>> model = TFTapasForQuestionAnswering.from_pretrained(model_name)
|
||||
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
|
||||
|
||||
>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
|
||||
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
|
||||
>>> table = pd.DataFrame.from_dict(data)
|
||||
>>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="tf")
|
||||
>>> outputs = model(**inputs)
|
||||
>>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
|
||||
... inputs,
|
||||
... outputs.logits,
|
||||
... outputs.logits_aggregation
|
||||
... )
|
||||
|
||||
>>> # let's print out the results:
|
||||
>>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
|
||||
>>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
|
||||
|
||||
>>> answers = []
|
||||
>>> for coordinates in predicted_answer_coordinates:
|
||||
... if len(coordinates) == 1:
|
||||
... # only a single cell:
|
||||
... answers.append(table.iat[coordinates[0]])
|
||||
... else:
|
||||
... # multiple cells
|
||||
... cell_values = []
|
||||
... for coordinate in coordinates:
|
||||
... cell_values.append(table.iat[coordinate])
|
||||
... answers.append(", ".join(cell_values))
|
||||
|
||||
>>> display(table)
|
||||
>>> print("")
|
||||
>>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
|
||||
... print(query)
|
||||
... if predicted_agg == "NONE":
|
||||
... print("Predicted answer: " + answer)
|
||||
... else:
|
||||
... print("Predicted answer: " + predicted_agg + " > " + answer)
|
||||
What is the name of the first actor?
|
||||
Predicted answer: Brad Pitt
|
||||
How many movies has George Clooney played in?
|
||||
Predicted answer: COUNT > 69
|
||||
What is the total number of movies?
|
||||
Predicted answer: SUM > 87, 53, 69
|
||||
```
|
||||
|
||||
In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such that the `prev_labels` token types can be overwritten by the predicted `labels` of the previous table-question pair. Again, more info can be found in [this notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) (for PyTorch) and [this notebook](https://github.com/kamalkraj/Tapas-Tutorial/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb) (for TensorFlow).
|
||||
|
||||
## TAPAS specific outputs
|
||||
[[autodoc]] models.tapas.modeling_tapas.TableQuestionAnsweringOutput
|
||||
|
||||
## TapasConfig
|
||||
[[autodoc]] TapasConfig
|
||||
|
||||
## TapasTokenizer
|
||||
[[autodoc]] TapasTokenizer
|
||||
- __call__
|
||||
- convert_logits_to_predictions
|
||||
- save_vocabulary
|
||||
|
||||
## TapasModel
|
||||
[[autodoc]] TapasModel
|
||||
- forward
|
||||
|
||||
## TapasForMaskedLM
|
||||
[[autodoc]] TapasForMaskedLM
|
||||
- forward
|
||||
|
||||
## TapasForSequenceClassification
|
||||
[[autodoc]] TapasForSequenceClassification
|
||||
- forward
|
||||
|
||||
## TapasForQuestionAnswering
|
||||
[[autodoc]] TapasForQuestionAnswering
|
||||
- forward
|
||||
|
||||
## TFTapasModel
|
||||
[[autodoc]] TFTapasModel
|
||||
- call
|
||||
|
||||
## TFTapasForMaskedLM
|
||||
[[autodoc]] TFTapasForMaskedLM
|
||||
- call
|
||||
|
||||
## TFTapasForSequenceClassification
|
||||
[[autodoc]] TFTapasForSequenceClassification
|
||||
- call
|
||||
|
||||
## TFTapasForQuestionAnswering
|
||||
[[autodoc]] TFTapasForQuestionAnswering
|
||||
- call
|
||||
435
docs/source/model_doc/tapas.rst
Normal file
435
docs/source/model_doc/tapas.rst
Normal file
@@ -0,0 +1,435 @@
|
||||
TAPAS
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
.. note::
|
||||
|
||||
This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
|
||||
breaking changes to fix them in the future.
|
||||
|
||||
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The TAPAS model was proposed in `TAPAS: Weakly Supervised Table Parsing via Pre-training
|
||||
<https://www.aclweb.org/anthology/2020.acl-main.398>`__ by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller,
|
||||
Francesco Piccinno and Julian Martin Eisenschlos. It's a BERT-based model specifically designed (and pre-trained) for
|
||||
answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 token types
|
||||
that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset
|
||||
comprising millions of tables from English Wikipedia and corresponding texts. For question answering, TAPAS has 2 heads
|
||||
on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or
|
||||
summing) among selected cells. TAPAS has been fine-tuned on several datasets: `SQA
|
||||
<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__ (Sequential Question Answering by Microsoft), `WTQ
|
||||
<https://github.com/ppasupat/WikiTableQuestions>`__ (Wiki Table Questions by Stanford University) and `WikiSQL
|
||||
<https://github.com/salesforce/WikiSQL>`__ (by Salesforce). It achieves state-of-the-art on both SQA and WTQ, while
|
||||
having comparable performance to SOTA on WikiSQL, with a much simpler architecture.
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the
|
||||
collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations
|
||||
instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition,
|
||||
the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we
|
||||
present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak
|
||||
supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation
|
||||
operator to such selection. TAPAS extends BERT's architecture to encode tables as input, initializes from an effective
|
||||
joint pre-training of text segments and tables crawled from Wikipedia, and is trained end-to-end. We experiment with
|
||||
three different semantic parsing datasets, and find that TAPAS outperforms or rivals semantic parsing models by
|
||||
improving state-of-the-art accuracy on SQA from 55.1 to 67.2 and performing on par with the state-of-the-art on WIKISQL
|
||||
and WIKITQ, but with a simpler model architecture. We additionally find that transfer learning, which is trivial in our
|
||||
setting, from WIKISQL to WIKITQ, yields 48.7 accuracy, 4.2 points above the state-of-the-art.*
|
||||
|
||||
In addition, the authors have further pre-trained TAPAS to recognize **table entailment**, by creating a balanced
|
||||
dataset of millions of automatically created training examples which are learned in an intermediate step prior to
|
||||
fine-tuning. The authors of TAPAS call this further pre-training intermediate pre-training (since TAPAS is first
|
||||
pre-trained on MLM, and then on another dataset). They found that intermediate pre-training further improves
|
||||
performance on SQA, achieving a new state-of-the-art as well as state-of-the-art on `TabFact
|
||||
<https://github.com/wenhuchen/Table-Fact-Checking>`__, a large-scale dataset with 16k Wikipedia tables for table
|
||||
entailment (a binary classification task). For more details, see their follow-up paper: `Understanding tables with
|
||||
intermediate pre-training <https://www.aclweb.org/anthology/2020.findings-emnlp.27/>`__ by Julian Martin Eisenschlos,
|
||||
Syrine Krichene and Thomas Müller.
|
||||
|
||||
This model was contributed by `nielsr <https://huggingface.co/nielsr>`__. The original code can be found `here
|
||||
<https://github.com/google-research/tapas>`__.
|
||||
|
||||
Tips:
|
||||
|
||||
- TAPAS is a model that uses relative position embeddings by default (restarting the position embeddings at every cell
|
||||
of the table). Note that this is something that was added after the publication of the original TAPAS paper.
|
||||
According to the authors, this usually results in a slightly better performance, and allows you to encode longer
|
||||
sequences without running out of embeddings. This is reflected in the ``reset_position_index_per_cell`` parameter of
|
||||
:class:`~transformers.TapasConfig`, which is set to ``True`` by default. The default versions of the models available
|
||||
in the `model hub <https://huggingface.co/models?search=tapas>`_ all use relative position embeddings. You can still
|
||||
use the ones with absolute position embeddings by passing in an additional argument ``revision="no_reset"`` when
|
||||
calling the ``.from_pretrained()`` method. Note that it's usually advised to pad the inputs on the right rather than
|
||||
the left.
|
||||
- TAPAS is based on BERT, so ``TAPAS-base`` for example corresponds to a ``BERT-base`` architecture. Of course,
|
||||
TAPAS-large will result in the best performance (the results reported in the paper are from TAPAS-large). Results of
|
||||
the various sized models are shown on the `original Github repository <https://github.com/google-research/tapas>`_.
|
||||
- TAPAS has checkpoints fine-tuned on SQA, which are capable of answering questions related to a table in a
|
||||
conversational set-up. This means that you can ask follow-up questions such as "what is his age?" related to the
|
||||
previous question. Note that the forward pass of TAPAS is a bit different in case of a conversational set-up: in that
|
||||
case, you have to feed every table-question pair one by one to the model, such that the `prev_labels` token type ids
|
||||
can be overwritten by the predicted `labels` of the model to the previous question. See "Usage" section for more
|
||||
info.
|
||||
- TAPAS is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
|
||||
efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
|
||||
with a causal language modeling (CLM) objective are better in that regard.
|
||||
|
||||
|
||||
Usage: fine-tuning
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Here we explain how you can fine-tune :class:`~transformers.TapasForQuestionAnswering` on your own dataset.
|
||||
|
||||
**STEP 1: Choose one of the 3 ways in which you can use TAPAS - or experiment**
|
||||
|
||||
Basically, there are 3 different ways in which one can fine-tune :class:`~transformers.TapasForQuestionAnswering`,
|
||||
corresponding to the different datasets on which Tapas was fine-tuned:
|
||||
|
||||
1. SQA: if you're interested in asking follow-up questions related to a table, in a conversational set-up. For example
|
||||
if you first ask "what's the name of the first actor?" then you can ask a follow-up question such as "how old is
|
||||
he?". Here, questions do not involve any aggregation (all questions are cell selection questions).
|
||||
2. WTQ: if you're not interested in asking questions in a conversational set-up, but rather just asking questions
|
||||
related to a table, which might involve aggregation, such as counting a number of rows, summing up cell values or
|
||||
averaging cell values. You can then for example ask "what's the total number of goals Cristiano Ronaldo made in his
|
||||
career?". This case is also called **weak supervision**, since the model itself must learn the appropriate
|
||||
aggregation operator (SUM/COUNT/AVERAGE/NONE) given only the answer to the question as supervision.
|
||||
3. WikiSQL-supervised: this dataset is based on WikiSQL with the model being given the ground truth aggregation
|
||||
operator during training. This is also called **strong supervision**. Here, learning the appropriate aggregation
|
||||
operator is much easier.
|
||||
|
||||
To summarize:
|
||||
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
| **Task** | **Example dataset** | **Description** |
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
| Conversational | SQA | Conversational, only cell selection questions |
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
| Weak supervision for aggregation | WTQ | Questions might involve aggregation, and the model must learn this given only the answer as supervision |
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
| Strong supervision for aggregation | WikiSQL-supervised | Questions might involve aggregation, and the model must learn this given the gold aggregation operator |
|
||||
+------------------------------------+----------------------+-------------------------------------------------------------------------------------------------------------------+
|
||||
|
||||
Initializing a model with a pre-trained base and randomly initialized classification heads from the model hub can be
|
||||
done as follows (be sure to have installed the `torch-scatter dependency <https://github.com/rusty1s/pytorch_scatter>`_
|
||||
for your environment):
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering
|
||||
|
||||
>>> # for example, the base sized model with default SQA configuration
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base')
|
||||
|
||||
>>> # or, the base sized model with WTQ configuration
|
||||
>>> config = TapasConfig.from_pretrained('google/tapas-base-finetuned-wtq')
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
|
||||
>>> # or, the base sized model with WikiSQL configuration
|
||||
>>> config = TapasConfig('google-base-finetuned-wikisql-supervised')
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
|
||||
|
||||
Of course, you don't necessarily have to follow one of these three ways in which TAPAS was fine-tuned. You can also
|
||||
experiment by defining any hyperparameters you want when initializing :class:`~transformers.TapasConfig`, and then
|
||||
create a :class:`~transformers.TapasForQuestionAnswering` based on that configuration. For example, if you have a
|
||||
dataset that has both conversational questions and questions that might involve aggregation, then you can do it this
|
||||
way. Here's an example:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering
|
||||
|
||||
>>> # you can initialize the classification heads any way you want (see docs of TapasConfig)
|
||||
>>> config = TapasConfig(num_aggregation_labels=3, average_logits_per_cell=True, select_one_column=False)
|
||||
>>> # initializing the pre-trained base sized model with our custom classification heads
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained('google/tapas-base', config=config)
|
||||
|
||||
What you can also do is start from an already fine-tuned checkpoint. A note here is that the already fine-tuned
|
||||
checkpoint on WTQ has some issues due to the L2-loss which is somewhat brittle. See `here
|
||||
<https://github.com/google-research/tapas/issues/91#issuecomment-735719340>`__ for more info.
|
||||
|
||||
For a list of all pre-trained and fine-tuned TAPAS checkpoints available in the HuggingFace model hub, see `here
|
||||
<https://huggingface.co/models?search=tapas>`__.
|
||||
|
||||
**STEP 2: Prepare your data in the SQA format**
|
||||
|
||||
Second, no matter what you picked above, you should prepare your dataset in the `SQA format
|
||||
<https://www.microsoft.com/en-us/download/details.aspx?id=54253>`__. This format is a TSV/CSV file with the following
|
||||
columns:
|
||||
|
||||
- ``id``: optional, id of the table-question pair, for bookkeeping purposes.
|
||||
- ``annotator``: optional, id of the person who annotated the table-question pair, for bookkeeping purposes.
|
||||
- ``position``: integer indicating if the question is the first, second, third,... related to the table. Only required
|
||||
in case of conversational setup (SQA). You don't need this column in case you're going for WTQ/WikiSQL-supervised.
|
||||
- ``question``: string
|
||||
- ``table_file``: string, name of a csv file containing the tabular data
|
||||
- ``answer_coordinates``: list of one or more tuples (each tuple being a cell coordinate, i.e. row, column pair that is
|
||||
part of the answer)
|
||||
- ``answer_text``: list of one or more strings (each string being a cell value that is part of the answer)
|
||||
- ``aggregation_label``: index of the aggregation operator. Only required in case of strong supervision for aggregation
|
||||
(the WikiSQL-supervised case)
|
||||
- ``float_answer``: the float answer to the question, if there is one (np.nan if there isn't). Only required in case of
|
||||
weak supervision for aggregation (such as WTQ and WikiSQL)
|
||||
|
||||
The tables themselves should be present in a folder, each table being a separate csv file. Note that the authors of the
|
||||
TAPAS algorithm used conversion scripts with some automated logic to convert the other datasets (WTQ, WikiSQL) into the
|
||||
SQA format. The author explains this `here
|
||||
<https://github.com/google-research/tapas/issues/50#issuecomment-705465960>`__. Interestingly, these conversion scripts
|
||||
are not perfect (the ``answer_coordinates`` and ``float_answer`` fields are populated based on the ``answer_text``),
|
||||
meaning that WTQ and WikiSQL results could actually be improved.
|
||||
|
||||
**STEP 3: Convert your data into PyTorch tensors using TapasTokenizer**
|
||||
|
||||
Third, given that you've prepared your data in this TSV/CSV format (and corresponding CSV files containing the tabular
|
||||
data), you can then use :class:`~transformers.TapasTokenizer` to convert table-question pairs into :obj:`input_ids`,
|
||||
:obj:`attention_mask`, :obj:`token_type_ids` and so on. Again, based on which of the three cases you picked above,
|
||||
:class:`~transformers.TapasForQuestionAnswering` requires different inputs to be fine-tuned:
|
||||
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
| **Task** | **Required inputs** |
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
| Conversational | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels`` |
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
| Weak supervision for aggregation | ``input_ids``, ``attention_mask``, ``token_type_ids``, ``labels``, ``numeric_values``, |
|
||||
| | ``numeric_values_scale``, ``float_answer`` |
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
| Strong supervision for aggregation | ``input ids``, ``attention mask``, ``token type ids``, ``labels``, ``aggregation_labels`` |
|
||||
+------------------------------------+----------------------------------------------------------------------------------------------+
|
||||
|
||||
:class:`~transformers.TapasTokenizer` creates the ``labels``, ``numeric_values`` and ``numeric_values_scale`` based on
|
||||
the ``answer_coordinates`` and ``answer_text`` columns of the TSV file. The ``float_answer`` and ``aggregation_labels``
|
||||
are already in the TSV file of step 2. Here's an example:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasTokenizer
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> model_name = 'google/tapas-base'
|
||||
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
|
||||
|
||||
>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
|
||||
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
|
||||
>>> answer_coordinates = [[(0, 0)], [(2, 1)], [(0, 1), (1, 1), (2, 1)]]
|
||||
>>> answer_text = [["Brad Pitt"], ["69"], ["209"]]
|
||||
>>> table = pd.DataFrame.from_dict(data)
|
||||
>>> inputs = tokenizer(table=table, queries=queries, answer_coordinates=answer_coordinates, answer_text=answer_text, padding='max_length', return_tensors='pt')
|
||||
>>> inputs
|
||||
{'input_ids': tensor([[ ... ]]), 'attention_mask': tensor([[...]]), 'token_type_ids': tensor([[[...]]]),
|
||||
'numeric_values': tensor([[ ... ]]), 'numeric_values_scale: tensor([[ ... ]]), labels: tensor([[ ... ]])}
|
||||
|
||||
Note that :class:`~transformers.TapasTokenizer` expects the data of the table to be **text-only**. You can use
|
||||
``.astype(str)`` on a dataframe to turn it into text-only data. Of course, this only shows how to encode a single
|
||||
training example. It is advised to create a PyTorch dataset and a corresponding dataloader:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> import torch
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> tsv_path = "your_path_to_the_tsv_file"
|
||||
>>> table_csv_path = "your_path_to_a_directory_containing_all_csv_files"
|
||||
|
||||
>>> class TableDataset(torch.utils.data.Dataset):
|
||||
... def __init__(self, data, tokenizer):
|
||||
... self.data = data
|
||||
... self.tokenizer = tokenizer
|
||||
...
|
||||
... def __getitem__(self, idx):
|
||||
... item = data.iloc[idx]
|
||||
... table = pd.read_csv(table_csv_path + item.table_file).astype(str) # be sure to make your table data text only
|
||||
... encoding = self.tokenizer(table=table,
|
||||
... queries=item.question,
|
||||
... answer_coordinates=item.answer_coordinates,
|
||||
... answer_text=item.answer_text,
|
||||
... truncation=True,
|
||||
... padding="max_length",
|
||||
... return_tensors="pt"
|
||||
... )
|
||||
... # remove the batch dimension which the tokenizer adds by default
|
||||
... encoding = {key: val.squeeze(0) for key, val in encoding.items()}
|
||||
... # add the float_answer which is also required (weak supervision for aggregation case)
|
||||
... encoding["float_answer"] = torch.tensor(item.float_answer)
|
||||
... return encoding
|
||||
...
|
||||
... def __len__(self):
|
||||
... return len(self.data)
|
||||
|
||||
>>> data = pd.read_csv(tsv_path, sep='\t')
|
||||
>>> train_dataset = TableDataset(data, tokenizer)
|
||||
>>> train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32)
|
||||
|
||||
Note that here, we encode each table-question pair independently. This is fine as long as your dataset is **not
|
||||
conversational**. In case your dataset involves conversational questions (such as in SQA), then you should first group
|
||||
together the ``queries``, ``answer_coordinates`` and ``answer_text`` per table (in the order of their ``position``
|
||||
index) and batch encode each table with its questions. This will make sure that the ``prev_labels`` token types (see
|
||||
docs of :class:`~transformers.TapasTokenizer`) are set correctly. See `this notebook
|
||||
<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__
|
||||
for more info.
|
||||
|
||||
**STEP 4: Train (fine-tune) TapasForQuestionAnswering**
|
||||
|
||||
You can then fine-tune :class:`~transformers.TapasForQuestionAnswering` using native PyTorch as follows (shown here for
|
||||
the weak supervision for aggregation case):
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasConfig, TapasForQuestionAnswering, AdamW
|
||||
|
||||
>>> # this is the default WTQ configuration
|
||||
>>> config = TapasConfig(
|
||||
... num_aggregation_labels = 4,
|
||||
... use_answer_as_supervision = True,
|
||||
... answer_loss_cutoff = 0.664694,
|
||||
... cell_selection_preference = 0.207951,
|
||||
... huber_loss_delta = 0.121194,
|
||||
... init_cell_selection_weights_to_zero = True,
|
||||
... select_one_column = True,
|
||||
... allow_empty_column_selection = False,
|
||||
... temperature = 0.0352513,
|
||||
... )
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained("google/tapas-base", config=config)
|
||||
|
||||
>>> optimizer = AdamW(model.parameters(), lr=5e-5)
|
||||
|
||||
>>> for epoch in range(2): # loop over the dataset multiple times
|
||||
... for idx, batch in enumerate(train_dataloader):
|
||||
... # get the inputs;
|
||||
... input_ids = batch["input_ids"]
|
||||
... attention_mask = batch["attention_mask"]
|
||||
... token_type_ids = batch["token_type_ids"]
|
||||
... labels = batch["labels"]
|
||||
... numeric_values = batch["numeric_values"]
|
||||
... numeric_values_scale = batch["numeric_values_scale"]
|
||||
... float_answer = batch["float_answer"]
|
||||
|
||||
... # zero the parameter gradients
|
||||
... optimizer.zero_grad()
|
||||
|
||||
... # forward + backward + optimize
|
||||
... outputs = model(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids,
|
||||
... labels=labels, numeric_values=numeric_values, numeric_values_scale=numeric_values_scale,
|
||||
... float_answer=float_answer)
|
||||
... loss = outputs.loss
|
||||
... loss.backward()
|
||||
... optimizer.step()
|
||||
|
||||
Usage: inference
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
Here we explain how you can use :class:`~transformers.TapasForQuestionAnswering` for inference (i.e. making predictions
|
||||
on new data). For inference, only ``input_ids``, ``attention_mask`` and ``token_type_ids`` (which you can obtain using
|
||||
:class:`~transformers.TapasTokenizer`) have to be provided to the model to obtain the logits. Next, you can use the
|
||||
handy ``convert_logits_to_predictions`` method of :class:`~transformers.TapasTokenizer` to convert these into predicted
|
||||
coordinates and optional aggregation indices.
|
||||
|
||||
However, note that inference is **different** depending on whether or not the setup is conversational. In a
|
||||
non-conversational set-up, inference can be done in parallel on all table-question pairs of a batch. Here's an example
|
||||
of that:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> from transformers import TapasTokenizer, TapasForQuestionAnswering
|
||||
>>> import pandas as pd
|
||||
|
||||
>>> model_name = 'google/tapas-base-finetuned-wtq'
|
||||
>>> model = TapasForQuestionAnswering.from_pretrained(model_name)
|
||||
>>> tokenizer = TapasTokenizer.from_pretrained(model_name)
|
||||
|
||||
>>> data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
|
||||
>>> queries = ["What is the name of the first actor?", "How many movies has George Clooney played in?", "What is the total number of movies?"]
|
||||
>>> table = pd.DataFrame.from_dict(data)
|
||||
>>> inputs = tokenizer(table=table, queries=queries, padding='max_length', return_tensors="pt")
|
||||
>>> outputs = model(**inputs)
|
||||
>>> predicted_answer_coordinates, predicted_aggregation_indices = tokenizer.convert_logits_to_predictions(
|
||||
... inputs,
|
||||
... outputs.logits.detach(),
|
||||
... outputs.logits_aggregation.detach()
|
||||
... )
|
||||
|
||||
>>> # let's print out the results:
|
||||
>>> id2aggregation = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3:"COUNT"}
|
||||
>>> aggregation_predictions_string = [id2aggregation[x] for x in predicted_aggregation_indices]
|
||||
|
||||
>>> answers = []
|
||||
>>> for coordinates in predicted_answer_coordinates:
|
||||
... if len(coordinates) == 1:
|
||||
... # only a single cell:
|
||||
... answers.append(table.iat[coordinates[0]])
|
||||
... else:
|
||||
... # multiple cells
|
||||
... cell_values = []
|
||||
... for coordinate in coordinates:
|
||||
... cell_values.append(table.iat[coordinate])
|
||||
... answers.append(", ".join(cell_values))
|
||||
|
||||
>>> display(table)
|
||||
>>> print("")
|
||||
>>> for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):
|
||||
... print(query)
|
||||
... if predicted_agg == "NONE":
|
||||
... print("Predicted answer: " + answer)
|
||||
... else:
|
||||
... print("Predicted answer: " + predicted_agg + " > " + answer)
|
||||
What is the name of the first actor?
|
||||
Predicted answer: Brad Pitt
|
||||
How many movies has George Clooney played in?
|
||||
Predicted answer: COUNT > 69
|
||||
What is the total number of movies?
|
||||
Predicted answer: SUM > 87, 53, 69
|
||||
|
||||
In case of a conversational set-up, then each table-question pair must be provided **sequentially** to the model, such
|
||||
that the ``prev_labels`` token types can be overwritten by the predicted ``labels`` of the previous table-question
|
||||
pair. Again, more info can be found in `this notebook
|
||||
<https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb>`__.
|
||||
|
||||
|
||||
Tapas specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.models.tapas.modeling_tapas.TableQuestionAnsweringOutput
|
||||
:members:
|
||||
|
||||
|
||||
TapasConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasConfig
|
||||
:members:
|
||||
|
||||
|
||||
TapasTokenizer
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasTokenizer
|
||||
:members: __call__, convert_logits_to_predictions, save_vocabulary
|
||||
|
||||
|
||||
TapasModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasModel
|
||||
:members: forward
|
||||
|
||||
|
||||
TapasForMaskedLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasForMaskedLM
|
||||
:members: forward
|
||||
|
||||
|
||||
TapasForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasForSequenceClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
TapasForQuestionAnswering
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TapasForQuestionAnswering
|
||||
:members: forward
|
||||
@@ -1,101 +0,0 @@
|
||||
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
|
||||
License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
|
||||
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License. -->
|
||||
|
||||
# TrOCR
|
||||
|
||||
## Overview
|
||||
|
||||
The TrOCR model was proposed in [TrOCR: Transformer-based Optical Character Recognition with Pre-trained
|
||||
Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
|
||||
Zhoujun Li, Furu Wei. TrOCR consists of an image Transformer encoder and an autoregressive text Transformer decoder to
|
||||
perform [optical character recognition (OCR)](https://en.wikipedia.org/wiki/Optical_character_recognition).
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Text recognition is a long-standing research problem for document digitalization. Existing approaches for text recognition
|
||||
are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language
|
||||
model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end
|
||||
text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the
|
||||
Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but
|
||||
effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments
|
||||
show that the TrOCR model outperforms the current state-of-the-art models on both printed and handwritten text recognition
|
||||
tasks.*
|
||||
|
||||
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/trocr_architecture.jpg"
|
||||
alt="drawing" width="600"/>
|
||||
|
||||
<small> TrOCR architecture. Taken from the [original paper](https://arxiv.org/abs/2109.10282). </small>
|
||||
|
||||
Please refer to the [`VisionEncoderDecoder`] class on how to use this model.
|
||||
|
||||
This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found
|
||||
[here](https://github.com/microsoft/unilm/tree/6f60612e7cc86a2a1ae85c47231507a587ab4e01/trocr).
|
||||
|
||||
Tips:
|
||||
|
||||
- The quickest way to get started with TrOCR is by checking the [tutorial
|
||||
notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/TrOCR), which show how to use the model
|
||||
at inference time as well as fine-tuning on custom data.
|
||||
- TrOCR is pre-trained in 2 stages before being fine-tuned on downstream datasets. It achieves state-of-the-art results
|
||||
on both printed (e.g. the [SROIE dataset](https://paperswithcode.com/dataset/sroie) and handwritten (e.g. the [IAM
|
||||
Handwriting dataset](https://fki.tic.heia-fr.ch/databases/iam-handwriting-database>) text recognition tasks. For more
|
||||
information, see the [official models](https://huggingface.co/models?other=trocr>).
|
||||
- TrOCR is always used within the [VisionEncoderDecoder](./model_doc/visionencoderdecoder) framework.
|
||||
|
||||
## Inference
|
||||
|
||||
TrOCR's [`VisionEncoderDecoder`] model accepts images as input and makes use of
|
||||
[`~generation_utils.GenerationMixin.generate`] to autoregressively generate text given the input image.
|
||||
|
||||
The [`ViTFeatureExtractor`] class is responsible for preprocessing the input image and
|
||||
[`RobertaTokenizer`] decodes the generated target tokens to the target string. The
|
||||
[`TrOCRProcessor`] wraps [`ViTFeatureExtractor`] and [`RobertaTokenizer`]
|
||||
into a single instance to both extract the input features and decode the predicted token ids.
|
||||
|
||||
- Step-by-step Optical Character Recognition (OCR)
|
||||
|
||||
``` py
|
||||
>>> from transformers import TrOCRProcessor, VisionEncoderDecoderModel
|
||||
>>> import requests
|
||||
>>> from PIL import Image
|
||||
|
||||
>>> processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten")
|
||||
>>> model = VisionEncoderDecoderModel.from_pretrained("microsoft/trocr-base-handwritten")
|
||||
|
||||
>>> # load image from the IAM dataset url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"
|
||||
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
|
||||
|
||||
>>> pixel_values = processor(image, return_tensors="pt").pixel_values
|
||||
>>> generated_ids = model.generate(pixel_values)
|
||||
|
||||
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
||||
```
|
||||
|
||||
See the [model hub](https://huggingface.co/models?filter=trocr) to look for TrOCR checkpoints.
|
||||
|
||||
## TrOCRConfig
|
||||
|
||||
[[autodoc]] TrOCRConfig
|
||||
|
||||
## TrOCRProcessor
|
||||
|
||||
[[autodoc]] TrOCRProcessor
|
||||
- __call__
|
||||
- from_pretrained
|
||||
- save_pretrained
|
||||
- batch_decode
|
||||
- decode
|
||||
- as_target_processor
|
||||
|
||||
## TrOCRForCausalLM
|
||||
|
||||
[[autodoc]] TrOCRForCausalLM
|
||||
- forward
|
||||
@@ -1,88 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
UniSpeech
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The UniSpeech model was proposed in `UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
|
||||
<https://arxiv.org/abs/2101.07597>`__ by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael
|
||||
Zeng, Xuedong Huang .
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both
|
||||
unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive
|
||||
self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture
|
||||
information more correlated with phonetic structures and improve the generalization across languages and domains. We
|
||||
evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The
|
||||
results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech
|
||||
recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all
|
||||
testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task,
|
||||
i.e., a relative word error rate reduction of 6% against the previous approach.*
|
||||
|
||||
Tips:
|
||||
|
||||
- UniSpeech is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please
|
||||
use :class:`~transformers.Wav2Vec2Processor` for the feature extraction.
|
||||
- UniSpeech model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
|
||||
decoded using :class:`~transformers.Wav2Vec2CTCTokenizer`.
|
||||
|
||||
This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The Authors' code can be
|
||||
found `here <https://github.com/microsoft/UniSpeech/tree/main/UniSpeech>`__.
|
||||
|
||||
|
||||
UniSpeechConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechConfig
|
||||
:members:
|
||||
|
||||
|
||||
UniSpeech specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.models.unispeech.modeling_unispeech.UniSpeechBaseModelOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.models.unispeech.modeling_unispeech.UniSpeechForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
|
||||
UniSpeechModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechModel
|
||||
:members: forward
|
||||
|
||||
|
||||
UniSpeechForCTC
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechForCTC
|
||||
:members: forward
|
||||
|
||||
|
||||
UniSpeechForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechForSequenceClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
UniSpeechForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechForPreTraining
|
||||
:members: forward
|
||||
@@ -1,92 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
UniSpeech-SAT
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The UniSpeech-SAT model was proposed in `UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware
|
||||
Pre-Training <https://arxiv.org/abs/2110.05752>`__ by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen,
|
||||
Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu .
|
||||
|
||||
The abstract from the paper is the following:
|
||||
|
||||
*Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled
|
||||
data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in
|
||||
speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In
|
||||
this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are
|
||||
introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi-task learning to
|
||||
the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function.
|
||||
Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where
|
||||
additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed
|
||||
methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves
|
||||
state-of-the-art performance in universal representation learning, especially for speaker identification oriented
|
||||
tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training
|
||||
dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.*
|
||||
|
||||
Tips:
|
||||
|
||||
- UniSpeechSat is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
|
||||
Please use :class:`~transformers.Wav2Vec2Processor` for the feature extraction.
|
||||
- UniSpeechSat model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be
|
||||
decoded using :class:`~transformers.Wav2Vec2CTCTokenizer`.
|
||||
- UniSpeechSat performs especially well on speaker verification, speaker identification, and speaker diarization tasks.
|
||||
|
||||
This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The Authors' code can be
|
||||
found `here <https://github.com/microsoft/UniSpeech/tree/main/UniSpeech-SAT>`__.
|
||||
|
||||
|
||||
UniSpeechSatConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechSatConfig
|
||||
:members:
|
||||
|
||||
|
||||
UniSpeechSat specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatBaseModelOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput
|
||||
:members:
|
||||
|
||||
|
||||
UniSpeechSatModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechSatModel
|
||||
:members: forward
|
||||
|
||||
|
||||
UniSpeechSatForCTC
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechSatForCTC
|
||||
:members: forward
|
||||
|
||||
|
||||
UniSpeechSatForSequenceClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechSatForSequenceClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
UniSpeechSatForPreTraining
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.UniSpeechSatForPreTraining
|
||||
:members: forward
|
||||
@@ -1,56 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
VisionTextDualEncoder
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
Overview
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
The :class:`~transformers.VisionTextDualEncoderModel` can be used to initialize a vision-text dual encoder model with
|
||||
any pretrained vision autoencoding model as the vision encoder (*e.g.* :doc:`ViT <vit>`, :doc:`BEiT <beit>`, :doc:`DeiT
|
||||
<deit>`) and any pretrained text autoencoding model as the text encoder (*e.g.* :doc:`RoBERTa <roberta>`, :doc:`BERT
|
||||
<bert>`). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
|
||||
to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
|
||||
downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
|
||||
training and then can be used for zero-shot vision tasks such image-classification or retrieval.
|
||||
|
||||
In `LiT: Zero-Shot Transfer with Locked-image Text Tuning <https://arxiv.org/abs/2111.07991>`__ it is shown how
|
||||
leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvment on
|
||||
new zero-shot vision tasks such as image classification or retrieval.
|
||||
|
||||
VisionTextDualEncoderConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.VisionTextDualEncoderConfig
|
||||
:members:
|
||||
|
||||
|
||||
VisionTextDualEncoderProcessor
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.VisionTextDualEncoderProcessor
|
||||
:members:
|
||||
|
||||
|
||||
VisionTextDualEncoderModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.VisionTextDualEncoderModel
|
||||
:members: forward
|
||||
|
||||
|
||||
FlaxVisionTextDualEncoderModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxVisionTextDualEncoderModel
|
||||
:members: __call__
|
||||
@@ -1,48 +0,0 @@
|
||||
..
|
||||
Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Vision Encoder Decoder Models
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
The :class:`~transformers.VisionEncoderDecoderModel` can be used to initialize an image-to-text-sequence model with any
|
||||
pretrained vision autoencoding model as the encoder (*e.g.* :doc:`ViT <vit>`, :doc:`BEiT <beit>`, :doc:`DeiT <deit>`)
|
||||
and any pretrained language model as the decoder (*e.g.* :doc:`RoBERTa <roberta>`, :doc:`GPT2 <gpt2>`, :doc:`BERT
|
||||
<bert>`).
|
||||
|
||||
The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for
|
||||
example) `TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
|
||||
<https://arxiv.org/abs/2109.10282>`__ by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
|
||||
Zhoujun Li, Furu Wei.
|
||||
|
||||
An example of how to use a :class:`~transformers.VisionEncoderDecoderModel` for inference can be seen in :doc:`TrOCR
|
||||
<trocr>`.
|
||||
|
||||
|
||||
VisionEncoderDecoderConfig
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.VisionEncoderDecoderConfig
|
||||
:members:
|
||||
|
||||
|
||||
VisionEncoderDecoderModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.VisionEncoderDecoderModel
|
||||
:members: forward, from_encoder_decoder_pretrained
|
||||
|
||||
|
||||
FlaxVisionEncoderDecoderModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.FlaxVisionEncoderDecoderModel
|
||||
:members: __call__, from_encoder_decoder_pretrained
|
||||
@@ -43,8 +43,6 @@ substantially fewer computational resources to train.*
|
||||
|
||||
Tips:
|
||||
|
||||
- Demo notebooks regarding inference as well as fine-tuning ViT on custom data can be found `here
|
||||
<https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer>`__.
|
||||
- To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
|
||||
which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image, which can be
|
||||
used for classification. The authors also add absolute position embeddings, and feed the resulting sequence of
|
||||
@@ -122,20 +120,6 @@ ViTForImageClassification
|
||||
:members: forward
|
||||
|
||||
|
||||
TFViTModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFViTModel
|
||||
:members: call
|
||||
|
||||
|
||||
TFViTForImageClassification
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.TFViTForImageClassification
|
||||
:members: call
|
||||
|
||||
|
||||
FlaxVitModel
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
|
||||
@@ -67,19 +67,9 @@ Wav2Vec2Processor
|
||||
:members: __call__, pad, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor
|
||||
|
||||
|
||||
Wav2Vec2ProcessorWithLM
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.Wav2Vec2ProcessorWithLM
|
||||
:members: __call__, pad, from_pretrained, save_pretrained, batch_decode, decode, as_target_processor
|
||||
|
||||
|
||||
Wav2Vec2 specific outputs
|
||||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. autoclass:: transformers.models.wav2vec2_with_lm.processing_wav2vec2_with_lm.Wav2Vec2DecoderWithLMOutput
|
||||
:members:
|
||||
|
||||
.. autoclass:: transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2BaseModelOutput
|
||||
:members:
|
||||
|
||||
|
||||
@@ -76,7 +76,7 @@ Transformers:
|
||||
It will store your access token in the Hugging Face cache folder (by default :obj:`~/.cache/`).
|
||||
|
||||
If you don't have an easy access to a terminal (for instance in a Colab session), you can find a token linked to your
|
||||
account by going on `huggingface.co <https://huggingface.co/>`, click on your avatar on the top left corner, then on
|
||||
acount by going on `huggingface.co <https://huggingface.co/>`, click on your avatar on the top left corner, then on
|
||||
`Edit profile` on the left, just beneath your profile picture. In the submenu `API Tokens`, you will find your API
|
||||
token that you can just copy.
|
||||
|
||||
@@ -90,7 +90,7 @@ Directly push your model to the hub
|
||||
picture-in-picture" allowfullscreen></iframe>
|
||||
|
||||
Once you have an API token (either stored in the cache or copied and pasted in your notebook), you can directly push a
|
||||
finetuned model you saved in :obj:`save_directory` by calling:
|
||||
finetuned model you saved in :obj:`save_drectory` by calling:
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
|
||||
@@ -80,7 +80,7 @@ Original GPT
|
||||
<a href="https://huggingface.co/models?filter=openai-gpt">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-openai--gpt-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/gpt">
|
||||
<a href="model_doc/gpt.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-openai--gpt-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -100,7 +100,7 @@ GPT-2
|
||||
<a href="https://huggingface.co/models?filter=gpt2">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-gpt2-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/gpt2">
|
||||
<a href="model_doc/gpt2.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-gpt2-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -122,7 +122,7 @@ CTRL
|
||||
<a href="https://huggingface.co/models?filter=ctrl">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/ctrl">
|
||||
<a href="model_doc/ctrl.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-ctrl-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -143,7 +143,7 @@ Transformer-XL
|
||||
<a href="https://huggingface.co/models?filter=transfo-xl">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-transfo--xl-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/transformerxl">
|
||||
<a href="model_doc/transformerxl.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-transfo--xl-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -174,7 +174,7 @@ Reformer
|
||||
<a href="https://huggingface.co/models?filter=reformer">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-reformer-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/reformer">
|
||||
<a href="model_doc/reformer.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-reformer-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -208,7 +208,7 @@ XLNet
|
||||
<a href="https://huggingface.co/models?filter=xlnet">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlnet-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/xlnet">
|
||||
<a href="model_doc/xlnet.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlnet-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -248,7 +248,7 @@ BERT
|
||||
<a href="https://huggingface.co/models?filter=bert">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-bert-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/bert">
|
||||
<a href="model_doc/bert.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bert-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -277,7 +277,7 @@ ALBERT
|
||||
<a href="https://huggingface.co/models?filter=albert">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-albert-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/albert">
|
||||
<a href="model_doc/albert.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-albert-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -306,7 +306,7 @@ RoBERTa
|
||||
<a href="https://huggingface.co/models?filter=roberta">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-roberta-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/roberta">
|
||||
<a href="model_doc/roberta.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-roberta-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -331,7 +331,7 @@ DistilBERT
|
||||
<a href="https://huggingface.co/models?filter=distilbert">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/distilbert">
|
||||
<a href="model_doc/distilbert.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-distilbert-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -356,11 +356,11 @@ ConvBERT
|
||||
<a href="https://huggingface.co/models?filter=convbert">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-convbert-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/convbert">
|
||||
<a href="model_doc/convbert.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-convbert-blueviolet">
|
||||
</a>
|
||||
|
||||
`ConvBERT: Improving BERT with Span-based Dynamic Convolution <https://arxiv.org/abs/2008.02496>`_, Zihang Jiang,
|
||||
`ConvBERT: Improving BERT with Span-based Dynamic Convolution <https://arxiv.org/abs/1910.01108>`_, Zihang Jiang,
|
||||
Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
|
||||
|
||||
Pre-trained language models like BERT and its variants have recently achieved impressive performance in various natural
|
||||
@@ -386,7 +386,7 @@ XLM
|
||||
<a href="https://huggingface.co/models?filter=xlm">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/xlm">
|
||||
<a href="model_doc/xlm.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -420,7 +420,7 @@ XLM-RoBERTa
|
||||
<a href="https://huggingface.co/models?filter=xlm-roberta">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm--roberta-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/xlmroberta">
|
||||
<a href="model_doc/xlmroberta.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm--roberta-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -442,7 +442,7 @@ FlauBERT
|
||||
<a href="https://huggingface.co/models?filter=flaubert">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-flaubert-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/flaubert">
|
||||
<a href="model_doc/flaubert.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-flaubert-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -460,7 +460,7 @@ ELECTRA
|
||||
<a href="https://huggingface.co/models?filter=electra">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-electra-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/electra">
|
||||
<a href="model_doc/electra.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-electra-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -484,7 +484,7 @@ Funnel Transformer
|
||||
<a href="https://huggingface.co/models?filter=funnel">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-funnel-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/funnel">
|
||||
<a href="model_doc/funnel.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-funnel-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -518,7 +518,7 @@ Longformer
|
||||
<a href="https://huggingface.co/models?filter=longformer">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-longformer-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/longformer">
|
||||
<a href="model_doc/longformer.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-longformer-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -558,7 +558,7 @@ BART
|
||||
<a href="https://huggingface.co/models?filter=bart">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-bart-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/bart">
|
||||
<a href="model_doc/bart.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bart-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -585,7 +585,7 @@ Pegasus
|
||||
<a href="https://huggingface.co/models?filter=pegasus">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-pegasus-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/pegasus">
|
||||
<a href="model_doc/pegasus.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-pegasus-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -616,7 +616,7 @@ MarianMT
|
||||
<a href="https://huggingface.co/models?filter=marian">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/marian">
|
||||
<a href="model_doc/marian.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-marian-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -635,7 +635,7 @@ T5
|
||||
<a href="https://huggingface.co/models?filter=t5">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-t5-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/t5">
|
||||
<a href="model_doc/t5.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-t5-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -668,7 +668,7 @@ MT5
|
||||
<a href="https://huggingface.co/models?filter=mt5">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-mt5-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/mt5">
|
||||
<a href="model_doc/mt5.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mt5-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -689,7 +689,7 @@ MBart
|
||||
<a href="https://huggingface.co/models?filter=mbart">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-mbart-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/mbart">
|
||||
<a href="model_doc/mbart.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-mbart-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -718,7 +718,7 @@ ProphetNet
|
||||
<a href="https://huggingface.co/models?filter=prophetnet">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-prophetnet-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/prophetnet">
|
||||
<a href="model_doc/prophetnet.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-prophetnet-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -743,7 +743,7 @@ XLM-ProphetNet
|
||||
<a href="https://huggingface.co/models?filter=xprophetnet">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-xprophetnet-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/xlmprophetnet">
|
||||
<a href="model_doc/xlmprophetnet.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xprophetnet-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -781,7 +781,7 @@ model know which part of the input vector corresponds to the text and which to t
|
||||
The pretrained model only works for classification.
|
||||
|
||||
..
|
||||
More information in this :doc:`model documentation <model_doc/mmbt>`. TODO: write this page
|
||||
More information in this :doc:`model documentation </model_doc/mmbt.html>`. TODO: write this page
|
||||
|
||||
.. _retrieval-based-models:
|
||||
|
||||
@@ -799,7 +799,7 @@ DPR
|
||||
<a href="https://huggingface.co/models?filter=dpr">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-dpr-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/dpr">
|
||||
<a href="model_doc/dpr.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-dpr-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -828,7 +828,7 @@ RAG
|
||||
<a href="https://huggingface.co/models?filter=rag">
|
||||
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-rag-blueviolet">
|
||||
</a>
|
||||
<a href="model_doc/rag">
|
||||
<a href="model_doc/rag.html">
|
||||
<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-rag-blueviolet">
|
||||
</a>
|
||||
|
||||
@@ -877,7 +877,7 @@ Some preselected input tokens are also given global attention: for those few tok
|
||||
all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in
|
||||
their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask:
|
||||
|
||||
.. image:: /imgs/local_attention_mask.png
|
||||
.. image:: imgs/local_attention_mask.png
|
||||
:scale: 50 %
|
||||
:align: center
|
||||
|
||||
|
||||
@@ -1,118 +0,0 @@
|
||||
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
-->
|
||||
|
||||
# Multi-lingual models
|
||||
|
||||
[[open-in-colab]]
|
||||
|
||||
Most of the models available in this library are mono-lingual models (English, Chinese and German). A few multi-lingual
|
||||
models are available and have a different mechanisms than mono-lingual models. This page details the usage of these
|
||||
models.
|
||||
|
||||
## XLM
|
||||
|
||||
XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
|
||||
be split in two categories: the checkpoints that make use of language embeddings, and those that don't
|
||||
|
||||
### XLM & Language Embeddings
|
||||
|
||||
This section concerns the following checkpoints:
|
||||
|
||||
- `xlm-mlm-ende-1024` (Masked language modeling, English-German)
|
||||
- `xlm-mlm-enfr-1024` (Masked language modeling, English-French)
|
||||
- `xlm-mlm-enro-1024` (Masked language modeling, English-Romanian)
|
||||
- `xlm-mlm-xnli15-1024` (Masked language modeling, XNLI languages)
|
||||
- `xlm-mlm-tlm-xnli15-1024` (Masked language modeling + Translation, XNLI languages)
|
||||
- `xlm-clm-enfr-1024` (Causal language modeling, English-French)
|
||||
- `xlm-clm-ende-1024` (Causal language modeling, English-German)
|
||||
|
||||
These checkpoints require language embeddings that will specify the language used at inference time. These language
|
||||
embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
|
||||
these tensors depend on the language used and are identifiable using the `lang2id` and `id2lang` attributes from
|
||||
the tokenizer.
|
||||
|
||||
Here is an example using the `xlm-clm-enfr-1024` checkpoint (Causal language modeling, English-French):
|
||||
|
||||
|
||||
```py
|
||||
>>> import torch
|
||||
>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
|
||||
|
||||
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
|
||||
>>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
|
||||
```
|
||||
|
||||
The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
|
||||
`lang2id` attribute:
|
||||
|
||||
```py
|
||||
>>> print(tokenizer.lang2id)
|
||||
{'en': 0, 'fr': 1}
|
||||
```
|
||||
|
||||
These ids should be used when passing a language parameter during a model pass. Let's define our inputs:
|
||||
|
||||
```py
|
||||
>>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
|
||||
```
|
||||
|
||||
We should now define the language embedding by using the previously defined language id. We want to create a tensor
|
||||
filled with the appropriate language ids, of the same size as input_ids. For english, the id is 0:
|
||||
|
||||
```py
|
||||
>>> language_id = tokenizer.lang2id['en'] # 0
|
||||
>>> langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0])
|
||||
|
||||
>>> # We reshape it to be of size (batch_size, sequence_length)
|
||||
>>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
|
||||
```
|
||||
|
||||
You can then feed it all as input to your model:
|
||||
|
||||
```py
|
||||
>>> outputs = model(input_ids, langs=langs)
|
||||
```
|
||||
|
||||
The example [run_generation.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-generation/run_generation.py) can generate text
|
||||
using the CLM checkpoints from XLM, using the language embeddings.
|
||||
|
||||
### XLM without Language Embeddings
|
||||
|
||||
This section concerns the following checkpoints:
|
||||
|
||||
- `xlm-mlm-17-1280` (Masked language modeling, 17 languages)
|
||||
- `xlm-mlm-100-1280` (Masked language modeling, 100 languages)
|
||||
|
||||
These checkpoints do not require language embeddings at inference time. These models are used to have generic sentence
|
||||
representations, differently from previously-mentioned XLM checkpoints.
|
||||
|
||||
|
||||
## BERT
|
||||
|
||||
BERT has two checkpoints that can be used for multi-lingual tasks:
|
||||
|
||||
- `bert-base-multilingual-uncased` (Masked language modeling + Next sentence prediction, 102 languages)
|
||||
- `bert-base-multilingual-cased` (Masked language modeling + Next sentence prediction, 104 languages)
|
||||
|
||||
These checkpoints do not require language embeddings at inference time. They should identify the language used in the
|
||||
context and infer accordingly.
|
||||
|
||||
## XLM-RoBERTa
|
||||
|
||||
XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
|
||||
over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
|
||||
labeling and question answering.
|
||||
|
||||
Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:
|
||||
|
||||
- `xlm-roberta-base` (Masked language modeling, 100 languages)
|
||||
- `xlm-roberta-large` (Masked language modeling, 100 languages)
|
||||
129
docs/source/multilingual.rst
Normal file
129
docs/source/multilingual.rst
Normal file
@@ -0,0 +1,129 @@
|
||||
..
|
||||
Copyright 2020 The HuggingFace Team. All rights reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
||||
the License. You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
||||
specific language governing permissions and limitations under the License.
|
||||
|
||||
Multi-lingual models
|
||||
=======================================================================================================================
|
||||
|
||||
Most of the models available in this library are mono-lingual models (English, Chinese and German). A few multi-lingual
|
||||
models are available and have a different mechanisms than mono-lingual models. This page details the usage of these
|
||||
models.
|
||||
|
||||
The two models that currently support multiple languages are BERT and XLM.
|
||||
|
||||
XLM
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
XLM has a total of 10 different checkpoints, only one of which is mono-lingual. The 9 remaining model checkpoints can
|
||||
be split in two categories: the checkpoints that make use of language embeddings, and those that don't
|
||||
|
||||
XLM & Language Embeddings
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
This section concerns the following checkpoints:
|
||||
|
||||
- ``xlm-mlm-ende-1024`` (Masked language modeling, English-German)
|
||||
- ``xlm-mlm-enfr-1024`` (Masked language modeling, English-French)
|
||||
- ``xlm-mlm-enro-1024`` (Masked language modeling, English-Romanian)
|
||||
- ``xlm-mlm-xnli15-1024`` (Masked language modeling, XNLI languages)
|
||||
- ``xlm-mlm-tlm-xnli15-1024`` (Masked language modeling + Translation, XNLI languages)
|
||||
- ``xlm-clm-enfr-1024`` (Causal language modeling, English-French)
|
||||
- ``xlm-clm-ende-1024`` (Causal language modeling, English-German)
|
||||
|
||||
These checkpoints require language embeddings that will specify the language used at inference time. These language
|
||||
embeddings are represented as a tensor that is of the same shape as the input ids passed to the model. The values in
|
||||
these tensors depend on the language used and are identifiable using the ``lang2id`` and ``id2lang`` attributes from
|
||||
the tokenizer.
|
||||
|
||||
Here is an example using the ``xlm-clm-enfr-1024`` checkpoint (Causal language modeling, English-French):
|
||||
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> import torch
|
||||
>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
|
||||
|
||||
>>> tokenizer = XLMTokenizer.from_pretrained("xlm-clm-enfr-1024")
|
||||
>>> model = XLMWithLMHeadModel.from_pretrained("xlm-clm-enfr-1024")
|
||||
|
||||
|
||||
The different languages this model/tokenizer handles, as well as the ids of these languages are visible using the
|
||||
``lang2id`` attribute:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> print(tokenizer.lang2id)
|
||||
{'en': 0, 'fr': 1}
|
||||
|
||||
|
||||
These ids should be used when passing a language parameter during a model pass. Let's define our inputs:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
|
||||
|
||||
|
||||
We should now define the language embedding by using the previously defined language id. We want to create a tensor
|
||||
filled with the appropriate language ids, of the same size as input_ids. For english, the id is 0:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> language_id = tokenizer.lang2id['en'] # 0
|
||||
>>> langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0])
|
||||
|
||||
>>> # We reshape it to be of size (batch_size, sequence_length)
|
||||
>>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
|
||||
|
||||
|
||||
You can then feed it all as input to your model:
|
||||
|
||||
.. code-block::
|
||||
|
||||
>>> outputs = model(input_ids, langs=langs)
|
||||
|
||||
|
||||
The example :prefix_link:`run_generation.py <examples/pytorch/text-generation/run_generation.py>` can generate text
|
||||
using the CLM checkpoints from XLM, using the language embeddings.
|
||||
|
||||
XLM without Language Embeddings
|
||||
-----------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
This section concerns the following checkpoints:
|
||||
|
||||
- ``xlm-mlm-17-1280`` (Masked language modeling, 17 languages)
|
||||
- ``xlm-mlm-100-1280`` (Masked language modeling, 100 languages)
|
||||
|
||||
These checkpoints do not require language embeddings at inference time. These models are used to have generic sentence
|
||||
representations, differently from previously-mentioned XLM checkpoints.
|
||||
|
||||
|
||||
BERT
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
BERT has two checkpoints that can be used for multi-lingual tasks:
|
||||
|
||||
- ``bert-base-multilingual-uncased`` (Masked language modeling + Next sentence prediction, 102 languages)
|
||||
- ``bert-base-multilingual-cased`` (Masked language modeling + Next sentence prediction, 104 languages)
|
||||
|
||||
These checkpoints do not require language embeddings at inference time. They should identify the language used in the
|
||||
context and infer accordingly.
|
||||
|
||||
XLM-RoBERTa
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
XLM-RoBERTa was trained on 2.5TB of newly created clean CommonCrawl data in 100 languages. It provides strong gains
|
||||
over previously released multi-lingual models like mBERT or XLM on downstream tasks like classification, sequence
|
||||
labeling and question answering.
|
||||
|
||||
Two XLM-RoBERTa checkpoints can be used for multi-lingual tasks:
|
||||
|
||||
- ``xlm-roberta-base`` (Masked language modeling, 100 languages)
|
||||
- ``xlm-roberta-large`` (Masked language modeling, 100 languages)
|
||||
@@ -35,7 +35,7 @@ The following is the brief description of the main concepts that will be describ
|
||||
1. DataParallel (DP) - the same setup is replicated multiple times, and each being fed a slice of the data. The processing is done in parallel and all setups are synchronized at the end of each training step.
|
||||
2. TensorParallel (TP) - each tensor is split up into multiple chunks, so instead of having the whole tensor reside on a single gpu, each shard of the tensor resides on its designated gpu. During processing each shard gets processed separately and in parallel on different GPUs and the results are synced at the end of the step. This is what one may call horizontal parallelism, as the splitting happens on horizontal level.
|
||||
3. PipelineParallel (PP) - the model is split up vertically (layer-level) across multiple GPUs, so that only one or several layers of the model are places on a single gpu. Each gpu processes in parallel different stages of the pipeline and working on a small chunk of the batch.
|
||||
4. Zero Redundancy Optimizer (ZeRO) - Also performs sharding of the tensors somewhat similar to TP, except the whole tensor gets reconstructed in time for a forward or backward computation, therefore the model doesn't need to be modified. It also supports various offloading techniques to compensate for limited GPU memory.
|
||||
4. Zero Redundancy Optimizer (ZeRO) - Also performs sharding of the tensors somewhat similar to TP, except the whole tensor gets reconstructed in time for a forward or backward computation, therefore the model does't need to be modified. It also supports various offloading techniques to compensate for limited GPU memory.
|
||||
5. Sharded DDP - is another name for the foundational ZeRO concept as used by various other implementations of ZeRO.
|
||||
|
||||
|
||||
@@ -46,7 +46,7 @@ Most users with just 2 GPUs already enjoy the increased training speed up thanks
|
||||
## ZeRO Data Parallel
|
||||
|
||||
ZeRO-powered data parallelism (ZeRO-DP) is described on the following diagram from this [blog post](https://www.microsoft.com/en-us/research/blog/zero-deepspeed-new-system-optimizations-enable-training-models-with-over-100-billion-parameters/)
|
||||
|
||||
|
||||
|
||||
It can be difficult to wrap one's head around it, but in reality the concept is quite simple. This is just the usual DataParallel (DP), except, instead of replicating the full model params, gradients and optimizer states, each GPU stores only a slice of it. And then at run-time when the full layer params are needed just for the given layer, all GPUs synchronize to give each other parts that they miss - this is it.
|
||||
|
||||
@@ -110,7 +110,7 @@ To me this sounds like an efficient group backpacking weight distribution strate
|
||||
2. person B carries the stove
|
||||
3. person C carries the axe
|
||||
|
||||
Now each night they all share what they have with others and get from others what they don't have, and in the morning they pack up their allocated type of gear and continue on their way. This is Sharded DDP / Zero DP.
|
||||
Now each night they all share what they have with others and get from others what the don't have, and in the morning they pack up their allocated type of gear and continue on their way. This is Sharded DDP / Zero DP.
|
||||
|
||||
Compare this strategy to the simple one where each person has to carry their own tent, stove and axe, which would be far more inefficient. This is DataParallel (DP and DDP) in Pytorch.
|
||||
|
||||
@@ -122,7 +122,7 @@ Implementations:
|
||||
|
||||
- [DeepSpeed](https://www.deepspeed.ai/features/#the-zero-redundancy-optimizer) ZeRO-DP stages 1+2+3
|
||||
- [Fairscale](https://github.com/facebookresearch/fairscale/#optimizer-state-sharding-zero) ZeRO-DP stages 1+2+3
|
||||
- [`transformers` integration](main_classes/trainer#trainer-integrations)
|
||||
- [`transformers` integration](https://huggingface.co/transformers/master/main_classes/trainer.html#trainer-integrations)
|
||||
|
||||
## Naive Model Parallel (Vertical) and Pipeline Parallel
|
||||
|
||||
@@ -140,7 +140,7 @@ we just sliced it in 2 vertically, placing layers 0-3 onto GPU0 and 4-7 to GPU1.
|
||||
|
||||
Now while data travels from layer 0 to 1, 1 to 2 and 2 to 3 this is just the normal model. But when data needs to pass from layer 3 to layer 4 it needs to travel from GPU0 to GPU1 which introduces a communication overhead. If the participating GPUs are on the same compute node (e.g. same physical machine) this copying is pretty fast, but if the GPUs are located on different compute nodes (e.g. multiple machines) the communication overhead could be significantly larger.
|
||||
|
||||
Then layers 4 to 5 to 6 to 7 are as a normal model would have and when the 7th layer completes we often need to send the data back to layer 0 where the labels are (or alternatively send the labels to the last layer). Now the loss can be computed and the optimizer can do its work.
|
||||
Then layers 4 to 5 to 6 to 7 are as a normal model would have and when the 7th layer completes we often need to send the data back to layer 0 where the labels are (or alternatively send the labels to the the last layer). Now the loss can be computed and the optimizer can do its work.
|
||||
|
||||
Problems:
|
||||
- the main deficiency and why this one is called "naive" MP, is that all but one GPU is idle at any given moment. So if 4 GPUs are used, it's almost identical to quadrupling the amount of memory of a single GPU, and ignoring the rest of the hardware. Plus there is the overhead of copying the data between devices. So 4x 6GB cards will be able to accommodate the same size as 1x 24GB card using naive MP, except the latter will complete the training faster, since it doesn't have the data copying overhead. But, say, if you have 40GB cards and need to fit a 45GB model you can with 4x 40GB cards (but barely because of the gradient and optimizer states)
|
||||
@@ -150,7 +150,7 @@ Pipeline Parallel (PP) is almost identical to a naive MP, but it solves the GPU
|
||||
|
||||
The following illustration from the [GPipe paper](https://ai.googleblog.com/2019/03/introducing-gpipe-open-source-library.html) shows the naive MP on the top, and PP on the bottom:
|
||||
|
||||
|
||||
|
||||
|
||||
It's easy to see from the bottom diagram how PP has less dead zones, where GPUs are idle. The idle parts are referred to as the "bubble".
|
||||
|
||||
@@ -170,44 +170,27 @@ With `chunks=1` you end up with the naive MP, which is very inefficient. With a
|
||||
|
||||
While the diagram shows that there is a bubble of "dead" time that can't be parallelized because the last `forward` stage has to wait for `backward` to complete the pipeline, the purpose of finding the best value for `chunks` is to enable a high concurrent GPU utilization across all participating GPUs which translates to minimizing the size of the bubble.
|
||||
|
||||
There are 2 groups of solutions - the traditional Pipeline API and the more modern solutions that make things much easier for the end user.
|
||||
|
||||
Traditional Pipeline API solutions:
|
||||
- PyTorch
|
||||
- FairScale
|
||||
- DeepSpeed
|
||||
- Megatron-LM
|
||||
|
||||
Modern solutions:
|
||||
- Varuna
|
||||
- Sagemaker
|
||||
|
||||
Problems with traditional Pipeline API solutions:
|
||||
Problems:
|
||||
- have to modify the model quite heavily, because Pipeline requires one to rewrite the normal flow of modules into a `nn.Sequential` sequence of the same, which may require changes to the design of the model.
|
||||
- currently the Pipeline API is very restricted. If you had a bunch of python variables being passed in the very first stage of the Pipeline, you will have to find a way around it. Currently, the pipeline interface requires either a single Tensor or a tuple of Tensors as the only input and output. These tensors must have a batch size as the very first dimension, since pipeline is going to chunk the mini batch into micro-batches. Possible improvements are being discussed here https://github.com/pytorch/pytorch/pull/50693
|
||||
- conditional control flow at the level of pipe stages is not possible - e.g., Encoder-Decoder models like T5 require special workarounds to handle a conditional encoder stage.
|
||||
- have to arrange each layer so that the output of one model becomes an input to the other model.
|
||||
|
||||
We are yet to experiment with Varuna and SageMaker but their papers report that they have overcome the list of problems mentioned above and that they require much smaller changes to the user's model.
|
||||
- have to arrange each layer so that the output of one model becomes an input to the other model
|
||||
|
||||
Implementations:
|
||||
- [Pytorch](https://pytorch.org/docs/stable/pipeline.html) (initial support in pytorch-1.8, and progressively getting improved in 1.9 and more so in 1.10). Some [examples](https://github.com/pytorch/pytorch/blob/master/benchmarks/distributed/pipeline/pipe.py)
|
||||
- [FairScale](https://fairscale.readthedocs.io/en/latest/tutorials/pipe.html)
|
||||
- [DeepSpeed](https://www.deepspeed.ai/tutorials/pipeline/)
|
||||
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) has an internal implementation - no API.
|
||||
- [Varuna](https://github.com/microsoft/varuna)
|
||||
- [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.
|
||||
|
||||
🤗 Transformers status: as of this writing none of the models supports full-PP. GPT2 and T5 models have naive PP support. The main obstacle is being unable to convert the models to `nn.Sequential` and have all the inputs to be Tensors. This is because currently the models include many features that make the conversion very complicated, and will need to be removed to accomplish that.
|
||||
|
||||
Other approaches:
|
||||
|
||||
DeepSpeed, Varuna and SageMaker use the concept of an [Interleaved Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html)
|
||||
|
||||
DeepSpeed and SageMaker use the concept of an [Interleaved Pipeline](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html)
|
||||
|
||||
|
||||
Here the bubble (idle time) is further minimized by prioritizing backward passes.
|
||||
|
||||
Varuna further tries to improve the schedule by using simulations to discover the most efficient scheduling.
|
||||
According to [the same document](https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html), it might be able to automate the non `nn.Sequential` model conversion to pipeline. The only problem is that this is currently only available at AWS, so you can't run it on your own hardware.
|
||||
|
||||
|
||||
## Tensor Parallelism
|
||||
@@ -221,31 +204,28 @@ The main building block of any transformer is a fully connected `nn.Linear` foll
|
||||
Following the Megatron's paper notation, we can write the dot-product part of it as `Y = GeLU(XA)`, where `X` and `Y` are the input and output vectors, and `A` is the weight matrix.
|
||||
|
||||
If we look at the computation in matrix form, it's easy to see how the matrix multiplication can be split between multiple GPUs:
|
||||
|
||||
|
||||
|
||||
If we split the weight matrix `A` column-wise across `N` GPUs and perform matrix multiplications `XA_1` through `XA_n` in parallel, then we will end up with `N` output vectors `Y_1, Y_2, ..., Y_n` which can be fed into `GeLU` independently:
|
||||
|
||||
|
||||
|
||||
Using this principle, we can update an MLP of arbitrary depth, without the need for any synchronization between GPUs until the very end, where we need to reconstruct the output vector from shards. The Megatron-LM paper authors provide a helpful illustration for that:
|
||||
|
||||
|
||||
|
||||
Parallelizing the multi-headed attention layers is even simpler, since they are already inherently parallel, due to having multiple independent heads!
|
||||
|
||||
|
||||
|
||||
Special considerations: TP requires very fast network, and therefore it's not advisable to do TP across more than one node. Practically, if a node has 4 GPUs, the highest TP degree is therefore 4. If you need a TP degree of 8, you need to use nodes that have at least 8 GPUs.
|
||||
|
||||
This section is based on the original much more [detailed TP overview](https://github.com/huggingface/transformers/issues/10321#issuecomment-783543530).
|
||||
by [@anton-l](https://github.com/anton-l).
|
||||
|
||||
SageMaker combines TP with DP for a more efficient processing.
|
||||
|
||||
Alternative names:
|
||||
- DeepSpeed calls it [tensor slicing](https://www.deepspeed.ai/features/#model-parallelism)
|
||||
|
||||
Implementations:
|
||||
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) has an internal implementation, as it's very model-specific
|
||||
- [parallelformers](https://github.com/tunib-ai/parallelformers) (only inference at the moment)
|
||||
- [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.
|
||||
|
||||
🤗 Transformers status:
|
||||
- core: not yet implemented in the core
|
||||
@@ -258,7 +238,7 @@ Implementations:
|
||||
|
||||
The following diagram from the DeepSpeed [pipeline tutorial](https://www.deepspeed.ai/tutorials/pipeline/) demonstrates how one combines DP with PP.
|
||||
|
||||
|
||||
|
||||
|
||||
Here it's important to see how DP rank 0 doesn't see GPU2 and DP rank 1 doesn't see GPU3. To DP there is just GPUs 0 and 1 where it feeds data as if there were just 2 GPUs. GPU0 "secretly" offloads some of its load to GPU2 using PP. And GPU1 does the same by enlisting GPU3 to its aid.
|
||||
|
||||
@@ -267,8 +247,6 @@ Since each dimension requires at least 2 GPUs, here you'd need at least 4 GPUs.
|
||||
Implementations:
|
||||
- [DeepSpeed](https://github.com/microsoft/DeepSpeed)
|
||||
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
|
||||
- [Varuna](https://github.com/microsoft/varuna)
|
||||
- [SageMaker](https://arxiv.org/abs/2111.05972)
|
||||
|
||||
🤗 Transformers status: not yet implemented
|
||||
|
||||
@@ -277,7 +255,7 @@ Implementations:
|
||||
|
||||
To get an even more efficient training a 3D parallelism is used where PP is combined with TP and DP. This can be seen in the following diagram.
|
||||
|
||||
|
||||
|
||||
|
||||
This diagram is from a blog post [3D parallelism: Scaling to trillion-parameter models](https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/), which is a good read as well.
|
||||
|
||||
@@ -286,8 +264,6 @@ Since each dimension requires at least 2 GPUs, here you'd need at least 8 GPUs.
|
||||
Implementations:
|
||||
- [DeepSpeed](https://github.com/microsoft/DeepSpeed) - DeepSpeed also includes an even more efficient DP, which they call ZeRO-DP.
|
||||
- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
|
||||
- [Varuna](https://github.com/microsoft/varuna)
|
||||
- [SageMaker](https://arxiv.org/abs/2111.05972)
|
||||
|
||||
🤗 Transformers status: not yet implemented, since we have no PP and TP.
|
||||
|
||||
@@ -296,7 +272,7 @@ Implementations:
|
||||
|
||||
One of the main features of DeepSpeed is ZeRO, which is a super-scalable extension of DP. It has already been discussed in [ZeRO Data Parallel](#zero-data-parallel). Normally it's a standalone feature that doesn't require PP or TP. But it can be combined with PP and TP.
|
||||
|
||||
When ZeRO-DP is combined with PP (and optionally TP) it typically enables only ZeRO stage 1 (optimizer sharding).
|
||||
When ZeRO-DP is combined with PP (and optinally TP) it typically enables only ZeRO stage 1 (optimizer sharding).
|
||||
|
||||
While it's theoretically possible to use ZeRO stage 2 (gradient sharding) with Pipeline Parallelism, it will have bad performance impacts. There would need to be an additional reduce-scatter collective for every micro-batch to aggregate the gradients before sharding, which adds a potentially significant communication overhead. By nature of Pipeline Parallelism, small micro-batches are used and instead the focus is on trying to balance arithmetic intensity (micro-batch size) with minimizing the Pipeline bubble (number of micro-batches). Therefore those communication costs are going to hurt.
|
||||
|
||||
@@ -320,29 +296,14 @@ Paper: ["Beyond Data and Model Parallelism for Deep Neural Networks" by Zhihao J
|
||||
|
||||
It performs a sort of 4D Parallelism over Sample-Operator-Attribute-Parameter.
|
||||
|
||||
1. Sample = Data Parallelism (sample-wise parallel)
|
||||
2. Operator = Parallelize a single operation into several sub-operations
|
||||
3. Attribute = Data Parallelism (length-wise parallel)
|
||||
4. Parameter = Model Parallelism (regardless of dimension - horizontal or vertical)
|
||||
1. Sample = Data Parallelism
|
||||
2. Operator = part vertical Layer Parallelism, but it can split the layer too - more refined level
|
||||
3. Attribute = horizontal Model Parallelism (Megatron-LM style)
|
||||
4. Parameter = Sharded model params
|
||||
|
||||
Examples:
|
||||
* Sample
|
||||
and they are working on Pipeline Parallelism. I guess ZeRO-DP is Sample+Parameter in this context.
|
||||
|
||||
Let's take 10 batches of sequence length 512. If we parallelize them by sample dimension into 2 devices, we get 10 x 512 which becomes be 5 x 2 x 512.
|
||||
|
||||
* Operator
|
||||
|
||||
If we perform layer normalization, we compute std first and mean second, and then we can normalize data. Operator parallelism allows computing std and mean in parallel. So if we parallelize them by operator dimension into 2 devices (cuda:0, cuda:1), first we copy input data into both devices, and cuda:0 computes std, cuda:1 computes mean at the same time.
|
||||
|
||||
* Attribute
|
||||
|
||||
We have 10 batches of 512 length. If we parallelize them by attribute dimension into 2 devices, 10 x 512 will be 10 x 2 x 256.
|
||||
|
||||
* Parameter
|
||||
|
||||
It is similar with tensor model parallelism or naive layer-wise model parallelism.
|
||||
|
||||
|
||||
|
||||
|
||||
The significance of this framework is that it takes resources like (1) GPU/TPU/CPU vs. (2) RAM/DRAM vs. (3) fast-intra-connect/slow-inter-connect and it automatically optimizes all these algorithmically deciding which parallelisation to use where.
|
||||
|
||||
@@ -355,7 +316,7 @@ So the promise is very attractive - it runs a 30min simulation on the cluster of
|
||||
|
||||
## Which Strategy To Use When
|
||||
|
||||
Here is a very rough outline at which parallelism strategy to use when. The first on each list is typically faster.
|
||||
Here is a very rough outlook at which parallelism strategy to use when. The first on the list is typically faster.
|
||||
|
||||
**⇨ Single GPU**
|
||||
|
||||
@@ -366,11 +327,7 @@ Here is a very rough outline at which parallelism strategy to use when. The firs
|
||||
* Model doesn't fit onto a single GPU:
|
||||
|
||||
1. ZeRO + Offload CPU and optionally NVMe
|
||||
2. as above plus Memory Centric Tiling (see below for details) if the largest layer can't fit into a single GPU
|
||||
|
||||
* Largest Layer not fitting into a single GPU:
|
||||
|
||||
1. ZeRO - Enable [Memory Centric Tiling](https://deepspeed.readthedocs.io/en/latest/zero3.html#memory-centric-tiling) (MCT). It allows you to run arbitrarily large layers by automatically splitting them and executing them sequentially. MCT reduces the number of parameters that are live on a GPU, but it does not affect the activation memory. As this need is very rare as of this writing a manual override of `torch.nn.Linear` needs to be done by the user.
|
||||
|
||||
**⇨ Single Node / Multi-GPU**
|
||||
|
||||
@@ -385,14 +342,7 @@ Here is a very rough outline at which parallelism strategy to use when. The firs
|
||||
2. ZeRO
|
||||
3. TP
|
||||
|
||||
With very fast intra-node connectivity of NVLINK or NVSwitch all three should be mostly on par, without these PP will be faster than TP or ZeRO. The degree of TP may also make a difference. Best to experiment to find the winner on your particular setup.
|
||||
|
||||
TP is almost always used within a single node. That is TP size <= gpus per node.
|
||||
|
||||
* Largest Layer not fitting into a single GPU:
|
||||
|
||||
1. If not using ZeRO - must use TP, as PP alone won't be able to fit.
|
||||
2. With ZeRO see the same entry for "Single GPU" above
|
||||
With very fast intra-node connectivity of NVLINK or NVSwitch all three should be mostly on par, without these PP will be faster than TP and ZeRO. The degree of TP may also make a difference. Best to experiment to find the winner on your particular setup.
|
||||
|
||||
|
||||
**⇨ Multi-Node / Multi-GPU**
|
||||
|
||||
@@ -52,10 +52,9 @@ Software:
|
||||
- Pipeline Parallelism
|
||||
- Tensor Parallelism
|
||||
- Low-memory Optimizers
|
||||
- fp16/bf16 (smaller data/faster throughput)
|
||||
- tf32 (faster throughput)
|
||||
- fp16/bf16 (smaller data)
|
||||
- Gradient checkpointing
|
||||
- Sparsity
|
||||
|
||||
|
||||
|
||||
## Hardware
|
||||
@@ -162,100 +161,19 @@ Software: `pytorch-1.8-to-be` + `cuda-11.0` / `transformers==4.3.0.dev0`
|
||||
|
||||
## Software
|
||||
|
||||
|
||||
### Anatomy of Model's Operations
|
||||
|
||||
Transformers architecture includes 3 main groups of operations grouped below by compute-intensity.
|
||||
|
||||
1. **Tensor Contractions**
|
||||
|
||||
Linear layers and components of Multi-Head Attention all do batched **matrix-matrix multiplications**. These operations are the most compute-intensive part of training a transformer.
|
||||
|
||||
2. **Statistical Normalizations**
|
||||
|
||||
Softmax and layer normalization are less compute-intensive than tensor contractions, and involve one or more **reduction operations**, the result of which is then applied via a map.
|
||||
|
||||
3. **Element-wise Operators**
|
||||
|
||||
These are the remaining operators: **biases, dropout, activations, and residual connections**. These are the least compute-intensive operations.
|
||||
|
||||
This knowledge can be helpful to know when analyzing performance bottlenecks.
|
||||
|
||||
This summary is derived from [Data Movement Is All You Need: A Case Study on Optimizing Transformers 2020](https://arxiv.org/abs/2007.00072)
|
||||
|
||||
|
||||
|
||||
### Anatomy of Model's Memory
|
||||
|
||||
The components on GPU memory are the following:
|
||||
1. model weights
|
||||
2. optimizer states
|
||||
3. gradients
|
||||
4. forward activations saved for gradient computation
|
||||
5. temporary buffers
|
||||
6. functionality-specific memory
|
||||
|
||||
A typical model trained in mixed precision with AdamW requires 18 bytes per model parameter plus activation memory.
|
||||
|
||||
For inference there are no optimizer states and gradients, so we can subtract those. And thus we end up with 6 bytes per model parameter for mixed precision inference, plus activation memory.
|
||||
|
||||
Let's look at the details.
|
||||
|
||||
#### Model Weights
|
||||
|
||||
- 4 bytes * number of parameters for fp32 training
|
||||
- 6 bytes * number of parameters for mixed precision training
|
||||
|
||||
#### Optimizer States
|
||||
|
||||
- 8 bytes * number of parameters for normal AdamW (maintains 2 states)
|
||||
- 2 bytes * number of parameters for 8-bit AdamW optimizers like [bitsandbytes](https://github.com/facebookresearch/bitsandbytes)
|
||||
- 4 bytes * number of parameters for optimizers like SGD (maintains only 1 state)
|
||||
|
||||
#### Gradients
|
||||
|
||||
- 4 bytes * number of parameters for either fp32 or mixed precision training
|
||||
|
||||
#### Forward Activations
|
||||
|
||||
- size depends on many factors, the key ones being sequence length, hidden size and batch size.
|
||||
|
||||
There are the input and output that are being passed and returned by the forward and the backward functions and the forward activations saved for gradient computation.
|
||||
|
||||
#### Temporary Memory
|
||||
|
||||
Additionally there are all kinds of temporary variables which get released once the calculation is done, but in the moment these could require additional memory and could push to OOM. Therefore when coding it's crucial to think strategically about such temporary variables and sometimes to explicitly free those as soon as they are no longer needed.
|
||||
|
||||
#### Functionality-specific memory
|
||||
|
||||
Then your software could have special memory needs. For example, when generating text using beam search, the software needs to maintain multiple copies of inputs and outputs.
|
||||
|
||||
|
||||
- the model weights
|
||||
- the forward activations saved for gradient computation
|
||||
- the gradients
|
||||
- the optimizer state
|
||||
|
||||
### `forward` vs `backward` Execution Speed
|
||||
|
||||
For convolutions and linear layers there are 2x flops in the backward compared to the forward, which generally translates into ~2x slower (sometimes more, because sizes in the backward tend to be more awkward). Activations are usually bandwidth-limited, and it’s typical for an activation to have to read more data in the backward than in the forward (e.g. activation forward reads once, writes once, activation backward reads twice, gradOutput and output of the forward, and writes once, gradInput).
|
||||
|
||||
|
||||
### Floating Data Types
|
||||
|
||||
Here are the commonly used floating point data types choice of which impacts both memory usage and throughput:
|
||||
|
||||
- fp32 (`float32`)
|
||||
- fp16 (`float16`)
|
||||
- bf16 (`bfloat16`)
|
||||
- tf32 (CUDA internal data type)
|
||||
|
||||
Here is a diagram that shows how these data types correlate to each other.
|
||||
|
||||
|
||||
|
||||
(source: [NVIDIA Blog](https://developer.nvidia.com/blog/accelerating-ai-training-with-tf32-tensor-cores/))
|
||||
|
||||
While fp16 and fp32 have been around for quite some time, bf16 and tf32 are only available on the Ampere architecture GPUS. TPUs support bf16 as well.
|
||||
|
||||
|
||||
#### fp16
|
||||
### fp16
|
||||
|
||||
AMP = Automatic Mixed Precision
|
||||
|
||||
@@ -267,8 +185,6 @@ If we look at what's happening with FP16 training (mixed precision) we have:
|
||||
|
||||
So the savings only happen for the forward activations saved for the backward computation, and there is a slight overhead because the model weights are stored both in half- and full-precision.
|
||||
|
||||
In 🤗 Transformers fp16 mixed precision is enabled by passing `--fp16` to the 🤗 Trainer.
|
||||
|
||||
Now let's look at a simple text-classification fine-tuning on 2 GPUs (I'm giving the command for reference):
|
||||
```
|
||||
export BS=16
|
||||
@@ -301,92 +217,15 @@ Summary: FP16 with apex or AMP will only give you some memory savings with a rea
|
||||
|
||||
Additionally, under mixed precision when possible, it's important that the batch size is a multiple of 8 to efficiently use tensor cores.
|
||||
|
||||
Note that in some situations the speed up can be as big as 5x when using mixed precision. e.g. we have observed that while using [Megatron-Deepspeed](https://github.com/bigscience-workshop/Megatron-DeepSpeed).
|
||||
|
||||
Some amazing tutorials to read on mixed precision:
|
||||
- @sgugger wrote a great explanation of mixed precision [here](https://docs.fast.ai/callback.fp16.html#A-little-bit-of-theory)
|
||||
- Aleksey Bilogur's [A developer-friendly guide to mixed precision training with PyTorch](https://spell.ml/blog/mixed-precision-training-with-pytorch-Xuk7YBEAACAASJam)
|
||||
|
||||
##### fp16 caching
|
||||
### fp16 caching
|
||||
|
||||
pytorch `autocast` which performs AMP include a caching feature, which speed things up by caching fp16-converted values. Here is the full description from this [comment](https://discuss.pytorch.org/t/autocast-and-torch-no-grad-unexpected-behaviour/93475/3):
|
||||
|
||||
Autocast maintains a cache of the FP16 casts of model parameters (leaves). This helps streamline parameter reuse: if the same FP32 param is used in several different FP16list ops, like several matmuls, instead of re-casting the param to FP16 on entering each matmul, the cast will occur on the first matmul, the casted FP16 copy will be cached, and for all later matmuls the FP16 copy will be reused. The cache is maintained only within a particular outermost autocast context. When you exit the autocast context the cache is dropped. For recommended usage, in which autocast wraps the forward pass, and then you exit the context before calling backward(), this means the cache only lasts the duration of the forward pass each iteration, and will be rebuilt next iteration. (The cache of FP16-casted copies MUST be rebuilt each iteration. The FP32 parameters get updated by the optimizer, so the FP16 copies must be recreated, otherwise the FP16 values will be stale.)
|
||||
|
||||
##### fp16 Inference
|
||||
|
||||
While normally inference is done with fp16/amp as with training, it's also possible to use the full fp16 mode without using mixed precision. This is especially a good fit if the pretrained model weights are already in fp16. So a lot less memory is used: 2 bytes per parameter vs 6 bytes with mixed precision!
|
||||
|
||||
How good the results this will deliver will depend on the model. If it can handle fp16 without overflows and accuracy issues, then it'll definitely better to use the full fp16 mode.
|
||||
|
||||
For example, LayerNorm has to be done in fp32 and recent pytorch (1.10+) has been fixed to do that regardless of the input types, but earlier pytorch versions accumulate in the input type which can be an issue.
|
||||
|
||||
In 🤗 Transformers the full fp16 inference is enabled by passing `--fp16_full_eval` to the 🤗 Trainer.
|
||||
|
||||
|
||||
#### bf16
|
||||
|
||||
If you own Ampere or newer hardware you can start using bf16 for your training and evaluation. While bf16 has a worse precision than fp16, it has a much much bigger dynamic range. Therefore, if in the past you were experiencing overflow issues while training the model, bf16 will prevent this from happening most of the time. Remember that in fp16 the biggest number you can have is `65535` and any number above that will overflow. A bf16 number can be as large as `3.39e+38` (!) which is about the same as fp32 - because both have 8-bits used for the numerical range.
|
||||
|
||||
Automatic Mixed Precision (AMP) is the same as with fp16, except it'll use bf16.
|
||||
|
||||
Thanks to the fp32-like dynamic range with bf16 mixed precision loss scaling is no longer needed.
|
||||
|
||||
If you have tried to finetune models pre-trained under bf16 mixed precision (e.g. T5) it's very likely that you have encountered overflow issues. Now you should be able to finetune those models without any issues.
|
||||
|
||||
That said, also be aware that if you pre-trained a model in bf16, it's likely to have overflow issues if someone tries to finetune it in fp16 down the road. So once started on the bf16-mode path it's best to remain on it and not switch to fp16.
|
||||
|
||||
In 🤗 Transformers bf16 mixed precision is enabled by passing `--bf16` to the 🤗 Trainer.
|
||||
|
||||
If you use your own trainer, this is just:
|
||||
|
||||
```
|
||||
from torch.cuda.amp import autocast
|
||||
with autocast(dtype=torch.bfloat16):
|
||||
loss, outputs = ...
|
||||
```
|
||||
|
||||
If you need to switch a tensor to bf16, it's just: `t.to(dtype=torch.bfloat16)`
|
||||
|
||||
Here is how you can check if your setup supports bf16:
|
||||
|
||||
```
|
||||
python -c 'import transformers; print(f"BF16 support is {transformers.file_utils.is_torch_bf16_available()}")'
|
||||
```
|
||||
|
||||
On the other hand bf16 has a much worse precision than fp16, so there are certain situations where you'd still want to use fp16 and not bf16.
|
||||
|
||||
|
||||
##### bf16 Inference
|
||||
|
||||
Same as with fp16, you can do inference in either the mixed precision bf16 or using the full bf16 mode. The same caveats apply. For details see [fp16 Inference](#fp16-inference).
|
||||
|
||||
In 🤗 Transformers the full bf16 inference is enabled by passing `--bf16_full_eval` to the 🤗 Trainer.
|
||||
|
||||
|
||||
#### tf32
|
||||
|
||||
The Ampere hardware uses a magical data type called tf32. It has the same numerical range as fp32 (8-bits), but instead of 23 bits precision it has only 10 bits (same as fp16). In total it uses only 19 bits.
|
||||
|
||||
It's magical in the sense that you can use the normal fp32 training and/or inference code and by enabling tf32 support you can get up to 3x throughput improvement. All you need to do is to add this to your code:
|
||||
|
||||
```
|
||||
import torch
|
||||
torch.backends.cuda.matmul.allow_tf32 = True
|
||||
```
|
||||
|
||||
When this is done CUDA will automatically switch to using tf32 instead of fp32 where it's possible. This, of course, assumes that the used GPU is from the Ampere series.
|
||||
|
||||
Like all cases with reduced precision this may or may not be satisfactory for your needs, so you have to experiment and see. According to [NVIDIA research](https://developer.nvidia.com/blog/accelerating-ai-training-with-tf32-tensor-cores/) the majority of machine learning training shouldn't be impacted and showed the same perplexity and convergence as the fp32 training.
|
||||
|
||||
If you're already using fp16 or bf16 mixed precision it may help with the throughput as well.
|
||||
|
||||
You can enable this mode in the 🤗 Trainer with `--tf32`, or disable it with `--tf32 0` or `--no_tf32`.
|
||||
By default the PyTorch default is used.
|
||||
|
||||
Note: tf32 mode is internal to CUDA and can't be accessed directly via `tensor.to(dtype=torch.tf32)` as `torch.tf32` doesn't exit.
|
||||
|
||||
Note: you need `torch>=1.7` to enjoy this feature.
|
||||
Autocast maintains a cache of the FP16 casts of model params (leaves). This helps streamline parameter reuse: if the same FP32 param is used in several different FP16list ops, like several matmuls, instead of re-casting the param to FP16 on entering each matmul, the cast will occur on the first matmul, the casted FP16 copy will be cached, and for all later matmuls the FP16 copy will be reused. The cache is maintained only within a particular outermost autocast context. When you exit the autocast context the cache is dropped. For recommended usage, in which autocast wraps the forward pass, and then you exit the context before calling backward(), this means the cache only lasts the duration of the forward pass each iteration, and will be rebuilt next iteration. (The cache of FP16-casted copies MUST be rebuilt each iteration. The FP32 params get updated by the optimizer, so the FP16 copies must be recreated, otherwise the FP16 values will be stale.)
|
||||
|
||||
|
||||
### Gradient Checkpointing
|
||||
@@ -513,38 +352,6 @@ One of the important requirements to reach great training speed is the ability t
|
||||
pytorch-nightly introduced `torch.optim._multi_tensor` which should significantly speed up the optimizers for situations with lots of small feature tensors. It should eventually become the default, but if you want to experiment with it sooner and don't mind using the bleed-edge, see: https://github.com/huggingface/transformers/issues/9965
|
||||
|
||||
|
||||
### Sparsity
|
||||
|
||||
#### Mixture of Experts
|
||||
|
||||
Quite a few of the recent papers reported a 4-5x training speedup and a faster inference by integrating
|
||||
Mixture of Experts (MoE) into the Transformer models.
|
||||
|
||||
Since it has been discovered that more parameters lead to better performance, this technique allows to increase the number of parameters by an order of magnitude without increasing training costs.
|
||||
|
||||
In this approach every other FFN layer is replaced with a MoE Layer which consists of many experts, with a gated function that trains each expert in a balanced way depending on the input token's position in a sequence.
|
||||
|
||||
|
||||
|
||||
(source: [GLAM](https://ai.googleblog.com/2021/12/more-efficient-in-context-learning-with.html))
|
||||
|
||||
You can find exhaustive details and comparison tables in the papers listed at the end of this section.
|
||||
|
||||
The main drawback of this approach is that it requires staggering amounts of GPU memory - almost an order of magnitude larger than its dense equivalent. Various distillation and approaches are proposed to how to overcome the much higher memory requirements.
|
||||
|
||||
There is direct trade-off though, you can use just a few experts with a 2-3x smaller base model instead of dozens or hundreds experts leading to a 5x smaller model and thus increase the training speed moderately while increasing the memory requirements moderately as well.
|
||||
|
||||
Most related papers and implementations are built around Tensorflow/TPUs:
|
||||
|
||||
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://arxiv.org/abs/2006.16668)
|
||||
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961)
|
||||
- [GLaM: Generalist Language Model (GLaM)](https://ai.googleblog.com/2021/12/more-efficient-in-context-learning-with.html)
|
||||
|
||||
And for Pytorch DeepSpeed has built one as well: [Mixture of Experts](https://www.deepspeed.ai/tutorials/mixture-of-experts/) - blog posts: [1](https://www.microsoft.com/en-us/research/blog/deepspeed-powers-8x-larger-moe-model-training-with-high-performance/), [2](https://www.microsoft.com/en-us/research/publication/scalable-and-efficient-moe-training-for-multitask-multilingual-models/) and specific deployment with large transformer-based natural language generation models: [blog post](https://www.deepspeed.ai/news/2021/12/09/deepspeed-moe-nlg.html), [Megatron-Deepspeed branch](Thttps://github.com/microsoft/Megatron-DeepSpeed/tree/moe-training).
|
||||
|
||||
|
||||
|
||||
|
||||
## Contribute
|
||||
|
||||
This document is far from being complete and a lot more needs to be added, so if you have additions or corrections to make please don't hesitate to open a PR or if you aren't sure start an Issue and we can discuss the details there.
|
||||
|
||||
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