[Doctests] Fix ignore bug and add more doc tests (#15911)

* finish speech doc tests

* finish

* boom

* Update src/transformers/models/speech_to_text/modeling_speech_to_text.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

.circleci/config.yml

@@ -81,10 +81,9 @@ jobs:
             - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
             - 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-1.11.0+cpu.html
+            - 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 git+https://github.com/huggingface/accelerate
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -120,10 +119,9 @@ jobs:
             - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
             - 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-1.11.0+cpu.html
+            - 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 git+https://github.com/huggingface/accelerate
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -138,7 +136,7 @@ jobs:
     run_tests_torch_and_flax:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
             RUN_PT_FLAX_CROSS_TESTS: yes
@@ -154,9 +152,8 @@ jobs:
             - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
             - 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-1.11.0+cpu.html
+            - 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 git+https://github.com/huggingface/accelerate
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -176,7 +173,7 @@ jobs:
     run_tests_torch_and_flax_all:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
             RUN_PT_FLAX_CROSS_TESTS: yes
@@ -192,9 +189,8 @@ jobs:
             - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
             - 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-1.11.0+cpu.html
+            - 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 git+https://github.com/huggingface/accelerate
             - save_cache:
                 key: v0.4-{{ checksum "setup.py" }}
                 paths:
@@ -221,12 +217,11 @@ jobs:
                   keys:
                       - v0.4-torch-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
-            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng time
+            - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
             - 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-1.11.0+cpu.html
+            - 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 git+https://github.com/huggingface/accelerate
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
@@ -261,9 +256,8 @@ jobs:
             - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
             - 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-1.11.0+cpu.html
+            - 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 git+https://github.com/huggingface/accelerate
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
                   paths:
@@ -426,7 +420,7 @@ jobs:
             - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
             - 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-1.11.0+cpu.html
+            - 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
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
@@ -463,7 +457,7 @@ jobs:
             - run: sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng
             - 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-1.11.0+cpu.html
+            - 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
             - save_cache:
                   key: v0.4-torch-{{ checksum "setup.py" }}
@@ -555,7 +549,7 @@ jobs:
                       - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install .[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]
+            - run: pip install .[ja,testing,sentencepiece,jieba,spacy,ftfy]
             - run: python -m unidic download
             - save_cache:
                   key: v0.4-custom_tokenizers-{{ checksum "setup.py" }}
@@ -577,7 +571,7 @@ jobs:
     run_examples_torch:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
             TRANSFORMERS_IS_CI: yes
@@ -612,7 +606,7 @@ jobs:
     run_examples_torch_all:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
             TRANSFORMERS_IS_CI: yes
@@ -789,7 +783,7 @@ jobs:
                       - v0.4-torch-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install .[torch,testing,sentencepiece,onnxruntime,vision,rjieba]
+            - run: pip install .[torch,testing,sentencepiece,onnxruntime]
             - save_cache:
                   key: v0.4-onnx-{{ checksum "setup.py" }}
                   paths:
@@ -822,7 +816,7 @@ jobs:
                       - v0.4-torch-{{ checksum "setup.py" }}
                       - v0.4-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install .[torch,testing,sentencepiece,onnxruntime,vision]
+            - run: pip install .[torch,testing,sentencepiece,onnxruntime]
             - save_cache:
                   key: v0.4-onnx-{{ checksum "setup.py" }}
                   paths:
@@ -837,7 +831,7 @@ jobs:
     check_code_quality:
         working_directory: ~/transformers
         docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
         resource_class: large
         environment:
             TRANSFORMERS_IS_CI: yes
@@ -858,12 +852,12 @@ jobs:
             - run: isort --check-only examples tests src utils
             - run: python utils/custom_init_isort.py --check_only
             - run: flake8 examples tests src utils
-            - run: doc-builder style src/transformers docs/source --max_len 119 --check_only --path_to_docs docs/source
+            - 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:3.7
+            - image: circleci/python:3.6
         resource_class: large
         environment:
             TRANSFORMERS_IS_CI: yes
@@ -885,7 +879,6 @@ jobs:
             - run: python utils/check_dummies.py
             - run: python utils/check_repo.py
             - run: python utils/check_inits.py
-            - run: python utils/check_config_docstrings.py
             - run: make deps_table_check_updated
             - run: python utils/tests_fetcher.py --sanity_check
 
@@ -920,7 +913,7 @@ jobs:
                   path: ~/transformers/test_preparation.txt
             - run: |
                   if [ -f test_list.txt ]; then
-                    python -m pytest -n 1 tests/models/*layoutlmv2* --dist=loadfile -s --make-reports=tests_layoutlmv2 --durations=100
+                    python -m pytest -n 1 tests/*layoutlmv2* --dist=loadfile -s --make-reports=tests_layoutlmv2 --durations=100
                   fi
             - store_artifacts:
                   path: ~/transformers/tests_output.txt
@@ -930,7 +923,7 @@ jobs:
 # TPU JOBS
     run_examples_tpu:
         docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
         environment:
             OMP_NUM_THREADS: 1
             TRANSFORMERS_IS_CI: yes
@@ -950,7 +943,7 @@ jobs:
 
     cleanup-gke-jobs:
         docker:
-            - image: circleci/python:3.7
+            - image: circleci/python:3.6
         steps:
             - gcp-gke/install
             - gcp-gke/update-kubeconfig-with-credentials:
@@ -962,7 +955,7 @@ workflow_filters: &workflow_filters
     filters:
         branches:
             only:
-                - main
+                - master
 workflows:
     version: 2
     build_and_test:
@@ -989,7 +982,7 @@ workflows:
                 filters:
                     branches:
                         only:
-                            - main
+                            - master
         jobs:
             - run_examples_torch_all
             - run_examples_flax_all
@@ -1011,7 +1004,7 @@ workflows:
 #                filters:
 #                    branches:
 #                        only:
-#                            - main
+#                            - master
 #        jobs:
 #            - cleanup-gke-jobs
 #            - run_examples_tpu

.github/ISSUE_TEMPLATE/---new-benchmark.md

@@ -0,0 +1,22 @@
+---
+name: "\U0001F5A5 New benchmark"
+about: Benchmark a part of this library and share your results
+title: "[Benchmark]"
+labels: ''
+assignees: ''
+
+---
+
+# 🖥 Benchmarking `transformers`
+
+## Benchmark
+
+Which part of `transformers` did you benchmark?
+
+## Set-up
+
+What did you run your benchmarks on? Please include details, such as: CPU, GPU? If using multiple GPUs, which parallelization did you use?
+
+## Results
+
+Put your results here!

.github/ISSUE_TEMPLATE/--new-model-addition.md

@@ -0,0 +1,20 @@
+---
+name: "\U0001F31F New model addition"
+about: Submit a proposal/request to implement a new Transformer-based model
+title: ''
+labels: New model
+assignees: ''
+
+---
+
+# 🌟 New model addition
+
+## Model description
+
+<!-- Important information -->
+
+## Open source status
+
+* [ ] the model implementation is available: (give details)
+* [ ] the model weights are available: (give details)
+* [ ] who are the authors: (mention them, if possible by @gh-username)

.github/ISSUE_TEMPLATE/bug-report.md

@@ -0,0 +1,106 @@
+---
+name: "\U0001F41B Bug Report"
+about: Submit a bug report to help us improve transformers
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+
+## Environment info
+<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
+     Don't forget to fill out the missing fields in that output! -->
+
+- `transformers` version:
+- Platform:
+- Python version:
+- PyTorch version (GPU?):
+- Tensorflow version (GPU?):
+- Using GPU in script?:
+- Using distributed or parallel set-up in script?:
+
+### Who can help
+<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
+ If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
+ Please tag fewer than 3 people.
+
+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.
+
+Library:
+
+- Benchmarks: @patrickvonplaten
+- Deepspeed: @stas00
+- Ray/raytune: @richardliaw, @amogkam
+- Text generation: @patrickvonplaten @narsil
+- Tokenizers: @SaulLu
+- Trainer: @sgugger
+- Pipelines: @Narsil
+- Speech: @patrickvonplaten, @anton-l
+- Vision: @NielsRogge, @sgugger
+
+Documentation: @sgugger
+
+Model hub:
+
+- for issues with a model, report at https://discuss.huggingface.co/ and tag the model's creator.
+
+HF projects:
+
+- datasets: [different repo](https://github.com/huggingface/datasets)
+- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
+
+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
+
+ -->
+
+## Information
+
+Model I am using (Bert, XLNet ...):
+
+The problem arises when using:
+* [ ] the official example scripts: (give details below)
+* [ ] my own modified scripts: (give details below)
+
+The tasks I am working on is:
+* [ ] an official GLUE/SQUaD task: (give the name)
+* [ ] my own task or dataset: (give details below)
+
+## To reproduce
+
+Steps to reproduce the behavior:
+
+1.
+2.
+3.
+
+<!-- If you have code snippets, error messages, stack traces please provide them here as well.
+     Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+     Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.-->
+
+## Expected behavior
+
+<!-- A clear and concise description of what you would expect to happen. -->

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,121 +0,0 @@
-name: "\U0001F41B Bug Report"
-description: Submit a bug report to help us import transformers
-labels: [ "bug" ]
-body:
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
-      render: shell
-      placeholder: transformers version, platform, python version, ...
-    validations:
-      required: true
-
-  - type: textarea
-    id: who-can-help
-    attributes:
-      label: Who can help?
-      description: |
-        Your issue will be replied to more quickly if you can figure out the right person to tag with @
-        If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
-        Please tag fewer than 3 people.
-        
-        Models:
-
-          - ALBERT, BERT, XLM, DeBERTa, DeBERTa-v2, ELECTRA, MobileBert, SqueezeBert: `@LysandreJik`
-          - T5, Pegasus, EncoderDecoder: `@patrickvonplaten`
-          - Blenderbot, MBART, BART, Marian, Pegasus: `@patil-suraj`
-          - Reformer, TransfoXL, XLNet, FNet: `@patrickvonplaten`
-          - Longformer, BigBird: `@ydshieh`
-          - FSMT: `@stas00`
-          - Funnel: `@sgugger`
-          - GPT-2, GPT: `@patil-suraj`, `@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, UniSpeech, UniSpeechSAT, SEW, SEW-D: `@patrickvonplaten`, `@anton-l`
-          - SpeechEncoderDecoder, Speech2Text, Speech2Text2: `@sanchit-gandhi`, `@patrickvonplaten`, `@anton-l`
-          
-          If the model isn't in the list, ping `@LysandreJik` who will redirect you to the correct contributor.
-
-        Library:
-          - Benchmarks: `@patrickvonplaten`
-          - Deepspeed: `@stas00`
-          - Ray/raytune: `@richardliaw`, `@amogkam`
-          - Text generation: `@patrickvonplaten`, `@Narsil`, `@gante`
-          - Tokenizers: `@SaulLu`
-          - Trainer: `@sgugger`
-          - Pipelines: `@Narsil`
-          - Speech: `@patrickvonplaten`, `@anton-l`, `@sanchit-gandhi`
-          - Vision: `@NielsRogge`, `@sgugger`
-
-        Documentation: `@sgugger`, `@stevhliu`
-
-        Model hub:
-
-          - for issues with a model, report at https://discuss.huggingface.co/ and tag the model's creator.
-
-        HF projects:
-
-          - datasets: [different repo](https://github.com/huggingface/datasets)
-          - rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
-
-        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`
-      placeholder: "@Username ..."
-
-  - type: checkboxes
-    id: information-scripts-examples
-    attributes:
-      label: Information
-      description: 'The problem arises when using:'
-      options:
-        - label: "The official example scripts"
-        - label: "My own modified scripts"
-
-  - type: checkboxes
-    id: information-tasks
-    attributes:
-      label: Tasks
-      description: "The tasks I am working on are:"
-      options:
-        - label: "An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)"
-        - label: "My own task or dataset (give details below)"
-
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Reproduction
-      description: |
-        Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        If you have code snippets, error messages, stack traces please provide them here as well.
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-          
-          1.
-          2.
-          3.
-          
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."
-      render: shell

.github/ISSUE_TEMPLATE/config.yml

@@ -1,9 +0,0 @@
-blank_issues_enabled: true
-version: 2.1
-contact_links:
-  - name: Website Related
-    url: https://github.com/huggingface/hub-docs/issues
-    about: Feature requests and bug reports related to the website
-  - name: Forum
-    url: https://discuss.huggingface.co/
-    about: General usage questions and community discussions

.github/ISSUE_TEMPLATE/feature-request.md

@@ -0,0 +1,25 @@
+---
+name: "\U0001F680 Feature request"
+about: Submit a proposal/request for a new transformers feature
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+# 🚀 Feature request
+
+<!-- A clear and concise description of the feature proposal.
+     Please provide a link to the paper and code in case they exist. -->
+
+## Motivation
+
+<!-- Please outline the motivation for the proposal. Is your feature request
+     related to a problem? e.g., I'm always frustrated when [...]. If this is related
+     to another GitHub issue, please link here too. -->
+
+## Your contribution
+
+<!-- Is there any way that you could help, e.g. by submitting a PR?
+     Make sure to read the CONTRIBUTING.MD readme:
+     https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md -->

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,31 +0,0 @@
-name: "\U0001F680 Feature request"
-description: Submit a proposal/request for a new transformers feature
-labels: [ "feature" ]
-body:
-  - type: textarea
-    id: feature-request
-    validations:
-      required: true
-    attributes:
-      label: Feature request
-      description: |
-        A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist.
-
-  - type: textarea
-    id: motivation
-    validations:
-      required: true
-    attributes:
-      label: Motivation
-      description: |
-        Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too.
-        
-
-  - type: textarea
-    id: contribution
-    validations:
-      required: true
-    attributes:
-      label: Your contribution
-      description: |
-        Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md)

.github/ISSUE_TEMPLATE/migration.md

@@ -0,0 +1,58 @@
+---
+name: "\U0001F4DA Migration from pytorch-pretrained-bert or pytorch-transformers"
+about: Report a problem when migrating from pytorch-pretrained-bert or pytorch-transformers
+  to transformers
+title: ''
+labels: Migration
+assignees: ''
+
+---
+
+# 📚 Migration
+
+## Information
+
+<!-- Important information -->
+
+Model I am using (Bert, XLNet ...):
+
+Language I am using the model on (English, Chinese ...):
+
+The problem arises when using:
+* [ ] the official example scripts: (give details below)
+* [ ] my own modified scripts: (give details below)
+
+The tasks I am working on is:
+* [ ] an official GLUE/SQUaD task: (give the name)
+* [ ] my own task or dataset: (give details below)
+
+## Details
+
+<!-- A clear and concise description of the migration issue.
+    If you have code snippets, please provide it here as well.
+    Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
+    Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
+    -->
+
+## Environment info
+<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
+     Don't forget to fill out the missing fields in that output! -->
+ 
+- `transformers` version:
+- Platform:
+- Python version:
+- PyTorch version (GPU?):
+- Tensorflow version (GPU?):
+- Using GPU in script?:
+- Using distributed or parallel set-up in script?:
+
+<!-- IMPORTANT: which version of the former library do you use? -->
+* `pytorch-transformers` or `pytorch-pretrained-bert` version (or branch):
+
+
+## Checklist
+
+- [ ] I have read the migration guide in the readme.
+ ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers);
+  [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))
+- [ ] I checked if a related official extension example runs on my machine.

.github/ISSUE_TEMPLATE/migration.yml

@@ -1,72 +0,0 @@
-name: "\U0001F4DA Migration from pytorch-pretrained-bert or pytorch-transformers"
-description: Report a problem when migrating from pytorch-pretrained-bert or pytorch-transformers to transformers
-labels: [ "migration" ]
-body:
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
-      render: shell
-      placeholder: transformers version, platform, python version, ...
-    validations:
-      required: true
-
-  - type: checkboxes
-    id: information-scripts-examples
-    attributes:
-      label: Information
-      description: 'The problem arises when using:'
-      options:
-        - label: "The official example scripts"
-        - label: "My own modified scripts"
-
-  - type: checkboxes
-    id: information-tasks
-    attributes:
-      label: Tasks
-      description: "The tasks I am working on are:"
-      options:
-        - label: "An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)"
-        - label: "My own task or dataset (give details below)"
-
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Reproduction
-      description: |
-        Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        If you have code snippets, error messages, stack traces please provide them here as well.
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Do not use screenshots, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-          
-          1.
-          2.
-          3.
-          
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."
-      render: shell
-
-  - type: checkboxes
-    id: checklist
-    attributes:
-      label: Checklist
-      options:
-        - label: "I have read the migration guide in the readme.
- ([pytorch-transformers](https://github.com/huggingface/transformers#migrating-from-pytorch-transformers-to-transformers);
-  [pytorch-pretrained-bert](https://github.com/huggingface/transformers#migrating-from-pytorch-pretrained-bert-to-transformers))"
-          required: true
-        - label: "I checked if a related official extension example runs on my machine."
-          required: true

.github/ISSUE_TEMPLATE/new-model-addition.yml

@@ -1,31 +0,0 @@
-name: "\U0001F31F New model addition"
-description: Submit a proposal/request to implement a new model
-labels: [ "New model" ]
-
-body:
-  - type: textarea
-    id: description-request
-    validations:
-      required: true
-    attributes:
-      label: Model description
-      description: |
-        Put any and all important information relative to the model
-
-  - type: checkboxes
-    id: information-tasks
-    attributes:
-      label: Open source status
-      description: |
-          Please note that if the model implementation isn't available or if the weights aren't open-source, we are less likely to implement it in `transformers`.
-      options:
-        - label: "The model implementation is available"
-        - label: "The model weights are available"
-
-  - type: textarea
-    id: additional-info
-    attributes:
-      label: Provide useful links for the implementation
-      description: |
-        Please provide information regarding the implementation, the weights, and the authors.
-        Please mention the authors by @gh-username if you're aware of their usernames.

.github/ISSUE_TEMPLATE/question-help.md

@@ -0,0 +1,26 @@
+---
+name: "❓ Questions & Help"
+about: Post your general questions on the Hugging Face forum: https://discuss.huggingface.co/
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+# ❓ Questions & Help
+
+<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
+     new models, benchmarks, and migration questions. For all other questions,
+     we direct you to the Hugging Face forum: https://discuss.huggingface.co/ .
+     -->
+
+## Details
+
+<!-- Description of your issue -->
+
+<!-- You should first ask your question on the forum, and only if
+     you didn't get an answer after a few days ask it here on GitHub. -->
+
+**A link to original question on the forum**:
+
+<!-- Your issue will be closed if you don't fill this part. -->

.github/PULL_REQUEST_TEMPLATE.md

@@ -17,13 +17,13 @@ Fixes # (issue)
 
 ## Before submitting
 - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
-- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
+- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),
       Pull Request section?
 - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
       to it if that's the case.
 - [ ] Did you make sure to update the documentation with your changes? Here are the
-      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
-      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
+      [documentation guidelines](https://github.com/huggingface/transformers/tree/master/docs), and
+      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/master/docs#writing-source-documentation).
 - [ ] Did you write any new necessary tests?
 
 

.github/workflows/add-model-like.yml

@@ -3,7 +3,7 @@ name: Add model like runner
 on:
   push:
     branches:
-      - main
+      - master
   pull_request:
     paths:
       - "src/**"
@@ -18,52 +18,35 @@ jobs:
     steps:
       - uses: actions/checkout@v2
 
-      - name: Install dependencies
-        run: |
-          sudo apt -y update && sudo apt install -y libsndfile1-dev
-
-      - name: Load cached virtual environment
+      - name: Loading cache.
         uses: actions/cache@v2
         id: cache
         with:
-          path: ~/venv/
-          key: v3-tests_model_like-${{ hashFiles('setup.py') }}
+          path: ~/.cache/pip
+          key: v1-tests_model_like
+          restore-keys: |
+            v1-tests_model_like-${{ hashFiles('setup.py') }}
+            v1-tests_model_like
 
-      - name: Create virtual environment on cache miss
-        if: steps.cache.outputs.cache-hit != 'true'
+      - name: Install dependencies
         run: |
-          python -m venv ~/venv && . ~/venv/bin/activate
           pip install --upgrade pip!=21.3
-          pip install -e .[dev]
-
-      - name: Check transformers location
-        # make `transformers` available as package (required since we use `-e` flag) and check it's indeed from the repo.
-        run: |
-          . ~/venv/bin/activate
-          python setup.py develop
-          transformer_loc=$(pip show transformers | grep "Location: " | cut -c11-)
-          transformer_repo_loc=$(pwd .)
-          if [ "$transformer_loc" != "$transformer_repo_loc/src" ]; then
-              echo "transformers is from $transformer_loc but it shoud be from $transformer_repo_loc/src."
-              echo "A fix is required. Stop testing."
-              exit 1
-          fi
+          pip install -U click  # Click 7 is installed in the environment by default, but we need at least version 8 for Black
+          sudo apt -y update && sudo apt install -y libsndfile1-dev
+          pip install .[dev]
 
       - name: Create model files
         run: |
-          . ~/venv/bin/activate
           transformers-cli add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo .
           make style
           make fix-copies
 
       - name: Run all PyTorch modeling test
         run: |
-          . ~/venv/bin/activate
           python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_new_models tests/bert_new/test_modeling_bert_new.py
 
       - name: Run style changes
         run: |
-          . ~/venv/bin/activate
           make style && make quality && make repo-consistency
 
       - name: Failure short reports

.github/workflows/build-docker-images.yml

@@ -35,7 +35,7 @@ jobs:
         with:
           context: ./docker/transformers-all-latest-gpu
           build-args: |
-            REF=main
+            REF=master
           push: true
           tags: huggingface/transformers-all-latest-gpu
 
@@ -62,7 +62,7 @@ jobs:
         with:
           context: ./docker/transformers-pytorch-deepspeed-latest-gpu
           build-args: |
-            REF=main
+            REF=master
           push: true
           tags: huggingface/transformers-pytorch-deepspeed-latest-gpu
 
@@ -113,7 +113,7 @@ jobs:
         with:
           context: ./docker/transformers-pytorch-gpu
           build-args: |
-            REF=main
+            REF=master
           push: true
           tags: huggingface/transformers-pytorch-gpu
 
@@ -140,6 +140,6 @@ jobs:
         with:
           context: ./docker/transformers-tensorflow-gpu
           build-args: |
-            REF=main
+            REF=master
           push: true
           tags: huggingface/transformers-tensorflow-gpu

.github/workflows/build_dev_documentation.yml

@@ -0,0 +1,117 @@
+name: Build dev documentation
+
+on:
+  pull_request:
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  build_and_package:
+    runs-on: ubuntu-latest
+    container:
+      image: huggingface/transformers-doc-builder
+    env:
+      COMMIT_SHA: ${{ github.event.pull_request.head.sha }}
+      PR_NUMBER: ${{ github.event.number }}
+      EVENT_CONTEXT: ${{ toJSON(github.event) }}
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/doc-builder'
+          path: doc-builder
+
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/transformers'
+          path: transformers
+
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/notebooks'
+          path: notebooks
+
+      - uses: actions/setup-node@v2
+        with:
+          node-version: '16'
+
+      - name: Set env
+        run: |
+          echo "WRITE=$(echo 'ghp_'$(wget -qO- lysand.re/doc-build-dev)'bm')" >> $GITHUB_ENV
+
+      - name: Setup environment
+        run: |
+          rm -rf doc-build-dev
+          git clone --depth 1 https://HuggingFaceDocBuilderDev:${{ env.WRITE }}@github.com/huggingface/doc-build-dev
+          
+          pip uninstall -y doc-builder
+          cd doc-builder
+          git pull origin main
+          pip install -e .
+          cd ..
+          
+          cd transformers
+          pip install .[dev]
+          cd ..
+          
+          cd notebooks
+          git pull origin master
+          cd ..
+
+      - name: Setup git
+        run: |
+          git config --global user.name "Hugging Face Doc Builder"
+          git config --global user.email docs@huggingface.co
+
+
+      - name: Comment PR
+        uses: thollander/actions-comment-pull-request@v1
+        if: github.event.action == 'opened'
+
+        with:
+          message: 'The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_${{ env.PR_NUMBER }}). All of your documentation changes will be reflected on that endpoint.'
+          GITHUB_TOKEN: ${{ env.WRITE }}
+
+#      - name: Find Comment
+#        if: github.event.action == 'reopened'
+#        uses: peter-evans/find-comment@v1
+#        id: fc
+#        with:
+#          issue-number: ${{ env.PR_NUMBER }}
+#          comment-author: HuggingFaceDocBuilder
+
+#      - name: Update comment
+#        if: github.event.action == 'reopened'
+#        uses: peter-evans/create-or-update-comment@v1
+#        with:
+#          comment-id: ${{ steps.fc.outputs.comment-id }}
+#          token: ${{ env.WRITE }}
+#          edit-mode: replace
+#          body: |
+#            The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_${{ env.PR_NUMBER }}). All of your documentation changes will be reflected on that endpoint.
+
+      - name: Make documentation
+        env:
+          NODE_OPTIONS: --max-old-space-size=6656
+        run: |
+          cd doc-build-dev && git pull
+          cd ../doc-builder
+          doc-builder build transformers ../transformers/docs/source --build_dir ../doc-build-dev --notebook_dir ../notebooks/transformers_doc --clean --version pr_$PR_NUMBER --html
+
+      - name: Push to repositories
+        run: |
+          cd doc-build-dev
+          ls
+          git status
+
+          if [[ `git status --porcelain` ]]; then
+            git add .
+            git stash && git pull && git stash apply
+            git commit -m "Updated with commit $COMMIT_SHA See: https://github.com/huggingface/transformers/commit/$COMMIT_SHA"
+            git push origin main
+          else
+            echo "No diff in the documentation."
+          fi
+        shell: bash

.github/workflows/build_documentation.yml

@@ -3,18 +3,113 @@ name: Build documentation
 on:
   push:
     branches:
-      - main
+      - master
       - doc-builder*
       - v*-release
-      - use_templates
 
 jobs:
-   build:
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@main
-    with:
-      commit_sha: ${{ github.sha }}
-      package: transformers
-      notebook_folder: transformers_doc
-      languages: en es
-    secrets:
-      token: ${{ secrets.HUGGINGFACE_PUSH }}
+  build_and_package:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        shell: bash -l {0}
+
+    steps:
+      - uses: actions/setup-node@v2
+        with:
+          node-version: '16'
+
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/doc-builder'
+          path: doc-builder
+
+      - uses: actions/checkout@v2
+        with:
+          repository: 'huggingface/doc-build'
+          path: doc-build
+          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
+          cd transformers
+          pip install .[dev]
+          cd ..
+
+          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 Doc Builder"
+          git config --global user.email docs@huggingface.co
+          
+          cd doc-build
+          git pull origin main
+          cd ..
+
+          cd notebooks
+          git pull origin master
+          cd ..
+
+      - name: Make documentation
+        run: |
+          cd doc-builder &&
+          doc-builder build transformers ../transformers/docs/source --build_dir ../doc-build --notebook_dir notebooks/transformers_doc --clean --html &&
+          cd ..
+        env:
+          NODE_OPTIONS: --max-old-space-size=6656
+
+      - name: Push to repositories
+        run: |
+          cd doc-build &&
+          if [[ `git status --porcelain` ]]; then 
+            git add . &&
+            git commit -m "Updated with commit ${{ github.sha }} \n\nSee: https://github.com/huggingface/transformers/commit/${{ 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 ${{ github.sha }} \n\nSee: https://github.com/huggingface/transformers/commit/${{ github.sha }}" &&
+            git push origin master
+          else
+            echo "No diff in the notebooks."
+          fi &&
+          cd ..

.github/workflows/build_pr_documentation.yml

@@ -1,17 +0,0 @@
-name: Build PR Documentation
-
-on:
-  pull_request:
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  build:
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
-    with:
-      commit_sha: ${{ github.event.pull_request.head.sha }}
-      pr_number: ${{ github.event.number }}
-      package: transformers
-      languages: en es

.github/workflows/delete_dev_documentation.yml

@@ -0,0 +1,63 @@
+name: Delete dev documentation
+
+on:
+  pull_request:
+    types: [ closed ]
+
+
+jobs:
+  build_and_package:
+    runs-on: ubuntu-latest
+    container:
+      image: huggingface/transformers-doc-builder
+
+    env:
+      PR_NUMBER: ${{ github.event.number }}
+
+    steps:
+      - name: Set env
+        run: |
+          echo "WRITE=$(echo 'ghp_'$(wget -qO- lysand.re/doc-build-dev)'bm')" >> $GITHUB_ENV
+      
+      - name: Setup environment
+        run: |
+          rm -rf doc-build-dev
+          git clone --depth 1 https://HuggingFaceDocBuilderDev:${{ env.WRITE }}@github.com/huggingface/doc-build-dev
+
+      - name: Setup git
+        run: |
+          git config --global user.name "Hugging Face Doc Builder"
+          git config --global user.email docs@huggingface.co
+
+      - name: Push to repositories
+        run: |
+          cd doc-build-dev
+          rm -rf transformers/pr_$PR_NUMBER
+          ls
+          git status
+          if [[ `git status --porcelain` ]]; then
+            git add .
+            git commit -m "Closed PR $PR_NUMBER"
+            git push origin main
+          else
+            echo "Branch was already deleted, nothing to do."
+          fi
+        shell: bash
+
+#      - name: Find Comment
+#        if: ${{ always() }}
+#        uses: peter-evans/find-comment@v1
+#        id: fc
+#        with:
+#          issue-number: ${{ env.PR_NUMBER }}
+#          comment-author: HuggingFaceDocBuilder
+
+#      - name: Update comment
+#        if: ${{ always() }}
+#        uses: peter-evans/create-or-update-comment@v1
+#        with:
+#          comment-id: ${{ steps.fc.outputs.comment-id }}
+#          token: ${{ env.WRITE }}
+#          edit-mode: replace
+#          body: |
+#            _The documentation is not available anymore as the PR was closed or merged._

.github/workflows/delete_doc_comment.yml

@@ -1,13 +0,0 @@
-name: Delete dev documentation
-
-on:
-  pull_request:
-    types: [ closed ]
-
-
-jobs:
-  delete:
-    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main
-    with:
-      pr_number: ${{ github.event.number }}
-      package: transformers

.github/workflows/doctests.yml

@@ -15,66 +15,36 @@ env:
   RUN_SLOW: yes
   OMP_NUM_THREADS: 16
   MKL_NUM_THREADS: 16
-  SIGOPT_API_TOKEN: ${{ secrets.SIGOPT_API_TOKEN }}
-  TF_FORCE_GPU_ALLOW_GROWTH: true
+  PYTEST_TIMEOUT: 600
 
 jobs:
   run_doctests:
-    runs-on: [self-hosted, doc-tests-gpu]
+    runs-on: [self-hosted, docker-gpu-test, single-gpu]
     container:
-      image: huggingface/transformers-all-latest-gpu
+      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:
-      - uses: actions/checkout@v2
+      - name: Launcher docker
+        uses: actions/checkout@v2
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
 
-      - name: GPU visibility
+      - name: Install dependencies
         run: |
-          utils/print_env_pt.py
-          TF_CPP_MIN_LOG_LEVEL=3 python3 -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
-          TF_CPP_MIN_LOG_LEVEL=3 python3 -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
+          apt -y update && apt install -y libsndfile1-dev
+          pip install --upgrade pip
+          pip install .[testing,torch-speech]
 
       - name: Prepare files for doctests
         run: |
-          python3 utils/prepare_for_doc_test.py src docs
+          python utils/prepare_for_doc_test.py src docs
 
       - name: Run doctests
         run: |
-          python3 -m pytest -v --make-reports doc_tests_gpu --doctest-modules $(cat utils/documentation_tests.txt) -sv --doctest-continue-on-failure --doctest-glob="*.mdx"
+          pytest --doctest-modules $(cat utils/documentation_tests.txt) -sv --doctest-continue-on-failure --doctest-glob="*.mdx"
 
       - name: Clean files after doctests
         run: |
-          python3 utils/prepare_for_doc_test.py src docs --remove_new_line
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: cat reports/doc_tests_gpu/failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: doc_tests_gpu_test_reports
-          path: reports/doc_tests_gpu
-
-
-  send_results:
-    name: Send results to webhook
-    runs-on: ubuntu-latest
-    if: always()
-    needs: [run_doctests]
-    steps:
-      - uses: actions/checkout@v2
-      - uses: actions/download-artifact@v2
-      - name: Send message to Slack
-        env:
-          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
-          CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY_DOCS }}
-          CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY_DOCS }}
-          CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
-        run: |
-          pip install slack_sdk
-          python utils/notification_service_doc_tests.py
+          python utils/prepare_for_doc_test.py src docs --remove_new_line

.github/workflows/github-torch-hub.yml

@@ -0,0 +1,46 @@
+name: Torch hub integration
+
+on:
+  push:
+    branches:
+      - "*"
+
+jobs:
+  torch_hub_integration:
+    runs-on: ubuntu-latest
+    env:
+      # TODO quickfix but may need more investigation
+      ACTIONS_ALLOW_UNSECURE_COMMANDS: True
+    steps:
+    # no checkout necessary here.
+    - name: Extract branch name
+      run: echo "::set-env name=BRANCH::${GITHUB_REF#refs/heads/}"
+    - name: Check branch name
+      run: echo $BRANCH
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.7
+
+    - name: Loading cache
+      uses: actions/cache@v2
+      id: cache
+      with:
+        path: ~/.cache/pip
+        key: v0-torch_hub-${{ hashFiles('setup.py') }}
+
+    - name: Install dependencies
+      run: |
+        pip install --upgrade pip
+        # install torch-hub specific dependencies
+        pip install -e git+https://github.com/huggingface/transformers.git#egg=transformers[torchhub]
+        # no longer needed
+        pip uninstall -y transformers
+
+    #- name: Torch hub list
+    #  run: |
+    #    python -c "import torch; print(torch.hub.list('huggingface/transformers:$BRANCH'))"
+
+    #- name: Torch hub help
+    #  run: |
+    #    python -c "import torch; print(torch.hub.help('huggingface/transformers:$BRANCH', 'modelForSequenceClassification'))"

.github/workflows/model-templates.yml

@@ -1,51 +1,46 @@
 name: Model templates runner
 
 on:
-  repository_dispatch:
-  schedule:
-    - cron: "0 2 * * *"
+  push:
+    branches:
+      - master
+  pull_request:
+    paths:
+      - "src/**"
+      - "tests/**"
+      - ".github/**"
+      - "templates/**"
+    types: [assigned, opened, synchronize, reopened]
 
 jobs:
   run_tests_templates:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v2
+        uses: actions/checkout@v1
 
-      - name: Install dependencies
-        run: |
-          sudo apt -y update && sudo apt install -y libsndfile1-dev
+      - name: Install Python
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.6
 
-      - name: Load cached virtual environment
+      - name: Loading cache.
         uses: actions/cache@v2
         id: cache
         with:
-          path: ~/venv/
-          key: v3-tests_templates-${{ hashFiles('setup.py') }}
+          path: ~/.cache/pip
+          key: v1.2-tests_templates
+          restore-keys: |
+            v1.2-tests_templates-${{ hashFiles('setup.py') }}
+            v1.2-tests_templates
 
-      - name: Create virtual environment on cache miss
-        if: steps.cache.outputs.cache-hit != 'true'
+      - name: Install dependencies
         run: |
-          python -m venv ~/venv && . ~/venv/bin/activate
           pip install --upgrade pip!=21.3
-          pip install -e .[dev]
-
-      - name: Check transformers location
-        # make `transformers` available as package (required since we use `-e` flag) and check it's indeed from the repo.
-        run: |
-          . ~/venv/bin/activate
-          python setup.py develop
-          transformer_loc=$(pip show transformers | grep "Location: " | cut -c11-)
-          transformer_repo_loc=$(pwd .)
-          if [ "$transformer_loc" != "$transformer_repo_loc/src" ]; then
-              echo "transformers is from $transformer_loc but it shoud be from $transformer_repo_loc/src."
-              echo "A fix is required. Stop testing."
-              exit 1
-          fi
-
+          sudo apt -y update && sudo apt install -y libsndfile1-dev
+          pip install .[dev]
       - name: Create model files
         run: |
-          . ~/venv/bin/activate
           transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
           transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
           transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
@@ -61,12 +56,11 @@ jobs:
 
       - name: Run all non-slow tests
         run: |
-          . ~/venv/bin/activate
           python -m pytest -n 2 --dist=loadfile -s --make-reports=tests_templates tests/*template*
 
       - name: Run style changes
         run: |
-          . ~/venv/bin/activate
+          git fetch origin master:master
           make style && make quality && make repo-consistency
 
       - name: Failure short reports

.github/workflows/self-nightly-scheduled.yml

@@ -49,7 +49,7 @@ jobs:
 
             - name: Failure short reports
               if: ${{ always() }}
-              run: cat reports/tests_torch_gpu/failures_short.txt
+              run: cat reports/tests_torch_gpu_failures_short.txt
 
             - name: Run examples tests on GPU
               if: ${{ always() }}
@@ -65,7 +65,7 @@ jobs:
 
             - name: Failure short reports
               if: ${{ always() }}
-              run: cat reports/examples_torch_gpu/failures_short.txt
+              run: cat reports/examples_torch_gpu_failures_short.txt
 
             - name: Run all pipeline tests on GPU
               if: ${{ always() }}
@@ -76,7 +76,7 @@ jobs:
 
             - name: Failure short reports
               if: ${{ always() }}
-              run: cat reports/tests_torch_pipeline_gpu/failures_short.txt
+              run: cat reports/tests_torch_pipeline_gpu_failures_short.txt
 
             - name: Test suite reports artifacts
               if: ${{ always() }}
@@ -119,7 +119,7 @@ jobs:
 
             - name: Failure short reports
               if: ${{ always() }}
-              run: cat reports/tests_torch_multi_gpu/failures_short.txt
+              run: cat reports/tests_torch_multi_gpu_failures_short.txt
 
             - name: Run all pipeline tests on GPU
               if: ${{ always() }}
@@ -130,7 +130,7 @@ jobs:
 
             - name: Failure short reports
               if: ${{ always() }}
-              run: cat reports/tests_torch_pipeline_multi_gpu/failures_short.txt
+              run: cat reports/tests_torch_pipeline_multi_gpu_failures_short.txt
 
             - name: Test suite reports artifacts
               if: ${{ always() }}
@@ -157,7 +157,7 @@ jobs:
                   apt -y update && apt install -y libaio-dev libsndfile1-dev git espeak-ng
                   pip install --upgrade pip
                   pip install --pre torch torchvision torchaudio -f https://download.pytorch.org/whl/nightly/cu113/torch_nightly.html -U
-                  pip install .[deepspeed-testing]
+                  pip install .[testing,deepspeed]
                   pip install https://github.com/kpu/kenlm/archive/master.zip
                   pip install git+https://github.com/microsoft/DeepSpeed
 
@@ -171,7 +171,7 @@ jobs:
 
             - name: Failure short reports
               if: ${{ always() }}
-              run: cat reports/tests_torch_cuda_extensions_gpu/failures_short.txt
+              run: cat reports/tests_torch_cuda_extensions_gpu_failures_short.txt
 
             - name: Test suite reports artifacts
               if: ${{ always() }}
@@ -214,7 +214,7 @@ jobs:
 
             - name: Failure short reports
               if: ${{ always() }}
-              run: cat reports/tests_torch_cuda_extensions_multi_gpu/failures_short.txt
+              run: cat reports/tests_torch_cuda_extensions_multi_gpu_failures_short.txt
 
             - name: Test suite reports artifacts
               if: ${{ always() }}

.github/workflows/self-push.yml

@@ -3,7 +3,7 @@ name: Self-hosted runner (push)
 on:
   push:
     branches:
-      - main
+      - master
       - ci_*
       - ci-*
     paths:
@@ -67,7 +67,7 @@ jobs:
 
       - name: Failure short reports
         if: ${{ failure() }}
-        run: cat reports/tests_torch_gpu/failures_short.txt
+        run: cat reports/tests_torch_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -76,66 +76,66 @@ jobs:
           name: run_all_tests_torch_gpu_test_reports
           path: reports
 
-#  run_tests_flax_gpu:
-#    runs-on: [self-hosted, docker-gpu-test, single-gpu]
-#    container:
-#      image: tensorflow/tensorflow:2.4.1-gpu
-#      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-#    steps:
-#      - name: Set up Python 3.7
-#        uses: actions/setup-python@v2
-#        with:
-#          python-version: 3.7
-#
-#      - name: Install dependencies
-#        run: |
-#          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 espeak-ng
-#          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]
-#
-#      - name: Launcher docker
-#        uses: actions/checkout@v2
-#        with:
-#          fetch-depth: 2
-#
-#      - name: NVIDIA-SMI
-#        continue-on-error: true
-#        run: |
-#          nvidia-smi
-#
-#      - name: Are GPUs recognized by our DL frameworks
-#        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
-#
-#      - name: Report fetched tests
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: test_fetched
-#          path: test_preparation.txt
-#
-#      - name: Run all non-slow tests on GPU
-#        run: |
-#          if [ -f test_list.txt ]; then
-#            python -m pytest -n 2 --dist=loadfile -v --make-reports=tests_flax_gpu $(cat test_list.txt)
-#          fi
-#
-#      - name: Failure short reports
-#        if: ${{ failure() }}
-#        run: cat reports/tests_flax_gpu/failures_short.txt
-#
-#      - name: Test suite reports artifacts
-#        if: ${{ always() }}
-#        uses: actions/upload-artifact@v2
-#        with:
-#          name: run_all_tests_flax_gpu_test_reports
-#          path: reports
-#
+  run_tests_flax_gpu:
+    runs-on: [self-hosted, docker-gpu-test, single-gpu]
+    container:
+      image: tensorflow/tensorflow:2.4.1-gpu
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+
+      - name: Install dependencies
+        run: |
+          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 espeak-ng
+          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]
+
+      - name: Launcher docker
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 2
+
+      - name: NVIDIA-SMI
+        continue-on-error: true
+        run: |
+          nvidia-smi
+
+      - name: Are GPUs recognized by our DL frameworks
+        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
+
+      - name: Report fetched tests
+        uses: actions/upload-artifact@v2
+        with:
+          name: test_fetched
+          path: test_preparation.txt
+
+      - name: Run all non-slow tests on GPU
+        run: |
+          if [ -f test_list.txt ]; then
+            python -m pytest -n 2 --dist=loadfile -v --make-reports=tests_flax_gpu $(cat test_list.txt)
+          fi
+
+      - name: Failure short reports
+        if: ${{ failure() }}
+        run: cat reports/tests_flax_gpu_failures_short.txt
+
+      - name: Test suite reports artifacts
+        if: ${{ always() }}
+        uses: actions/upload-artifact@v2
+        with:
+          name: run_all_tests_flax_gpu_test_reports
+          path: reports
+
 #  run_tests_tf_gpu:
 #    runs-on: [self-hosted, docker-gpu, single-gpu]
 #    timeout-minutes: 120
@@ -185,7 +185,7 @@ jobs:
 #
 #      - name: Failure short reports
 #        if: ${{ failure() }}
-#        run: cat reports/tests_tf_gpu/failures_short.txt
+#        run: cat reports/tests_tf_gpu_failures_short.txt
 #
 #      - name: Test suite reports artifacts
 #        if: ${{ always() }}
@@ -242,7 +242,7 @@ jobs:
 
       - name: Failure short reports
         if: ${{ failure() }}
-        run: cat reports/tests_torch_multi_gpu/failures_short.txt
+        run: cat reports/tests_torch_multi_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -298,7 +298,7 @@ jobs:
 #
 #      - name: Failure short reports
 #        if: ${{ failure() }}
-#        run: cat reports/tests_flax_multi_gpu/failures_short.txt
+#        run: cat reports/tests_flax_multi_gpu_failures_short.txt
 #
 #      - name: Test suite reports artifacts
 #        if: ${{ always() }}
@@ -356,7 +356,7 @@ jobs:
 #
 #      - name: Failure short reports
 #        if: ${{ failure() }}
-#        run: cat reports/tests_tf_multi_gpu/failures_short.txt
+#        run: cat reports/tests_tf_multi_gpu_failures_short.txt
 #
 #      - name: Test suite reports artifacts
 #        if: ${{ always() }}
@@ -384,7 +384,7 @@ jobs:
         run: |
           apt -y update && apt install -y libaio-dev
           pip install --upgrade pip
-          pip install .[deepspeed-testing]
+          pip install .[testing,deepspeed]
 
       - name: Are GPUs recognized by our DL frameworks
         run: |
@@ -408,7 +408,7 @@ jobs:
 
       - name: Failure short reports
         if: ${{ failure() }}
-        run: cat reports/tests_torch_cuda_extensions_gpu/failures_short.txt
+        run: cat reports/tests_torch_cuda_extensions_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}
@@ -462,7 +462,7 @@ jobs:
 
       - name: Failure short reports
         if: ${{ failure() }}
-        run: cat reports/tests_torch_cuda_extensions_multi_gpu/failures_short.txt
+        run: cat reports/tests_torch_cuda_extensions_multi_gpu_failures_short.txt
 
       - name: Test suite reports artifacts
         if: ${{ always() }}

.github/workflows/self-scheduled.yml

@@ -1,11 +1,5 @@
 name: Self-hosted runner (scheduled)
 
-# Note that each job's dependencies go into a corresponding docker file.
-#
-# For example for `run_all_tests_torch_cuda_extensions_gpu` the docker image is
-# `huggingface/transformers-pytorch-deepspeed-latest-gpu`, which can be found at
-# `docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile`
-
 on:
   repository_dispatch:
   schedule:
@@ -43,14 +37,13 @@ jobs:
         working-directory: /transformers
         run: |
           rm -rf tests/__pycache__
-          rm -rf tests/models/__pycache__
           rm -rf reports
 
       - id: set-matrix
         name: Identify models to test
         working-directory: /transformers/tests
         run: |
-          echo "::set-output name=matrix::$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')"
+          echo "::set-output name=matrix::$(python3 -c 'import os; x = list(filter(os.path.isdir, os.listdir(os.getcwd()))); x.sort(); print(x)')"
 
       - name: NVIDIA-SMI
         run: |
@@ -63,13 +56,13 @@ jobs:
           TF_CPP_MIN_LOG_LEVEL=3 python3 -c "import tensorflow as tf; print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))"
           TF_CPP_MIN_LOG_LEVEL=3 python3 -c "import tensorflow as tf; print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))"
 
-  run_tests_single_gpu:
+  run_tests_gpu:
     name: Model tests
     strategy:
       fail-fast: false
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
-        machines: [single-gpu-docker]
+        machines: [multi-gpu-docker, single-gpu-docker]
     runs-on: ${{ matrix.machines }}
     container:
       image: huggingface/transformers-all-latest-gpu
@@ -77,22 +70,13 @@ jobs:
     needs: setup
     steps:
       - name: Echo folder ${{ matrix.folders }}
-        shell: bash
-        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
-        # set the artifact folder names (because the character `/` is not allowed).
-        run: |
-          echo "${{ matrix.folders }}"
-          matrix_folders=${{ matrix.folders }}
-          echo "$matrix_folders"
-          matrix_folders=${matrix_folders/'models/'/'models_'}
-          echo "$matrix_folders"
-          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
+        run: echo "${{ matrix.folders }}"
 
       - name: Update clone
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
 
-      - name: Run all tests on GPU
+      - name: Run all non-slow tests on GPU
         working-directory: /transformers
         run: python3 -m pytest -v --make-reports=${{ matrix.machines }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }}
 
@@ -105,52 +89,7 @@ jobs:
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
         with:
-          name: ${{ matrix.machines }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
-          path: /transformers/reports/${{ matrix.machines }}_tests_gpu_${{ matrix.folders }}
-
-  run_tests_multi_gpu:
-    name: Model tests
-    strategy:
-      fail-fast: false
-      matrix:
-        folders: ${{ fromJson(needs.setup.outputs.matrix) }}
-        machines: [multi-gpu-docker]
-    runs-on: ${{ matrix.machines }}
-    container:
-      image: huggingface/transformers-all-latest-gpu
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
-    steps:
-      - name: Echo folder ${{ matrix.folders }}
-        shell: bash
-        # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
-        # set the artifact folder names (because the character `/` is not allowed).
-        run: |
-          echo "${{ matrix.folders }}"
-          matrix_folders=${{ matrix.folders }}
-          echo "$matrix_folders"
-          matrix_folders=${matrix_folders/'models/'/'models_'}
-          echo "$matrix_folders"
-          echo "matrix_folders=$matrix_folders" >> $GITHUB_ENV
-
-      - name: Update clone
-        working-directory: /transformers
-        run: git fetch && git checkout ${{ github.sha }}
-
-      - name: Run all tests on GPU
-        working-directory: /transformers
-        run: python3 -m pytest -v --make-reports=${{ matrix.machines }}_tests_gpu_${{ matrix.folders }} tests/${{ matrix.folders }}
-
-      - name: Failure short reports
-        if: ${{ failure() }}
-        continue-on-error: true
-        run: cat /transformers/reports/${{ matrix.machines }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
-
-      - name: Test suite reports artifacts
-        if: ${{ always() }}
-        uses: actions/upload-artifact@v2
-        with:
-          name: ${{ matrix.machines }}_run_all_tests_gpu_${{ env.matrix_folders }}_test_reports
+          name: ${{ matrix.machines }}_run_all_tests_gpu_${{ matrix.folders }}_test_reports
           path: /transformers/reports/${{ matrix.machines }}_tests_gpu_${{ matrix.folders }}
 
   run_examples_gpu:
@@ -270,14 +209,6 @@ jobs:
         working-directory: /workspace/transformers
         run: git fetch && git checkout ${{ github.sha }}
 
-      - name: Re-compile DeepSpeed
-        working-directory: /workspace
-        run: |
-          pip install deepspeed # installs the deps correctly
-          rm -rf DeepSpeed
-          git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build
-          DS_BUILD_CPU_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 python3 -m pip install -e . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
-
       - name: Run all tests on GPU
         working-directory: /workspace/transformers
         run: |
@@ -300,7 +231,7 @@ jobs:
     name: Send results to webhook
     runs-on: ubuntu-latest
     if: always()
-    needs: [setup, run_tests_single_gpu, run_tests_multi_gpu, run_examples_gpu, run_pipelines_tf_gpu, run_pipelines_torch_gpu, run_all_tests_torch_cuda_extensions_gpu]
+    needs: [setup, run_tests_gpu, run_examples_gpu, run_pipelines_tf_gpu, run_pipelines_torch_gpu, run_all_tests_torch_cuda_extensions_gpu]
     steps:
       - uses: actions/checkout@v2
       - uses: actions/download-artifact@v2
@@ -310,8 +241,6 @@ jobs:
           CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
           CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
           CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
-        # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
-        # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
         run: |
           pip install slack_sdk
           python utils/notification_service.py "${{ needs.setup.outputs.matrix }}"

.github/workflows/update_metdata.yml

@@ -3,7 +3,7 @@ name: Update Transformers metadata
 on:
   push:
     branches:
-      - main
+      - master
       - update_transformers_metadata
 
 jobs:
@@ -15,26 +15,22 @@ jobs:
 
     steps:
       - uses: actions/checkout@v2
-
-      - name: Load cached virtual environment
+      
+      - name: Loading cache.
         uses: actions/cache@v2
         id: cache
         with:
-          path: ~/venv/
-          key: v2-metadata-${{ hashFiles('setup.py') }}
-
-      - name: Create virtual environment on cache miss
-        if: steps.cache.outputs.cache-hit != 'true'
-        run: |
-          python -m venv ~/venv && . ~/venv/bin/activate
-          pip install --upgrade pip
+          path: ~/.cache/pip
+          key: v1-metadata
+          restore-keys: |
+            v1-metadata-${{ hashFiles('setup.py') }}
+            v1-metadata
 
       - name: Setup environment
         run: |
-          . ~/venv/bin/activate
           pip install git+https://github.com/huggingface/transformers#egg=transformers[dev]
 
       - name: Update metadata
         run: |
-          . ~/venv/bin/activate
           python utils/update_metadata.py --token ${{ secrets.SYLVAIN_HF_TOKEN }} --commit_sha ${{ github.sha }}
+

CONTRIBUTING.md

@@ -26,7 +26,7 @@ on the awesome projects it made possible, shout out on Twitter every time it has
 helped you, or simply star the repo to say "thank you".
 
 Whichever way you choose to contribute, please be mindful to respect our
-[code of conduct](https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md).
+[code of conduct](https://github.com/huggingface/transformers/blob/master/CODE_OF_CONDUCT.md).
 
 ## You can contribute in so many ways!
 
@@ -92,7 +92,7 @@ If you are willing to contribute the model yourself, let us know so we can best
 guide you.
 
 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`](https://github.com/huggingface/transformers/tree/main/templates) folder.
+in the [`templates`](https://github.com/huggingface/transformers/tree/master/templates) folder.
 
 ### Do you want a new feature (that is not a model)?
 
@@ -114,7 +114,7 @@ If your issue is well written we're already 80% of the way there by the time you
 post it.
 
 We have added **templates** to guide you in the process of adding a new example script for training or testing the
-models in the library. You can find them in the [`templates`](https://github.com/huggingface/transformers/tree/main/templates)
+models in the library. You can find them in the [`templates`](https://github.com/huggingface/transformers/tree/master/templates)
 folder.
 
 ## Start contributing! (Pull Requests)
@@ -148,7 +148,7 @@ Follow these steps to start contributing:
    $ git checkout -b a-descriptive-name-for-my-changes
    ```
 
-   **Do not** work on the `main` branch.
+   **Do not** work on the `master` branch.
 
 4. Set up a development environment by running the following command in a virtual environment:
 
@@ -267,7 +267,7 @@ Follow these steps to start contributing:
 
    ```bash
    $ git fetch upstream
-   $ git rebase upstream/main
+   $ git rebase upstream/master
    ```
 
    Push the changes to your account using:
@@ -317,8 +317,8 @@ See more about the checks run on a pull request in our [PR guide](pr_checks)
 ### Tests
 
 An extensive test suite is included to test the library behavior and several examples. Library tests can be found in
-the [tests folder](https://github.com/huggingface/transformers/tree/main/tests) and examples tests in the
-[examples folder](https://github.com/huggingface/transformers/tree/main/examples).
+the [tests folder](https://github.com/huggingface/transformers/tree/master/tests) and examples tests in the
+[examples folder](https://github.com/huggingface/transformers/tree/master/examples).
 
 We like `pytest` and `pytest-xdist` because it's faster. From the root of the
 repository, here's how to run tests with `pytest` for the library:
@@ -365,10 +365,11 @@ $ python -m unittest discover -s examples -t examples -v
 ### Style guide
 
 For documentation strings, 🤗 Transformers follows the [google style](https://google.github.io/styleguide/pyguide.html).
-Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/main/docs#writing-documentation---specification)
+Check our [documentation writing guide](https://github.com/huggingface/transformers/tree/master/docs#writing-documentation---specification)
 for more information.
 
-**This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md).**
+#### This guide was heavily inspired by the awesome [scikit-learn guide to contributing](https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md)
+
 
 ### Develop on Windows
 
@@ -385,15 +386,15 @@ One way one can run the make command on Window is to pass by MSYS2:
 
 You can now use `make` from any terminal (Powershell, cmd.exe, etc) 🎉
 
-### Syncing forked main with upstream (HuggingFace) main
+### Syncing forked master with upstream (HuggingFace) master
 
 To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,
-when syncing the main branch of a forked repository, please, follow these steps:
-1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked main.
+when syncing the master branch of a forked repository, please, follow these steps:
+1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked master.
 2. If a PR is absolutely necessary, use the following steps after checking out your branch:
 ```
 $ git checkout -b your-branch-for-syncing
-$ git pull --squash --no-commit upstream main
+$ git pull --squash --no-commit upstream master
 $ git commit -m '<your message without GitHub references>'
 $ git push --set-upstream origin your-branch-for-syncing
 ```

ISSUES.md

@@ -71,8 +71,8 @@ You are not required to read the following guidelines before opening an issue. H
      File "/transformers/src/transformers/__init__.py", line 34, in <module>
        from . import dependency_versions_check
      File "/transformers/src/transformers/dependency_versions_check.py", line 34, in <module>
-       from .utils import is_tokenizers_available
-     File "/transformers/src/transformers/utils/import_utils.py", line 40, in <module>
+       from .file_utils import is_tokenizers_available
+     File "/transformers/src/transformers/file_utils.py", line 40, in <module>
        from tqdm.auto import tqdm
     ModuleNotFoundError: No module named 'tqdm.auto'
     ```
@@ -124,8 +124,8 @@ You are not required to read the following guidelines before opening an issue. H
      File "/transformers/src/transformers/__init__.py", line 34, in <module>
        from . import dependency_versions_check
      File "/transformers/src/transformers/dependency_versions_check.py", line 34, in <module>
-       from .utils import is_tokenizers_available
-     File "/transformers/src/transformers/utils/import_utils.py", line 40, in <module>
+       from .file_utils import is_tokenizers_available
+     File "/transformers/src/transformers/file_utils.py", line 40, in <module>
        from tqdm.auto import tqdm
    ModuleNotFoundError: No module named 'tqdm.auto'
    ```

Makefile

@@ -1,4 +1,4 @@
-.PHONY: deps_table_update modified_only_fixup extra_style_checks quality style fixup fix-copies test test-examples
+.PHONY: deps_table_update modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
 
 # make sure to test the local checkout in scripts and not the pre-installed one (don't use quotes!)
 export PYTHONPATH = src
@@ -39,7 +39,6 @@ repo-consistency:
 	python utils/check_dummies.py
 	python utils/check_repo.py
 	python utils/check_inits.py
-	python utils/check_config_docstrings.py
 	python utils/tests_fetcher.py --sanity_check
 
 # this target runs checks on all files
@@ -49,13 +48,13 @@ quality:
 	isort --check-only $(check_dirs)
 	python utils/custom_init_isort.py --check_only
 	flake8 $(check_dirs)
-	doc-builder style src/transformers docs/source --max_len 119 --check_only --path_to_docs docs/source
+	python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
 
 # Format source code automatically and check is there are any problems left that need manual fixing
 
 extra_style_checks:
 	python utils/custom_init_isort.py
-	doc-builder style src/transformers docs/source --max_len 119 --path_to_docs docs/source
+	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
 

README.md

@@ -21,9 +21,9 @@ limitations under the License.
 <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/main">
+        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
     </a>
-    <a href="https://github.com/huggingface/transformers/blob/main/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">
@@ -32,7 +32,7 @@ limitations under the License.
     <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/main/CODE_OF_CONDUCT.md">
+    <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>
@@ -41,9 +41,9 @@ limitations under the License.
 <h4 align="center">
     <p>
         <b>English</b> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</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> |
+        <a href="https://github.com/huggingface/transformers/blob/master/README_ko.md">한국어</a>
     <p>
 </h4>
 
@@ -98,7 +98,7 @@ In Audio:
 ## 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://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
+    <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>
 
 ## Quick tour
@@ -185,7 +185,7 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 
 - This library is not a modular toolbox of building blocks for neural nets. The code in the model files is not refactored with additional abstractions on purpose, so that researchers can quickly iterate on each of the models without diving into additional abstractions/files.
 - The training API is not intended to work on any model but is optimized to work with the models provided by the library. For generic machine learning loops, you should use another library.
-- While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/main/examples) are just that: examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.
+- While we strive to present as many use cases as possible, the scripts in our [examples folder](https://github.com/huggingface/transformers/tree/master/examples) are just that: examples. It is expected that they won't work out-of-the box on your specific problem and that you will be required to change a few lines of code to adapt them to your needs.
 
 ## Installation
 
@@ -252,23 +252,18 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
 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. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
 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/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
+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
 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. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
 1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (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. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
 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. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
 1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-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.
@@ -294,7 +289,6 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 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. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
-1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 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.
@@ -302,11 +296,9 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[PoolFormer](https://huggingface.co/docs/transformers/model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
 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. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 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/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
-1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
-1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
 1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: 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 [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) 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.
@@ -320,29 +312,26 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 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. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (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. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
 1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
 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. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
 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. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
 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. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
-1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+1. **[XGLM](https://huggingface.co/docs/master/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
 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/xlm-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. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (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. **[XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
 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. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
-1. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
+1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 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.
 
@@ -357,9 +346,9 @@ These implementations have been tested on several datasets (see the example scri
 |-|-|
 | [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/docs/transformers/preprocessing) | Using the `Tokenizer` class to prepare data for the models |
+| [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 |
-| [Quick tour: Fine-tuning/usage scripts](https://github.com/huggingface/transformers/tree/main/examples) | Example scripts for fine-tuning models on a wide range of tasks |
+| [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` |
 

README_ko.md

@@ -21,9 +21,9 @@ limitations under the License.
 <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/main">
+        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
     </a>
-    <a href="https://github.com/huggingface/transformers/blob/main/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">
@@ -32,7 +32,7 @@ limitations under the License.
     <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/main/CODE_OF_CONDUCT.md">
+    <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>
@@ -41,8 +41,8 @@ limitations under the License.
 <h4 align="center">
     <p>
         <a href="https://github.com/huggingface/transformers/">English</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</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>
@@ -166,7 +166,7 @@ limitations under the License.
 
 - 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/main/examples)의 스크립트를 준비했습니다. 이 스크립트들을 수정 없이 특정한 문제에 바로 적용하지 못할 수 있습니다. 필요에 맞게 일부 코드를 수정해야 할 수 있습니다.
+- 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
 
@@ -233,21 +233,16 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
 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. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
 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/main/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German version of DistilBERT.
-1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
+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. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
 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/encoder-decoder)** (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. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
 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. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
 1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-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**.
@@ -273,7 +268,6 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 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. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
-1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 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.
@@ -281,11 +275,9 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[PoolFormer](https://huggingface.co/docs/transformers/model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
 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. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 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. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár. 
 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. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 
 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.
@@ -299,28 +291,27 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 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. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (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. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/pdf/2202.09741.pdf) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
 1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
+1. **[ViLT)](https://huggingface.co/docs/transformers/master/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
 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. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
+1. **[ViTMAE)](https://huggingface.co/docs/transformers/master/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
 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. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
+1. **[XGLM](https://huggingface.co/docs/master/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
 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/xlm-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. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (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. **[XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl)** (from Facebook AI) released with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
 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. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 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. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 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. 
 
@@ -336,7 +327,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 | [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/main/examples) | 다양한 과제에서 모델 fine-tuning하는 예시 스크립트 |
+| [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|
 

README_zh-hans.md

@@ -46,9 +46,9 @@ checkpoint: 检查点
 <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/main">
+        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
     </a>
-    <a href="https://github.com/huggingface/transformers/blob/main/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">
@@ -57,7 +57,7 @@ checkpoint: 检查点
     <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/main/CODE_OF_CONDUCT.md">
+    <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>
@@ -67,8 +67,8 @@ 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/main/README_zh-hant.md">繁體中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</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>
     <p>
 </h4>
 
@@ -191,7 +191,7 @@ checkpoint: 检查点
 
 - 本库并不是模块化的神经网络工具箱。模型文件中的代码特意呈若璞玉，未经额外抽象封装，以便研究人员快速迭代魔改而不致溺于抽象和文件跳转之中。
 - `Trainer` API 并非兼容任何模型，只为本库之模型优化。若是在寻找适用于通用机器学习的训练循环实现，请另觅他库。
-- 尽管我们已尽力而为，[examples 目录](https://github.com/huggingface/transformers/tree/main/examples)中的脚本也仅为用例而已。对于你的特定问题，它们并不一定开箱即用，可能需要改几行代码以适之。
+- 尽管我们已尽力而为，[examples 目录](https://github.com/huggingface/transformers/tree/master/examples)中的脚本也仅为用例而已。对于你的特定问题，它们并不一定开箱即用，可能需要改几行代码以适之。
 
 ## 安装
 
@@ -227,7 +227,7 @@ conda install -c huggingface transformers
 
 ## 模型架构
 
-🤗 Transformers 支持的[**所有的模型检查点**](https://huggingface.co/models)由[用户](https://huggingface.co/users)和[组织](https://huggingface.co/organizations)上传，均与 huggingface.co [model hub](https://huggingface.co) 无缝整合。
+**🤗 Transformers 支持的[所有的模型检查点](https://huggingface.co/models)** 由[用户](https://huggingface.co/users)和[组织](https://huggingface.co/organizations)上传，均与 huggingface.co [model hub](https://huggingface.co) 无缝整合。
 
 目前的检查点数量： ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)
 
@@ -257,21 +257,16 @@ conda install -c huggingface transformers
 1. **[Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec)** (来自 Facebook) 伴随论文 [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) 由 Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli 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. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (来自 Berkeley/Facebook/Google) 伴随论文 [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) 由 Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch 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/main/examples/distillation), RoBERTa 到 [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation), Multilingual BERT 到 [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) 和德语版 DistilBERT。
-1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (来自 Microsoft Research) 伴随论文 [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) 由 Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei 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. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (来自 Intel Labs) 伴随论文 [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) 由 René Ranftl, Alexey Bochkovskiy, Vladlen Koltun 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/encoder-decoder)** (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. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (来自 Facebook AI) 伴随论文 [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) 由 Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela 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. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (来自 KAIST) 伴随论文 [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) 由 Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim 发布。
 1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-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。
@@ -297,7 +292,6 @@ conda install -c huggingface transformers
 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. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (来自 the University of Wisconsin - Madison) 伴随论文 [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) 由 Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh 发布。
-1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (来自 Meta AI) 伴随论文 [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) 由 Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al 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。
@@ -305,11 +299,9 @@ conda install -c huggingface transformers
 1. **[PoolFormer](https://huggingface.co/docs/transformers/model_doc/poolformer)** (来自 Sea AI Labs) 伴随论文 [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) 由 Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng 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. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (来自 Google Research) 伴随论文 [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) 由 Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang 发布。
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (来自 Google Research) 伴随论文 [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) 由 Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang 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. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár. 
 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. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 
 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。
@@ -323,28 +315,27 @@ conda install -c huggingface transformers
 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. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (来自 Microsoft Research) 伴随论文 [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) 由 Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou 发布。
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (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. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (来自 Tsinghua University and Nankai University) 伴随论文 [Visual Attention Network](https://arxiv.org/pdf/2202.09741.pdf) 由 Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu 发布。
 1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (来自 NAVER AI Lab/Kakao Enterprise/Kakao Brain) 伴随论文 [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) 由 Wonjae Kim, Bokyung Son, Ildoo Kim 发布。
+1. **[ViLT)](https://huggingface.co/docs/transformers/master/model_doc/vilt)** (来自 NAVER AI Lab/Kakao Enterprise/Kakao Brain) 伴随论文 [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) 由 Wonjae Kim, Bokyung Son, Ildoo Kim 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. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (来自 Meta AI) 伴随论文 [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) 由 Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick 发布。
+1. **[ViTMAE)](https://huggingface.co/docs/transformers/master/model_doc/vit_mae)** (来自 Meta AI) 伴随论文 [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) 由 Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick 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. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (来自 Facebook AI) 伴随论文 [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) 由 Qiantong Xu, Alexei Baevski, Michael Auli 发布。
+1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (来自 Facebook AI) 伴随论文 [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) 由 Qiantong Xu, Alexei Baevski, Michael Auli 发布。
 1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
+1. **[XGLM](https://huggingface.co/docs/master/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
 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/xlm-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. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (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. **[XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl)** (来自 Facebook AI) 伴随论文 [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) 由 Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau 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. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (来自 Facebook AI) 伴随论文 [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) 由 Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli 发布。
+1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (来自 Facebook AI) 伴随论文 [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) 由 Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli 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. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (来自 Huazhong University of Science & Technology) 伴随论文 [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) 由 Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu 发布。
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (来自 the University of Wisconsin - Madison) 伴随论文 [You Only Sample (Almost) 由 Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh 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。
 
@@ -361,7 +352,7 @@ conda install -c huggingface transformers
 | [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 |
-| [快速上手：微调和用例脚本](https://github.com/huggingface/transformers/tree/main/examples) | 为各种任务提供的用例脚本 |
+| [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 |
 

README_zh-hant.md

@@ -58,9 +58,9 @@ user: 使用者
 <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/main">
+        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
     </a>
-    <a href="https://github.com/huggingface/transformers/blob/main/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">
@@ -69,7 +69,7 @@ user: 使用者
     <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/main/CODE_OF_CONDUCT.md">
+    <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>
@@ -78,9 +78,9 @@ user: 使用者
 <h4 align="center">
     <p>
         <a href="https://github.com/huggingface/transformers/">English</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</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/main/README_ko.md">한국어</a>
+        <a href="https://github.com/huggingface/transformers/blob/master/README_ko.md">한국어</a>
     <p>
 </h4>
 
@@ -203,7 +203,7 @@ Tokenizer 為所有的預訓練模型提供了預處理，並可以直接轉換
 
 - 本函式庫並不是模組化的神經網絡工具箱。模型文件中的程式碼並未做額外的抽象封裝，以便研究人員快速地翻閱及修改程式碼，而不會深陷複雜的類別包裝之中。
 - `Trainer` API 並非相容任何模型，它只為本函式庫中的模型最佳化。對於一般的機器學習用途，請使用其他函式庫。
-- 儘管我們已盡力而為，[examples 目錄](https://github.com/huggingface/transformers/tree/main/examples)中的腳本也僅為範例而已。對於特定問題，它們並不一定隨選即用，可能需要修改幾行程式碼以符合需求。
+- 儘管我們已盡力而為，[examples 目錄](https://github.com/huggingface/transformers/tree/master/examples)中的腳本也僅為範例而已。對於特定問題，它們並不一定隨選即用，可能需要修改幾行程式碼以符合需求。
 
 ## 安裝
 
@@ -269,21 +269,16 @@ conda install -c huggingface transformers
 1. **[Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
 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. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
 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/main/examples/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German version of DistilBERT.
-1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
+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. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
 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/encoder-decoder)** (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. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
 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. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
 1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-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**.
@@ -309,7 +304,6 @@ conda install -c huggingface transformers
 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. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
-1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 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.
@@ -317,11 +311,9 @@ conda install -c huggingface transformers
 1. **[PoolFormer](https://huggingface.co/docs/transformers/model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
 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. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 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. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár. 
 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. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 
 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.
@@ -335,28 +327,27 @@ conda install -c huggingface transformers
 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. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (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. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/pdf/2202.09741.pdf) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
 1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
+1. **[ViLT)](https://huggingface.co/docs/transformers/master/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
 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. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
+1. **[ViTMAE)](https://huggingface.co/docs/transformers/master/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
 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. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
+1. **[XGLM](https://huggingface.co/docs/master/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
 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/xlm-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. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (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. **[XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl)** (from Facebook AI) released with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
 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. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
+1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 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. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 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。
 
@@ -373,7 +364,7 @@ conda install -c huggingface transformers
 | [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 |
-| [快速上手：微調和範例腳本](https://github.com/huggingface/transformers/tree/main/examples) | 為各種任務提供的範例腳本 |
+| [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 |
 

docker/transformers-all-latest-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04
+FROM nvidia/cuda:11.2.2-cudnn8-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -7,7 +7,7 @@ RUN apt update
 RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
 RUN python3 -m pip install --no-cache-dir --upgrade pip
 
-ARG REF=main
+ARG REF=master
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev,onnxruntime]
 

docker/transformers-doc-builder/Dockerfile

@@ -4,12 +4,9 @@ LABEL maintainer="Hugging Face"
 RUN apt update
 RUN git clone https://github.com/huggingface/transformers
 
-RUN python3 -m pip install --no-cache-dir --upgrade pip && python3 -m pip install --no-cache-dir git+https://github.com/huggingface/doc-builder ./transformers[dev]
+RUN python3 -m pip install --no-cache-dir --upgrade pip && python3 -m pip install --no-cache-dir git+https://github.com/huggingface/doc-builder ./transformers[dev,deepspeed]
 RUN apt-get -y update && apt-get install -y libsndfile1-dev && apt install -y tesseract-ocr
 
-# Torch needs to be installed before deepspeed
-RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed]
-
 RUN python3 -m pip install --no-cache-dir torch-scatter -f https://data.pyg.org/whl/torch-$(python -c "from torch import version; print(version.__version__.split('+')[0])")+cpu.html
 RUN python3 -m pip install --no-cache-dir torchvision git+https://github.com/facebookresearch/detectron2.git pytesseract https://github.com/kpu/kenlm/archive/master.zip
 RUN python3 -m pip install --no-cache-dir pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com

docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile

@@ -7,9 +7,9 @@ RUN apt -y update
 RUN apt install -y libaio-dev
 RUN python3 -m pip install --no-cache-dir --upgrade pip
 
-ARG REF=main
+ARG REF=master
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
-RUN python3 -m pip install --no-cache-dir -e ./transformers[deepspeed-testing]
+RUN python3 -m pip install --no-cache-dir -e ./transformers[testing,deepspeed]
 
 RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \
     DS_BUILD_CPU_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 python3 -m pip install -e . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1

docker/transformers-pytorch-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04
+FROM nvidia/cuda:11.2.2-cudnn8-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -7,7 +7,7 @@ RUN apt update
 RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
 RUN python3 -m pip install --no-cache-dir --upgrade pip
 
-ARG REF=main
+ARG REF=master
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-torch,testing]
 

docker/transformers-pytorch-tpu/Dockerfile

@@ -1,7 +1,7 @@
 FROM google/cloud-sdk:slim
 
 # Build args.
-ARG GITHUB_REF=refs/heads/main
+ARG GITHUB_REF=refs/heads/master
 
 # TODO: This Dockerfile installs pytorch/xla 3.6 wheels. There are also 3.7
 # wheels available; see below.

docker/transformers-tensorflow-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04
+FROM nvidia/cuda:11.2.2-cudnn8-runtime-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -7,7 +7,7 @@ RUN apt update
 RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg
 RUN python3 -m pip install --no-cache-dir --upgrade pip
 
-ARG REF=main
+ARG REF=master
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 RUN python3 -m pip install --no-cache-dir -e ./transformers[dev-tensorflow,testing]
 

docs/README.md

@@ -63,7 +63,7 @@ will see a bot add a comment to a link where the documentation with your changes
 Accepted files are Markdown (.md or .mdx).
 
 Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
-the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/main/docs/source/_toctree.yml) file.
+the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/transformers/blob/master/docs/source/_toctree.yml) file.
 
 ## Renaming section headers and moving sections
 
@@ -88,7 +88,7 @@ Sections that were moved:
 
 Use the relative style to link to the new file so that the versioned docs continue to work.
 
-For an example of a rich moved sections set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/main_classes/trainer.mdx).
+For an example of a rich moved sections set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/master/docs/source/main_classes/trainer.mdx).
 
 
 ## Writing Documentation - Specification
@@ -108,11 +108,6 @@ Make sure to put your new file under the proper section. It's unlikely to go in
 depending on the intended targets (beginners, more advanced users or researchers) it should go in section two, three or
 four.
 
-### Translating
-
-When translating, refer to the guide at [./TRANSLATING.md](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md).
-
-
 ### Adding a new model
 
 When adding a new model:
@@ -177,9 +172,9 @@ adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`funct
 function to be in the main package.
 
 If you want to create a link to some internal class or function, you need to
-provide its path. For instance: \[\`utils.ModelOutput\`\]. This will be converted into a link with
-`utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are
-linking to in the description, add a ~: \[\`~utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.
+provide its path. For instance: \[\`file_utils.ModelOutput\`\]. This will be converted into a link with
+`file_utils.ModelOutput` in the description. To get rid of the path and only keep the name of the object you are
+linking to in the description, add a ~: \[\`~file_utils.ModelOutput\`\] will generate a link with `ModelOutput` in the description.
 
 The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
 
@@ -348,20 +343,7 @@ contains the example docstring to the [documentation_tests.txt](../utils/documen
 
 ### For Python files
 
-You will first need to run the following command (from the root of the repository) to prepare the doc file (doc-testing needs to add additional lines that we don't include in the doc source files):
-
-```bash
-python utils/prepare_for_doc_test.py src docs
-```
-
-If you work on a specific python module, say `modeling_wav2vec2.py`, you can run the command as follows (to avoid the unnecessary temporary changes in irrelevant files):
-
-```bash
-python utils/prepare_for_doc_test.py src/transformers/utils/doc.py src/transformers/models/wav2vec2/modeling_wav2vec2.py
-```
-(`utils/doc.py` should always be included)
-
-Then you can run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance:
+You can run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance:
 
 ```bash
 pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py -sv --doctest-continue-on-failure
@@ -373,12 +355,6 @@ If you want to isolate a specific docstring, just add `::` after the file name t
 pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py::transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward -sv --doctest-continue-on-failure
 ```
 
-Once you're done, you can run the following command (still from the root of the repository) to undo the changes made by the first command before committing:
-
-```bash
-python utils/prepare_for_doc_test.py src docs --remove_new_line
-```
-
 ### For Markdown files
 
 You will first need to run the following command (from the root of the repository) to prepare the doc file (doc-testing needs to add additional lines that we don't include in the doc source files):

docs/TRANSLATING.md

@@ -1,58 +0,0 @@
-### Translating the Transformers documentation into your language
-
-As part of our mission to democratize machine learning, we'd love to make the Transformers library available in many more languages! Follow the steps below if you want to help translate the documentation into your language 🙏.
-
-**🗞️ Open an issue**
-
-To get started, navigate to the [Issues](https://github.com/huggingface/transformers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the "Translation template" from the "New issue" button.
-
-Once an issue exists, post a comment to indicate which chapters you'd like to work on, and we'll add your name to the list.
-
-
-**🍴 Fork the repository**
-
-First, you'll need to [fork the Transformers repo](https://docs.github.com/en/get-started/quickstart/fork-a-repo). You can do this by clicking on the **Fork** button on the top-right corner of this repo's page.
-
-Once you've forked the repo, you'll want to get the files on your local machine for editing. You can do that by cloning the fork with Git as follows:
-
-```bash
-git clone https://github.com/YOUR-USERNAME/transformers.git
-```
-
-**📋 Copy-paste the English version with a new language code**
-
-The documentation files are in one leading directory:
-
-- [`docs/source`](https://github.com/huggingface/transformers/tree/main/docs/source): All the documentation materials are organized here by language.
-
-You'll only need to copy the files in the [`docs/source/en`](https://github.com/huggingface/transformers/tree/main/docs/source/en) directory, so first navigate to your fork of the repo and run the following:
-
-```bash
-cd ~/path/to/transformers/docs
-cp -r source/en source/LANG-ID
-```
-
-Here, `LANG-ID` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.
-
-**✍️ Start translating**
-
-The fun part comes - translating the text!
-
-The first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website. 
-
-> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source/LANG-ID/` directory!
-
-The fields you should add are `local` (with the name of the file containing the translation; e.g. `autoclass_tutorial`), and `title` (with the title of the doc in your language; e.g. `Load pretrained instances with an AutoClass`) -- as a reference, here is the `_toctree.yml` for [English](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml):
-
-```yaml
-- sections:
-  - local: pipeline_tutorial # Do not change this! Use the same name for your .md file
-    title: Pipelines for inference # Translate this!
-    ...
-  title: Tutorials # Translate this!
-```
-
-Once you have translated the `_toctree.yml` file, you can start translating the [MDX](https://mdxjs.com/) files associated with your docs chapter.
-
-> 🙋 If you'd like others to help you with the translation, you can either [open an issue](https://github.com/huggingface/transformers/issues) or tag @[espejelomar](https://twitter.com/espejelomar)
- on Twitter to gain some visibility.

docs/source/_config.py

@@ -6,9 +6,4 @@ INSTALL_CONTENT = """
 # ! pip install git+https://github.com/huggingface/transformers.git
 """
 
-notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]
-black_avoid_patterns = {
-    "{processor_class}": "FakeProcessorClass",
-    "{model_class}": "FakeModelClass",
-    "{object_class}": "FakeObjectClass",    
-}
+notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]

docs/source/_toctree.yml

@@ -5,6 +5,10 @@
     title: Quick tour
   - local: installation
     title: Installation
+  - local: philosophy
+    title: Philosophy
+  - local: glossary
+    title: Glossary
   title: Get started
 - sections:
   - local: pipeline_tutorial
@@ -13,20 +17,32 @@
     title: Load pretrained instances with an AutoClass
   - local: preprocessing
     title: Preprocess
+  - local: task_summary
+    title: Summary of the tasks
+  - local: model_summary
+    title: Summary of the models
   - local: training
-    title: Fine-tune a pretrained model
+    title: Fine-tuning a pretrained model
   - local: accelerate
     title: Distributed training with 🤗 Accelerate
   - local: model_sharing
     title: Share a model
+  - local: tokenizer_summary
+    title: Summary of the tokenizers
+  - local: multilingual
+    title: Multi-lingual models
   title: Tutorials
 - sections:
-  - local: fast_tokenizers
-    title: "Use tokenizers from 🤗 Tokenizers"
   - local: create_a_model
-    title: Create a custom architecture
-  - local: custom_models
-    title: Sharing custom models
+    title: Create a custom model
+  - local: multilingual
+    title: Inference for multilingual models
+  - local: examples
+    title: Examples
+  - local: troubleshooting
+    title: Troubleshooting
+  - local: custom_datasets
+    title: Fine-tuning with custom datasets
   - sections:
     - local: tasks/sequence_classification
       title: Text classification
@@ -42,70 +58,48 @@
       title: Summarization
     - local: tasks/multiple_choice
       title: Multiple choice
-    - local: tasks/audio_classification
-      title: Audio classification
-    - local: tasks/asr
-      title: Automatic speech recognition
-    - local: tasks/image_classification
-      title: Image classification
     title: Fine-tune for downstream tasks
-  - local: run_scripts
-    title: Train with a script
-  - local: sagemaker
-    title: Run training on Amazon SageMaker
-  - local: multilingual
-    title: Inference for multilingual models
-  - local: converting_tensorflow_models
-    title: Converting TensorFlow Checkpoints
-  - local: serialization
-    title: Export 🤗 Transformers models
-  - local: performance
-    title: 'Performance and Scalability: How To Fit a Bigger Model and Train It Faster'
-  - local: big_models
-    title: Instantiating a big model
-  - local: parallelism
-    title: Model Parallelism
-  - local: benchmarks
-    title: Benchmarks
-  - local: migration
-    title: Migrating from previous packages
-  - local: troubleshooting
-    title: Troubleshoot
-  - local: debugging
-    title: Debugging
   - local: notebooks
     title: "🤗 Transformers Notebooks"
+  - local: sagemaker
+    title: Run training on Amazon SageMaker
   - local: community
     title: Community
+  - local: converting_tensorflow_models
+    title: Converting Tensorflow Checkpoints
+  - local: migration
+    title: Migrating from previous packages
   - local: contributing
     title: How to contribute to transformers?
   - local: add_new_model
     title: "How to add a model to 🤗 Transformers?"
   - local: add_new_pipeline
     title: "How to add a pipeline to 🤗 Transformers?"
+  - local: fast_tokenizers
+    title: "Using tokenizers from 🤗 Tokenizers"
+  - local: performance
+    title: 'Performance and Scalability: How To Fit a Bigger Model and Train It Faster'
+  - local: parallelism
+    title: Model Parallelism
   - local: testing
     title: Testing
+  - local: debugging
+    title: Debugging
+  - local: serialization
+    title: Exporting 🤗 Transformers models
+  - local: custom_models
+    title: Sharing custom models
   - local: pr_checks
     title: Checks on a Pull Request
   title: How-to guides
 - sections:
-  - local: philosophy
-    title: Philosophy
-  - local: glossary
-    title: Glossary
-  - local: task_summary
-    title: Summary of the tasks
-  - local: model_summary
-    title: Summary of the models
-  - local: tokenizer_summary
-    title: Summary of the tokenizers
-  - local: pad_truncation
-    title: Padding and truncation
   - local: bertology
     title: BERTology
   - local: perplexity
     title: Perplexity of fixed-length models
-  title: Conceptual guides
+  - local: benchmarks
+    title: Benchmarks
+  title: Research
 - sections:
   - sections:
     - local: main_classes/callback
@@ -120,8 +114,6 @@
       title: Logging
     - local: main_classes/model
       title: Models
-    - local: main_classes/text_generation
-      title: Text Generation
     - local: main_classes/onnx
       title: ONNX
     - local: main_classes/optimizer_schedules
@@ -194,8 +186,6 @@
       title: DeBERTa
     - local: model_doc/deberta-v2
       title: DeBERTa-v2
-    - local: model_doc/decision_transformer
-      title: Decision Transformer
     - local: model_doc/deit
       title: DeiT
     - local: model_doc/detr
@@ -204,28 +194,20 @@
       title: DialoGPT
     - local: model_doc/distilbert
       title: DistilBERT
-    - local: model_doc/dit
-      title: DiT
     - local: model_doc/dpr
       title: DPR
-    - local: model_doc/dpt
-      title: DPT
     - local: model_doc/electra
       title: ELECTRA
     - local: model_doc/encoder-decoder
       title: Encoder Decoder Models
     - local: model_doc/flaubert
       title: FlauBERT
-    - local: model_doc/flava
-      title: FLAVA
     - local: model_doc/fnet
       title: FNet
     - local: model_doc/fsmt
       title: FSMT
     - local: model_doc/funnel
       title: Funnel Transformer
-    - local: model_doc/glpn
-      title: GLPN
     - local: model_doc/herbert
       title: HerBERT
     - local: model_doc/ibert
@@ -259,9 +241,11 @@
     - local: model_doc/megatron_gpt2
       title: MegatronGPT2
     - local: model_doc/mluke
-      title: mLUKE
+      title: MLUKE
     - local: model_doc/mobilebert
       title: MobileBERT
+    - local: model_doc/mluke
+      title: mLUKE
     - local: model_doc/mpnet
       title: MPNet
     - local: model_doc/mt5
@@ -270,8 +254,6 @@
       title: Nyströmformer
     - local: model_doc/openai-gpt
       title: OpenAI GPT
-    - local: model_doc/opt
-      title: OPT
     - local: model_doc/gpt2
       title: OpenAI GPT2
     - local: model_doc/gptj
@@ -302,10 +284,6 @@
       title: Reformer
     - local: model_doc/rembert
       title: RemBERT
-    - local: model_doc/regnet
-      title: RegNet
-    - local: model_doc/resnet
-      title: ResNet
     - local: model_doc/retribert
       title: RetriBERT
     - local: model_doc/roberta
@@ -336,8 +314,6 @@
       title: T5v1.1
     - local: model_doc/tapas
       title: TAPAS
-    - local: model_doc/tapex
-      title: TAPEX
     - local: model_doc/transfo-xl
       title: Transformer XL
     - local: model_doc/trocr
@@ -346,8 +322,6 @@
       title: UniSpeech
     - local: model_doc/unispeech-sat
       title: UniSpeech-SAT
-    - local: model_doc/van
-      title: VAN
     - local: model_doc/vilt
       title: ViLT
     - local: model_doc/vision-encoder-decoder
@@ -382,8 +356,6 @@
       title: XLSR-Wav2Vec2
     - local: model_doc/xls_r
       title: XLS-R
-    - local: model_doc/yolos
-      title: YOLOS
     - local: model_doc/yoso
       title: YOSO
     title: Models

docs/source/add_new_model.mdx

@@ -19,7 +19,7 @@ independently. Thus, for some new models that the community wants to be added to
 model to 🤗 Transformers.
 
 If this sounds like something you would be interested in, feel free to check out the currently open
-“calls-for-model-addition” [here](https://github.com/huggingface/transformers/tree/main/templates/adding_a_new_model/open_model_proposals/README.md)
+“calls-for-model-addition” [here](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model/open_model_proposals/README.md)
 and to contact us.
 
 If selected, you will then work closely with one member of the Hugging Face team to integrate the model into 🤗
@@ -95,24 +95,6 @@ different formats - the model to a *pytorch_model.bin* file and the configuratio
 [`~PretrainedConfig.save_pretrained`], so that both model and configuration are saved.
 
 
-### Code style
-
-When coding your new model, keep in mind that Transformers is an opinionated library and we have a few quirks of our
-own regarding how code should be written :-)
-
-1. The forward pass of your model should be fully written in the modeling file while being fully independent of other
-   models in the library. If you want to reuse a block from another model, copy the code and paste it with a
-   `# Copied from` comment on top (see [here](https://github.com/huggingface/transformers/blob/v4.17.0/src/transformers/models/roberta/modeling_roberta.py#L160)
-   for a good example).
-2. The code should be fully understandable, even by a non-native English speaker. This means you should pick
-   descriptive variable names and avoid abbreviations. As an example, `activation` is preferred to `act`.
-   One-letter variable names are strongly discouraged unless it's an index in a for loop. 
-3. More generally we prefer longer explicit code to short magical one.
-4. Avoid subclassing `nn.Sequential` in PyTorch but subclass `nn.Module` and write the forward pass, so that anyone
-   using your code can quickly debug it by adding print statements or breaking points.
-5. Your function signature should be type-annotated. For the rest, good variable names are way more readable and
-   understandable than type annotations.
-
 ### Overview of tokenizers
 
 Not quite ready yet :-( This section will be added soon!
@@ -381,7 +363,7 @@ important. Here is some advice is to make your debugging environment as efficien
   original code so that you can directly input the ids instead of an input string.
 - Make sure that the model in your debugging setup is **not** in training mode, which often causes the model to yield
   random outputs due to multiple dropout layers in the model. Make sure that the forward pass in your debugging
-  environment is **deterministic** so that the dropout layers are not used. Or use *transformers.utils.set_seed*
+  environment is **deterministic** so that the dropout layers are not used. Or use *transformers.file_utils.set_seed*
   if the old and new implementations are in the same framework.
 
 The following section gives you more specific details/tips on how you can do this for *brand_new_bert*.
@@ -398,12 +380,15 @@ In the special case that you are adding a model whose architecture exactly match
 existing model you only have to add a conversion script as described in [this section](#write-a-conversion-script).
 In this case, you can just re-use the whole model architecture of the already existing model.
 
-Otherwise, let's start generating a new model. You have two choices here:
+Otherwise, let's start generating a new model with the amazing Cookiecutter!
 
-- `transformers-cli add-new-model-like` to add a new model like an existing one
-- `transformers-cli add-new-model` to add a new model from our template (will look like BERT or Bart depending on the type of model you select)
+**Use the Cookiecutter to automatically generate the model's code**
 
-In both cases, you will be prompted with a questionnaire to fill the basic information of your model. The second command requires to install `cookiecutter`, you can find more information on it [here](https://github.com/huggingface/transformers/tree/main/templates/adding_a_new_model).
+To begin with head over to the [🤗 Transformers templates](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model) to make use of our
+`cookiecutter` implementation to automatically generate all the relevant files for your model. Again, we recommend
+only adding the PyTorch version of the model at first. Make sure you follow the instructions of the `README.md` on
+the [🤗 Transformers templates](https://github.com/huggingface/transformers/tree/master/templates/adding_a_new_model)
+carefully.
 
 **Open a Pull Request on the main huggingface/transformers repo**
 
@@ -413,7 +398,7 @@ side-by-side on integrating the model into 🤗 Transformers.
 
 You should do the following:
 
-1. Create a branch with a descriptive name from your main branch
+1. Create a branch with a descriptive name from your master branch
 
 ```bash
 git checkout -b add_brand_new_bert
@@ -426,11 +411,11 @@ git add .
 git commit
 ```
 
-3. Fetch and rebase to current main
+3. Fetch and rebase to current master
 
 ```bash
 git fetch upstream
-git rebase upstream/main
+git rebase upstream/master
 ```
 
 4. Push the changes to your account using:
@@ -446,12 +431,12 @@ git push -u origin a-descriptive-name-for-my-changes
 6. Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page.
 
 In the following, whenever you have done some progress, don't forget to commit your work and push it to your account so
-that it shows in the pull request. Additionally, you should make sure to update your work with the current main from
+that it shows in the pull request. Additionally, you should make sure to update your work with the current master from
 time to time by doing:
 
 ```bash
 git fetch upstream
-git merge upstream/main
+git merge upstream/master
 ```
 
 In general, all questions you might have regarding the model or your implementation should be asked in your PR and
@@ -509,7 +494,7 @@ slightly adapt it for your use case. Don't hesitate to ask the Hugging Face team
 existing conversion script for your model.
 
 - If you are porting a model from TensorFlow to PyTorch, a good starting point might be BERT's conversion script [here](https://github.com/huggingface/transformers/blob/7acfa95afb8194f8f9c1f4d2c6028224dbed35a2/src/transformers/models/bert/modeling_bert.py#L91)
-- If you are porting a model from PyTorch to PyTorch, a good starting point might be BART's conversion script [here](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py)
+- If you are porting a model from PyTorch to PyTorch, a good starting point might be BART's conversion script [here](https://github.com/huggingface/transformers/blob/master/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py)
 
 In the following, we'll quickly explain how PyTorch models store layer weights and define layer names. In PyTorch, the
 name of a layer is defined by the name of the class attribute you give the layer. Let's define a dummy model in
@@ -834,7 +819,7 @@ fine-tuned on a downstream task. This is not mandatory to merge your PR, but ver
 
 **14. Submit your finished PR**
 
-You're done programming now and can move to the last step, which is getting your PR merged into main. Usually, the
+You're done programming now and can move to the last step, which is getting your PR merged into master. Usually, the
 Hugging Face team should have helped you already at this point, but it is worth taking some time to give your finished
 PR a nice description and eventually add comments to your code, if you want to point out certain design choices to your
 reviewer.

docs/source/autoclass_tutorial.mdx

@@ -77,30 +77,13 @@ Load a processor with [`AutoProcessor.from_pretrained`]:
 
 ## AutoModel
 
-<frameworkcontent>
-<pt>
 Finally, the `AutoModelFor` classes let you load a pretrained model for a given task (see [here](model_doc/auto) for a complete list of available tasks). For example, load a model for sequence classification with [`AutoModelForSequenceClassification.from_pretrained`]:
 
 ```py
 >>> from transformers import AutoModelForSequenceClassification
 
 >>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Easily reuse the same checkpoint to load an architecture for a different task:
-
-```py
->>> from transformers import AutoModelForTokenClassification
-
->>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Generally, we recommend using the `AutoTokenizer` class and the `AutoModelFor` class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next [tutorial](preprocessing), learn how to use your newly loaded tokenizer, feature extractor and processor to preprocess a dataset for fine-tuning.
-</pt>
-<tf>
-Finally, the `TFAutoModelFor` classes let you load a pretrained model for a given task (see [here](model_doc/auto) for a complete list of available tasks). For example, load a model for sequence classification with [`TFAutoModelForSequenceClassification.from_pretrained`]:
-
-```py
+===PT-TF-SPLIT===
 >>> from transformers import TFAutoModelForSequenceClassification
 
 >>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
@@ -109,11 +92,13 @@ Finally, the `TFAutoModelFor` classes let you load a pretrained model for a give
 Easily reuse the same checkpoint to load an architecture for a different task:
 
 ```py
+>>> from transformers import AutoModelForTokenClassification
+
+>>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
+===PT-TF-SPLIT===
 >>> from transformers import TFAutoModelForTokenClassification
 
 >>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
 ```
 
-Generally, we recommend using the `AutoTokenizer` class and the `TFAutoModelFor` class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next [tutorial](preprocessing), learn how to use your newly loaded tokenizer, feature extractor and processor to preprocess a dataset for fine-tuning.
-</tf>
-</frameworkcontent>
+Generally, we recommend using the `AutoTokenizer` class and the `AutoModelFor` class to load pretrained instances of models. This will ensure you load the correct architecture every time. In the next [tutorial](preprocessing), learn how to use your newly loaded tokenizer, feature extractor and processor to preprocess a dataset for fine-tuning.

docs/source/benchmarks.mdx

@@ -23,7 +23,7 @@ and memory complexity of Transformer models.
 
 Let's take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks.
 
-A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found [here](https://github.com/huggingface/notebooks/tree/main/examples/benchmark.ipynb).
+A notebook explaining in more detail how to benchmark 🤗 Transformers models can be found [here](https://github.com/huggingface/notebooks/tree/master/examples/benchmark.ipynb).
 
 ## How to benchmark 🤗 Transformers models
 
@@ -39,17 +39,12 @@ backward pass.
 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.
 
-<frameworkcontent>
-<pt>
 ```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>
-```py
+===PT-TF-SPLIT===
 >>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
 
 >>> args = TensorFlowBenchmarkArguments(
@@ -57,8 +52,6 @@ The benchmark classes [`PyTorchBenchmark`] and [`TensorFlowBenchmark`] expect an
 ... )
 >>> benchmark = TensorFlowBenchmark(args)
 ```
-</tf>
-</frameworkcontent>
 
 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
@@ -70,10 +63,11 @@ and `src/transformers/benchmark/benchmark_args_tf.py` (for Tensorflow). Alternat
 commands from root will print out a descriptive list of all configurable parameters for PyTorch and Tensorflow
 respectively.
 
-<frameworkcontent>
-<pt>
 ```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()`.
@@ -124,18 +118,8 @@ bert-base-uncased          8              512            1539
 - gpu_power_watts: 280.0
 - gpu_performance_state: 2
 - use_tpu: False
-```
-</pt>
-<tf>
-```bash
-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)
+===PT-TF-SPLIT===
 >>> results = benchmark.run()
 >>> print(results)
 ====================       INFERENCE - SPEED - RESULT       ====================
@@ -182,8 +166,6 @@ bert-base-uncased          8              512            1770
 - gpu_performance_state: 2
 - use_tpu: False
 ```
-</tf>
-</frameworkcontent>
 
 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
@@ -197,8 +179,6 @@ Instead of benchmarking pre-trained models via their model identifier, _e.g._ `b
 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.
 
-<frameworkcontent>
-<pt>
 ```py
 >>> from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments, BertConfig
 
@@ -270,10 +250,8 @@ bert-6-lay                 8              512            1359
 - gpu_power_watts: 280.0
 - gpu_performance_state: 2
 - use_tpu: False
-```
-</pt>
-<tf>
-```py
+
+===PT-TF-SPLIT===
 >>> from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments, BertConfig
 
 >>> args = TensorFlowBenchmarkArguments(
@@ -345,8 +323,6 @@ bert-6-lay                 8              512            1540
 - gpu_performance_state: 2
 - use_tpu: False
 ```
-</tf>
-</frameworkcontent>
 
 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
@@ -379,5 +355,5 @@ available [here](https://docs.google.com/spreadsheets/d/1sryqufw2D0XlUH4sq3e9Wnx
 
 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/main/examples/pytorch/benchmarking/README.md).
-- [TensorFlow Benchmarking Results](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/benchmarking/README.md).
+- [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).

docs/source/bertology.mdx

@@ -32,5 +32,5 @@ help people access the inner representations, mainly adapted from the great work
 - retrieving heads output values and gradients to be able to compute head importance score and prune head as explained
   in https://arxiv.org/abs/1905.10650.
 
-To help you understand and use these features, we have added a specific example script: [bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) while extract information and prune a model pre-trained on
+To help you understand and use these features, we have added a specific example script: [bertology.py](https://github.com/huggingface/transformers/tree/master/examples/research_projects/bertology/run_bertology.py) while extract information and prune a model pre-trained on
 GLUE.

docs/source/contributing.md

@@ -0,0 +1 @@
+../../CONTRIBUTING.md

docs/source/converting_tensorflow_models.mdx

@@ -27,12 +27,12 @@ The documentation below reflects the **transformers-cli convert** command format
 ## 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
-[convert_bert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) script.
+[convert_bert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/master/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 [quicktour](quicktour) , [run_glue.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_glue.py) ).
+be imported using `from_pretrained()` (see example in [quicktour](quicktour) , [run_glue.py](https://github.com/huggingface/transformers/tree/master/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 (\
@@ -56,7 +56,7 @@ You can download Google's pre-trained models for the conversion [here](https://g
 ## ALBERT
 
 Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
-[convert_albert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/main/src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py) script.
+[convert_albert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/master/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

docs/source/create_a_model.mdx

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Create a custom architecture
+# Create a custom model
 
 An [`AutoClass`](model_doc/auto) automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an `AutoClass` to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a custom 🤗 Transformers model from just a few base classes. This could be particularly useful for anyone who is interested in studying, training or experimenting with a 🤗 Transformers model. In this guide, dive deeper into creating a custom model without an `AutoClass`. Learn how to:
 
@@ -107,8 +107,6 @@ You can also save your configuration file as a dictionary or even just the diffe
 
 The next step is to create a [model](main_classes/models). The model - also loosely referred to as the architecture - defines what each layer is doing and what operations are happening. Attributes like `num_hidden_layers` from the configuration are used to define the architecture. Every model shares the base class [`PreTrainedModel`] and a few common methods like resizing input embeddings and pruning self-attention heads. In addition, all models are also either a [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html), [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) or [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/flax.linen.html#module) subclass. This means models are compatible with each of their respective framework's usage.
 
-<frameworkcontent>
-<pt>
 Load your custom configuration attributes into the model:
 
 ```py
@@ -116,26 +114,7 @@ Load your custom configuration attributes into the model:
 
 >>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
 >>> model = DistilBertModel(my_config)
-```
-
-This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.
-
-Create a pretrained model with [`~PreTrainedModel.from_pretrained`]:
-
-```py
->>> model = DistilBertModel.from_pretrained("distilbert-base-uncased")
-```
-
-When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:
-
-```py
->>> model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
-```
-</pt>
-<tf>
-Load your custom configuration attributes into the model:
-
-```py
+===PT-TF-SPLIT===
 >>> from transformers import TFDistilBertModel
 
 >>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
@@ -144,32 +123,36 @@ Load your custom configuration attributes into the model:
 
 This creates a model with random values instead of pretrained weights. You won't be able to use this model for anything useful yet until you train it. Training is a costly and time-consuming process. It is generally better to use a pretrained model to obtain better results faster, while using only a fraction of the resources required for training.
 
-Create a pretrained model with [`~TFPreTrainedModel.from_pretrained`]:
+Create a pretrained model with [`~PreTrainedModel.from_pretrained`]:
 
 ```py
+>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased")
+===PT-TF-SPLIT===
 >>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
 ```
 
 When you load pretrained weights, the default model configuration is automatically loaded if the model is provided by 🤗 Transformers. However, you can still replace - some or all of - the default model configuration attributes with your own if you'd like:
 
 ```py
+>>> model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
+===PT-TF-SPLIT===
 >>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
 ```
-</tf>
-</frameworkcontent>
 
 ### Model heads
 
 At this point, you have a base DistilBERT model which outputs the *hidden states*. The hidden states are passed as inputs to a model head to produce the final output. 🤗 Transformers provides a different model head for each task as long as a model supports the task (i.e., you can't use DistilBERT for a sequence-to-sequence task like translation).
 
-<frameworkcontent>
-<pt>
 For example, [`DistilBertForSequenceClassification`] is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs.
 
 ```py
 >>> from transformers import DistilBertForSequenceClassification
 
 >>> model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
+===PT-TF-SPLIT===
+>>> from transformers import TFDistilBertForSequenceClassification
+
+>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
 ```
 
 Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [`DistilBertForQuestionAnswering`] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.
@@ -178,26 +161,11 @@ Easily reuse this checkpoint for another task by switching to a different model
 >>> from transformers import DistilBertForQuestionAnswering
 
 >>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
-```
-</pt>
-<tf>
-For example, [`TFDistilBertForSequenceClassification`] is a base DistilBERT model with a sequence classification head. The sequence classification head is a linear layer on top of the pooled outputs.
-
-```py
->>> from transformers import TFDistilBertForSequenceClassification
-
->>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Easily reuse this checkpoint for another task by switching to a different model head. For a question answering task, you would use the [`TFDistilBertForQuestionAnswering`] model head. The question answering head is similar to the sequence classification head except it is a linear layer on top of the hidden states output.
-
-```py
+===PT-TF-SPLIT===
 >>> from transformers import TFDistilBertForQuestionAnswering
 
 >>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert-base-uncased")
 ```
-</tf>
-</frameworkcontent>
 
 ## Tokenizer
 

docs/source/custom_datasets.mdx

@@ -0,0 +1,702 @@
+<!--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])
+            else:
+                label_ids.append(-100)
+            previous_word_idx = 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,
+)
+```

docs/source/custom_models.mdx

@@ -106,7 +106,7 @@ directly upload your config to the Hub.
 
 Now that we have our ResNet configuration, we can go on writing the model. We will actually write two: one that
 extracts the hidden features from a batch of images (like [`BertModel`]) and one that is suitable for image
-classification (like [`BertForSequenceClassification`]).
+classification (like [`BertModelForSequenceClassification`]).
 
 As we mentioned before, we'll only write a loose wrapper of the model to keep it simple for this example. The only
 thing we need to do before writing this class is a map between the block types and actual block classes. Then the

docs/source/debugging.mdx

@@ -17,7 +17,7 @@ specific language governing permissions and limitations under the License.
 When training or inferencing with `DistributedDataParallel` and multiple GPU, if you run into issue of inter-communication between processes and/or nodes, you can use the following script to diagnose network issues.
 
 ```bash
-wget https://raw.githubusercontent.com/huggingface/transformers/main/scripts/distributed/torch-distributed-gpu-test.py
+wget https://raw.githubusercontent.com/huggingface/transformers/master/scripts/distributed/torch-distributed-gpu-test.py
 ```
 
 For example to test how 2 GPUs interact do:

docs/source/en/_config.py

@@ -1,14 +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}]
-black_avoid_patterns = {
-    "{processor_class}": "FakeProcessorClass",
-    "{model_class}": "FakeModelClass",
-    "{object_class}": "FakeObjectClass",    
-}

docs/source/en/big_models.mdx

@@ -1,128 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Instantiating a big model
-
-When you want to use a very big pretrained model, one challenge is to minimize the use of the RAM. The usual workflow
-from PyTorch is:
-
-1. Create your model with random weights.
-2. Load your pretrained weights.
-3. Put those pretrained weights in your random model.
-
-Step 1 and 2 both require a full version of the model in memory, which is not a problem in most cases, but if your model starts weighing several GigaBytes, those two copies can make you got our of RAM. Even worse, if you are using `torch.distributed` to launch a distributed training, each process will load the pretrained model and store these two copies in RAM.
-
-<Tip>
-
-Note that the randomly created model is initialized with "empty" tensors, which take the space in memory without filling it (thus the random values are whatever was in this chunk of memory at a given time). The random initialization following the appropriate distribution for the kind of model/parameters instatiated (like a normal distribution for instance) is only performed after step 3 on the non-initialized weights, to be as fast as possible! 
-
-</Tip>
-
-In this guide, we explore the solutions Transformers offer to deal with this issue. Note that this is an area of active development, so the APIs explained here may change slightly in the future.
-
-## Sharded checkpoints
-
-Since version 4.18.0, model checkpoints that end up taking more than 10GB of space are automatically sharded in smaller pieces. In terms of having one single checkpoint when you do `model.save_pretrained(save_dir)`, you will end up with several partial checkpoints (each of which being of size < 10GB) and an index that maps parameter names to the files they are stored in.
-
-You can control the maximum size before sharding with the `max_shard_size` parameter, so for the sake of an example, we'll use a normal-size models with a small shard size: let's take a traditional BERT model.
-
-```py
-from transformers import AutoModel
-
-model = AutoModel.from_pretrained("bert-base-cased")
-```
-
-If you save it using [`~PreTrainedModel.save_pretrained`], you will get a new folder with two files: the config of the model and its weights:
-
-```py
->>> import os
->>> import tempfile
-
->>> with tempfile.TemporaryDirectory() as tmp_dir:
-...     model.save_pretrained(tmp_dir)
-...     print(sorted(os.listdir(tmp_dir)))
-['config.json', 'pytorch_model.bin']
-```
-
-Now let's use a maximum shard size of 200MB:
-
-```py
->>> with tempfile.TemporaryDirectory() as tmp_dir:
-...     model.save_pretrained(tmp_dir, max_shard_size="200MB")
-...     print(sorted(os.listdir(tmp_dir)))
-['config.json', 'pytorch_model-00001-of-00003.bin', 'pytorch_model-00002-of-00003.bin', 'pytorch_model-00003-of-00003.bin', 'pytorch_model.bin.index.json']
-```
-
-On top of the configuration of the model, we see three different weights files, and an `index.json` file which is our index. A checkpoint like this can be fully reloaded using the [`~PreTrainedModel.from_pretrained`] method:
-
-```py
->>> with tempfile.TemporaryDirectory() as tmp_dir:
-...     model.save_pretrained(tmp_dir, max_shard_size="200MB")
-...     new_model = AutoModel.from_pretrained(tmp_dir)
-```
-
-The main advantage of doing this for big models is that during step 2 of the workflow shown above, each shard of the checkpoint is loaded after the previous one, capping the memory usage in RAM to the model size plus the size of the biggest shard.
-
-Beind the scenes, the index file is used to determine which keys are in the checkpoint, and where the corresponding weights are stored. We can load that index like any json and get a dictionary:
-
-```py
->>> import json
-
->>> with tempfile.TemporaryDirectory() as tmp_dir:
-...     model.save_pretrained(tmp_dir, max_shard_size="200MB")
-...     with open(os.path.join(tmp_dir, "pytorch_model.bin.index.json"), "r") as f:
-...         index = json.load(f)
-
->>> print(index.keys())
-dict_keys(['metadata', 'weight_map'])
-```
-
-The metadata just consists of the total size of the model for now. We plan to add several other informations in the future:
-
-```py
->>> index["metadata"]
-{'total_size': 433245184}
-```
-
-The weights map is the main part of this index, which maps each parameter name (as usually found in a PyTorch model `state_dict`) to the file it's stored in:
-
-```py
->>> index["weight_map"]
-{'embeddings.LayerNorm.bias': 'pytorch_model-00001-of-00003.bin',
- 'embeddings.LayerNorm.weight': 'pytorch_model-00001-of-00003.bin',
- ...
-```
-
-If you want to directly load such a sharded checkpoint inside a model without using [`~PreTrainedModel.from_pretrained`] (like you would do `model.load_state_dict()` for a full checkpoint) you should use [`~modeling_utils.load_sharded_checkpoint`]:
-
-```py
->>> from transformers.modeling_utils import load_sharded_checkpoint
-
->>> with tempfile.TemporaryDirectory() as tmp_dir:
-...     model.save_pretrained(tmp_dir, max_shard_size="200MB")
-...     load_sharded_checkpoint(model, tmp_dir)
-```
-
-## Low memory loading
-
-Sharded checkpoints reduce the memory usage during step 2 of the worflow mentioned above, but when loadin a pretrained model, why keep the random weights in memory? The option `low_cpu_mem_usage` will destroy the weights of the randomly initialized model, then progressively load the weights inside, then perform a random initialization for potential missing weights (if you are loadding a model with a newly initialized head for a fine-tuning task for instance).
-
-It's very easy to use, just add `low_cpu_mem_usage=True` to your call to [`~PreTrainedModel.from_pretrained`]:
-
-```py
-from transformers import AutoModelForSequenceClas
-
-model = AutoModel.from_pretrained("bert-base-cased", low_cpu_mem_usage=True)
-```
-
-This can be used in conjunction with a sharded checkpoint.
-

docs/source/en/contributing.md

@@ -1 +0,0 @@
-../../../CONTRIBUTING.md

docs/source/en/main_classes/text_generation.mdx

@@ -1,40 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Generation
-
-Each framework has a generate method for auto-regressive text generation implemented in their respective `GenerationMixin` class:
-
-- PyTorch [`~generation_utils.GenerationMixin.generate`] is implemented in [`~generation_utils.GenerationMixin`].
-- TensorFlow [`~generation_tf_utils.TFGenerationMixin.generate`] is implemented in [`~generation_tf_utils.TFGenerationMixin`].
-- Flax/JAX [`~generation_flax_utils.FlaxGenerationMixin.generate`] is implemented in [`~generation_flax_utils.FlaxGenerationMixin`].
-
-## GenerationMixin
-
-[[autodoc]] generation_utils.GenerationMixin
-	- generate
-	- greedy_search
-	- sample
-	- beam_search
-	- beam_sample
-	- group_beam_search
-	- constrained_beam_search
-
-## TFGenerationMixin
-
-[[autodoc]] generation_tf_utils.TFGenerationMixin
-	- generate
-
-## FlaxGenerationMixin
-
-[[autodoc]] generation_flax_utils.FlaxGenerationMixin
-	- generate

docs/source/en/model_doc/byt5.mdx

@@ -1,147 +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.
--->
-
-# ByT5
-
-## Overview
-
-The ByT5 model was presented in [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.
-
-The abstract from the paper is the following:
-
-*Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units.
-Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from
-the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they
-can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by
-removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token
-sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of
-operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with
-minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count,
-training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level
-counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on
-tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of
-pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our
-experiments.*
-
-This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten). The original code can be
-found [here](https://github.com/google-research/byt5).
-
-ByT5's architecture is based on the T5v1.1 model, so one can refer to [T5v1.1's documentation page](t5v1.1). They
-only differ in how inputs should be prepared for the model, see the code examples below.
-
-Since ByT5 was pre-trained unsupervisedly, there's no real advantage to using a task prefix during single-task
-fine-tuning. If you are doing multi-task fine-tuning, you should use a prefix.
-
-
-### Example
-
-ByT5 works on raw UTF-8 bytes, so it can be used without a tokenizer:
-
-```python
->>> from transformers import T5ForConditionalGeneration
->>> import torch
-
->>> model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
-
->>> num_special_tokens = 3
->>> # Model has 3 special tokens which take up the input ids 0,1,2 of ByT5.
->>> # => Need to shift utf-8 character encodings by 3 before passing ids to model.
-
->>> input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + num_special_tokens
-
->>> labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + num_special_tokens
-
->>> loss = model(input_ids, labels=labels).loss
->>> loss.item()
-2.66
-```
-
-For batched inference and training it is however recommended to make use of the tokenizer:
-
-```python
->>> from transformers import T5ForConditionalGeneration, AutoTokenizer
-
->>> model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
->>> tokenizer = AutoTokenizer.from_pretrained("google/byt5-small")
-
->>> model_inputs = tokenizer(
-...     ["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt"
-... )
->>> labels_dict = tokenizer(
-...     ["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt"
-... )
->>> labels = labels_dict.input_ids
-
->>> loss = model(**model_inputs, labels=labels).loss
->>> loss.item()
-17.9
-```
-
-Similar to [T5](t5), ByT5 was trained on the span-mask denoising task. However, 
-since the model works directly on characters, the pretraining task is a bit 
-different. Let's corrupt some characters of the 
-input sentence `"The dog chases a ball in the park."` and ask ByT5 to predict them 
-for us.
-
-```python
->>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
->>> import torch
-
->>> tokenizer = AutoTokenizer.from_pretrained("google/byt5-base")
->>> model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-base")
-
->>> input_ids_prompt = "The dog chases a ball in the park."
->>> input_ids = tokenizer(input_ids_prompt).input_ids
-
->>> # Note that we cannot add "{extra_id_...}" to the string directly
->>> # as the Byte tokenizer would incorrectly merge the tokens
->>> # For ByT5, we need to work directly on the character level
->>> # Contrary to T5, ByT5 does not use sentinel tokens for masking, but instead
->>> # uses final utf character ids.
->>> # UTF-8 is represented by 8 bits and ByT5 has 3 special tokens.
->>> # => There are 2**8+2 = 259 input ids and mask tokens count down from index 258.
->>> # => mask to "The dog [258]a ball [257]park."
-
->>> input_ids = torch.tensor([input_ids[:8] + [258] + input_ids[14:21] + [257] + input_ids[28:]])
->>> input_ids
-tensor([[ 87, 107, 104,  35, 103, 114, 106,  35, 258,  35, 100,  35, 101, 100, 111, 111, 257,  35, 115, 100, 117, 110,  49,   1]])
-
->>> # ByT5 produces only one char at a time so we need to produce many more output characters here -> set `max_length=100`.
->>> output_ids = model.generate(input_ids, max_length=100)[0].tolist()
->>> output_ids
-[0, 258, 108, 118,  35, 119, 107, 104,  35, 114, 113, 104,  35, 122, 107, 114,  35, 103, 114, 104, 118, 257,  35, 108, 113,  35, 119, 107, 104,  35, 103, 108, 118, 102, 114, 256, 108, 113,  35, 119, 107, 104, 35, 115, 100, 117, 110,  49,  35,  87, 107, 104,  35, 103, 114, 106, 35, 108, 118,  35, 119, 107, 104,  35, 114, 113, 104,  35, 122, 107, 114,  35, 103, 114, 104, 118,  35, 100,  35, 101, 100, 111, 111,  35, 108, 113, 255,  35, 108, 113,  35, 119, 107, 104,  35, 115, 100, 117, 110,  49]
-
->>> # ^- Note how 258 descends to 257, 256, 255
-
->>> # Now we need to split on the sentinel tokens, let's write a short loop for this
->>> output_ids_list = []
->>> start_token = 0
->>> sentinel_token = 258
->>> while sentinel_token in output_ids:
-...     split_idx = output_ids.index(sentinel_token)
-...     output_ids_list.append(output_ids[start_token:split_idx])
-...     start_token = split_idx
-...     sentinel_token -= 1
-
->>> output_ids_list.append(output_ids[start_token:])
->>> output_string = tokenizer.batch_decode(output_ids_list)
->>> output_string
-['<pad>', 'is the one who does', ' in the disco', 'in the park. The dog is the one who does a ball in', ' in the park.']
-```
-
-
-## ByT5Tokenizer
-
-[[autodoc]] ByT5Tokenizer
-
-See [`ByT5Tokenizer`] for all details.

docs/source/en/model_doc/decision_transformer.mdx

@@ -1,51 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Decision Transformer
-
-## Overview
-
-The Decision Transformer model was proposed in [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345)  
-by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-
-The abstract from the paper is the following:
-
-*We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. 
-This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances
- in language modeling such as GPT-x and BERT. In particular, we present Decision Transformer, an architecture that 
- casts the problem of RL as conditional sequence modeling. Unlike prior approaches to RL that fit value functions or 
- compute policy gradients, Decision Transformer simply outputs the optimal actions by leveraging a causally masked 
- Transformer. By conditioning an autoregressive model on the desired return (reward), past states, and actions, our 
- Decision Transformer model can generate future actions that achieve the desired return. Despite its simplicity, 
- Decision Transformer matches or exceeds the performance of state-of-the-art model-free offline RL baselines on 
- Atari, OpenAI Gym, and Key-to-Door tasks.*
-
-Tips:
-
-This version of the model is for tasks where the state is a vector, image-based states will come soon.
-
-This model was contributed by [edbeeching](https://huggingface.co/edbeeching). The original code can be found [here](https://github.com/kzl/decision-transformer).
-
-## DecisionTransformerConfig
-
-[[autodoc]] DecisionTransformerConfig
-
-
-## DecisionTransformerGPT2Model
-
-[[autodoc]] DecisionTransformerGPT2Model
-    - forward
-
-## DecisionTransformerModel
-
-[[autodoc]] DecisionTransformerModel
-    - forward

docs/source/en/model_doc/dit.mdx

@@ -1,67 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# DiT
-
-## Overview
-
-DiT was proposed in [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
-DiT applies the self-supervised objective of [BEiT](beit) (BERT pre-training of Image Transformers) to 42 million document images, allowing for state-of-the-art results on tasks including:
-
-- document image classification: the [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/) dataset (a collection of
-  400,000 images belonging to one of 16 classes).
-- document layout analysis: the [PubLayNet](https://github.com/ibm-aur-nlp/PubLayNet) dataset (a collection of more
-  than 360,000 document images constructed by automatically parsing PubMed XML files).
-- table detection: the [ICDAR 2019 cTDaR](https://github.com/cndplab-founder/ICDAR2019_cTDaR) dataset (a collection of
-  600 training images and 240 testing images).
-
-The abstract from the paper is the following:
-
-*Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we propose DiT, a self-supervised pre-trained Document Image Transformer model using large-scale unlabeled text images for Document AI tasks, which is essential since no supervised counterparts ever exist due to the lack of human labeled document images. We leverage DiT as the backbone network in a variety of vision-based Document AI tasks, including document image classification, document layout analysis, as well as table detection. Experiment results have illustrated that the self-supervised pre-trained DiT model achieves new state-of-the-art results on these downstream tasks, e.g. document image classification (91.11 → 92.69), document layout analysis (91.0 → 94.9) and table detection (94.23 → 96.55). *
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dit_architecture.jpg"
-alt="drawing" width="600"/> 
-
-<small> Summary of the approach. Taken from the [original paper](https://arxiv.org/abs/2203.02378). </small>
-
-One can directly use the weights of DiT with the AutoModel API:
-
-```python
-from transformers import AutoModel
-
-model = AutoModel.from_pretrained("microsoft/dit-base")
-```
-
-This will load the model pre-trained on masked image modeling. Note that this won't include the language modeling head on top, used to predict visual tokens.
-
-To include the head, you can load the weights into a `BeitForMaskedImageModeling` model, like so:
-
-```python
-from transformers import BeitForMaskedImageModeling
-
-model = BeitForMaskedImageModeling.from_pretrained("microsoft/dit-base")
-```
-
-You can also load a fine-tuned model from the [hub](https://huggingface.co/models?other=dit), like so:
-
-```python
-from transformers import AutoModelForImageClassification
-
-model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
-```
-
-This particular checkpoint was fine-tuned on [RVL-CDIP](https://www.cs.cmu.edu/~aharley/rvl-cdip/), an important benchmark for document image classification.
-A notebook that illustrates inference for document image classification can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DiT/Inference_with_DiT_(Document_Image_Transformer)_for_document_image_classification.ipynb).
-
-As DiT's architecture is equivalent to that of BEiT, one can refer to [BEiT's documentation page](beit) 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/tree/master/dit).

docs/source/en/model_doc/dpt.mdx

@@ -1,57 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# DPT
-
-## Overview
-
-The DPT model was proposed in [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
-DPT is a model that leverages the [Vision Transformer (ViT)](vit) as backbone for dense prediction tasks like semantic segmentation and depth estimation.
-
-The abstract from the paper is the following:
-
-*We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense vision transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art.*
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg"
-alt="drawing" width="600"/> 
-
-<small> DPT architecture. Taken from the <a href="https://arxiv.org/abs/2103.13413" target="_blank">original paper</a>. </small>
-
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/isl-org/DPT).
-
-## DPTConfig
-
-[[autodoc]] DPTConfig
-
-
-## DPTFeatureExtractor
-
-[[autodoc]] DPTFeatureExtractor
-    - __call__
-
-
-## DPTModel
-
-[[autodoc]] DPTModel
-    - forward
-
-
-## DPTForDepthEstimation
-
-[[autodoc]] DPTForDepthEstimation
-    - forward
-
-
-## DPTForSemanticSegmentation
-
-[[autodoc]] DPTForSemanticSegmentation
-    - forward

docs/source/en/model_doc/flava.mdx

@@ -1,96 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# FLAVA
-
-## Overview
-
-The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
-
-The paper aims at creating a single unified foundation model which can work across vision, language 
-as well as vision-and-language multimodal tasks.
-
-The abstract from the paper is the following:
-
-*State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety 
-of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal 
-(with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising 
-direction would be to use a single holistic universal model, as a "foundation", that targets all modalities 
-at once -- a true vision and language foundation model should be good at vision tasks, language tasks, and 
-cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate 
-impressive performance on a wide range of 35 tasks spanning these target modalities.*
-
-
-This model was contributed by [aps](https://huggingface.co/aps). The original code can be found [here](https://github.com/facebookresearch/multimodal/tree/main/examples/flava).
-
-
-## FlavaConfig
-
-[[autodoc]] FlavaConfig
-
-## FlavaTextConfig
-
-[[autodoc]] FlavaTextConfig
-
-## FlavaImageConfig
-
-[[autodoc]] FlavaImageConfig
-
-## FlavaMultimodalConfig
-
-[[autodoc]] FlavaMultimodalConfig
-
-## FlavaImageCodebookConfig
-
-[[autodoc]] FlavaImageCodebookConfig
-
-## FlavaProcessor
-
-[[autodoc]] FlavaProcessor
-
-## FlavaFeatureExtractor
-
-[[autodoc]] FlavaFeatureExtractor
-
-## FlavaForPreTraining
-
-[[autodoc]] FlavaForPreTraining
-    - forward
-
-## FlavaModel
-
-[[autodoc]] FlavaModel
-    - forward
-    - get_text_features
-    - get_image_features
-
-## FlavaImageCodebook
-
-[[autodoc]] FlavaImageCodebook
-    - forward
-    - get_codebook_indices
-    - get_codebook_probs
-
-## FlavaTextModel
-
-[[autodoc]] FlavaTextModel
-    - forward
-
-## FlavaImageModel
-
-[[autodoc]] FlavaImageModel
-    - forward
-
-## FlavaMultimodalModel
-
-[[autodoc]] FlavaMultimodalModel
-    - forward

docs/source/en/model_doc/glpn.mdx

@@ -1,61 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# GLPN
-
-<Tip>
-
-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 it in the future. If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title).
-
-</Tip>
-
-## Overview
-
-The GLPN model was proposed in [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436)  by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
-GLPN combines [SegFormer](segformer)'s hierarchical mix-Transformer with a lightweight decoder for monocular depth estimation. The proposed decoder shows better performance than the previously proposed decoders, with considerably
-less computational complexity.
-
-The abstract from the paper is the following:
-
-*Depth estimation from a single image is an important task that can be applied to various fields in computer vision, and has grown rapidly with the development of convolutional neural networks. In this paper, we propose a novel structure and training strategy for monocular depth estimation to further improve the prediction accuracy of the network. We deploy a hierarchical transformer encoder to capture and convey the global context, and design a lightweight yet powerful decoder to generate an estimated depth map while considering local connectivity. By constructing connected paths between multi-scale local features and the global decoding stream with our proposed selective feature fusion module, the network can integrate both representations and recover fine details. In addition, the proposed decoder shows better performance than the previously proposed decoders, with considerably less computational complexity. Furthermore, we improve the depth-specific augmentation method by utilizing an important observation in depth estimation to enhance the model. Our network achieves state-of-the-art performance over the challenging depth dataset NYU Depth V2. Extensive experiments have been conducted to validate and show the effectiveness of the proposed approach. Finally, our model shows better generalisation ability and robustness than other comparative models.*
-
-Tips:
-
-- A notebook illustrating inference with [`GLPNForDepthEstimation`] can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/GLPN/GLPN_inference_(depth_estimation).ipynb).
-- One can use [`GLPNFeatureExtractor`] to prepare images for the model.
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/glpn_architecture.jpg"
-alt="drawing" width="600"/> 
-
-<small> Summary of the approach. Taken from the <a href="https://arxiv.org/abs/2201.07436" target="_blank">original paper</a>. </small>
-
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/vinvino02/GLPDepth).
-
-## GLPNConfig
-
-[[autodoc]] GLPNConfig
-
-## GLPNFeatureExtractor
-
-[[autodoc]] GLPNFeatureExtractor
-    - __call__
-
-## GLPNModel
-
-[[autodoc]] GLPNModel
-    - forward
-
-## GLPNForDepthEstimation
-
-[[autodoc]] GLPNForDepthEstimation
-    - forward

docs/source/en/model_doc/opt.mdx

@@ -1,47 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# OPT
-
-## Overview
-
-The OPT model was proposed in [Open Pre-trained Transformer Language Models](https://arxiv.org/pdf/2205.01068) by Meta AI.
-OPT is a series of open-sourced large causal language models which perform similar in performance to GPT3. 
-
-
-The abstract from the paper is the following:
-
-*Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models.*
-
-Tips:
-- OPT has the same architecture as [`BartDecoder`].
-- Contrary to GPT2, OPT adds the EOS token `</s>` to the beginning of every prompt. **Note**: Make sure to pass `use_fast=False` when loading OPT's tokenizer with [`AutoTokenizer`] to get the correct tokenizer.
-
-This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ), [Younes Belkada](https://huggingface.co/ybelkada), and [Patrick Von Platen](https://huggingface.co/patrickvonplaten).
-The original code can be found [here](https://github.com/facebookresearch/metaseq).
-
-
-## OPTConfig
-
-[[autodoc]] OPTConfig
-
-## OPTModel
-
-[[autodoc]] OPTModel
-    - forward
-
-
-## OPTForCausalLM
-
-[[autodoc]] OPTForCausalLM
-    - forward
-

docs/source/en/model_doc/regnet.mdx

@@ -1,48 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# RegNet
-
-## Overview
-
-The RegNet model was proposed in [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
-
-The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
-
-The abstract from the paper is the following:
-
-*In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.*
-
-Tips:
-
-- One can use [`AutoFeatureExtractor`] to prepare images for the model.
-- The huge 10B model from [Self-supervised Pretraining of Visual Features in the Wild](https://arxiv.org/abs/2103.01988), trained on one billion Instagram images, is available on the [hub](https://huggingface.co/facebook/regnet-y-10b-seer)
-
-This model was contributed by [Francesco](https://huggingface.co/Francesco).
-The original code can be found [here](https://github.com/facebookresearch/pycls).
-
-
-## RegNetConfig
-
-[[autodoc]] RegNetConfig
-
-
-## RegNetModel
-
-[[autodoc]] RegNetModel
-    - forward
-
-
-## RegNetForImageClassification
-
-[[autodoc]] RegNetForImageClassification
-    - forward

docs/source/en/model_doc/resnet.mdx

@@ -1,50 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# ResNet
-
-## Overview
-
-The ResNet model was proposed in [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. Our implementation follows the small changes made by [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch), we apply the `stride=2` for downsampling in bottleneck's `3x3` conv and not in the first `1x1`. This is generally known as "ResNet v1.5".
-
-ResNet introduced residual connections, they allow to train networks with an unseen number of layers (up to 1000). ResNet won the 2015 ILSVRC & COCO competition, one important milestone in deep computer vision.
-
-The abstract from the paper is the following:
-
-*Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.
-The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.*
-
-Tips:
-
-- One can use [`AutoFeatureExtractor`] to prepare images for the model.
-
-The figure below illustrates the architecture of ResNet. Taken from the [original paper](https://arxiv.org/abs/1512.03385).
-
-<img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/resnet_architecture.png"/>
-
-This model was contributed by [Francesco](https://huggingface.co/Francesco). The original code can be found [here](https://github.com/KaimingHe/deep-residual-networks).
-
-## ResNetConfig
-
-[[autodoc]] ResNetConfig
-
-
-## ResNetModel
-
-[[autodoc]] ResNetModel
-    - forward
-
-
-## ResNetForImageClassification
-
-[[autodoc]] ResNetForImageClassification
-    - forward

docs/source/en/model_doc/t5.mdx

@@ -1,373 +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.
--->
-
-# T5
-
-## Overview
-
-The T5 model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
-Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu.
-
-The abstract from the paper is the following:
-
-*Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream
-task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning
-has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of
-transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a
-text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled datasets, transfer
-approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration
-with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering
-summarization, question answering, text classification, and more. To facilitate future work on transfer learning for
-NLP, we release our dataset, pre-trained models, and code.*
-
-Tips:
-
-- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which
-each task is converted into a text-to-text format. T5 works well on a variety of tasks out-of-the-box by prepending a
-different prefix to the input corresponding to each task, e.g., for translation: *translate English to German: ...*,
-for summarization: *summarize: ...*.
-
-- T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.
-
-- See the [training](#training), [inference](#inference) and [scripts](#scripts) sections below for all details regarding usage.
-
-T5 comes in different sizes:
-
-- [t5-small](https://huggingface.co/t5-small)
-
-- [t5-base](https://huggingface.co/t5-base)
-
-- [t5-large](https://huggingface.co/t5-large)
-
-- [t5-3b](https://huggingface.co/t5-3b)
-
-- [t5-11b](https://huggingface.co/t5-11b).
-
-Based on the original T5 model, Google has released some follow-up works:
-
-- **T5v1.1**: T5v1.1 is an improved version of T5 with some architectural tweaks, and is pre-trained on C4 only without
-  mixing in the supervised tasks. Refer to the documentation of T5v1.1 which can be found [here](t5v1.1).
-
-- **mT5**: mT5 is a multilingual T5 model. It is pre-trained on the mC4 corpus, which includes 101 languages. Refer to
-  the documentation of mT5 which can be found [here](mt5).
-
-- **byT5**: byT5 is a T5 model pre-trained on byte sequences rather than SentencePiece subword token sequences. Refer
-  to the documentation of byT5 which can be found [here](byt5).
-
-All checkpoints can be found on the [hub](https://huggingface.co/models?search=t5).
-
-This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/google-research/text-to-text-transfer-transformer).
-
-<a id='training'></a>
-
-## Training
-
-T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher
-forcing. This means that for training, we always need an input sequence and a corresponding target sequence. The input
-sequence is fed to the model using `input_ids`. The target sequence is shifted to the right, i.e., prepended by a
-start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target
-sequence is then appended by the EOS token and corresponds to the `labels`. The PAD token is hereby used as the
-start-sequence token. T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
-
-One can use [`T5ForConditionalGeneration`] (or the Tensorflow/Flax variant), which includes the
-language modeling head on top of the decoder.
-
-- Unsupervised denoising training
-
-In this setup, spans of the input sequence are masked by so-called sentinel tokens (*a.k.a* unique mask tokens) and
-the output sequence is formed as a concatenation of the same sentinel tokens and the *real* masked tokens. Each
-sentinel token represents a unique mask token for this sentence and should start with `<extra_id_0>`,
-`<extra_id_1>`, ... up to `<extra_id_99>`. As a default, 100 sentinel tokens are available in
-[`T5Tokenizer`].
-
-For instance, the sentence "The cute dog walks in the park" with the masks put on "cute dog" and "the" should be
-processed as follows:
-
-```python
->>> from transformers import T5Tokenizer, T5ForConditionalGeneration
-
->>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
->>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
->>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
->>> labels = tokenizer("<extra_id_0> cute dog <extra_id_1> the <extra_id_2>", return_tensors="pt").input_ids
-
->>> # the forward function automatically creates the correct decoder_input_ids
->>> loss = model(input_ids=input_ids, labels=labels).loss
->>> loss.item()
-3.7837
-```
-
-If you're interested in pre-training T5 on a new corpus, check out the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling) script in the Examples
-directory.
-
-- Supervised training
-
-In this setup, the input sequence and output sequence are a standard sequence-to-sequence input-output mapping.
-Suppose that we want to fine-tune the model for translation for example, and we have a training example: the input
-sequence "The house is wonderful." and output sequence "Das Haus ist wunderbar.", then they should be prepared for
-the model as follows:
-
-```python
->>> from transformers import T5Tokenizer, T5ForConditionalGeneration
-
->>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
->>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
->>> input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
->>> labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids
-
->>> # the forward function automatically creates the correct decoder_input_ids
->>> loss = model(input_ids=input_ids, labels=labels).loss
->>> loss.item()
-0.2542
-```
-
-As you can see, only 2 inputs are required for the model in order to compute a loss: `input_ids` (which are the
-`input_ids` of the encoded input sequence) and `labels` (which are the `input_ids` of the encoded
-target sequence). The model will automatically create the `decoder_input_ids` based on the `labels`, by
-shifting them one position to the right and prepending the `config.decoder_start_token_id`, which for T5 is
-equal to 0 (i.e. the id of the pad token). Also note the task prefix: we prepend the input sequence with 'translate
-English to German: ' before encoding it. This will help in improving the performance, as this task prefix was used
-during T5's pre-training.
-
-However, the example above only shows a single training example. In practice, one trains deep learning models in
-batches. This entails that we must pad/truncate examples to the same length. For encoder-decoder models, one
-typically defines a `max_source_length` and `max_target_length`, which determine the maximum length of the
-input and output sequences respectively (otherwise they are truncated). These should be carefully set depending on
-the task.
-
-In addition, we must make sure that padding token id's of the `labels` are not taken into account by the loss
-function. In PyTorch and Tensorflow, this can be done by replacing them with -100, which is the `ignore_index`
-of the `CrossEntropyLoss`. In Flax, one can use the `decoder_attention_mask` to ignore padded tokens from
-the loss (see the [Flax summarization script](https://github.com/huggingface/transformers/tree/main/examples/flax/summarization) for details). We also pass
-`attention_mask` as additional input to the model, which makes sure that padding tokens of the inputs are
-ignored. The code example below illustrates all of this.
-
-```python
->>> from transformers import T5Tokenizer, T5ForConditionalGeneration
->>> import torch
-
->>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
->>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
->>> # the following 2 hyperparameters are task-specific
->>> max_source_length = 512
->>> max_target_length = 128
-
->>> # Suppose we have the following 2 training examples:
->>> input_sequence_1 = "Welcome to NYC"
->>> output_sequence_1 = "Bienvenue à NYC"
-
->>> input_sequence_2 = "HuggingFace is a company"
->>> output_sequence_2 = "HuggingFace est une entreprise"
-
->>> # encode the inputs
->>> task_prefix = "translate English to French: "
->>> input_sequences = [input_sequence_1, input_sequence_2]
-
->>> encoding = tokenizer(
-...     [task_prefix + sequence for sequence in input_sequences],
-...     padding="longest",
-...     max_length=max_source_length,
-...     truncation=True,
-...     return_tensors="pt",
-... )
-
->>> input_ids, attention_mask = encoding.input_ids, encoding.attention_mask
-
->>> # encode the targets
->>> target_encoding = tokenizer(
-...     [output_sequence_1, output_sequence_2], padding="longest", max_length=max_target_length, truncation=True
-... )
->>> labels = target_encoding.input_ids
-
->>> # replace padding token id's of the labels by -100 so it's ignored by the loss
->>> 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
->>> loss.item()
-0.188
-```
-
-Additional training tips:
-
-- T5 models need a slightly higher learning rate than the default one set in the `Trainer` when using the AdamW
-optimizer. Typically, 1e-4 and 3e-4 work well for most problems (classification, summarization, translation, question
-answering, question generation). Note that T5 was pre-trained using the AdaFactor optimizer.
-
-According to [this forum post](https://discuss.huggingface.co/t/t5-finetuning-tips/684), task prefixes matter when
-(1) doing multi-task training (2) your task is similar or related to one of the supervised tasks used in T5's
-pre-training mixture (see Appendix D of the [paper](https://arxiv.org/pdf/1910.10683.pdf) for the task prefixes
-used).
-
-If training on TPU, it is recommended to pad all examples of the dataset to the same length or make use of
-*pad_to_multiple_of* to have a small number of predefined bucket sizes to fit all examples in. Dynamically padding
-batches to the longest example is not recommended on TPU as it triggers a recompilation for every batch shape that is
-encountered during training thus significantly slowing down the training. only padding up to the longest example in a
-batch) leads to very slow training on TPU.
-
-<a id='inference'></a>
-
-## Inference
-
-At inference time, it is recommended to use [`~generation_utils.GenerationMixin.generate`]. This
-method takes care of encoding the input and feeding the encoded hidden states via cross-attention layers to the decoder
-and auto-regressively generates the decoder output. Check out [this blog post](https://huggingface.co/blog/how-to-generate) to know all the details about generating text with Transformers.
-There's also [this blog post](https://huggingface.co/blog/encoder-decoder#encoder-decoder) which explains how
-generation works in general in encoder-decoder models.
-
-```python
->>> from transformers import T5Tokenizer, T5ForConditionalGeneration
-
->>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
->>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
->>> input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids
->>> outputs = model.generate(input_ids)
->>> print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-Das Haus ist wunderbar.
-```
-
-Note that T5 uses the `pad_token_id` as the `decoder_start_token_id`, so when doing generation without using
-[`~generation_utils.GenerationMixin.generate`], make sure you start it with the `pad_token_id`.
-
-The example above only shows a single example. You can also do batched inference, like so:
-
-```python
->>> from transformers import T5Tokenizer, T5ForConditionalGeneration
-
->>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
->>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
->>> task_prefix = "translate English to German: "
->>> # use different length sentences to test batching
->>> sentences = ["The house is wonderful.", "I like to work in NYC."]
-
->>> inputs = tokenizer([task_prefix + sentence for sentence in sentences], return_tensors="pt", padding=True)
-
->>> output_sequences = model.generate(
-...     input_ids=inputs["input_ids"],
-...     attention_mask=inputs["attention_mask"],
-...     do_sample=False,  # disable sampling to test if batching affects output
-... )
-
->>> print(tokenizer.batch_decode(output_sequences, skip_special_tokens=True))
-['Das Haus ist wunderbar.', 'Ich arbeite gerne in NYC.']
-```
-
-Because T5 has been trained with the span-mask denoising objective,
-it can be used to predict the sentinel (masked-out) tokens during inference.
-The predicted tokens will then be placed between the sentinel tokens.
-
-```python
->>> from transformers import T5Tokenizer, T5ForConditionalGeneration
-
->>> tokenizer = T5Tokenizer.from_pretrained("t5-small")
->>> model = T5ForConditionalGeneration.from_pretrained("t5-small")
-
->>> input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids
-
->>> sequence_ids = model.generate(input_ids)
->>> sequences = tokenizer.batch_decode(sequence_ids)
->>> sequences
-['<pad> <extra_id_0> park offers<extra_id_1> the<extra_id_2> park.</s>']
-```
-
-
-<a id='scripts'></a>
-
-## Performance
-
-If you'd like a faster training and inference performance, install [apex](https://github.com/NVIDIA/apex#quick-start) and then the model will automatically use `apex.normalization.FusedRMSNorm` instead of `T5LayerNorm`. The former uses an optimized fused kernel which is several times faster than the latter.
-
-
-## Example scripts
-
-T5 is supported by several example scripts, both for pre-training and fine-tuning.
-
-- pre-training: the [run_t5_mlm_flax.py](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/run_t5_mlm_flax.py)
-  script allows you to further pre-train T5 or pre-train T5 from scratch on your own data. The [t5_tokenizer_model.py](https://github.com/huggingface/transformers/blob/main/examples/flax/language-modeling/t5_tokenizer_model.py)
-  script allows you to further train a T5 tokenizer or train a T5 Tokenizer from scratch on your own data. Note that
-  Flax (a neural network library on top of JAX) is particularly useful to train on TPU hardware.
-
-- fine-tuning: T5 is supported by the official summarization scripts ([PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization), [Tensorflow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization), and [Flax](https://github.com/huggingface/transformers/tree/main/examples/flax/summarization)) and translation scripts
-  ([PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/translation) and [Tensorflow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/translation)). These scripts allow
-  you to easily fine-tune T5 on custom data for summarization/translation.
-
-## T5Config
-
-[[autodoc]] T5Config
-
-## T5Tokenizer
-
-[[autodoc]] T5Tokenizer
-    - build_inputs_with_special_tokens
-    - get_special_tokens_mask
-    - create_token_type_ids_from_sequences
-    - save_vocabulary
-
-## T5TokenizerFast
-
-[[autodoc]] T5TokenizerFast
-
-## T5Model
-
-[[autodoc]] T5Model
-    - forward
-    - parallelize
-    - deparallelize
-
-## T5ForConditionalGeneration
-
-[[autodoc]] T5ForConditionalGeneration
-    - forward
-    - parallelize
-    - deparallelize
-
-## T5EncoderModel
-
-[[autodoc]] T5EncoderModel
-    - forward
-    - parallelize
-    - deparallelize
-
-## TFT5Model
-
-[[autodoc]] TFT5Model
-    - call
-
-## TFT5ForConditionalGeneration
-
-[[autodoc]] TFT5ForConditionalGeneration
-    - call
-
-## TFT5EncoderModel
-
-[[autodoc]] TFT5EncoderModel
-    - call
-
-## FlaxT5Model
-
-[[autodoc]] FlaxT5Model
-    - __call__
-    - encode
-    - decode
-
-## FlaxT5ForConditionalGeneration
-
-[[autodoc]] FlaxT5ForConditionalGeneration
-    - __call__
-    - encode
-    - decode

docs/source/en/model_doc/tapex.mdx

@@ -1,130 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# TAPEX
-
-## Overview
-
-The TAPEX model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu,
-Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. TAPEX pre-trains a BART model to solve synthetic SQL queries, after
-which it can be fine-tuned to answer natural language questions related to tabular data, as well as performing table fact checking. 
-
-TAPEX 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)
-- [TabFact](https://tabfact.github.io/) (by USCB NLP Lab).
-
-The abstract from the paper is the following:
-
-*Recent progress in language model pre-training has achieved a great success via leveraging large-scale unstructured textual data. However, it is
-still a challenge to apply pre-training on structured tabular data due to the absence of large-scale high-quality tabular data. In this paper, we
-propose TAPEX to show that table pre-training can be achieved by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically
-synthesizing executable SQL queries and their execution outputs. TAPEX addresses the data scarcity challenge via guiding the language model to mimic a SQL
-executor on the diverse, large-scale and high-quality synthetic corpus. We evaluate TAPEX on four benchmark datasets. Experimental results demonstrate that
-TAPEX outperforms previous table pre-training approaches by a large margin and achieves new state-of-the-art results on all of them. This includes improvements
-on the weakly-supervised WikiSQL denotation accuracy to 89.5% (+2.3%), the WikiTableQuestions denotation accuracy to 57.5% (+4.8%), the SQA denotation accuracy
-to 74.5% (+3.5%), and the TabFact accuracy to 84.2% (+3.2%). To our knowledge, this is the first work to exploit table pre-training via synthetic executable programs
-and to achieve new state-of-the-art results on various downstream tasks.*
-
-Tips:
-
-- TAPEX is a generative (seq2seq) model. One can directly plug in the weights of TAPEX into a BART model. 
-- TAPEX has checkpoints on the hub that are either pre-trained only, or fine-tuned on WTQ, SQA, WikiSQL and TabFact.
-- Sentences + tables are presented to the model as `sentence + " " + linearized table`. The linearized table has the following format: 
-  `col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ...`.
-- TAPEX has its own tokenizer, that allows to prepare all data for the model easily. One can pass Pandas DataFrames and strings to the tokenizer,
-  and it will automatically create the `input_ids` and `attention_mask` (as shown in the usage examples below). 
-
-## Usage: inference
-
-Below, we illustrate how to use TAPEX for table question answering. As one can see, one can directly plug in the weights of TAPEX into a BART model.
-We use the [Auto API](auto), which will automatically instantiate the appropriate tokenizer ([`TapexTokenizer`]) and model ([`BartForConditionalGeneration`]) for us,
-based on the configuration file of the checkpoint on the hub.
-
-```python
->>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
->>> import pandas as pd
-
->>> tokenizer = AutoTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
->>> model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/tapex-large-finetuned-wtq")
-
->>> # prepare table + question
->>> data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
->>> table = pd.DataFrame.from_dict(data)
->>> question = "how many movies does Leonardo Di Caprio have?"
-
->>> encoding = tokenizer(table, question, return_tensors="pt")
-
->>> # let the model generate an answer autoregressively
->>> outputs = model.generate(**encoding)
-
->>> # decode back to text
->>> predicted_answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
->>> print(predicted_answer)
-53
-```
-
-Note that [`TapexTokenizer`] also supports batched inference. Hence, one can provide a batch of different tables/questions, or a batch of a single table
-and multiple questions, or a batch of a single query and multiple tables. Let's illustrate this:
-
-```python
->>> # prepare table + question
->>> data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
->>> table = pd.DataFrame.from_dict(data)
->>> questions = [
-...     "how many movies does Leonardo Di Caprio have?",
-...     "which actor has 69 movies?",
-...     "what's the first name of the actor who has 87 movies?",
-... ]
->>> encoding = tokenizer(table, questions, padding=True, return_tensors="pt")
-
->>> # let the model generate an answer autoregressively
->>> outputs = model.generate(**encoding)
-
->>> # decode back to text
->>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
-[' 53', ' george clooney', ' brad pitt']
-```
-
-In case one wants to do table verification (i.e. the task of determining whether a given sentence is supported or refuted by the contents
-of a table), one can instantiate a [`BartForSequenceClassification`] model. TAPEX has checkpoints on the hub fine-tuned on TabFact, an important
-benchmark for table fact checking (it achieves 84% accuracy). The code example below again leverages the [Auto API](auto).
-
-```python
->>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
->>> tokenizer = AutoTokenizer.from_pretrained("microsoft/tapex-large-finetuned-tabfact")
->>> model = AutoModelForSequenceClassification.from_pretrained("microsoft/tapex-large-finetuned-tabfact")
-
->>> # prepare table + sentence
->>> data = {"Actors": ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], "Number of movies": ["87", "53", "69"]}
->>> table = pd.DataFrame.from_dict(data)
->>> sentence = "George Clooney has 30 movies"
-
->>> encoding = tokenizer(table, sentence, return_tensors="pt")
-
->>> # forward pass
->>> outputs = model(**encoding)
-
->>> # print prediction
->>> predicted_class_idx = outputs.logits[0].argmax(dim=0).item()
->>> print(model.config.id2label[predicted_class_idx])
-Refused
-```
-
-
-## TapexTokenizer
-
-[[autodoc]] TapexTokenizer
-    - __call__
-    - save_vocabulary

docs/source/en/model_doc/van.mdx

@@ -1,51 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# VAN
-
-## Overview
-
-The VAN model was proposed in [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
-
-This paper introduces a new attention layer based on convolution operations able to capture both local and distant relationships. This is done by combining normal and large kernel convolution layers. The latter uses a dilated convolution to capture distant correlations.
-
-The abstract from the paper is the following:
-
-*While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc. Code is available at [this https URL](https://github.com/Visual-Attention-Network/VAN-Classification).*
-
-Tips:
-
-- VAN does not have an embedding layer, thus the `hidden_states` will have a length equal to the number of stages.
-
-The figure below illustrates the architecture of a Visual Aattention Layer. Taken from the [original paper](https://arxiv.org/abs/2202.09741).
-
-<img width="600" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/van_architecture.png"/>
-
-This model was contributed by [Francesco](https://huggingface.co/Francesco). The original code can be found [here](https://github.com/Visual-Attention-Network/VAN-Classification).
-
-
-## VanConfig
-
-[[autodoc]] VanConfig
-
-
-## VanModel
-
-[[autodoc]] VanModel
-    - forward
-
-
-## VanForImageClassification
-
-[[autodoc]] VanForImageClassification
-    - forward
-

docs/source/en/model_doc/yolos.mdx

@@ -1,60 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# YOLOS
-
-## Overview
-
-The YOLOS model was proposed in [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
-YOLOS proposes to just leverage the plain [Vision Transformer (ViT)](vit) for object detection, inspired by DETR. It turns out that a base-sized encoder-only Transformer can also achieve 42 AP on COCO, similar to DETR and much more complex frameworks such as Faster R-CNN.
-
-The abstract from the paper is the following:
-
-*Can Transformer perform 2D object- and region-level recognition from a pure sequence-to-sequence perspective with minimal knowledge about the 2D spatial structure? To answer this question, we present You Only Look at One Sequence (YOLOS), a series of object detection models based on the vanilla Vision Transformer with the fewest possible modifications, region priors, as well as inductive biases of the target task. We find that YOLOS pre-trained on the mid-sized ImageNet-1k dataset only can already achieve quite competitive performance on the challenging COCO object detection benchmark, e.g., YOLOS-Base directly adopted from BERT-Base architecture can obtain 42.0 box AP on COCO val. We also discuss the impacts as well as limitations of current pre-train schemes and model scaling strategies for Transformer in vision through YOLOS.*
-
-Tips:
-
-- One can use [`YolosFeatureExtractor`] for preparing images (and optional targets) for the model. Contrary to [DETR](detr), YOLOS doesn't require a `pixel_mask` to be created.
-- Demo notebooks (regarding inference and fine-tuning on custom data) can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/YOLOS).
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/yolos_architecture.png"
-alt="drawing" width="600"/> 
-
-<small> YOLOS architecture. Taken from the <a href="https://arxiv.org/abs/2106.00666">original paper</a>.</small>
-
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/hustvl/YOLOS).
-
-## YolosConfig
-
-[[autodoc]] YolosConfig
-
-
-## YolosFeatureExtractor
-
-[[autodoc]] YolosFeatureExtractor
-    - __call__
-    - pad
-    - post_process
-    - post_process_segmentation
-    - post_process_panoptic
-
-
-## YolosModel
-
-[[autodoc]] YolosModel
-    - forward
-
-
-## YolosForObjectDetection
-
-[[autodoc]] YolosForObjectDetection
-    - forward

docs/source/en/notebooks.md

@@ -1 +0,0 @@
-../../../notebooks/README.md

docs/source/en/pad_truncation.mdx

@@ -1,66 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Padding and truncation
-
-Batched inputs are often different lengths, so they can't be converted to fixed-size tensors. Padding and truncation are strategies for dealing with this problem, to create rectangular tensors from batches of varying lengths. Padding adds a special **padding token** to ensure shorter sequences will have the same length as either the longest sequence in a batch or the maximum length accepted by the model. Truncation works in the other direction by truncating long sequences.
-
-In most cases, padding your batch to the length of the longest sequence and truncating to the maximum length a model can accept works pretty well. However, the API supports more strategies if you need them. The three arguments you need to are: `padding`, `truncation` and `max_length`.
-
-The `padding` argument controls padding. It can be a boolean or a string:
-
-  - `True` or `'longest'`: pad to the longest sequence in the batch (no padding is applied if you only provide
-    a single sequence).
-  - `'max_length'`: pad to a length specified by the `max_length` argument or the maximum length accepted
-    by the model if no `max_length` is provided (`max_length=None`). Padding will still be applied if you only provide a single sequence.
-  - `False` or `'do_not_pad'`: no padding is applied. This is the default behavior.
-
-The `truncation` argument controls truncation. It can be a boolean or a string:
-
-  - `True` or `'longest_first'`: truncate to a maximum length specified by the `max_length` argument or
-    the maximum length accepted by the model if no `max_length` is provided (`max_length=None`). This will
-    truncate token by token, removing a token from the longest sequence in the pair until the proper length is
-    reached.
-  - `'only_second'`: truncate to a maximum length specified by the `max_length` argument or the maximum
-    length accepted by the model if no `max_length` is provided (`max_length=None`). This will only truncate
-    the second sentence of a pair if a pair of sequences (or a batch of pairs of sequences) is provided.
-  - `'only_first'`: truncate to a maximum length specified by the `max_length` argument or the maximum
-    length accepted by the model if no `max_length` is provided (`max_length=None`). This will only truncate
-    the first sentence of a pair if a pair of sequences (or a batch of pairs of sequences) is provided.
-  - `False` or `'do_not_truncate'`: no truncation is applied. This is the default behavior.
-
-The `max_length` argument controls the length of the padding and truncation. It can be an integer or `None`, in which case it will default to the maximum length the model can accept. If the model has no specific maximum input length, truncation or padding to `max_length` is deactivated.
-
-The following table summarizes the recommended way to setup padding and truncation. If you use pairs of input sequences in any of the following examples, you can replace `truncation=True` by a `STRATEGY` selected in
-`['only_first', 'only_second', 'longest_first']`, i.e. `truncation='only_second'` or `truncation='longest_first'` to control how both sequences in the pair are truncated as detailed before.
-
-| Truncation                           | Padding                           | Instruction                                                                                 |
-|--------------------------------------|-----------------------------------|---------------------------------------------------------------------------------------------|
-| no truncation                        | no padding                        | `tokenizer(batch_sentences)`                                                           |
-|                                      | padding to max sequence in batch  | `tokenizer(batch_sentences, padding=True)` or                                          |
-|                                      |                                   | `tokenizer(batch_sentences, padding='longest')`                                        |
-|                                      | padding to max model input length | `tokenizer(batch_sentences, padding='max_length')`                                     |
-|                                      | padding to specific length        | `tokenizer(batch_sentences, padding='max_length', max_length=42)`                      |
-| truncation to max model input length | no padding                        | `tokenizer(batch_sentences, truncation=True)` or                                       |
-|                                      |                                   | `tokenizer(batch_sentences, truncation=STRATEGY)`                                      |
-|                                      | padding to max sequence in batch  | `tokenizer(batch_sentences, padding=True, truncation=True)` or                         |
-|                                      |                                   | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                        |
-|                                      | padding to max model input length | `tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
-|                                      |                                   | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
-|                                      | padding to specific length        | Not possible                                                                                |
-| truncation to specific length        | no padding                        | `tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
-|                                      |                                   | `tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
-|                                      | padding to max sequence in batch  | `tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
-|                                      |                                   | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |
-|                                      | padding to max model input length | Not possible                                                                                |
-|                                      | padding to specific length        | `tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` or  |
-|                                      |                                   | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |

docs/source/en/run_scripts.mdx

@@ -1,347 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Train with a script
-
-Along with the 🤗 Transformers [notebooks](./noteboks/README), there are also example scripts demonstrating how to train a model for a task with [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow), or [JAX/Flax](https://github.com/huggingface/transformers/tree/main/examples/flax).
-
-You will also find scripts we've used in our [research projects](https://github.com/huggingface/transformers/tree/main/examples/research_projects) and [legacy examples](https://github.com/huggingface/transformers/tree/main/examples/legacy) which are mostly community contributed. These scripts are not actively maintained and require a specific version of 🤗 Transformers that will most likely be incompatible with the latest version of the library.
-
-The example scripts are not expected to work out-of-the-box on every problem, and you may need to adapt the script to the problem you're trying to solve. To help you with this, most of the scripts fully expose how data is preprocessed, allowing you to edit it as necessary for your use case.
-
-For any feature you'd like to implement in an example script, please discuss it on the [forum](https://discuss.huggingface.co/) or in an [issue](https://github.com/huggingface/transformers/issues) before submitting a Pull Request. While we welcome bug fixes, it is unlikely we will merge a Pull Request that adds more functionality at the cost of readability.
-
-This guide will show you how to run an example summarization training script in [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summarization) and [TensorFlow](https://github.com/huggingface/transformers/tree/main/examples/tensorflow/summarization). All examples are expected to work with both frameworks unless otherwise specified.
-
-## Setup
-
-To successfully run the latest version of the example scripts, you have to **install 🤗 Transformers from source** in a new virtual environment:
-
-```bash
-git clone https://github.com/huggingface/transformers
-cd transformers
-pip install .
-```
-
-For older versions of the example scripts, click on the toggle below:
-
-<details>
-  <summary>Examples for older versions of 🤗 Transformers</summary>
-	<ul>
-		<li><a href="https://github.com/huggingface/transformers/tree/v4.5.1/examples">v4.5.1</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v4.4.2/examples">v4.4.2</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v4.3.3/examples">v4.3.3</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v4.2.2/examples">v4.2.2</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v4.1.1/examples">v4.1.1</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v4.0.1/examples">v4.0.1</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v3.5.1/examples">v3.5.1</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v3.4.0/examples">v3.4.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v3.3.1/examples">v3.3.1</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v3.2.0/examples">v3.2.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v3.1.0/examples">v3.1.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v3.0.2/examples">v3.0.2</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.11.0/examples">v2.11.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.10.0/examples">v2.10.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.9.1/examples">v2.9.1</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.8.0/examples">v2.8.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.7.0/examples">v2.7.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.6.0/examples">v2.6.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.5.1/examples">v2.5.1</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.4.0/examples">v2.4.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.3.0/examples">v2.3.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.2.0/examples">v2.2.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.1.0/examples">v2.1.1</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v2.0.0/examples">v2.0.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v1.2.0/examples">v1.2.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v1.1.0/examples">v1.1.0</a></li>
-		<li><a href="https://github.com/huggingface/transformers/tree/v1.0.0/examples">v1.0.0</a></li>
-	</ul>
-</details>
-
-Then switch your current clone of 🤗 Transformers to a specific version, like v3.5.1 for example:
-
-```bash
-git checkout tags/v3.5.1
-```
-
-After you've setup the correct library version, navigate to the example folder of your choice and install the example specific requirements:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Run a script
-
-<frameworkcontent>
-<pt>
-The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset with the [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
-
-```bash
-python examples/pytorch/summarization/run_summarization.py \
-    --model_name_or_path t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
-    --predict_with_generate
-```
-</pt>
-<tf>
-The example script downloads and preprocesses a dataset from the 🤗 [Datasets](https://huggingface.co/docs/datasets/) library. Then the script fine-tunes a dataset using Keras on an architecture that supports summarization. The following example shows how to fine-tune [T5-small](https://huggingface.co/t5-small) on the [CNN/DailyMail](https://huggingface.co/datasets/cnn_dailymail) dataset. The T5 model requires an additional `source_prefix` argument due to how it was trained. This prompt lets T5 know this is a summarization task.
-
-```bash
-python examples/tensorflow/summarization/run_summarization.py  \
-    --model_name_or_path t5-small \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --output_dir /tmp/tst-summarization  \
-    --per_device_train_batch_size 8 \
-    --per_device_eval_batch_size 16 \
-    --num_train_epochs 3 \
-    --do_train \
-    --do_eval
-```
-</tf>
-</frameworkcontent>
-
-## Distributed training and mixed precision
-
-The [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) supports distributed training and mixed precision, which means you can also use it in a script. To enable both of these features:
-
-- Add the `fp16` argument to enable mixed precision.
-- Set the number of GPUs to use with the `nproc_per_node` argument.
-
-```bash
-python -m torch.distributed.launch \
-    --nproc_per_node 8 pytorch/summarization/run_summarization.py \
-    --fp16 \
-    --model_name_or_path t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
-    --predict_with_generate
-```
-
-TensorFlow scripts utilize a [`MirroredStrategy`](https://www.tensorflow.org/guide/distributed_training#mirroredstrategy) for distributed training, and you don't need to add any additional arguments to the training script. The TensorFlow script will use multiple GPUs by default if they are available.
-
-## Run a script on a TPU
-
-<frameworkcontent>
-<pt>
-Tensor Processing Units (TPUs) are specifically designed to accelerate performance. PyTorch supports TPUs with the [XLA](https://www.tensorflow.org/xla) deep learning compiler (see [here](https://github.com/pytorch/xla/blob/master/README.md) for more details). To use a TPU, launch the `xla_spawn.py` script and use the `num_cores` argument to set the number of TPU cores you want to use.
-
-```bash
-python xla_spawn.py --num_cores 8 \
-    summarization/run_summarization.py \
-    --model_name_or_path t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
-    --predict_with_generate
-```
-</pt>
-<tf>
-Tensor Processing Units (TPUs) are specifically designed to accelerate performance. TensorFlow scripts utilize a [`TPUStrategy`](https://www.tensorflow.org/guide/distributed_training#tpustrategy) for training on TPUs. To use a TPU, pass the name of the TPU resource to the `tpu` argument.
-
-```bash
-python run_summarization.py  \
-    --tpu name_of_tpu_resource \
-    --model_name_or_path t5-small \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --output_dir /tmp/tst-summarization  \
-    --per_device_train_batch_size 8 \
-    --per_device_eval_batch_size 16 \
-    --num_train_epochs 3 \
-    --do_train \
-    --do_eval
-```
-</tf>
-</frameworkcontent>
-
-## Run a script with 🤗 Accelerate
-
-🤗 [Accelerate](https://huggingface.co/docs/accelerate/index.html) is a PyTorch-only library that offers a unified method for training a model on several types of setups (CPU-only, multiple GPUs, TPUs) while maintaining complete visibility into the PyTorch training loop. Make sure you have 🤗 Accelerate installed if you don't already have it:
-
-> Note: As Accelerate is rapidly developing, the git version of accelerate must be installed to run the scripts
-```bash
-pip install git+https://github.com/huggingface/accelerate
-```
-
-Instead of the `run_summarization.py` script, you need to use the `run_summarization_no_trainer.py` script. 🤗 Accelerate supported scripts will have a `task_no_trainer.py` file in the folder. Begin by running the following command to create and save a configuration file:
-
-```bash
-accelerate config
-```
-
-Test your setup to make sure it is configured correctly:
-
-```bash
-accelerate test
-```
-
-Now you are ready to launch the training:
-
-```bash
-accelerate launch run_summarization_no_trainer.py \
-    --model_name_or_path t5-small \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir ~/tmp/tst-summarization
-```
-
-## Use a custom dataset
-
-The summarization script supports custom datasets as long as they are a CSV or JSON Line file. When you use your own dataset, you need to specify several additional arguments:
-
-- `train_file` and `validation_file` specify the path to your training and validation files.
-- `text_column` is the input text to summarize.
-- `summary_column` is the target text to output.
-
-A summarization script using a custom dataset would look like this:
-
-```bash
-python examples/pytorch/summarization/run_summarization.py \
-    --model_name_or_path t5-small \
-    --do_train \
-    --do_eval \
-    --train_file path_to_csv_or_jsonlines_file \
-    --validation_file path_to_csv_or_jsonlines_file \
-    --text_column text_column_name \
-    --summary_column summary_column_name \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --overwrite_output_dir \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --predict_with_generate
-```
-
-## Test a script
-
-It is often a good idea to run your script on a smaller number of dataset examples to ensure everything works as expected before committing to an entire dataset which may take hours to complete. Use the following arguments to truncate the dataset to a maximum number of samples:
-
-- `max_train_samples`
-- `max_eval_samples`
-- `max_predict_samples`
-
-```bash
-python examples/pytorch/summarization/run_summarization.py \
-    --model_name_or_path t5-small \
-    --max_train_samples 50 \
-    --max_eval_samples 50 \
-    --max_predict_samples 50 \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
-    --predict_with_generate
-```
-
-Not all example scripts support the `max_predict_samples` argument. If you aren't sure whether your script supports this argument, add the `-h` argument to check:
-
-```bash
-examples/pytorch/summarization/run_summarization.py -h
-```
-
-## Resume training from checkpoint
-
-Another helpful option to enable is resuming training from a previous checkpoint. This will ensure you can pick up where you left off without starting over if your training gets interrupted. There are two methods to resume training from a checkpoint.
-
-The first method uses the `output_dir previous_output_dir` argument to resume training from the latest checkpoint stored in `output_dir`. In this case, you should remove `overwrite_output_dir`:
-
-```bash
-python examples/pytorch/summarization/run_summarization.py
-    --model_name_or_path t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --output_dir previous_output_dir \
-    --predict_with_generate
-```
-
-The second method uses the `resume_from_checkpoint path_to_specific_checkpoint` argument to resume training from a specific checkpoint folder.
-
-```bash
-python examples/pytorch/summarization/run_summarization.py
-    --model_name_or_path t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
-    --resume_from_checkpoint path_to_specific_checkpoint \
-    --predict_with_generate
-```
-
-## Share your model
-
-All scripts can upload your final model to the [Model Hub](https://huggingface.co/models). Make sure you are logged into Hugging Face before you begin:
-
-```bash
-huggingface-cli login
-```
-
-Then add the `push_to_hub` argument to the script. This argument will create a repository with your Hugging Face username and the folder name specified in `output_dir`.
-
-To give your repository a specific name, use the `push_to_hub_model_id` argument to add it. The repository will be automatically listed under your namespace.
-
-The following example shows how to upload a model with a specific repository name:
-
-```bash
-python examples/pytorch/summarization/run_summarization.py
-    --model_name_or_path t5-small \
-    --do_train \
-    --do_eval \
-    --dataset_name cnn_dailymail \
-    --dataset_config "3.0.0" \
-    --source_prefix "summarize: " \
-    --push_to_hub \
-    --push_to_hub_model_id finetuned-t5-cnn_dailymail \
-    --output_dir /tmp/tst-summarization \
-    --per_device_train_batch_size=4 \
-    --per_device_eval_batch_size=4 \
-    --overwrite_output_dir \
-    --predict_with_generate
-```

docs/source/en/tasks/asr.mdx

@@ -1,235 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Automatic speech recognition
-
-<Youtube id="TksaY_FDgnk"/>
-
-Automatic speech recognition (ASR) converts a speech signal to text. It is an example of a sequence-to-sequence task, going from a sequence of audio inputs to textual outputs. Voice assistants like Siri and Alexa utilize ASR models to assist users.
-
-This guide will show you how to fine-tune [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) on the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset to transcribe audio to text.
-
-<Tip>
-
-See the automatic speech recognition [task page](https://huggingface.co/tasks/automatic-speech-recognition) for more information about its associated models, datasets, and metrics.
-
-</Tip>
-
-## Load MInDS-14 dataset
-
-Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) from the 🤗 Datasets library:
-
-```py
->>> from datasets import load_dataset, Audio
-
->>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train")
-```
-
-Split this dataset into a train and test set:
-
-```py
->>> minds = minds.train_test_split(test_size=0.2)
-```
-
-Then take a look at the dataset:
-
-```py
->>> minds
-DatasetDict({
-    train: Dataset({
-        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
-        num_rows: 450
-    })
-    test: Dataset({
-        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
-        num_rows: 113
-    })
-})
-```
-
-While the dataset contains a lot of helpful information, like `lang_id` and `intent_class`, you will focus on the `audio` and `transcription` columns in this guide. Remove the other columns:
-
-```py
->>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"])
-```
-
-Take a look at the example again:
-
-```py
->>> minds["train"][0]
-{'audio': {'array': array([-0.00024414,  0.        ,  0.        , ...,  0.00024414,
-          0.00024414,  0.00024414], dtype=float32),
-  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
-  'sampling_rate': 8000},
- 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
- 'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
-```
-
-The `audio` column contains a 1-dimensional `array` of the speech signal that must be called to load and resample the audio file.
-
-## Preprocess
-
-Load the Wav2Vec2 processor to process the audio signal and transcribed text:
-
-```py
->>> from transformers import AutoProcessor
-
->>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
-```
-
-The [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset has a sampling rate of 8000khz. You will need to resample the dataset to use the pretrained Wav2Vec2 model:
-
-```py
->>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
->>> minds["train"][0]
-{'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ...,
-          2.78103951e-04,  2.38446111e-04,  1.18740834e-04], dtype=float32),
-  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
-  'sampling_rate': 16000},
- 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
- 'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
-```
-
-The preprocessing function needs to:
-
-1. Call the `audio` column to load and resample the audio file.
-2. Extract the `input_values` from the audio file.
-3. Typically, when you call the processor, you call the feature extractor. Since you also want to tokenize text, instruct the processor to call the tokenizer instead with a context manager.
-
-```py
->>> def prepare_dataset(batch):
-...     audio = batch["audio"]
-
-...     batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
-...     batch["input_length"] = len(batch["input_values"])
-
-...     with processor.as_target_processor():
-...         batch["labels"] = processor(batch["transcription"]).input_ids
-...     return batch
-```
-
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the map function by increasing the number of processes with `num_proc`. Remove the columns you don't need:
-
-```py
->>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4)
-```
-
-🤗 Transformers doesn't have a data collator for automatic speech recognition, so you will need to create one. You can adapt the [`DataCollatorWithPadding`] to create a batch of examples for automatic speech recognition. It will also dynamically pad your text and labels to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the `tokenizer` function by setting `padding=True`, dynamic padding is more efficient.
-
-Unlike other data collators, this specific data collator needs to apply a different padding method to `input_values` and `labels`. You can apply a different padding method with a context manager:
-
-```py
->>> import torch
-
->>> from dataclasses import dataclass, field
->>> from typing import Any, Dict, List, Optional, Union
-
-
->>> @dataclass
-... class DataCollatorCTCWithPadding:
-
-...     processor: AutoProcessor
-...     padding: Union[bool, str] = True
-
-...     def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
-...         # split inputs and labels since they have to be of different lengths and need
-...         # different padding methods
-...         input_features = [{"input_values": feature["input_values"]} for feature in features]
-...         label_features = [{"input_ids": feature["labels"]} for feature in features]
-
-...         batch = self.processor.pad(
-...             input_features,
-...             padding=self.padding,
-...             return_tensors="pt",
-...         )
-...         with self.processor.as_target_processor():
-...             labels_batch = self.processor.pad(
-...                 label_features,
-...                 padding=self.padding,
-...                 return_tensors="pt",
-...             )
-
-...         # replace padding with -100 to ignore loss correctly
-...         labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
-
-...         batch["labels"] = labels
-
-...         return batch
-```
-
-Create a batch of examples and dynamically pad them with `DataCollatorForCTCWithPadding`:
-
-```py
->>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)
-```
-
-## Train
-
-<frameworkcontent>
-<pt>
-Load Wav2Vec2 with [`AutoModelForCTC`]. For `ctc_loss_reduction`, it is often better to use the average instead of the default summation:
-
-```py
->>> from transformers import AutoModelForCTC, TrainingArguments, Trainer
-
->>> model = AutoModelForCTC.from_pretrained(
-...     "facebook/wav2vec2-base",
-...     ctc_loss_reduction="mean",
-...     pad_token_id=processor.tokenizer.pad_token_id,
-... )
-```
-
-<Tip>
-
-If you aren't familiar with fine-tuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#finetune-with-trainer)!
-
-</Tip>
-
-At this point, only three steps remain:
-
-1. Define your training hyperparameters in [`TrainingArguments`].
-2. Pass the training arguments to [`Trainer`] along with the model, datasets, tokenizer, and data collator.
-3. Call [`~Trainer.train`] to fine-tune your model.
-
-```py
->>> training_args = TrainingArguments(
-...     output_dir="./results",
-...     group_by_length=True,
-...     per_device_train_batch_size=16,
-...     evaluation_strategy="steps",
-...     num_train_epochs=3,
-...     fp16=True,
-...     gradient_checkpointing=True,
-...     learning_rate=1e-4,
-...     weight_decay=0.005,
-...     save_total_limit=2,
-... )
-
->>> trainer = Trainer(
-...     model=model,
-...     args=training_args,
-...     train_dataset=encoded_minds["train"],
-...     eval_dataset=encoded_minds["test"],
-...     tokenizer=processor.feature_extractor,
-...     data_collator=data_collator,
-... )
-
->>> trainer.train()
-```
-</pt>
-</frameworkcontent>
-
-<Tip>
-
-For a more in-depth example of how to fine-tune a model for automatic speech recognition, take a look at this blog [post](https://huggingface.co/blog/fine-tune-wav2vec2-english) for English ASR and this [post](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for multilingual ASR.
-
-</Tip>

docs/source/en/tasks/audio_classification.mdx

@@ -1,192 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Audio classification
-
-<Youtube id="KWwzcmG98Ds"/>
-
-Audio classification assigns a label or class to audio data. It is similar to text classification, except an audio input is continuous and must be discretized, whereas text can be split into tokens. Some practical applications of audio classification include identifying intent, speakers, and even animal species by their sounds.
-
-This guide will show you how to fine-tune [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) on the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) to classify intent.
-
-<Tip>
-
-See the audio classification [task page](https://huggingface.co/tasks/audio-classification) for more information about its associated models, datasets, and metrics.
-
-</Tip>
-
-## Load MInDS-14 dataset
-
-Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) from the 🤗 Datasets library:
-
-```py
->>> from datasets import load_dataset, Audio
-
->>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train")
-```
-
-Split this dataset into a train and test set:
-
-```py
->>> minds = minds.train_test_split(test_size=0.2)
-```
-
-Then take a look at the dataset:
-
-```py
->>> minds
-DatasetDict({
-    train: Dataset({
-        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
-        num_rows: 450
-    })
-    test: Dataset({
-        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
-        num_rows: 113
-    })
-})
-```
-
-While the dataset contains a lot of other useful information, like `lang_id` and `english_transcription`, you will focus on the `audio` and `intent_class` in this guide. Remove the other columns:
-
-```py
->>> minds = minds.remove_columns(["path", "transcription", "english_transcription", "lang_id"])
-```
-
-Take a look at an example now:
-
-```py
->>> minds["train"][0]
-{'audio': {'array': array([ 0.        ,  0.        ,  0.        , ..., -0.00048828,
-         -0.00024414, -0.00024414], dtype=float32),
-  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602b9a5fbb1e6d0fbce91f52.wav',
-  'sampling_rate': 8000},
- 'intent_class': 2}
-```
-
-The `audio` column contains a 1-dimensional `array` of the speech signal that must be called to load and resample the audio file. The `intent_class` column is an integer that represents the class id of intent. Create a dictionary that maps a label name to an integer and vice versa. The mapping will help the model recover the label name from the label number:
-
-```py
->>> labels = minds["train"].features["intent_class"].names
->>> label2id, id2label = dict(), dict()
->>> for i, label in enumerate(labels):
-...     label2id[label] = str(i)
-...     id2label[str(i)] = label
-```
-
-Now you can convert the label number to a label name for more information:
-
-```py
->>> id2label[str(2)]
-'app_error'
-```
-
-Each keyword - or label - corresponds to a number; `2` indicates `app_error` in the example above.
-
-## Preprocess
-
-Load the Wav2Vec2 feature extractor to process the audio signal:
-
-```py
->>> from transformers import AutoFeatureExtractor
-
->>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
-```
-
-The [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset has a sampling rate of 8000khz. You will need to resample the dataset to use the pretrained Wav2Vec2 model:
-
-```py
->>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
->>> minds["train"][0]
-{'audio': {'array': array([ 2.2098757e-05,  4.6582241e-05, -2.2803260e-05, ...,
-         -2.8419291e-04, -2.3305941e-04, -1.1425107e-04], dtype=float32),
-  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602b9a5fbb1e6d0fbce91f52.wav',
-  'sampling_rate': 16000},
- 'intent_class': 2}
-```
-
-The preprocessing function needs to:
-
-1. Call the `audio` column to load and if necessary resample the audio file.
-2. Check the sampling rate of the audio file matches the sampling rate of the audio data a model was pretrained with. You can find this information on the Wav2Vec2 [model card]((https://huggingface.co/facebook/wav2vec2-base)).
-3. Set a maximum input length so longer inputs are batched without being truncated.
-
-```py
->>> def preprocess_function(examples):
-...     audio_arrays = [x["array"] for x in examples["audio"]]
-...     inputs = feature_extractor(
-...         audio_arrays, sampling_rate=feature_extractor.sampling_rate, max_length=16000, truncation=True
-...     )
-...     return inputs
-```
-
-Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) function to apply the preprocessing function over the entire dataset. You can speed up the `map` function by setting `batched=True` to process multiple elements of the dataset at once. Remove the columns you don't need, and rename `intent_class` to `label` because that is what the model expects:
-
-```py
->>> encoded_minds = minds.map(preprocess_function, remove_columns="audio", batched=True)
->>> encoded_minds = encoded_minds.rename_column("intent_class", "label")
-```
-
-## Train
-
-<frameworkcontent>
-<pt>
-Load Wav2Vec2 with [`AutoModelForAudioClassification`]. Specify the number of labels, and pass the model the mapping between label number and label class:
-
-```py
->>> from transformers import AutoModelForAudioClassification, TrainingArguments, Trainer
-
->>> num_labels = len(id2label)
->>> model = AutoModelForAudioClassification.from_pretrained(
-...     "facebook/wav2vec2-base", num_labels=num_labels, label2id=label2id, id2label=id2label
-... )
-```
-
-<Tip>
-
-If you aren't familiar with fine-tuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#finetune-with-trainer)!
-
-</Tip>
-
-At this point, only three steps remain:
-
-1. Define your training hyperparameters in [`TrainingArguments`].
-2. Pass the training arguments to [`Trainer`] along with the model, datasets, and feature extractor.
-3. Call [`~Trainer.train`] to fine-tune your model.
-
-```py
->>> training_args = TrainingArguments(
-...     output_dir="./results",
-...     evaluation_strategy="epoch",
-...     save_strategy="epoch",
-...     learning_rate=3e-5,
-...     num_train_epochs=5,
-... )
-
->>> trainer = Trainer(
-...     model=model,
-...     args=training_args,
-...     train_dataset=encoded_minds["train"],
-...     eval_dataset=encoded_minds["test"],
-...     tokenizer=feature_extractor,
-... )
-
->>> trainer.train()
-```
-</pt>
-</frameworkcontent>
-
-<Tip>
-
-For a more in-depth example of how to fine-tune a model for audio classification, take a look at the corresponding [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/audio_classification.ipynb).
-
-</Tip>

docs/source/en/tasks/image_classification.mdx

@@ -1,174 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Image classification
-
-<Youtube id="tjAIM7BOYhw"/>
-
-Image classification assigns a label or class to an image. Unlike text or audio classification, the inputs are the pixel values that represent an image. There are many uses for image classification, like detecting damage after a disaster, monitoring crop health, or helping screen medical images for signs of disease.
-
-This guide will show you how to fine-tune [ViT](https://huggingface.co/docs/transformers/v4.16.2/en/model_doc/vit) on the [Food-101](https://huggingface.co/datasets/food101) dataset to classify a food item in an image.
-
-<Tip>
-
-See the image classification [task page](https://huggingface.co/tasks/audio-classification) for more information about its associated models, datasets, and metrics.
-
-</Tip>
-
-## Load Food-101 dataset
-
-Load only the first 5000 images of the Food-101 dataset from the 🤗 Datasets library since it is pretty large:
-
-```py
->>> from datasets import load_dataset
-
->>> food = load_dataset("food101", split="train[:5000]")
-```
-
-Split this dataset into a train and test set:
-
-```py
->>> food = food.train_test_split(test_size=0.2)
-```
-
-Then take a look at an example:
-
-```py
->>> food["train"][0]
-{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F52AFC8AC50>,
- 'label': 79}
-```
-
-The `image` field contains a PIL image, and each `label` is an integer that represents a class. Create a dictionary that maps a label name to an integer and vice versa. The mapping will help the model recover the label name from the label number:
-
-```py
->>> labels = food["train"].features["label"].names
->>> label2id, id2label = dict(), dict()
->>> for i, label in enumerate(labels):
-...     label2id[label] = str(i)
-...     id2label[str(i)] = label
-```
-
-Now you can convert the label number to a label name for more information:
-
-```py
->>> id2label[str(79)]
-'prime_rib'
-```
-
-Each food class - or label - corresponds to a number; `79` indicates a prime rib in the example above.
-
-## Preprocess
-
-Load the ViT feature extractor to process the image into a tensor:
-
-```py
->>> from transformers import AutoFeatureExtractor
-
->>> feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
-```
-
-Apply several image transformations to the dataset to make the model more robust against overfitting. Here you'll use torchvision's [`transforms`](https://pytorch.org/vision/stable/transforms.html) module. Crop a random part of the image, resize it, and normalize it with the image mean and standard deviation:
-
-```py
->>> from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
-
->>> normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
->>> _transforms = Compose([RandomResizedCrop(feature_extractor.size), ToTensor(), normalize])
-```
-
-Create a preprocessing function that will apply the transforms and return the `pixel_values` - the inputs to the model - of the image:
-
-```py
->>> def transforms(examples):
-...     examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
-...     del examples["image"]
-...     return examples
-```
-
-Use 🤗 Dataset's [`with_transform`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?#datasets.Dataset.with_transform) method to apply the transforms over the entire dataset. The transforms are applied on-the-fly when you load an element of the dataset:
-
-```py
->>> food = food.with_transform(transforms)
-```
-
-Use [`DefaultDataCollator`] to create a batch of examples. Unlike other data collators in 🤗 Transformers, the DefaultDataCollator does not apply additional preprocessing such as padding.
-
-```py
->>> from transformers import DefaultDataCollator
-
->>> data_collator = DefaultDataCollator()
-```
-
-## Train
-
-<frameworkcontent>
-<pt>
-Load ViT with [`AutoModelForImageClassification`]. Specify the number of labels, and pass the model the mapping between label number and label class:
-
-```py
->>> from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
-
->>> model = AutoModelForImageClassification.from_pretrained(
-...     "google/vit-base-patch16-224-in21k",
-...     num_labels=len(labels),
-...     id2label=id2label,
-...     label2id=label2id,
-... )
-```
-
-<Tip>
-
-If you aren't familiar with fine-tuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#finetune-with-trainer)!
-
-</Tip>
-
-At this point, only three steps remain:
-
-1. Define your training hyperparameters in [`TrainingArguments`]. It is important you don't remove unused columns because this will drop the `image` column. Without the `image` column, you can't create `pixel_values`. Set `remove_unused_columns=False` to prevent this behavior!
-2. Pass the training arguments to [`Trainer`] along with the model, datasets, tokenizer, and data collator.
-3. Call [`~Trainer.train`] to fine-tune your model.
-
-```py
->>> training_args = TrainingArguments(
-...     output_dir="./results",
-...     per_device_train_batch_size=16,
-...     evaluation_strategy="steps",
-...     num_train_epochs=4,
-...     fp16=True,
-...     save_steps=100,
-...     eval_steps=100,
-...     logging_steps=10,
-...     learning_rate=2e-4,
-...     save_total_limit=2,
-...     remove_unused_columns=False,
-... )
-
->>> trainer = Trainer(
-...     model=model,
-...     args=training_args,
-...     data_collator=data_collator,
-...     train_dataset=food["train"],
-...     eval_dataset=food["test"],
-...     tokenizer=feature_extractor,
-... )
-
->>> trainer.train()
-```
-</pt>
-</frameworkcontent>
-
-<Tip>
-
-For a more in-depth example of how to fine-tune a model for image classification, take a look at the corresponding [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb).
-
-</Tip>

docs/source/en/troubleshooting.mdx

@@ -1,176 +0,0 @@
-<!---
-Copyright 2022 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.
--->
-
-# Troubleshoot
-
-Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we've seen and how you can resolve them. However, this guide isn't meant to be a comprehensive collection of every 🤗 Transformers issue. For more help with troubleshooting your issue, try:
-
-<Youtube id="S2EEG3JIt2A"/>
-
-1. Asking for help on the [forums](https://discuss.huggingface.co/). There are specific categories you can post your question to, like [Beginners](https://discuss.huggingface.co/c/beginners/5) or [🤗 Transformers](https://discuss.huggingface.co/c/transformers/9). Make sure you write a good descriptive forum post with some reproducible code to maximize the likelihood that your problem is solved!
-
-<Youtube id="_PAli-V4wj0"/>
-
-2. Create an [Issue](https://github.com/huggingface/transformers/issues/new/choose) on the 🤗 Transformers repository if it is a bug related to the library. Try to include as much information describing the bug as possible to help us better figure out what's wrong and how we can fix it.
-
-3. Check the [Migration](migration) guide if you use an older version of 🤗 Transformers since some important changes have been introduced between versions.
-
-For more details about troubleshooting and getting help, take a look at [Chapter 8](https://huggingface.co/course/chapter8/1?fw=pt) of the Hugging Face course.
-
-
-## Firewalled environments
-
-Some GPU instances on cloud and intranet setups are firewalled to external connections, resulting in a connection error. When your script attempts to download model weights or datasets, the download will hang and then timeout with the following message:
-
-```
-ValueError: Connection error, and we cannot find the requested files in the cached path.
-Please try again or make sure your Internet connection is on.
-```
-
-In this case, you should try to run 🤗 Transformers on [offline mode](installation#offline-mode) to avoid the connection error.
-
-## CUDA out of memory
-
-Training large models with millions of parameters can be challenging without the appropriate hardware. A common error you may encounter when the GPU runs out of memory is:
-
-```
-CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 11.17 GiB total capacity; 9.70 GiB already allocated; 179.81 MiB free; 9.85 GiB reserved in total by PyTorch)
-```
-
-Here are some potential solutions you can try to lessen memory use:
-
-- Reduce the [`per_device_train_batch_size`](main_classes/trainer#transformers.TrainingArguments.per_device_train_batch_size) value in [`TrainingArguments`].
-- Try using [`gradient_accumulation_steps`](main_classes/trainer#transformers.TrainingArguments.gradient_accumulation_steps) in [`TrainingArguments`] to effectively increase overall batch size.
-
-<Tip>
-
-Refer to the Performance [guide](performance) for more details about memory-saving techniques.
-
-</Tip>
-
-## Unable to load a saved TensorFlow model
-
-TensorFlow's [model.save](https://www.tensorflow.org/tutorials/keras/save_and_load#save_the_entire_model) method will save the entire model - architecture, weights, training configuration - in a single file. However, when you load the model file again, you may run into an error because 🤗 Transformers may not load all the TensorFlow-related objects in the model file. To avoid issues with saving and loading TensorFlow models, we recommend you:
-
-- Save the model weights as a `h5` file extension with [`model.save_weights`](https://www.tensorflow.org/tutorials/keras/save_and_load#save_the_entire_model) and then reload the model with [`~TFPreTrainedModel.from_pretrained`]:
-
-```py
->>> from transformers import TFPreTrainedModel
->>> from tensorflow import keras
-
->>> model.save_weights("some_folder/tf_model.h5")
->>> model = TFPreTrainedModel.from_pretrained("some_folder")
-```
-
-- Save the model with [`~TFPretrainedModel.save_pretrained`] and load it again with [`~TFPreTrainedModel.from_pretrained`]:
-
-```py
->>> from transformers import TFPreTrainedModel
-
->>> model.save_pretrained("path_to/model")
->>> model = TFPreTrainedModel.from_pretrained("path_to/model")
-```
-
-## ImportError
-
-Another common error you may encounter, especially if it is a newly released model, is `ImportError`:
-
-```
-ImportError: cannot import name 'ImageGPTFeatureExtractor' from 'transformers' (unknown location)
-```
-
-For these error types, check to make sure you have the latest version of 🤗 Transformers installed to access the most recent models:
-
-```bash
-pip install transformers --upgrade
-```
-
-## CUDA error: device-side assert triggered
-
-Sometimes you may run into a generic CUDA error about an error in the device code.
-
-```
-RuntimeError: CUDA error: device-side assert triggered
-```
-
-You should try to run the code on a CPU first to get a more descriptive error message. Add the following environment variable to the beginning of your code to switch to a CPU:
-
-```py
->>> import os
-
->>> os.environ["CUDA_VISIBLE_DEVICES"] = ""
-```
-
-Another option is to get a better traceback from the GPU. Add the following environment variable to the beginning of your code to get the traceback to point to the source of the error:
-
-```py
->>> import os
-
->>> os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
-```
-
-## Incorrect output when padding tokens aren't masked
-
-In some cases, the output `hidden_state` may be incorrect if the `input_ids` include padding tokens. To demonstrate, load a model and tokenizer. You can access a model's `pad_token_id` to see its value. The `pad_token_id` may be `None` for some models, but you can always manually set it.
-
-```py
->>> from transformers import AutoModelForSequenceClassification
->>> import torch
-
->>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
->>> model.config.pad_token_id
-0
-```
-
-The following example shows the output without masking the padding tokens:
-
-```py
->>> input_ids = torch.tensor([[7592, 2057, 2097, 2393, 9611, 2115], [7592, 0, 0, 0, 0, 0]])
->>> output = model(input_ids)
->>> print(output.logits)
-tensor([[ 0.0082, -0.2307],
-        [ 0.1317, -0.1683]], grad_fn=<AddmmBackward0>)
-```
-
-Here is the actual output of the second sequence:
-
-```py
->>> input_ids = torch.tensor([[7592]])
->>> output = model(input_ids)
->>> print(output.logits)
-tensor([[-0.1008, -0.4061]], grad_fn=<AddmmBackward0>)
-```
-
-Most of the time, you should provide an `attention_mask` to your model to ignore the padding tokens to avoid this silent error. Now the output of the second sequence matches its actual output:
-
-<Tip>
-
-By default, the tokenizer creates an `attention_mask` for you based on your specific tokenizer's defaults.
-
-</Tip>
-
-```py
->>> attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0]])
->>> output = model(input_ids, attention_mask=attention_mask)
->>> print(output.logits)
-tensor([[ 0.0082, -0.2307],
-        [-0.1008, -0.4061]], grad_fn=<AddmmBackward0>)
-```
-
-🤗 Transformers doesn't automatically create an `attention_mask` to mask a padding token if it is provided because:
-
-- Some models don't have a padding token.
-- For some use-cases, users want a model to attend to a padding token.

docs/source/es/_config.py

@@ -1,14 +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}]
-black_avoid_patterns = {
-    "{processor_class}": "FakeProcessorClass",
-    "{model_class}": "FakeModelClass",
-    "{object_class}": "FakeObjectClass",
-}

docs/source/es/_toctree.yml

@@ -1,35 +0,0 @@
-- sections:
-  - local: index
-    title: 🤗 Transformers
-  - local: quicktour
-    title: Tour rápido
-  - local: installation
-    title: Instalación
-  title: Empezar
-- sections:
-  - local: pipeline_tutorial
-    title: Pipelines para inferencia
-  - local: autoclass_tutorial
-    title: Carga instancias preentrenadas con un AutoClass
-  - local: training
-    title: Fine-tuning a un modelo pre-entrenado
-  - local: accelerate
-    title: Entrenamiento distribuido con 🤗 Accelerate
-  title: Tutoriales
-- sections:
-  - local: fast_tokenizers
-    title: Usa tokenizadores de 🤗 Tokenizers
-  - sections:
-    - local: language_modeling
-      title: Modelado de lenguaje
-    title: Fine-tuning para tareas posteriores
-  - local: multilingual
-    title: Modelos multilingües para inferencia
-  title: Guías prácticas
-- sections:
-  - local: philosophy
-    title: Filosofía
-  title: Guías conceptuales
-
-
-

docs/source/es/accelerate.mdx

@@ -1,132 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Entrenamiento distribuido con 🤗 Accelerate
-
-El paralelismo ha emergido como una estrategia para entrenar modelos grandes en hardware limitado e incrementar la velocidad de entrenamiento en varios órdenes de magnitud. En Hugging Face creamos la biblioteca [🤗 Accelerate](https://huggingface.co/docs/accelerate/index.html) para ayudar a los usuarios a entrenar modelos 🤗 Transformers en cualquier tipo de configuración distribuida, ya sea en una máquina con múltiples GPUs o en múltiples GPUs distribuidas entre muchas máquinas. En este tutorial aprenderás cómo personalizar tu bucle de entrenamiento de PyTorch nativo para poder entrenar en entornos distribuidos.
-
-## Configuración
-
-Empecemos por instalar 🤗 Accelerate:
-
-```bash
-pip install accelerate
-```
-
-Luego, importamos y creamos un objeto [`Accelerator`](https://huggingface.co/docs/accelerate/accelerator.html#accelerate.Accelerator). `Accelerator` detectará automáticamente el tipo de configuración distribuida que tengas disponible e inicializará todos los componentes necesarios para el entrenamiento. No necesitas especificar el dispositivo en donde se debe colocar tu modelo.
-
-```py
->>> from accelerate import Accelerator
-
->>> accelerator = Accelerator()
-```
-
-## Prepárate para acelerar
-
-Pasa todos los objetos relevantes para el entrenamiento al método [`prepare`](https://huggingface.co/docs/accelerate/accelerator.html#accelerate.Accelerator.prepare). Esto incluye los DataLoaders de entrenamiento y evaluación, un modelo y un optimizador:
-
-```py
->>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
-...     train_dataloader, eval_dataloader, model, optimizer
-... )
-```
-
-## Backward
-
-Por último, reemplaza el típico `loss.backward()` en tu bucle de entrenamiento con el método [`backward`](https://huggingface.co/docs/accelerate/accelerator.html#accelerate.Accelerator.backward) de 🤗 Accelerate:
-
-```py
->>> for epoch in range(num_epochs):
-...     for batch in train_dataloader:
-...         outputs = model(**batch)
-...         loss = outputs.loss
-...         accelerator.backward(loss)
-
-...         optimizer.step()
-...         lr_scheduler.step()
-...         optimizer.zero_grad()
-...         progress_bar.update(1)
-```
-
-Como se puede ver en el siguiente código, ¡solo necesitas adicionar cuatro líneas de código a tu bucle de entrenamiento para habilitar el entrenamiento distribuido!
-
-```diff
-+ from accelerate import Accelerator
-  from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
-
-+ accelerator = Accelerator()
-
-  model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
-  optimizer = AdamW(model.parameters(), lr=3e-5)
-
-- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-- model.to(device)
-
-+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
-+     train_dataloader, eval_dataloader, model, optimizer
-+ )
-
-  num_epochs = 3
-  num_training_steps = num_epochs * len(train_dataloader)
-  lr_scheduler = get_scheduler(
-      "linear",
-      optimizer=optimizer,
-      num_warmup_steps=0,
-      num_training_steps=num_training_steps
-  )
-
-  progress_bar = tqdm(range(num_training_steps))
-
-  model.train()
-  for epoch in range(num_epochs):
-      for batch in train_dataloader:
--         batch = {k: v.to(device) for k, v in batch.items()}
-          outputs = model(**batch)
-          loss = outputs.loss
--         loss.backward()
-+         accelerator.backward(loss)
-
-          optimizer.step()
-          lr_scheduler.step()
-          optimizer.zero_grad()
-          progress_bar.update(1)
-```
-
-## Entrenamiento
-
-Una vez que hayas añadido las líneas de código relevantes, inicia el entrenamiento desde un script o notebook como Colaboratory.
-
-### Entrenar con un script
-
-Si estas corriendo tu entrenamiento desde un script ejecuta el siguiente comando para crear y guardar un archivo de configuración:
-
-```bash
-accelerate config
-```
-
-Comienza el entrenamiento con:
-
-```bash
-accelerate launch train.py
-```
-
-### Entrenar con un notebook
-
-🤗 Accelerate puede correr en un notebook si, por ejemplo, estás planeando utilizar las TPUs de Colaboratory. Encierra el código responsable del entrenamiento en una función y pásalo a `notebook_launcher`:
-
-```py
->>> from accelerate import notebook_launcher
-
->>> notebook_launcher(training_function)
-```
-
-Para obtener más información sobre 🤗 Accelerate y sus numerosas funciones, consulta la [documentación](https://huggingface.co/docs/accelerate/index.html).

docs/source/es/autoclass_tutorial.mdx

@@ -1,119 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Carga instancias preentrenadas con un AutoClass
-
-Con tantas arquitecturas diferentes de Transformer puede ser retador crear una para tu checkpoint. Como parte de la filosofía central de 🤗 Transformers para hacer que la biblioteca sea fácil, simple y flexible de usar; una `AutoClass` automáticamente infiere y carga la arquitectura correcta desde un checkpoint dado. El método `from_pretrained` te permite cargar rápidamente un modelo preentrenado para cualquier arquitectura, por lo que no tendrás que dedicar tiempo y recursos para entrenar uno desde cero. Producir este tipo de código con checkpoint implica que si funciona con uno, funcionará también con otro (siempre que haya sido entrenado para una tarea similar) incluso si la arquitectura es distinta.
-
-<Tip>
-
-Recuerda, la arquitectura se refiere al esqueleto del modelo y los checkpoints son los pesos para una arquitectura dada. Por ejemplo, [BERT](https://huggingface.co/bert-base-uncased) es una arquitectura, mientras que `bert-base-uncased` es un checkpoint. Modelo es un término general que puede significar una arquitectura o un checkpoint.
-
-</Tip>
-
-En este tutorial, aprende a:
-
-* Cargar un tokenizador preentrenado.
-* Cargar un extractor de características (feature extractor) preentrenado.
-* Cargar un procesador preentrenado.
-* Cargar un modelo preentrenado.
-
-## AutoTokenizer
-
-Casi cualquier tarea de Natural Language Processing comienza con un tokenizador. Un tokenizador convierte tu input a un formato que puede ser procesado por el modelo.
-
-Carga un tokenizador con [`AutoTokenizer.from_pretrained`]:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-```
-
-Luego tokeniza tu input como lo mostrado a continuación:
-
-```py
->>> sequence = "In a hole in the ground there lived a hobbit."
->>> print(tokenizer(sequence))
-{'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102], 
- 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
- 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-```
-
-## AutoFeatureExtractor
-
-Para tareas de audio y visión, un extractor de características procesa la señal de audio o imagen al formato de input correcto.
-
-Carga un extractor de características con [`AutoFeatureExtractor.from_pretrained`]:
-
-```py
->>> from transformers import AutoFeatureExtractor
-
->>> feature_extractor = AutoFeatureExtractor.from_pretrained(
-...     "ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
-... )
-```
-
-## AutoProcessor
-
-Las tareas multimodales requieren un procesador que combine dos tipos de herramientas de preprocesamiento. Por ejemplo, el modelo [LayoutLMV2](model_doc/layoutlmv2) requiere que un extractor de características maneje las imágenes y que un tokenizador maneje el texto; un procesador combina ambas.
-
-Carga un procesador con [`AutoProcessor.from_pretrained`]:
-
-```py
->>> from transformers import AutoProcessor
-
->>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
-```
-
-## AutoModel
-
-<frameworkcontent>
-<pt>
-Finalmente, las clases `AutoModelFor` te permiten cargar un modelo preentrenado para una tarea dada (revisa [aquí](model_doc/auto) para conocer la lista completa de tareas disponibles). Por ejemplo, cargue un modelo para clasificación de secuencias con [`AutoModelForSequenceClassification.from_pretrained`]:
-
-```py
->>> from transformers import AutoModelForSequenceClassification
-
->>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Reutilice fácilmente el mismo checkpoint para cargar una aquitectura para alguna tarea diferente:
-
-```py
->>> from transformers import AutoModelForTokenClassification
-
->>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Generalmente recomendamos utilizar las clases `AutoTokenizer` y `AutoModelFor` para cargar instancias preentrenadas de modelos. Ésto asegurará que cargues la arquitectura correcta en cada ocasión. En el siguiente [tutorial](preprocessing), aprende a usar tu tokenizador recién cargado, el extractor de características y el procesador para preprocesar un dataset para fine-tuning.
-</pt>
-<tf>
-Finalmente, la clase `TFAutoModelFor` te permite cargar tu modelo preentrenado para una tarea dada (revisa [aquí](model_doc/auto) para conocer la lista completa de tareas disponibles). Por ejemplo, carga un modelo para clasificación de secuencias con [`TFAutoModelForSequenceClassification.from_pretrained`]:
-
-```py
->>> from transformers import TFAutoModelForSequenceClassification
-
->>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Reutilice fácilmente el mismo checkpoint para cargar una aquitectura para alguna tarea diferente:
-
-```py
->>> from transformers import TFAutoModelForTokenClassification
-
->>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Generalmente recomendamos utilizar las clases `AutoTokenizer` y `TFAutoModelFor` para cargar instancias de modelos preentrenados. Ésto asegurará que cargues la arquitectura correcta cada vez. En el siguiente [tutorial](preprocessing), aprende a usar tu tokenizador recién cargado, el extractor de características y el procesador para preprocesar un dataset para fine-tuning.
-</tf>
-</frameworkcontent>

docs/source/es/fast_tokenizers.mdx

@@ -1,70 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Usa los tokenizadores de 🤗 Tokenizers
-
-[`PreTrainedTokenizerFast`] depende de la biblioteca [🤗 Tokenizers](https://huggingface.co/docs/tokenizers). Los tokenizadores obtenidos desde la biblioteca 🤗 Tokenizers pueden ser 
-cargados de forma muy sencilla en los 🤗 Transformers.
-
-Antes de entrar en detalles, comencemos creando un tokenizador dummy en unas cuantas líneas:
-
-```python
->>> from tokenizers import Tokenizer
->>> from tokenizers.models import BPE
->>> from tokenizers.trainers import BpeTrainer
->>> from tokenizers.pre_tokenizers import Whitespace
-
->>> tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
->>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
-
->>> tokenizer.pre_tokenizer = Whitespace()
->>> files = [...]
->>> tokenizer.train(files, trainer)
-```
-
-Ahora tenemos un tokenizador entrenado en los archivos que definimos. Lo podemos seguir utilizando en ese entorno de ejecución (runtime en inglés), o puedes guardarlo
-en un archivo JSON para reutilizarlo en un futuro.
-
-## Cargando directamente desde el objeto tokenizador 
-
-Veamos cómo utilizar este objeto tokenizador en la biblioteca 🤗 Transformers. La clase
-[`PreTrainedTokenizerFast`] permite una instanciación fácil, al aceptar el objeto
-*tokenizer* instanciado como argumento:
-
-```python
->>> from transformers import PreTrainedTokenizerFast
-
->>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
-```
-
-Este objeto ya puede ser utilizado con todos los métodos compartidos por los tokenizadores de 🤗 Transformers! Visita la [página sobre tokenizadores
-](main_classes/tokenizer) para más información.
-
-## Cargando desde un archivo JSON
-
-Para cargar un tokenizador desde un archivo JSON, comencemos por guardar nuestro tokenizador:
-
-```python
->>> tokenizer.save("tokenizer.json")
-```
-
-La localización (path en inglés) donde este archivo es guardado puede ser incluida en el método de inicialización de [`PreTrainedTokenizerFast`]
-utilizando el parámetro `tokenizer_file`:
-
-```python
->>> from transformers import PreTrainedTokenizerFast
-
->>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
-```
-
-Este objeto ya puede ser utilizado con todos los métodos compartidos por los tokenizadores de 🤗 Transformers! Visita la [página sobre tokenizadores
-](main_classes/tokenizer) para más información.

docs/source/es/index.mdx

@@ -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
-
-Machine Learning de última generación para PyTorch, TensorFlow y JAX.
-
-🤗 Transformers proporciona APIs para descargar y entrenar fácilmente modelos preentrenados de última generación. El uso de modelos  preentrenados puede reducir tus costos de cómputo, tu huella de carbono y ahorrarte tiempo al entrenar un modelo desde cero. Los modelos se pueden utilizar en diferentes modalidades, tales como:
-
-* 📝 Texto: clasificación de texto, extracción de información, respuesta a preguntas, resumir, traducción y generación de texto en más de 100 idiomas.
-* 🖼️ Imágenes: clasificación de imágenes, detección de objetos y segmentación.
-* 🗣️ Audio: reconocimiento de voz y clasificación de audio.
-* 🐙 Multimodal: respuesta a preguntas en tablas, reconocimiento óptico de caracteres, extracción de información de documentos escaneados, clasificación de videos y respuesta visual a preguntas.
-
-Nuestra biblioteca admite una integración perfecta entre tres de las bibliotecas de deep learning más populares: [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/) y [JAX](https://jax.readthedocs.io/en/latest/). Entrena tu modelo con tres líneas de código en un framework y cárgalo para inferencia con otro.
-Cada arquitectura de 🤗 Transformers se define en un módulo de Python independiente para que se puedan personalizar fácilmente para investigación y experimentos.
-
-## Si estás buscando soporte personalizado del equipo de Hugging Face
-
-<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>
-
-## Contenidos
-
-La documentación está organizada en cuatro partes:
-
-- **EMPEZAR** contiene un recorrido rápido e instrucciones de instalación para comenzar a usar 🤗 Transformers.
-- **TUTORIALES** son un excelente lugar para comenzar si eres nuevo en nuestra biblioteca. Esta sección te ayudará a obtener las habilidades básicas que necesitas para comenzar a usar 🤗 Transformers.
-- **GUÍAS PRÁCTICAS** te mostrará cómo lograr un objetivo específico, cómo hacer fine-tuning a un modelo preentrenado para el modelado de lenguaje o cómo crear un cabezal para un modelo personalizado.
-- **GUÍAS CONCEPTUALES** proporciona más discusión y explicación de los conceptos e ideas subyacentes detrás de los modelos, las tareas y la filosofía de diseño de 🤗 Transformers. 
-
-La biblioteca actualmente contiene implementaciones de JAX, PyTorch y TensorFlow, pesos de modelos preentrenados, scripts de uso y utilidades de conversión para los siguientes modelos.
-
-### Modelos compatibles
-
-<!--This list is updated automatically from the README with _make fix-copies_. Do not update manually! -->
-
-1. **[ALBERT](model_doc/albert)** (de Google Research y el Instituto Tecnológico de Toyota en Chicago) publicado con el paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), por Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-1. **[BART](model_doc/bart)** (de Facebook) publicado con el paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) por Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov y Luke Zettlemoyer.
-1. **[BARThez](model_doc/barthez)** (de École polytechnique) publicado con el paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) por Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
-1. **[BARTpho](model_doc/bartpho)** (de VinAI Research) publicado con el paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) por Nguyen Luong Tran, Duong Minh Le y Dat Quoc Nguyen.
-1. **[BEiT](model_doc/beit)** (de Microsoft) publicado con el paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) por Hangbo Bao, Li Dong, Furu Wei.
-1. **[BERT](model_doc/bert)** (de Google) publicado con el paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) por Jacob Devlin, Ming-Wei Chang, Kenton Lee y Kristina Toutanova.
-1. **[BERTweet](model_doc/bertweet)** (de VinAI Research) publicado con el paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) por Dat Quoc Nguyen, Thanh Vu y Anh Tuan Nguyen.
-1. **[BERT For Sequence Generation](model_doc/bert-generation)** (de Google) publicado con el paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) por Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[BigBird-RoBERTa](model_doc/big_bird)** (de Google Research) publicado con el paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) por 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)** (de Google Research) publicado con el paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) por 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)** (de Facebook) publicado con el paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) por 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)** (de Facebook) publicado con el paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) por 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)** (de Alexa) publicado con el paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) por Adrian de Wynter y Daniel J. Perry.
-1. **[ByT5](model_doc/byt5)** (de Google Research) publicado con el paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) por Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
-1. **[CamemBERT](model_doc/camembert)** (de Inria/Facebook/Sorbonne) publicado con el paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) por Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah y Benoît Sagot.
-1. **[CANINE](model_doc/canine)** (de Google Research) publicado con el paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) por Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
-1. **[ConvNeXT](model_doc/convnext)** (de Facebook AI) publicado con el paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) por Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
-1. **[CLIP](model_doc/clip)** (de OpenAI) publicado con el paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) por 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)** (de YituTech) publicado con el paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) por Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
-1. **[CPM](model_doc/cpm)** (de Universidad de Tsinghua) publicado con el paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) por 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)** (de Salesforce) publicado con el paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) por Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong y Richard Socher.
-1. **[Data2Vec](model_doc/data2vec)** (de Facebook) publicado con el paper [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) por Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
-1. **[DeBERTa](model_doc/deberta)** (de Microsoft) publicado con el paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) por Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[DeBERTa-v2](model_doc/deberta-v2)** (de Microsoft) publicado con el paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) por Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[Decision Transformer](model_doc/decision_transformer)** (de Berkeley/Facebook/Google) publicado con el paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) por Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[DiT](model_doc/dit)** (de Microsoft Research) publicado con el paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) por Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
-1. **[DeiT](model_doc/deit)** (de Facebook) publicado con el paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) por Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
-1. **[DETR](model_doc/detr)** (de Facebook) publicado con el paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) por Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
-1. **[DialoGPT](model_doc/dialogpt)** (de Microsoft Research) publicado con el paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) por Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-1. **[DistilBERT](model_doc/distilbert)** (de HuggingFace), publicado junto con el paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) por Victor Sanh, Lysandre Debut y Thomas Wolf. Se ha aplicado el mismo método para comprimir GPT2 en [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa en [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), BERT multilingüe en [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) y una versión alemana de DistilBERT.
-1. **[DPR](model_doc/dpr)** (de Facebook) publicado con el paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) por Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, y Wen-tau Yih.
-1. **[DPT](master/model_doc/dpt)** (de Intel Labs) publicado con el paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) por René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
-1. **[EncoderDecoder](model_doc/encoder-decoder)** (de Google Research) publicado con el paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) por Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[ELECTRA](model_doc/electra)** (de Google Research/Universidad de Stanford) publicado con el paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) por Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-1. **[FlauBERT](model_doc/flaubert)** (de CNRS) publicado con el paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) por 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)** (de Google Research) publicado con el paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) por James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
-1. **[Funnel Transformer](model_doc/funnel)** (de CMU/Google Brain) publicado con el paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) por Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-1. **[GLPN](model_doc/glpn)** (de KAIST) publicado con el paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) por Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
-1. **[GPT](model_doc/openai-gpt)** (de OpenAI) publicado con el paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) por Alec Radford, Karthik Narasimhan, Tim Salimans y Ilya Sutskever.
-1. **[GPT-2](model_doc/gpt2)** (de OpenAI) publicado con el paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) por Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** y Ilya Sutskever**.
-1. **[GPT-J](model_doc/gptj)** (de EleutherAI) publicado con el repositorio [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) por Ben Wang y Aran Komatsuzaki.
-1. **[GPT Neo](model_doc/gpt_neo)** (de EleutherAI) publicado en el paper [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) por Sid Black, Stella Biderman, Leo Gao, Phil Wang y Connor Leahy.
-1. **[Hubert](model_doc/hubert)** (de Facebook) publicado con el paper [HuBERT: Self-Supervised Speech Representation Learning por Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) por Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
-1. **[I-BERT](model_doc/ibert)** (de Berkeley) publicado con el paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) por Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
-1. **[ImageGPT](model_doc/imagegpt)** (de OpenAI) publicado con el paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) por Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
-1. **[LayoutLM](model_doc/layoutlm)** (de Microsoft Research Asia) publicado con el paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) por Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-1. **[LayoutLMv2](model_doc/layoutlmv2)** (de Microsoft Research Asia) publicado con el paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) por 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)** (de Microsoft Research Asia) publicado con el paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) por Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
-1. **[LED](model_doc/led)** (de AllenAI) publicado con el paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) por Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[Longformer](model_doc/longformer)** (de AllenAI) publicado con el paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) por Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LUKE](model_doc/luke)** (de Studio Ousia) publicado con el paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) por Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
-1. **[mLUKE](model_doc/mluke)** (de Studio Ousia) publicado con el paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) por Ryokan Ri, Ikuya Yamada, y Yoshimasa Tsuruoka.
-1. **[LXMERT](model_doc/lxmert)** (de UNC Chapel Hill) publicado con el paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) por Hao Tan y Mohit Bansal.
-1. **[M2M100](model_doc/m2m_100)** (de Facebook) publicado con el paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) por 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)** Modelos de traducción automática entrenados usando [OPUS](http://opus.nlpl.eu/) data por Jörg Tiedemann. El [Marian Framework](https://marian-nmt.github.io/) está siendo desarrollado por el equipo de traductores de Microsoft.
-1. **[MaskFormer](model_doc/maskformer)** (de Meta y UIUC) publicado con el paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) por Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
-1. **[MBart](model_doc/mbart)** (de Facebook) publicado con el paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) por Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-1. **[MBart-50](model_doc/mbart)** (de Facebook) publicado con el paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) por Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-1. **[Megatron-BERT](model_doc/megatron-bert)** (de NVIDIA) publicado con el paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) por Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper y Bryan Catanzaro.
-1. **[Megatron-GPT2](model_doc/megatron_gpt2)** (de NVIDIA) publicado con el paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) por Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper y Bryan Catanzaro.
-1. **[MPNet](model_doc/mpnet)** (de Microsoft Research) publicado con el paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) por Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-1. **[MT5](model_doc/mt5)** (de Google AI) publicado con el paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) por Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-1. **[Nyströmformer](model_doc/nystromformer)** (de la Universidad de Wisconsin - Madison) publicado con el paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) por Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
-1. **[Pegasus](model_doc/pegasus)** (de Google) publicado con el paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) por Jingqing Zhang, Yao Zhao, Mohammad Saleh y Peter J. Liu.
-1. **[Perceiver IO](model_doc/perceiver)** (de Deepmind) publicado con el paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) por 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)** (de VinAI Research) publicado con el paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) por Dat Quoc Nguyen y Anh Tuan Nguyen.
-1. **[PLBart](model_doc/plbart)** (de UCLA NLP) publicado con el paper [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) por Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
-1. **[PoolFormer](model_doc/poolformer)** (de Sea AI Labs) publicado con el paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) por Yu, Weihao y Luo, Mi y Zhou, Pan y Si, Chenyang y Zhou, Yichen y Wang, Xinchao y Feng, Jiashi y Yan, Shuicheng.
-1. **[ProphetNet](model_doc/prophetnet)** (de Microsoft Research) publicado con el paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) por Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang y Ming Zhou.
-1. **[QDQBert](model_doc/qdqbert)** (de NVIDIA) publicado con el paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) por Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev y Paulius Micikevicius.
-1. **[REALM](model_doc/realm.html)** (de Google Research) publicado con el paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) por Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat y Ming-Wei Chang.
-1. **[Reformer](model_doc/reformer)** (de Google Research) publicado con el paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) por Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RemBERT](model_doc/rembert)** (de Google Research) publicado con el paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821) por Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
-1. **[RegNet](model_doc/regnet)** (de META Platforms) publicado con el paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) por Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
-1. **[ResNet](model_doc/resnet)** (de Microsoft Research) publicado con el paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) por Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
-1. **[RoBERTa](model_doc/roberta)** (de Facebook), publicado junto con el paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) por 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)** (de ZhuiyiTechnology), publicado junto con el paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) por Jianlin Su y Yu Lu y Shengfeng Pan y Bo Wen y Yunfeng Liu.
-1. **[SegFormer](model_doc/segformer)** (de NVIDIA) publicado con el paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) por Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
-1. **[SEW](model_doc/sew)** (de ASAPP) publicado con el paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) por Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SEW-D](model_doc/sew_d)** (de ASAPP) publicado con el paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) por Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SpeechToTextTransformer](model_doc/speech_to_text)** (de Facebook), publicado junto con el paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) por Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-1. **[SpeechToTextTransformer2](model_doc/speech_to_text_2)** (de Facebook), publicado junto con el paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) por Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
-1. **[Splinter](model_doc/splinter)** (de Universidad de Tel Aviv), publicado junto con el paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) pory Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
-1. **[SqueezeBert](model_doc/squeezebert)** (de Berkeley) publicado con el paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) por Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, y Kurt W. Keutzer.
-1. **[Swin Transformer](model_doc/swin)** (de Microsoft) publicado con el paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) por Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
-1. **[T5](model_doc/t5)** (de Google AI) publicado con el paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) por Colin Raffel y Noam Shazeer y Adam Roberts y Katherine Lee y Sharan Narang y Michael Matena y Yanqi Zhou y Wei Li y Peter J. Liu.
-1. **[T5v1.1](model_doc/t5v1.1)** (de Google AI) publicado en el repositorio [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) por Colin Raffel y Noam Shazeer y Adam Roberts y Katherine Lee y Sharan Narang y Michael Matena y Yanqi Zhou y Wei Li y Peter J. Liu.
-1. **[TAPAS](model_doc/tapas)** (de Google AI) publicado con el paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) por Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno y Julian Martin Eisenschlos.
-1. **[TAPEX](model_doc/tapex)** (de Microsoft Research) publicado con el paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) por Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
-1. **[Transformer-XL](model_doc/transfo-xl)** (de Google/CMU) publicado con el paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) por Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-1. **[TrOCR](model_doc/trocr)** (de Microsoft), publicado junto con el paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) por Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
-1. **[UniSpeech](model_doc/unispeech)** (de Microsoft Research) publicado con el paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) por Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
-1. **[UniSpeechSat](model_doc/unispeech-sat)** (de Microsoft Research) publicado con el paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) por Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
-1. **[VAN](model_doc/van)** (de la Universidad de Tsinghua y la Universidad de Nankai) publicado con el paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) por Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
-1. **[ViLT](model_doc/vilt)** (de NAVER AI Lab/Kakao Enterprise/Kakao Brain) publicado con el paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) por Wonjae Kim, Bokyung Son, Ildoo Kim.
-1. **[Vision Transformer (ViT)](model_doc/vit)** (de Google AI) publicado con el paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) por 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. **[ViTMAE](model_doc/vit_mae)** (de Meta AI) publicado con el paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) por Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
-1. **[VisualBERT](model_doc/visual_bert)** (de UCLA NLP) publicado con el paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) por Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
-1. **[WavLM](model_doc/wavlm)** (de Microsoft Research) publicado con el paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) por Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[Wav2Vec2](model_doc/wav2vec2)** (de Facebook AI) publicado con el paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) por Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
-1. **[Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme)** (de Facebook AI) publicado con el paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) por Qiantong Xu, Alexei Baevski, Michael Auli.
-1. **[XGLM](model_doc/xglm)** (de Facebook AI) publicado con el paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) por Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
-1. **[XLM](model_doc/xlm)** (de Facebook) publicado junto con el paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) por Guillaume Lample y Alexis Conneau.
-1. **[XLM-ProphetNet](model_doc/xlm-prophetnet)** (de Microsoft Research) publicado con el paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) por Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang y Ming Zhou.
-1. **[XLM-RoBERTa](model_doc/xlm-roberta)** (de Facebook AI), publicado junto con el paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) por Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer y Veselin Stoyanov.
-1. **[XLM-RoBERTa-XL](model_doc/xlm-roberta-xl)** (de Facebook AI), publicado junto con el paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) por Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
-1. **[XLNet](model_doc/xlnet)** (de Google/CMU) publicado con el paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) por Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-1. **[XLSR-Wav2Vec2](model_doc/xlsr_wav2vec2)** (de Facebook AI) publicado con el paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) por Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
-1. **[XLS-R](model_doc/xls_r)** (de Facebook AI) publicado con el paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) por Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
-1. **[YOSO](model_doc/yoso)** (de la Universidad de Wisconsin-Madison) publicado con el paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) por Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
-
-
-### Frameworks compatibles
-
-La siguiente tabla representa el soporte actual en la biblioteca para cada uno de esos modelos, ya sea que tengan un tokenizador de Python (llamado "slow"). Un tokenizador "fast" respaldado por la biblioteca 🤗 Tokenizers, ya sea que tengan soporte en Jax (a través de
-Flax), PyTorch y/o TensorFlow.
-
-<!--This table is updated automatically from the auto modules with _make fix-copies_. Do not update manually!-->
-
-|            Modelo           | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
-|:---------------------------:|:--------------:|:--------------:|:---------------:|:------------------:|:------------:|
-|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|            BEiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
-|            BERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           BigBird           |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
-|       BigBirdPegasus        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|         Blenderbot          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|       BlenderbotSmall       |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|           Canine            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            CLIP             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|          ConvNext           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
-|            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|        Data2VecAudio        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|        Data2VecText         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|         DeBERTa-v2          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|    Decision Transformer     |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            DeiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            DETR             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|             DPT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-| FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|            FNet             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|            GLPN             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
-|            GPT-J            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-|           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
-|           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|         LayoutLMv2          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|            LUKE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|           M2M100            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
-|         MaskFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|        MegatronBert         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|            MPNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|        Nystromformer        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|          Perceiver          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           PLBart            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|         PoolFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           QDQBert           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|            Realm            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|           ResNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|          SegFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|             SEW             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            SEW-D            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|   Speech Encoder decoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
-|         Speech2Text         |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|        Speech2Text2         |       ✅       |       ❌       |       ❌        |         ❌         |      ❌      |
-|          Splinter           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|            Swin             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|            TAPEX            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|            TrOCR            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|          UniSpeech          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|        UniSpeechSat         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|             VAN             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            ViLT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-|    VisionTextDualEncoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
-|         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-|           ViTMAE            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
-|          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
-|            WavLM            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            XGLM             |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
-|             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|       XLM-RoBERTa-XL        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|            YOSO             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-
-<!-- End table-->

docs/source/es/installation.mdx

@@ -1,238 +0,0 @@
-<!---
-Copyright 2022 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.
--->
-
-# Guía de instalación
-
-En esta guía puedes encontrar información para instalar 🤗 Transformers para cualquier biblioteca de Machine Learning con la que estés trabajando. Además, encontrarás información sobre cómo establecer el caché y cómo configurar 🤗 Transformers para correrlo de manera offline (opcional).
-
-🤗 Transformers ha sido probada en Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, y Flax. Para instalar la biblioteca de deep learning con la que desees trabajar, sigue las instrucciones correspondientes listadas a continuación:
-
-* [PyTorch](https://pytorch.org/get-started/locally/)
-* [TensorFlow 2.0](https://www.tensorflow.org/install/pip)
-* [Flax](https://flax.readthedocs.io/en/latest/)
-
-## Instalación con pip
-
-Es necesario instalar 🤗 Transformers en un [entorno virtual](https://docs.python.org/3/library/venv.html). Si necesitas más información sobre entornos virtuales de Python, consulta esta [guía](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/
-). Un entorno virtual facilita el manejo de proyectos y evita problemas de compatibilidad entre dependencias.
-
-Comienza por crear un entorno virtual en el directorio de tu proyecto:
-
-```bash
-python -m venv .env
-```
-
-Activa el entorno virtual:
-
-```bash
-source .env/bin/activate
-```
-
-Ahora puedes instalar 🤗 Transformers con el siguiente comando:
-
-```bash
-pip install transformers
-```
-
-Solo para CPU, puedes instalar 🤗 Transformers y una biblioteca de deep learning con un comando de una sola línea.
-
-Por ejemplo, instala 🤗 Transformers y Pytorch:
-
-```bash
-pip install transformers[torch]
-```
-
-🤗 Transformers y TensorFlow 2.0:
-
-```bash
-pip install transformers[tf-cpu]
-```
-
-🤗 Transformers y Flax:
-
-```bash
-pip install transformers[flax]
-```
-
-Por último, revisa si 🤗 Transformers ha sido instalada exitosamente con el siguiente comando que descarga un modelo pre-entrenado:
-
-```bash
-python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('we love you'))"
-```
-Después imprime la etiqueta y el puntaje:
-
-```bash
-[{'label': 'POSITIVE', 'score': 0.9998704791069031}]
-```
-
-## Instalación desde la fuente
-
-Instala 🤗 Transformers desde la fuente con el siguiente comando:
-
-```bash
-pip install git+https://github.com/huggingface/transformers
-```
-
-El comando de arriba instala la versión `master` más actual en vez de la última versión estable. La versión `master` es útil para obtener los últimos avances de  🤗 Transformers. Por ejemplo, se puede dar el caso de que un error fue corregido después de la última versión estable pero aún no se ha liberado un nuevo lanzamiento. Sin embargo, existe la posibilidad de que la versión `master` no sea estable. El equipo trata de mantener la versión `master` operacional y la mayoría de los errores son resueltos en unas cuantas horas o un día. Si encuentras algún problema, por favor abre un [Issue](https://github.com/huggingface/transformers/issues) para que pueda ser corregido más rápido.
-
-Verifica si 🤗 Transformers está instalada apropiadamente con el siguiente comando:
-
-```bash
-python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
-```
-
-## Instalación editable
-
-Necesitarás una instalación editable si deseas:
-* Usar la versión `master` del código fuente.
-* Contribuir a 🤗 Transformers y necesitas probar cambios en el código.
-
-Clona el repositorio e instala 🤗 Transformers con los siguientes comandos:
-
-```bash
-git clone https://github.com/huggingface/transformers.git
-cd transformers
-pip install -e .
-```
-
-Éstos comandos van a ligar el directorio desde donde clonamos el repositorio al path de las bibliotecas de Python. Python ahora buscará dentro de la carpeta que clonaste además de los paths normales de la biblioteca. Por ejemplo, si los paquetes de Python se encuentran instalados en `~/anaconda3/envs/main/lib/python3.7/site-packages/`, Python también buscará en el directorio desde donde clonamos el repositorio `~/transformers/`.
-
-<Tip warning={true}>
-
-Debes mantener el directorio `transformers` si deseas seguir usando la biblioteca.
-
-</Tip>
-
-Puedes actualizar tu copia local a la última versión de 🤗 Transformers con el siguiente comando:
-
-```bash
-cd ~/transformers/
-git pull
-```
-
-El entorno de Python que creaste para la instalación de 🤗 Transformers encontrará la versión `master` en la siguiente ejecución.
-
-## Instalación con conda
-
-Puedes instalar 🤗 Transformers desde el canal de conda `huggingface` con el siguiente comando:
-
-```bash
-conda install -c huggingface transformers
-```
-
-## Configuración de Caché
-
-Los modelos preentrenados se descargan y almacenan en caché localmente en: `~/.cache/huggingface/transformers/`. Este es el directorio predeterminado proporcionado por la variable de entorno de shell `TRANSFORMERS_CACHE`. En Windows, el directorio predeterminado es dado por `C:\Users\username\.cache\huggingface\transformers`. Puedes cambiar las variables de entorno de shell que se muestran a continuación, en orden de prioridad, para especificar un directorio de caché diferente:
-
-1. Variable de entorno del shell (por defecto): `TRANSFORMERS_CACHE`.
-2. Variable de entorno del shell:`HF_HOME` + `transformers/`.
-3. Variable de entorno del shell: `XDG_CACHE_HOME` + `/huggingface/transformers`.
-
-<Tip>
-
-🤗 Transformers usará las variables de entorno de shell `PYTORCH_TRANSFORMERS_CACHE` o `PYTORCH_PRETRAINED_BERT_CACHE` si viene de una iteración anterior de la biblioteca y ha configurado esas variables de entorno, a menos que especifiques la variable de entorno de shell `TRANSFORMERS_CACHE`.
-    
-</Tip>
-
-
-## Modo Offline
-
-🤗 Transformers puede ejecutarse en un entorno con firewall o fuera de línea (offline) usando solo archivos locales. Configura la variable de entorno `TRANSFORMERS_OFFLINE=1` para habilitar este comportamiento.
-
-<Tip>
-
-Puedes añadir [🤗 Datasets](https://huggingface.co/docs/datasets/) al flujo de entrenamiento offline declarando la variable de entorno  `HF_DATASETS_OFFLINE=1`.
-
-</Tip>
-
-Por ejemplo, normalmente ejecutarías un programa en una red normal con firewall para instancias externas con el siguiente comando:
-
-```bash
-python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
-```
-
-Ejecuta este mismo programa en una instancia offline con el siguiente comando:
-
-```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
-python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...
-```
-
-El script ahora debería ejecutarse sin bloquearse ni esperar a que se agote el tiempo de espera porque sabe que solo debe buscar archivos locales.
-
-### Obtener modelos y tokenizers para uso offline
-
-Otra opción para usar 🤗 Transformers offline es descargando previamente los archivos y después apuntar al path local donde se encuentren. Hay tres maneras de hacer esto:
-
-* Descarga un archivo mediante la interfaz de usuario del [Model Hub](https://huggingface.co/models) haciendo click en el ícono ↓.
-
-    ![download-icon](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/download-icon.png)
-
-
-* Utiliza el flujo de [`PreTrainedModel.from_pretrained`] y [`PreTrainedModel.save_pretrained`]:
-    1. Descarga previamente los archivos con [`PreTrainedModel.from_pretrained`]:
-
-    ```py
-    >>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-    >>> tokenizer = AutoTokenizer.from_pretrained("bigscience/T0_3B")
-    >>> model = AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0_3B")
-    ```
-
-
-    2. Guarda los archivos en un directorio específico con [`PreTrainedModel.save_pretrained`]:
-
-    ```py
-    >>> tokenizer.save_pretrained("./your/path/bigscience_t0")
-    >>> model.save_pretrained("./your/path/bigscience_t0")
-    ```
-
-    3. Cuando te encuentres offline, recarga los archivos con [`PreTrainedModel.from_pretrained`] desde el directorio especificado: 
-
-    ```py
-    >>> tokenizer = AutoTokenizer.from_pretrained("./your/path/bigscience_t0")
-    >>> model = AutoModel.from_pretrained("./your/path/bigscience_t0")
-    ```
-
-* Descarga de manera programática los archivos con la biblioteca [huggingface_hub](https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub):
-
-    1. Instala la biblioteca [huggingface_hub](https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub) en tu entorno virtual: 
-
-    ```bash
-    python -m pip install huggingface_hub
-    ```
-
-    2. Utiliza la función [`hf_hub_download`](https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub) para descargar un archivo a un path específico. Por ejemplo, el siguiente comando descarga el archivo `config.json` del modelo [T0](https://huggingface.co/bigscience/T0_3B) al path deseado:
-
-    ```py
-    >>> from huggingface_hub import hf_hub_download
-
-    >>> hf_hub_download(repo_id="bigscience/T0_3B", filename="config.json", cache_dir="./your/path/bigscience_t0")
-    ```
-
-Una vez que el archivo se descargue y se almacene en caché localmente, especifica tu ruta local para cargarlo y usarlo:
-
-```py
->>> from transformers import AutoConfig
-
->>> config = AutoConfig.from_pretrained("./your/path/bigscience_t0/config.json")
-```
-
-<Tip>
-
-Para más detalles sobre cómo descargar archivos almacenados en el Hub consulta la sección [How to download files from the Hub](https://huggingface.co/docs/hub/how-to-downstream).
-
-</Tip>

docs/source/es/language_modeling.mdx

@@ -1,418 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Modelado de lenguaje
-
-El modelado de lenguaje predice palabras en un enunciado. Hay dos formas de modelado de lenguaje.
-
-<Youtube id="Vpjb1lu0MDk"/>
-
-El modelado de lenguaje causal predice el siguiente token en una secuencia de tokens, y el modelo solo puede considerar los tokens a la izquierda.
-
-<Youtube id="mqElG5QJWUg"/>
-
-El modelado de lenguaje por enmascaramiento predice un token enmascarado en una secuencia, y el modelo puede considerar los tokens bidireccionalmente.
-
-Esta guía te mostrará cómo realizar fine-tuning [DistilGPT2](https://huggingface.co/distilgpt2) para modelos de lenguaje causales y [DistilRoBERTa](https://huggingface.co/distilroberta-base) para modelos de lenguaje por enmascaramiento en el [r/askscience](https://www.reddit.com/r/askscience/) subdataset [ELI5](https://huggingface.co/datasets/eli5). 
-
-<Tip>
-
-Puedes realizar fine-tuning a otras arquitecturas para modelos de lenguaje como [GPT-Neo](https://huggingface.co/EleutherAI/gpt-neo-125M), [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) y [BERT](https://huggingface.co/bert-base-uncased) siguiendo los mismos pasos presentados en esta guía!
-
-Mira la [página de tarea](https://huggingface.co/tasks/text-generation) para generación de texto y la [página de tarea](https://huggingface.co/tasks/fill-mask) para modelos de lenguajes por enmascaramiento para obtener más información sobre los modelos, datasets, y métricas asociadas.
-
-</Tip>
-
-## Carga el dataset ELI5
-
-Carga solo los primeros 5000 registros desde la biblioteca 🤗 Datasets, dado que es bastante grande:
-
-```py
->>> from datasets import load_dataset
-
->>> eli5 = load_dataset("eli5", split="train_asks[:5000]")
-```
-
-Divide este dataset en subdatasets para el entrenamiento y el test:
-
-```py
-eli5 = eli5.train_test_split(test_size=0.2)
-```
-
-Luego observa un ejemplo:
-
-```py
->>> eli5["train"][0]
-{'answers': {'a_id': ['c3d1aib', 'c3d4lya'],
-  'score': [6, 3],
-  'text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
-   "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"]},
- 'answers_urls': {'url': []},
- 'document': '',
- 'q_id': 'nyxfp',
- 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
- 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']},
- 'subreddit': 'askscience',
- 'title': 'Few questions about this space walk photograph.',
- 'title_urls': {'url': []}}
-```
-
-Observa que `text` es un subcampo anidado dentro del diccionario `answers`. Cuando preproceses el dataset, deberás extraer el subcampo `text` en una columna aparte.
-
-## Preprocesamiento
-
-<Youtube id="ma1TrR7gE7I"/>
-
-Para modelados de lenguaje causales carga el tokenizador DistilGPT2 para procesar el subcampo `text`:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
-```
-
-<Youtube id="8PmhEIXhBvI"/>
-
-Para modelados de lenguaje por enmascaramiento carga el tokenizador DistilRoBERTa, en lugar de DistilGPT2:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
-```
-
-Extrae el subcampo `text` desde su estructura anidado con el método [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten):
-
-```py
->>> eli5 = eli5.flatten()
->>> eli5["train"][0]
-{'answers.a_id': ['c3d1aib', 'c3d4lya'],
- 'answers.score': [6, 3],
- 'answers.text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
-  "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"],
- 'answers_urls.url': [],
- 'document': '',
- 'q_id': 'nyxfp',
- 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
- 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'],
- 'subreddit': 'askscience',
- 'title': 'Few questions about this space walk photograph.',
- 'title_urls.url': []}
-```
-
-Cada subcampo es ahora una columna separada, como lo indica el prefijo `answers`. Observa que `answers.text` es una lista. En lugar de tokenizar cada enunciado por separado, convierte la lista en un string para tokenizarlos conjuntamente.
-
-Así es como puedes crear una función de preprocesamiento para convertir la lista en una cadena y truncar las secuencias para que no superen la longitud máxima de input de DistilGPT2:
-
-```py
->>> def preprocess_function(examples):
-...     return tokenizer([" ".join(x) for x in examples["answers.text"]], truncation=True)
-```
-
-Usa de 🤗 Datasets la función [`map`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map) para aplicar la función de preprocesamiento sobre el dataset en su totalidad. Puedes acelerar la función `map` configurando el argumento `batched=True` para procesar múltiples elementos del dataset a la vez y aumentar la cantidad de procesos con `num_proc`. Elimina las columnas que no necesitas:
-
-```py
->>> tokenized_eli5 = eli5.map(
-...     preprocess_function,
-...     batched=True,
-...     num_proc=4,
-...     remove_columns=eli5["train"].column_names,
-... )
-```
-
-Ahora necesitas una segunda función de preprocesamiento para capturar el texto truncado de cualquier ejemplo demasiado largo para evitar cualquier pérdida de información. Esta función de preprocesamiento debería:
-
-- Concatenar todo el texto.
-- Dividir el texto concatenado en trozos más pequeños definidos por un `block_size`.
-
-```py
->>> block_size = 128
-
-
->>> def group_texts(examples):
-...     concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
-...     total_length = len(concatenated_examples[list(examples.keys())[0]])
-...     result = {
-...         k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
-...         for k, t in concatenated_examples.items()
-...     }
-...     result["labels"] = result["input_ids"].copy()
-...     return result
-```
-
-Aplica la función `group_texts` sobre todo el dataset:
-
-```py
->>> lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)
-```
-
-Para modelados de lenguaje causales, usa [`DataCollatorForLanguageModeling`] para crear un lote de ejemplos. Esto también *rellenará dinámicamente* tu texto a la dimensión del elemento más largo del lote para que de esta manera tengan largo uniforme. Si bien es posible rellenar tu texto en la función `tokenizer` mediante el argumento `padding=True`, el rellenado dinámico es más eficiente. 
-
-<frameworkcontent>
-<pt>
-Puedes usar el token de final de secuencia como el token de relleno y asignar `mlm=False`. Esto usará los inputs como etiquetas movidas un elemento hacia la derecha:
-
-```py
->>> from transformers import DataCollatorForLanguageModeling
-
->>> tokenizer.pad_token = tokenizer.eos_token
->>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
-```
-
-Para modelados de lenguaje por enmascaramiento usa el mismo [`DataCollatorForLanguageModeling`] excepto que deberás especificar `mlm_probability` para enmascarar tokens aleatoriamente cada vez que iteras sobre los datos.
-
-```py
->>> from transformers import DataCollatorForLanguageModeling
-
->>> tokenizer.pad_token = tokenizer.eos_token
->>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15)
-```
-</pt>
-<tf>
-Puedes usar el token de final de secuencia como el token de relleno y asignar `mlm=False`. Esto usará los inputs como etiquetas movidas un elemento hacia la derecha:
-
-```py
->>> from transformers import DataCollatorForLanguageModeling
-
->>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
-```
-
-Para modelados de lenguajes por enmascaramiento usa el mismo [`DataCollatorForLanguageModeling`] excepto que deberás especificar `mlm_probability` para enmascarar tokens aleatoriamente cada vez que iteras sobre los datos.
-
-```py
->>> from transformers import DataCollatorForLanguageModeling
-
->>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
-```
-</tf>
-</frameworkcontent>
-
-## Modelado de lenguaje causal
-
-El modelado de lenguaje causal es frecuentemente utilizado para generación de texto. Esta sección te muestra cómo realizar fine-tuning a [DistilGPT2](https://huggingface.co/distilgpt2) para generar nuevo texto.
-
-### Entrenamiento
-
-<frameworkcontent>
-<pt>
-Carga DistilGPT2 con [`AutoModelForCausalLM`]:
-
-```py
->>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
-
->>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
-```
-
-<Tip>
-
-Si no estás familiarizado con el proceso de realizar fine-tuning sobre un modelo con [`Trainer`], considera el tutorial básico [aquí](../training#finetune-with-trainer)!
-
-</Tip>
-
-A este punto, solo faltan tres pasos:
-
-1. Definir tus hiperparámetros de entrenamiento en [`TrainingArguments`].
-2. Pasarle los argumentos de entrenamiento a [`Trainer`] junto con el modelo, dataset, y el data collator.
-3. Realiza la llamada [`~Trainer.train`] para realizar el fine-tuning sobre tu modelo.
-
-```py
->>> training_args = TrainingArguments(
-...     output_dir="./results",
-...     evaluation_strategy="epoch",
-...     learning_rate=2e-5,
-...     weight_decay=0.01,
-... )
-
->>> trainer = Trainer(
-...     model=model,
-...     args=training_args,
-...     train_dataset=lm_dataset["train"],
-...     eval_dataset=lm_dataset["test"],
-...     data_collator=data_collator,
-... )
-
->>> trainer.train()
-```
-</pt>
-<tf>
-Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator:
-
-```py
->>> tf_train_set = lm_dataset["train"].to_tf_dataset(
-...     columns=["attention_mask", "input_ids", "labels"],
-...     dummy_labels=True,
-...     shuffle=True,
-...     batch_size=16,
-...     collate_fn=data_collator,
-... )
-
->>> tf_test_set = lm_dataset["test"].to_tf_dataset(
-...     columns=["attention_mask", "input_ids", "labels"],
-...     dummy_labels=True,
-...     shuffle=False,
-...     batch_size=16,
-...     collate_fn=data_collator,
-... )
-```
-
-<Tip>
-
-Si no estás familiarizado con realizar fine-tuning de tus modelos con Keras, considera el tutorial básico [aquí](training#finetune-with-keras)!
-
-</Tip>
-
-Crea la función optimizadora, la tasa de aprendizaje, y algunos hiperparámetros de entrenamiento:
-
-```py
->>> from transformers import create_optimizer, AdamWeightDecay
-
->>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
-```
-
-Carga DistilGPT2 con [`TFAutoModelForCausalLM`]:
-
-```py
->>> from transformers import TFAutoModelForCausalLM
-
->>> model = TFAutoModelForCausalLM.from_pretrained("distilgpt2")
-```
-
-Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
-
-```py
->>> import tensorflow as tf
-
->>> model.compile(optimizer=optimizer)
-```
-
-Llama a [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) para realizar el fine-tuning del modelo:
-
-```py
->>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
-```
-</tf>
-</frameworkcontent>
-
-## Modelado de lenguaje por enmascaramiento
-
-El modelado de lenguaje por enmascaramiento es también conocido como una tarea de rellenar la máscara, pues predice un token enmascarado dada una secuencia. Los modelos de lenguaje por enmascaramiento requieren una buena comprensión del contexto de una secuencia entera, en lugar de solo el contexto a la izquierda. Esta sección te enseña como realizar el fine-tuning de [DistilRoBERTa](https://huggingface.co/distilroberta-base) para predecir una palabra enmascarada.
-
-### Entrenamiento
-
-<frameworkcontent>
-<pt>
-Carga DistilRoBERTa con [`AutoModelForMaskedlM`]:
-
-```py
->>> from transformers import AutoModelForMaskedLM
-
->>> model = AutoModelForMaskedLM.from_pretrained("distilroberta-base")
-```
-
-<Tip>
-
-Si no estás familiarizado con el proceso de realizar fine-tuning sobre un modelo con [`Trainer`], considera el tutorial básico [aquí](../training#finetune-with-trainer)!
-
-</Tip>
-
-A este punto, solo faltan tres pasos:
-
-1. Definir tus hiperparámetros de entrenamiento en [`TrainingArguments`].
-2. Pasarle los argumentos de entrenamiento a [`Trainer`] junto con el modelo, dataset, y el data collator.
-3. Realiza la llamada [`~Trainer.train`] para realizar el fine-tuning de tu modelo.
-
-```py
->>> training_args = TrainingArguments(
-...     output_dir="./results",
-...     evaluation_strategy="epoch",
-...     learning_rate=2e-5,
-...     num_train_epochs=3,
-...     weight_decay=0.01,
-... )
-
->>> trainer = Trainer(
-...     model=model,
-...     args=training_args,
-...     train_dataset=lm_dataset["train"],
-...     eval_dataset=lm_dataset["test"],
-...     data_collator=data_collator,
-... )
-
->>> trainer.train()
-```
-</pt>
-<tf>
-Para realizar el fine-tuning de un modelo en TensorFlow, comienza por convertir tus datasets al formato `tf.data.Dataset` con [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Especifica los inputs y etiquetas en `columns`, ya sea para mezclar el dataset, tamaño de lote, y el data collator:
-
-```py
->>> tf_train_set = lm_dataset["train"].to_tf_dataset(
-...     columns=["attention_mask", "input_ids", "labels"],
-...     dummy_labels=True,
-...     shuffle=True,
-...     batch_size=16,
-...     collate_fn=data_collator,
-... )
-
->>> tf_test_set = lm_dataset["test"].to_tf_dataset(
-...     columns=["attention_mask", "input_ids", "labels"],
-...     dummy_labels=True,
-...     shuffle=False,
-...     batch_size=16,
-...     collate_fn=data_collator,
-... )
-```
-
-<Tip>
-
-Si no estás familiarizado con realizar fine-tuning de tus modelos con Keras, considera el tutorial básico [aquí](training#finetune-with-keras)!
-
-</Tip>
-
-Crea la función optimizadora, la tasa de aprendizaje, y algunos hiperparámetros de entrenamiento:
-
-```py
->>> from transformers import create_optimizer, AdamWeightDecay
-
->>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
-```
-
-Carga DistilRoBERTa con [`TFAutoModelForMaskedLM`]:
-
-```py
->>> from transformers import TFAutoModelForMaskedLM
-
->>> model = TFAutoModelForCausalLM.from_pretrained("distilroberta-base")
-```
-
-Configura el modelo para entrenamiento con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
-
-```py
->>> import tensorflow as tf
-
->>> model.compile(optimizer=optimizer)
-```
-
-Llama a [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) para realizar el fine-tuning del modelo:
-
-```py
->>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
-```
-</tf>
-</frameworkcontent>
-
-<Tip>
-
-Para un ejemplo más profundo sobre cómo realizar el fine-tuning sobre un modelo de lenguaje causal, considera
-[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb)
-o [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb).
-
-</Tip>

docs/source/es/multilingual.mdx

@@ -1,175 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Modelos multilingües para inferencia
-
-[[open-in-colab]]
-
-Existen varios modelos multilingües en 🤗 Transformers y su uso para inferencia difiere de los modelos monolingües. Sin embargo, no *todos* los usos de los modelos multilingües son diferentes. Algunos modelos, como [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased), pueden utilizarse igual que un modelo monolingüe. Esta guía te enseñará cómo utilizar modelos multilingües cuyo uso difiere en la inferencia.
-
-## XLM
-
-XLM tiene diez checkpoints diferentes de los cuales solo uno es monolingüe. Los nueve checkpoints restantes del modelo pueden dividirse en dos categorías: los checkpoints que utilizan language embeddings y los que no.
-
-### XLM con language embeddings
-
-Los siguientes modelos XLM usan language embeddings para especificar el lenguaje utilizado en la inferencia:
-
-- `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)
-
-Los language embeddings son representados como un tensor de la mismas dimensiones que los `input_ids` pasados al modelo. Los valores de estos tensores dependen del idioma utilizado y se identifican mediante los atributos `lang2id` y `id2lang` del tokenizador.
-
-En este ejemplo, carga el checkpoint `xlm-clm-enfr-1024` (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")
-```
-
-El atributo `lang2id` del tokenizador muestra los idiomas de este modelo y sus ids:
-
-```py
->>> print(tokenizer.lang2id)
-{'en': 0, 'fr': 1}
-```
-
-A continuación, crea un input de ejemplo:
-
-```py
->>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")])  # batch size of 1
-```
-
-Establece el id del idioma, por ejemplo `"en"`, y utilízalo para definir el language embedding. El language embedding es un tensor lleno de `0` ya que es el id del idioma para inglés. Este tensor debe ser del mismo tamaño que `input_ids`. 
-
-```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)
-```
-
-Ahora puedes pasar los `input_ids` y el language embedding al modelo:
-
-```py
->>> outputs = model(input_ids, langs=langs)
-```
-
-El script [run_generation.py](https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-generation/run_generation.py) puede generar texto con language embeddings utilizando los checkpoints `xlm-clm`.
-
-### XLM sin language embeddings
-
-Los siguientes modelos XLM no requieren language embeddings durante la inferencia:
-
-- `xlm-mlm-17-1280` (modelado de lenguaje enmascarado, 17 idiomas)
-- `xlm-mlm-100-1280` (modelado de lenguaje enmascarado, 100 idiomas)
-
-Estos modelos se utilizan para representaciones genéricas de frases a diferencia de los anteriores checkpoints XLM.
-
-## BERT
-
-Los siguientes modelos de BERT pueden utilizarse para tareas multilingües:
-
-- `bert-base-multilingual-uncased` (modelado de lenguaje enmascarado + predicción de la siguiente oración, 102 idiomas)
-- `bert-base-multilingual-cased` (modelado de lenguaje enmascarado + predicción de la siguiente oración, 104 idiomas)
-
-Estos modelos no requieren language embeddings durante la inferencia. Deben identificar la lengua a partir del
-contexto e inferir en consecuencia.
-
-## XLM-RoBERTa
-
-Los siguientes modelos de XLM-RoBERTa pueden utilizarse para tareas multilingües:
-
-- `xlm-roberta-base` (modelado de lenguaje enmascarado, 100 idiomas)
-- `xlm-roberta-large` (Modelado de lenguaje enmascarado, 100 idiomas)
-
-XLM-RoBERTa se entrenó con 2,5 TB de datos CommonCrawl recién creados y depurados en 100 idiomas. Proporciona fuertes ventajas sobre los modelos multilingües publicados anteriormente como mBERT o XLM en tareas posteriores como la clasificación, el etiquetado de secuencias y la respuesta a preguntas.
-
-## M2M100
-
-Los siguientes modelos de M2M100 pueden utilizarse para traducción multilingüe:
-
-- `facebook/m2m100_418M` (traducción)
-- `facebook/m2m100_1.2B` (traducción)
-
-En este ejemplo, carga el checkpoint `facebook/m2m100_418M` para traducir del chino al inglés. Puedes establecer el idioma de origen en el tokenizador:
-
-```py
->>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
-
->>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
->>> chinese_text = "不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒."
-
->>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh")
->>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
-```
-
-Tokeniza el texto:
-
-```py
->>> encoded_zh = tokenizer(chinese_text, return_tensors="pt")
-```
-
-M2M100 fuerza el id del idioma de destino como el primer token generado para traducir al idioma de destino.. Establece el `forced_bos_token_id` a `en` en el método `generate` para traducir al inglés:
-
-```py
->>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
->>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.'
-```
-
-## MBart
-
-Los siguientes modelos de MBart pueden utilizarse para traducción multilingüe:
-
-- `facebook/mbart-large-50-one-to-many-mmt` (traducción automática multilingüe de uno a muchos, 50 idiomas)
-- `facebook/mbart-large-50-many-to-many-mmt` (traducción automática multilingüe de muchos a muchos, 50 idiomas)
-- `facebook/mbart-large-50-many-to-one-mmt` (traducción automática multilingüe muchos a uno, 50 idiomas)
-- `facebook/mbart-large-50` (traducción multilingüe, 50 idiomas)
-- `facebook/mbart-large-cc25`
-
-En este ejemplo, carga el checkpoint `facebook/mbart-large-50-many-to-many-mmt` para traducir del finlandés al inglés. Puedes establecer el idioma de origen en el tokenizador:
-
-```py
->>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
->>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
->>> fi_text = "Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia."
-
->>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI")
->>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
-```
-
-Tokeniza el texto:
-
-```py
->>> encoded_en = tokenizer(en_text, return_tensors="pt")
-```
-
-MBart fuerza el id del idioma de destino como el primer token generado para traducirlo. Establece el `forced_bos_token_id` a `en` en el método `generate` para traducir al inglés:
-
-```py
->>> generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id("en_XX"))
->>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-"Don't interfere with the wizard's affairs, because they are subtle, will soon get angry."
-```
-
-Si estás usando el checkpoint `facebook/mbart-large-50-many-to-one-mmt` no necesitas forzar el id del idioma de destino como el primer token generado, de lo contrario el uso es el mismo.

docs/source/es/philosophy.mdx

@@ -1,75 +0,0 @@
-<!--Copyright 2020 de The HuggingFace Team. Todos los derechos reservados
-
-Con licencia bajo la Licencia Apache, Versión 2.0 (la "Licencia"); No puedes usar este archivo excepto de conformidad con la Licencia.
-Puedes obtener una copia de la Licencia en
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Al menos que sea requrido por la ley aplicable o acordado por escrito, el software distribuido bajo la Licencia es distribuido sobre una BASE "AS IS", SIN GARANTIAS O CONDICIONES DE
-NINGÚN TIPO. Ver la Licencia para el idioma específico que rige los permisos y limitaciones bajo la Licencia.
--->
-
-# Filosofía
-
-🤗 Transformers es una biblioteca construida para:
-
-- Los investigadores y educadores de NLP que busquen usar/estudiar/extender modelos transformers a gran escala 
-- Profesionales que quieren optimizar esos modelos y/o ponerlos en producción 
-- Ingenieros que solo quieren descargar un modelo preentrenado y usarlo para resolver una tarea NLP dada. 
-
-La biblioteca fue diseñada con dos fuertes objetivos en mente:
-
-- Que sea tan fácil y rápida de utilizar como sea posible:
-
-  - Hemos limitado enormemente el número de abstracciones que el usuario tiene que aprender. De hecho, no hay casi abstracciones,
-    solo tres clases estándar necesarias para usar cada modelo: [configuration](main_classes/configuration),
-    [models](main_classes/model) y [tokenizer](main_classes/tokenizer).
-  - Todas estas clases pueden ser inicializadas de forma simple y unificada a partir de ejemplos pre-entrenados mediante el uso de un método
-    `from_pretrained()` común de solicitud que se encargará de descargar (si es necesario), almacenar y cargar la solicitud de clase relacionada y datos asociados
-    (configurations' hyper-parameters, tokenizers' vocabulary, and models' weights) a partir de un control pre-entrenado proporcionado en
-    [Hugging Face Hub](https://huggingface.co/models) o de tu propio control guardado.
-  - Por encima de esas tres clases estándar, la biblioteca proporciona dos APIs: [`pipeline`] para usar rápidamente un modelo (junto a su configuracion y tokenizer asociados)
-    sobre una tarea dada, y [`Trainer`]/`Keras.fit` para entrenar u optimizar de forma rápida un modelo dado.
-  - Como consecuencia, esta biblioteca NO es una caja de herramientas modular de bloques individuales para redes neuronales. Si quieres extender/construir sobre la biblioteca,
-    usa simplemente los módulos regulares de Python/PyTorch/TensorFlow/Keras y emplea las clases estándar de la biblioteca como punto de partida para reutilizar funcionalidades
-    tales como abrir/guardar modelo.
-    
-- Proporciona modelos modernos con rendimientos lo más parecido posible a los modelos originales:
-
-  - Proporcionamos al menos un ejemplo para cada arquitectura que reproduce un resultado proporcionado por los autores de dicha arquitectura.
-  - El código normalmente es parecido al código base original, lo cual significa que algún código Pytorch puede no ser tan 
-    *pytorchic* como podría ser por haber sido convertido a código TensorFlow, y viceversa. 
-
-Unos cuantos objetivos adicionales:
-
-- Exponer las características internas de los modelos de la forma más coherente posible:
-
-  - Damos acceso, mediante una sola API, a todos los estados ocultos y pesos de atención.
-  - Tokenizer y el modelo de API base están estandarizados para cambiar fácilmente entre modelos.
-
-- Incorporar una selección subjetiva de herramientas de gran potencial para la optimización/investigación de estos modelos:
-
-  - Una forma sencilla/coherente de añadir nuevos tokens al vocabulario e incrustraciones (embeddings, en inglés) para optimización.
-  - Formas sencillas de camuflar y reducir "transformer heads".
-
-- Cambiar fácilmente entre PyTorch y TensorFlow 2.0, permitiendo el entrenamiento usando un marco y la inferencia usando otro.
-
-## Conceptos principales 
-
-La biblioteca está construida alrededor de tres tipos de clases para cada modelo:
-
-- **Model classes** como [`BertModel`], que consisten en más de 30 modelos PyTorch ([torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module)) o modelos Keras ([tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model)) que funcionan con pesos pre-entrenados proporcionados en la
-  biblioteca.
-- **Configuration classes** como [`BertConfig`], que almacena todos los parámetros necesarios para construir un modelo. 
-  No siempre tienes que generarla tu. En particular, si estas usando un modelo pre-entrenado sin ninguna modificación,
-  la creación del modelo se encargará automáticamente de generar la configuración (que es parte del modelo).
-- **Tokenizer classes** como [`BertTokenizer`], que almacena el vocabulario para cada modelo y proporciona métodos para
-  codificar/decodificar strings en una lista de índices de "token embeddings" para ser empleados en un modelo.          
-
-Todas estas clases pueden ser generadas a partir de ejemplos pre-entrenados, y guardados localmente usando dos métodos:
-
-- `from_pretrained()` permite generar un modelo/configuración/tokenizer a partir de una versión pre-entrenada proporcionada ya sea por
-  la propia biblioteca (los modelos compatibles se pueden encontrar en [Model Hub](https://huggingface.co/models)) o
-  guardados localmente (o en un servidor) por el usuario. 
-- `save_pretrained()` permite guardar un modelo/configuración/tokenizer localmente, de forma que puede ser empleado de nuevo usando
-  `from_pretrained()`.

docs/source/es/pipeline_tutorial.mdx

@@ -1,139 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Pipelines para inferencia
-
-Un [pipeline] simplifica el uso de cualquier modelo del [Model Hub](https://huggingface.co/models) para la inferencia en una variedad de tareas como la generación de texto, la segmentación de imágenes y la clasificación de audio. Incluso si no tienes experiencia con una modalidad específica o no comprendes el código que alimenta los modelos, ¡aún puedes usarlos con el [pipeline]! Este tutorial te enseñará a:
-
-* Utilizar un [`pipeline`] para inferencia.
-* Utilizar un tokenizador o modelo específico.
-* Utilizar un [`pipeline`] para tareas de audio y visión.
-
-<Tip>
-
-Echa un vistazo a la documentación de [`pipeline`] para obtener una lista completa de tareas admitidas.
-
-</Tip>
-
-## Uso del pipeline
-
-Si bien cada tarea tiene un [`pipeline`] asociado, es más sencillo usar la abstracción general [`pipeline`] que contiene todos los pipelines de tareas específicas. El [`pipeline`] carga automáticamente un modelo predeterminado y un tokenizador con capacidad de inferencia para tu tarea.
-
-1. Comienza creando un [`pipeline`] y específica una tarea de inferencia:
-
-```py
->>> from transformers import pipeline
-
->>> generator = pipeline(task="text-generation")
-```
-
-2. Pasa tu texto de entrada al [`pipeline`]:
-
-```py
->>> generator("Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone")
-[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]
-```
-
-Si tienes más de una entrada, pásala como una lista:
-
-```py
->>> generator(
-...     [
-...         "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
-...         "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
-...     ]
-... )
-```
-
-Cualquier parámetro adicional para tu tarea también se puede incluir en el [`pipeline`]. La tarea `text-generation` tiene un método [`~generation_utils.GenerationMixin.generate`] con varios parámetros para controlar la salida. Por ejemplo, si deseas generar más de una salida, defínelo en el parámetro `num_return_sequences`:
-
-```py
->>> generator(
-...     "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
-...     num_return_sequences=2,
-... )
-```
-
-### Selecciona un modelo y un tokenizador
-
-El [`pipeline`] acepta cualquier modelo del [Model Hub](https://huggingface.co/models). Hay etiquetas en el Model Hub que te permiten filtrar por el modelo que te gustaría utilizar para tu tarea. Una vez que hayas elegido un modelo apropiado, cárgalo con la clase `AutoModelFor` y [`AutoTokenizer'] correspondientes. Por ejemplo, carga la clase [`AutoModelForCausalLM`] para una tarea de modelado de lenguaje causal:
-
-```py
->>> from transformers import AutoTokenizer, AutoModelForCausalLM
-
->>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
->>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
-```
-
-Crea un [`pipeline`] para tu tarea y específica el modelo y el tokenizador que cargaste:
-
-```py
->>> from transformers import pipeline
-
->>> generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)
-```
-
-Pasa tu texto de entrada a [`pipeline`] para generar algo de texto:
-
-```py
->>> generator("Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone")
-[{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]
-```
-
-## Pipeline de audio
-
-La flexibilidad de [`pipeline`] significa que también se puede extender a tareas de audio.
-
-Por ejemplo, clasifiquemos la emoción de un breve fragmento del famoso discurso de John F. Kennedy ["We choose to go to the Moon"](https://en.wikipedia.org/wiki/We_choose_to_go_to_the_Moon). Encuentra un modelo de [audio classification](https://huggingface.co/models?pipeline_tag=audio-classification) para reconocimiento de emociones en el Model Hub y cárgalo en el [`pipeline`]:
-
-```py
->>> from transformers import pipeline
-
->>> audio_classifier = pipeline(
-...     task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
-... )
-```
-
-Pasa el archivo de audio al [`pipeline`]:
-
-```py
->>> audio_classifier("jfk_moon_speech.wav")
-[{'label': 'calm', 'score': 0.13856211304664612},
- {'label': 'disgust', 'score': 0.13148026168346405},
- {'label': 'happy', 'score': 0.12635163962841034},
- {'label': 'angry', 'score': 0.12439591437578201},
- {'label': 'fearful', 'score': 0.12404385954141617}]
-```
-
-## Pipeline de visión
-
-Finalmente, utilizar un [`pipeline`] para tareas de visión es prácticamente idéntico.
-
-Específica tu tarea de visión y pasa tu imagen al clasificador. La imagen puede ser un enlace o una ruta local a la imagen. Por ejemplo, ¿qué especie de gato se muestra a continuación?
-
-![pipeline-cat-chonk](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg)
-
-```py
->>> from transformers import pipeline
-
->>> vision_classifier = pipeline(task="image-classification")
->>> vision_classifier(
-...     images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
-... )
-[{'label': 'lynx, catamount', 'score': 0.4403027892112732},
- {'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
-  'score': 0.03433405980467796},
- {'label': 'snow leopard, ounce, Panthera uncia',
-  'score': 0.032148055732250214},
- {'label': 'Egyptian cat', 'score': 0.02353910356760025},
- {'label': 'tiger cat', 'score': 0.023034192621707916}]
-```

docs/source/es/quicktour.mdx

@@ -1,391 +0,0 @@
-<!--Copyright 2022 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.
--->
-
-# Quick tour
-
-[[open-in-colab]]
-
-¡Entra en marcha con los 🤗 Transformers! Comienza usando [`pipeline`] para una inferencia veloz, carga un modelo preentrenado y un tokenizador con una [AutoClass](./model_doc/auto) para resolver tu tarea de texto, visión o audio.
-
-<Tip>
-
-Todos los ejemplos de código presentados en la documentación tienen un botón arriba a la izquierda para elegir entre Pytorch y TensorFlow.
-Si no fuese así, se espera que el código funcione para ambos backends sin ningún cambio.
-
-</Tip>
-
-## Pipeline
-
-[`pipeline`] es la forma más fácil de usar un modelo preentrenado para una tarea dada.
-
-<Youtube id="tiZFewofSLM"/>
-
-El [`pipeline`] soporta muchas tareas comunes listas para usar:
-
-**Texto**:
-* Análisis de Sentimiento (Sentiment Analysis, en inglés): clasifica la polaridad de un texto dado.
-* Generación de Texto (Text Generation, en inglés): genera texto a partir de un input dado.
-* Reconocimiento de Entidades (Name Entity Recognition o NER, en inglés): etiqueta cada palabra con la entidad que representa (persona, fecha, ubicación, etc.).
-* Responder Preguntas (Question answering, en inglés): extrae la respuesta del contexto dado un contexto y una pregunta.
-* Rellenar Máscara (Fill-mask, en inglés): rellena el espacio faltante dado un texto con palabras enmascaradas.
-* Resumir (Summarization, en inglés): genera un resumen de una secuencia larga de texto o un documento.
-* Traducción (Translation, en inglés): traduce un texto a otro idioma.
-* Extracción de Características (Feature Extraction, en inglés): crea una representación tensorial del texto.
-
-**Imagen**:
-* Clasificación de Imágenes (Image Classification, en inglés): clasifica una imagen.
-* Segmentación de Imágenes (Image Segmentation, en inglés): clasifica cada pixel de una imagen.
-* Detección de Objetos (Object Detection, en inglés): detecta objetos dentro de una imagen.
-
-**Audio**:
-* Clasificación de Audios (Audio Classification, en inglés): asigna una etiqueta a un segmento de audio.
-* Reconocimiento de Voz Automático (Automatic Speech Recognition o ASR, en inglés): transcribe datos de audio a un texto.
-
-<Tip>
-
-Para más detalles acerca del [`pipeline`] y tareas asociadas, consulta la documentación [aquí](./main_classes/pipelines).
-
-</Tip>
-
-### Uso del Pipeline
-
-En el siguiente ejemplo, usarás el [`pipeline`] para análisis de sentimiento.
-
-Instala las siguientes dependencias si aún no lo has hecho:
-
-<frameworkcontent>
-<pt>
-```bash
-pip install torch
-```
-</pt>
-<tf>
-```bash
-pip install tensorflow
-```
-</tf>
-</frameworkcontent>
-
-Importa [`pipeline`] y especifica la tarea que deseas completar:
-
-```py
->>> from transformers import pipeline
-
->>> clasificador = pipeline("sentiment-analysis", model="pysentimiento/robertuito-sentiment-analysis")
-```
-
-El pipeline descarga y almacena en caché el [modelo preentrenado](https://huggingface.co/pysentimiento/robertuito-sentiment-analysis) y tokeniza para análisis de sentimiento. Si no hubieramos elegido un modelo el pipeline habría elegido uno por defecto. Ahora puedes usar `clasificador` en tu texto objetivo:
-
-```py
->>> clasificador("Estamos muy felices de mostrarte la biblioteca de 🤗 Transformers.")
-[{'label': 'POS', 'score': 0.9916}]
-```
-
-Para más de un enunciado, entrega una lista al [`pipeline`] que devolverá una lista de diccionarios:
-
-El [`pipeline`] también puede iterar sobre un dataset entero. Comienza instalando la biblioteca [🤗 Datasets](https://huggingface.co/docs/datasets/):
-
-```bash
-pip install datasets
-```
-
-Crea un [`pipeline`] con la tarea que deseas resolver y el modelo que quieres usar. Coloca el parámetro `device` a `0` para poner los tensores en un dispositivo CUDA:
-
-```py
->>> import torch
->>> from transformers import pipeline
-
->>> reconocedor_de_voz = pipeline(
-...     "automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish", device=0
-... )
-```
-
-A continuación, carga el dataset (ve 🤗 Datasets [Quick Start](https://huggingface.co/docs/datasets/quickstart.html) para más detalles) sobre el que quisieras iterar. Por ejemplo, vamos a cargar el dataset [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14):
-
-```py
->>> from datasets import load_dataset, Audio
-
->>> dataset = load_dataset("PolyAI/minds14", name="es-ES", split="train")  # doctest: +IGNORE_RESULT
-```
-
-Debemos asegurarnos de que la frecuencia de muestreo del conjunto de datos coincide con la frecuencia de muestreo con la que se entrenó `jonatasgrosman/wav2vec2-large-xlsr-53-spanish`.
-
-```py
->>> dataset = dataset.cast_column("audio", Audio(sampling_rate=reconocedor_de_voz.feature_extractor.sampling_rate))
-```
-
-Los archivos de audio se cargan y remuestrean automáticamente cuando llamamos a la columna `"audio"`.
-Extraigamos las matrices de onda cruda (raw waveform, en inglés) de las primeras 4 muestras y pasémosla como una lista al pipeline:
-
-```py
->>> resultado = reconocedor_de_voz(dataset[:4]["audio"])
->>> print([d["text"] for d in resultado])
-['ahora buenas e a ver  tengo un problema como vuestra aplicación resulta que que quiero hacer una transferencia bancaria a una cuenta conocida pero me da error la aplicación a ver que a ver que puede ser', 'la aplicación no cargue salda de mi nueva cuenta', 'hola tengo un problema con la aplicación no carga y y tampoco veo que carga el saldo de mi cuenta nueva dice que la aplicación está siendo reparada y ahora no puedo aceder a mi cuenta no necesito inmediatamente', 'ora buena la aplicación no se carga la viladad no carga el saldo de mi cuenta nueva dice que la villadenta siendo reparada y oro no puede hacer a mi cuenta']
-```
-
-Para un dataset más grande, donde los inputs son de mayor tamaño (como en habla/audio o visión), querrás pasar un generador en lugar de una lista que carga todos los inputs en memoria. Ve la [documentación del pipeline](./main_classes/pipelines) para más información.
-
-### Usa otro modelo y otro tokenizador en el pipeline
-
-El [`pipeline`] puede acomodarse a cualquier modelo del [Model Hub](https://huggingface.co/models) haciendo más fácil adaptar el [`pipeline`] para otros casos de uso. Por ejemplo, si quisieras un modelo capaz de manejar texto en francés, usa los tags en el Model Hub para filtrar entre los modelos apropiados. El resultado mejor filtrado devuelve un [modelo BERT](https://huggingface.co/nlptown/bert-base-multilingual-uncased-sentiment) multilingual fine-tuned para el análisis de sentimiento. Genial, ¡vamos a usar este modelo!
-
-```py
->>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
-```
-
-<frameworkcontent>
-<pt>
-Usa [`AutoModelForSequenceClassification`] y ['AutoTokenizer'] para cargar un modelo preentrenado y un tokenizador asociado (más en un `AutoClass` debajo):
-
-```py
->>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
->>> model = AutoModelForSequenceClassification.from_pretrained(model_name)
->>> tokenizer = AutoTokenizer.from_pretrained(model_name)
-```
-
-</pt>
-
-<tf>
-Usa [`TFAutoModelForSequenceClassification`] y ['AutoTokenizer'] para cargar un modelo preentrenado y un tokenizador asociado (más en un `TFAutoClass` debajo):
-
-```py
->>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-
->>> model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
->>> tokenizer = AutoTokenizer.from_pretrained(model_name)
-```
-
-</tf>
-</frameworkcontent>
-
-Después puedes especificar el modelo y el tokenizador en el [`pipeline`], y aplicar el `classifier` en tu texto objetivo:
-
-```py
->>> classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
->>> classifier("Nous sommes très heureux de vous présenter la bibliothèque 🤗 Transformers.")
-[{'label': '5 stars', 'score': 0.7273}]
-```
-
-Si no pudieras encontrar el modelo para tu caso respectivo de uso necesitarás ajustar un modelo preentrenado a tus datos. Mira nuestro [tutorial de fine-tuning](./training) para aprender cómo. Finalmente, después de que has ajustado tu modelo preentrenado, ¡por favor considera compartirlo (ve el tutorial [aquí](./model_sharing)) con la comunidad en el Model Hub para democratizar el NLP! 🤗
-
-## AutoClass
-
-<Youtube id="AhChOFRegn4"/>
-
-Debajo del capó, las clases [`AutoModelForSequenceClassification`] y [`AutoTokenizer`] trabajan juntas para dar poder al [`pipeline`]. Una [AutoClass](./model_doc/auto) es un atajo que automáticamente recupera la arquitectura de un modelo preentrenado con su nombre o el path. Sólo necesitarás seleccionar el `AutoClass` apropiado para tu tarea y tu tokenizador asociado con [`AutoTokenizer`].
-
-Regresemos a nuestro ejemplo y veamos cómo puedes usar el `AutoClass` para reproducir los resultados del [`pipeline`].
-
-### AutoTokenizer
-
-Un tokenizador es responsable de procesar el texto a un formato que sea entendible para el modelo. Primero, el tokenizador separará el texto en palabras llamadas *tokens*. Hay múltiples reglas que gobiernan el proceso de tokenización incluyendo el cómo separar una palabra y en qué nivel (aprende más sobre tokenización [aquí](./tokenizer_summary)). Lo más importante es recordar que necesitarás instanciar el tokenizador con el mismo nombre del modelo para asegurar que estás usando las mismas reglas de tokenización con las que el modelo fue preentrenado.
-
-Carga un tokenizador con [`AutoTokenizer`]:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> nombre_del_modelo = "nlptown/bert-base-multilingual-uncased-sentiment"
->>> tokenizer = AutoTokenizer.from_pretrained(nombre_del_modelo)
-```
-
-Después, el tokenizador convierte los tokens a números para construir un tensor que servirá como input para el modelo. Esto es conocido como el *vocabulario* del modelo.
-
-Pasa tu texto al tokenizador:
-
-```py
->>> encoding = tokenizer("Estamos muy felices de mostrarte la biblioteca de 🤗 Transformers.")
->>> print(encoding)
-{'input_ids': [101, 10602, 14000, 13653, 43353, 10107, 10102, 47201, 10218, 10106, 18283, 10102, 100, 58263, 119, 102],
- 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
- 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-```
-
-El tokenizador devolverá un diccionario conteniendo:
-
-* [input_ids](./glossary#input-ids): representaciones numéricas de los tokens.
-* [atttention_mask](.glossary#attention-mask): indica cuáles tokens deben ser atendidos.
-
-Como con el [`pipeline`], el tokenizador aceptará una lista de inputs. Además, el tokenizador también puede rellenar (pad, en inglés) y truncar el texto para devolver un lote (batch, en inglés) de longitud uniforme:
-
-<frameworkcontent>
-<pt>
-```py
->>> pt_batch = tokenizer(
-...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
-...     padding=True,
-...     truncation=True,
-...     max_length=512,
-...     return_tensors="pt",
-... )
-```
-</pt>
-<tf>
-```py
->>> tf_batch = tokenizer(
-...     ["We are very happy to show you the 🤗 Transformers library.", "We hope you don't hate it."],
-...     padding=True,
-...     truncation=True,
-...     max_length=512,
-...     return_tensors="tf",
-... )
-```
-</tf>
-</frameworkcontent>
-
-Lee el tutorial de [preprocessing](./preprocessing) para más detalles acerca de la tokenización.
-
-### AutoModel
-
-<frameworkcontent>
-<pt>
-🤗 Transformers provee una forma simple y unificada de cargar tus instancias preentrenadas. Esto significa que puedes cargar un [`AutoModel`] como cargarías un [`AutoTokenizer`]. La única diferencia es seleccionar el [`AutoModel`] correcto para la tarea. Ya que estás clasificando texto, o secuencias, carga [`AutoModelForSequenceClassification`]:
-
-```py
->>> from transformers import AutoModelForSequenceClassification
-
->>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
->>> pt_model = AutoModelForSequenceClassification.from_pretrained(model_name)
-```
-
-<Tip>
-
-Ve el [task summary](./task_summary) para revisar qué clase del [`AutoModel`] deberías usar para cada tarea.
-
-</Tip>
-
-Ahora puedes pasar tu lote (batch) preprocesado de inputs directamente al modelo. Solo tienes que desempacar el diccionario añadiendo `**`:
-
-```py
->>> pt_outputs = pt_model(**pt_batch)
-```
-
-El modelo producirá las activaciones finales en el atributo `logits`. Aplica la función softmax a `logits` para obtener las probabilidades:
-
-```py
->>> from torch import nn
-
->>> pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
->>> print(pt_predictions)
-tensor([[0.0021, 0.0018, 0.0115, 0.2121, 0.7725],
-        [0.2084, 0.1826, 0.1969, 0.1755, 0.2365]], grad_fn=<SoftmaxBackward0>)
-```
-</pt>
-<tf>
-🤗 Transformers provee una forma simple y unificada de cargar tus instancias preentrenadas. Esto significa que puedes cargar un [`TFAutoModel`] como cargarías un [`AutoTokenizer`]. La única diferencia es seleccionar el [`TFAutoModel`] correcto para la tarea. Ya que estás clasificando texto, o secuencias, carga [`TFAutoModelForSequenceClassification`]:
-
-```py
->>> from transformers import TFAutoModelForSequenceClassification
-
->>> model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
->>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
-```
-
-<Tip>
-  Ve el [task summary](./task_summary) para revisar qué clase del [`AutoModel`]
-  deberías usar para cada tarea.
-</Tip>
-
-Ahora puedes pasar tu lote preprocesado de inputs directamente al modelo pasando las llaves del diccionario directamente a los tensores:
-
-```py
->>> tf_outputs = tf_model(tf_batch)
-```
-
-El modelo producirá las activaciones finales en el atributo `logits`. Aplica la función softmax a `logits` para obtener las probabilidades:
-
-```py
->>> import tensorflow as tf
-
->>> tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
->>> print(tf.math.round(tf_predictions * 10**4) / 10**4)
-tf.Tensor(
-[[0.0021 0.0018 0.0116 0.2121 0.7725]
- [0.2084 0.1826 0.1969 0.1755  0.2365]], shape=(2, 5), dtype=float32)
-```
-</tf>
-</frameworkcontent>
-
-<Tip>
-
-Todos los modelos de 🤗 Transformers (PyTorch o TensorFlow) producirán los tensores *antes* de la función de activación
-final (como softmax) porque la función de activación final es comúnmente fusionada con la pérdida.
-
-</Tip>
-
-Los modelos son [`torch.nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) o [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) estándares así que podrás usarlos en tu training loop usual. Sin embargo, para facilitar las cosas, 🤗 Transformers provee una clase [`Trainer`] para PyTorch que añade funcionalidades para entrenamiento distribuido, precición mixta, y más. Para TensorFlow, puedes usar el método `fit` desde [Keras](https://keras.io/). Consulta el [tutorial de entrenamiento](./training) para más detalles.
-
-<Tip>
-
-Los outputs del modelo de 🤗 Transformers son dataclasses especiales por lo que sus atributos pueden ser completados en un IDE.
-Los outputs del modelo también se comportan como tuplas o diccionarios (e.g., puedes indexar con un entero, un slice o una cadena) en cuyo caso los atributos que son `None` son ignorados.
-
-</Tip>
-
-### Guarda un modelo
-
-<frameworkcontent>
-<pt>
-Una vez que se haya hecho fine-tuning a tu modelo puedes guardarlo con tu tokenizador usando [`PreTrainedModel.save_pretrained`]:
-
-```py
->>> pt_save_directory = "./pt_save_pretrained"
->>> tokenizer.save_pretrained(pt_save_directory)  # doctest: +IGNORE_RESULT
->>> pt_model.save_pretrained(pt_save_directory)
-```
-
-Cuando quieras usar el modelo otra vez cárgalo con [`PreTrainedModel.from_pretrained`]:
-
-```py
->>> pt_model = AutoModelForSequenceClassification.from_pretrained("./pt_save_pretrained")
-```
-
-</pt>
-
-<tf>
-Una vez que se haya hecho fine-tuning a tu modelo puedes guardarlo con tu tokenizador usando [`TFPreTrainedModel.save_pretrained`]:
-
-```py
->>> tf_save_directory = "./tf_save_pretrained"
->>> tokenizer.save_pretrained(tf_save_directory)  # doctest: +IGNORE_RESULT
->>> tf_model.save_pretrained(tf_save_directory)
-```
-
-Cuando quieras usar el modelo otra vez cárgalo con [`TFPreTrainedModel.from_pretrained`]:
-
-```py
->>> tf_model = TFAutoModelForSequenceClassification.from_pretrained("./tf_save_pretrained")
-```
-</tf>
-</frameworkcontent>
-
-Una característica particularmente interesante de 🤗 Transformers es la habilidad de guardar el modelo y cargarlo como un modelo de PyTorch o TensorFlow. El parámetro `from_pt` o `from_tf` puede convertir el modelo de un framework al otro:
-
-<frameworkcontent>
-<pt>
-```py
->>> from transformers import AutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained(tf_save_directory)
->>> pt_model = AutoModelForSequenceClassification.from_pretrained(tf_save_directory, from_tf=True)
-```
-</pt>
-<tf>
-```py
->>> from transformers import TFAutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained(pt_save_directory)
->>> tf_model = TFAutoModelForSequenceClassification.from_pretrained(pt_save_directory, from_pt=True)
-```
-</tf>
-</frameworkcontent>

docs/source/es/training.mdx

@@ -1,367 +0,0 @@
-<!--Copyright 2022 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 a un modelo pre-entrenado
-
-[[open-in-colab]]
-
-El uso de un modelo pre-entrenado tiene importantes ventajas. Reduce los costos de computación, la huella de carbono, y te permite utilizar modelos de última generación sin tener que entrenar uno desde cero. 🤗 Transformers proporciona acceso a miles de modelos pre-entrenados en una amplia gama de tareas. Cuando utilizas un modelo pre-entrenado, lo entrenas con un dataset específico para tu tarea. Esto se conoce como fine-tuning, una técnica de entrenamiento increíblemente poderosa. En este tutorial haremos fine-tuning a un modelo pre-entrenado con un framework de Deep Learning de tu elección:
-
-* Fine-tuning a un modelo pre-entrenado con 🤗 Transformers [`Trainer`].
-* Fine-tuning a un modelo pre-entrenado en TensorFlow con Keras.
-* Fine-tuning a un modelo pre-entrenado en PyTorch nativo.
-
-<a id='data-processing'></a>
-
-## Prepara un dataset
-
-<Youtube id="_BZearw7f0w"/>
-
-Antes de aplicar fine-tuning a un modelo pre-entrenado, descarga un dataset y prepáralo para el entrenamiento. El tutorial anterior nos enseñó cómo procesar los datos para el entrenamiento, y ahora es la oportunidad de poner a prueba estas habilidades.
-
-Comienza cargando el dataset de [Yelp Reviews](https://huggingface.co/datasets/yelp_review_full):
-
-```py
->>> from datasets import load_dataset
-
->>> dataset = load_dataset("yelp_review_full")
->>> dataset[100]
-{'label': 0,
- 'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}
-```
-
-Como ya sabes, necesitas un tokenizador para procesar el texto e incluir una estrategia para el padding y el truncamiento, para manejar cualquier longitud de secuencia variable. Para procesar tu dataset en un solo paso, utiliza el método de 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/process.html#map) para aplicar una función de preprocesamiento sobre todo el dataset:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-
-
->>> def tokenize_function(examples):
-...     return tokenizer(examples["text"], padding="max_length", truncation=True)
-
-
->>> tokenized_datasets = dataset.map(tokenize_function, batched=True)
-```
-
-Si lo deseas, puedes crear un subconjunto más pequeño del dataset completo para aplicarle fine-tuning y así reducir el tiempo.
-
-```py
->>> small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
->>> small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
-```
-
-<a id='trainer'></a>
-
-## Fine-tuning con `Trainer`
-
-<Youtube id="nvBXf7s7vTI"/>
-
-🤗 Transformers proporciona una clase [`Trainer`] optimizada para el entrenamiento de modelos de 🤗 Transformers, haciendo más fácil el inicio del entrenamiento sin necesidad de escribir manualmente tu propio ciclo. La API del [`Trainer`] soporta una amplia gama de opciones de entrenamiento y características como el logging, el gradient accumulation y el mixed precision.
-
-Comienza cargando tu modelo y especifica el número de labels previstas. A partir del [Card Dataset](https://huggingface.co/datasets/yelp_review_full#data-fields) de Yelp Review, que como ya sabemos tiene 5 labels:
-
-```py
->>> from transformers import AutoModelForSequenceClassification
-
->>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
-```
-
-<Tip>
-
-Verás una advertencia acerca de que algunos de los pesos pre-entrenados que no están siendo utilizados y que algunos pesos están siendo inicializados al azar. 
-No te preocupes, esto es completamente normal. El head/cabezal pre-entrenado del modelo BERT se descarta y se sustituye por un head de clasificación inicializado aleatoriamente. Puedes aplicar fine-tuning a este nuevo head del modelo en tu tarea de clasificación de secuencias haciendo transfer learning del modelo pre-entrenado.
-
-</Tip>
-
-### Hiperparámetros de entrenamiento
-
-A continuación, crea una clase [`TrainingArguments`] que contenga todos los hiperparámetros que puedes ajustar así como los indicadores para activar las diferentes opciones de entrenamiento. Para este tutorial puedes empezar con los [hiperparámetros](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) de entrenamiento por defecto, pero siéntete libre de experimentar con ellos para encontrar tu configuración óptima.
-
-Especifica dónde vas a guardar los checkpoints de tu entrenamiento:
-
-```py
->>> from transformers import TrainingArguments
-
->>> training_args = TrainingArguments(output_dir="test_trainer")
-```
-
-### Métricas
-
-El [`Trainer`] no evalúa automáticamente el rendimiento del modelo durante el entrenamiento. Tendrás que pasarle a [`Trainer`] una función para calcular y hacer un reporte de las métricas. La librería de 🤗 Datasets proporciona una función de [`accuracy`](https://huggingface.co/metrics/accuracy) simple que puedes cargar con la función `load_metric` (ver este [tutorial](https://huggingface.co/docs/datasets/metrics.html) para más información):
-
-```py
->>> import numpy as np
->>> from datasets import load_metric
-
->>> metric = load_metric("accuracy")
-```
-
-Define la función `compute` en `metric` para calcular el accuracy de tus predicciones. Antes de pasar tus predicciones a `compute`, necesitas convertir las predicciones a logits (recuerda que todos los modelos de 🤗 Transformers devuelven logits).
-
-```py
->>> def compute_metrics(eval_pred):
-...     logits, labels = eval_pred
-...     predictions = np.argmax(logits, axis=-1)
-...     return metric.compute(predictions=predictions, references=labels)
-```
-
-Si quieres controlar tus métricas de evaluación durante el fine-tuning, especifica el parámetro `evaluation_strategy` en tus argumentos de entrenamiento para que el modelo tenga en cuenta la métrica de evaluación al final de cada época:
-
-```py
->>> from transformers import TrainingArguments
-
->>> training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
-```
-
-### Trainer
-
-Crea un objeto [`Trainer`] con tu modelo, argumentos de entrenamiento, conjuntos de datos de entrenamiento y de prueba, y tu función de evaluación:
-
-```py
->>> trainer = Trainer(
-...     model=model,
-...     args=training_args,
-...     train_dataset=small_train_dataset,
-...     eval_dataset=small_eval_dataset,
-...     compute_metrics=compute_metrics,
-... )
-```
-
-A continuación, aplica fine-tuning a tu modelo llamando [`~transformers.Trainer.train`]:
-
-```py
->>> trainer.train()
-```
-
-<a id='keras'></a>
-
-## Fine-tuning con Keras
-
-<Youtube id="rnTGBy2ax1c"/>
-
-Los modelos de 🤗 Transformers también permiten realizar el entrenamiento en TensorFlow con la API de Keras. Sólo es necesario hacer algunos cambios antes de hacer fine-tuning.
-
-### Convierte el dataset al formato de TensorFlow
-
-El [`DefaultDataCollator`] junta los tensores en un batch para que el modelo se entrene en él. Asegúrate de especificar `return_tensors` para devolver los tensores de TensorFlow:
-
-```py
->>> from transformers import DefaultDataCollator
-
->>> data_collator = DefaultDataCollator(return_tensors="tf")
-```
-
-<Tip>
-
-[`Trainer`] utiliza [`DataCollatorWithPadding`] por defecto por lo que no es necesario especificar explícitamente un intercalador de datos (data collator, en inglés).
-
-</Tip>
-
-A continuación, convierte los datasets tokenizados en datasets de TensorFlow con el método [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Especifica tus entradas en `columns` y tu etiqueta en `label_cols`:
-
-```py
->>> tf_train_dataset = small_train_dataset.to_tf_dataset(
-...     columns=["attention_mask", "input_ids", "token_type_ids"],
-...     label_cols=["labels"],
-...     shuffle=True,
-...     collate_fn=data_collator,
-...     batch_size=8,
-... )
-
->>> tf_validation_dataset = small_eval_dataset.to_tf_dataset(
-...     columns=["attention_mask", "input_ids", "token_type_ids"],
-...     label_cols=["labels"],
-...     shuffle=False,
-...     collate_fn=data_collator,
-...     batch_size=8,
-... )
-```
-
-### Compila y ajusta
-
-Carguemos un modelo TensorFlow con el número esperado de labels:
-
-```py
->>> import tensorflow as tf
->>> from transformers import TFAutoModelForSequenceClassification
-
->>> model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
-```
-
-A continuación, compila y aplica fine-tuning a tu modelo con [`fit`](https://keras.io/api/models/model_training_apis/) como lo harías con cualquier otro modelo de Keras:
-
-```py
->>> model.compile(
-...     optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
-...     loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
-...     metrics=tf.metrics.SparseCategoricalAccuracy(),
-... )
-
->>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3)
-```
-
-<a id='pytorch_native'></a>
-
-## Fine-tune en PyTorch nativo
-
-<Youtube id="Dh9CL8fyG80"/>
-
-El [`Trainer`] se encarga del ciclo de entrenamiento y permite aplicar fine-tuning a un modelo en una sola línea de código. Para los usuarios que prefieren escribir tu propio ciclo de entrenamiento, también puedes aplicar fine-tuning a un modelo de 🤗 Transformers en PyTorch nativo.
-
-En este punto, es posible que necesites reiniciar tu notebook o ejecutar el siguiente código para liberar algo de memoria:
-
-```py
-del model
-del pytorch_model
-del trainer
-torch.cuda.empty_cache()
-```
-
-A continuación, haremos un post-procesamiento manual al `tokenized_dataset` y así prepararlo para el entrenamiento.
-
-1. Elimina la columna de `text` porque el modelo no acepta texto en crudo como entrada:
-
-    ```py
-    >>> tokenized_datasets = tokenized_datasets.remove_columns(["text"])
-    ```
-
-2. Cambia el nombre de la columna de `label` a `labels` porque el modelo espera que el argumento se llame `labels`:
-
-    ```py
-    >>> tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
-    ```
-
-3. Establece el formato del dataset para devolver tensores PyTorch en lugar de listas:
-
-    ```py
-    >>> tokenized_datasets.set_format("torch")
-    ```
-
-A continuación, crea un subconjunto más pequeño del dataset, como se ha mostrado anteriormente, para acelerar el fine-tuning:
-
-```py
->>> small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
->>> small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
-```
-
-### DataLoader
-
-Crea un `DataLoader` para tus datasets de entrenamiento y de prueba para poder iterar sobre batches de datos:
-
-```py
->>> from torch.utils.data import DataLoader
-
->>> train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
->>> eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)
-```
-
-Carga tu modelo con el número de labels previstas:
-
-```py
->>> from transformers import AutoModelForSequenceClassification
-
->>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
-```
-
-### Optimiza y progrma el learning rate
-
-Crea un optimizador y el learning rate para aplicar fine-tuning al modelo. Vamos a utilizar el optimizador [`AdamW`](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html) de PyTorch:
-
-```py
->>> from torch.optim import AdamW
-
->>> optimizer = AdamW(model.parameters(), lr=5e-5)
-```
-
-Crea el learning rate desde el [`Trainer`]:
-
-```py
->>> from transformers import get_scheduler
-
->>> num_epochs = 3
->>> num_training_steps = num_epochs * len(train_dataloader)
->>> lr_scheduler = get_scheduler(
-...     name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps
-... )
-```
-
-Por último, especifica el `device` o entorno de ejecución para utilizar una GPU si tienes acceso a una. De lo contrario, el entrenamiento en una CPU puede llevarte varias horas en lugar de un par de minutos.
-
-```py
->>> import torch
-
->>> device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
->>> model.to(device)
-```
-
-<Tip>
-
-Consigue acceso gratuito a una GPU en la nube si es que no tienes este recurso de forma local con un notebook alojado en [Colaboratory](https://colab.research.google.com/) o [SageMaker StudioLab](https://studiolab.sagemaker.aws/).
-
-</Tip>
-
-Genial, ¡ahora estamos listos entrenar! 🥳
-
-### Ciclo de entrenamiento
-
-Para hacer un seguimiento al progreso del entrenamiento, utiliza la librería [tqdm](https://tqdm.github.io/) para añadir una barra de progreso sobre el número de pasos de entrenamiento:
-
-```py
->>> from tqdm.auto import tqdm
-
->>> progress_bar = tqdm(range(num_training_steps))
-
->>> model.train()
->>> for epoch in range(num_epochs):
-...     for batch in train_dataloader:
-...         batch = {k: v.to(device) for k, v in batch.items()}
-...         outputs = model(**batch)
-...         loss = outputs.loss
-...         loss.backward()
-
-...         optimizer.step()
-...         lr_scheduler.step()
-...         optimizer.zero_grad()
-...         progress_bar.update(1)
-```
-
-### Métricas
-
-De la misma manera que necesitas añadir una función de evaluación al [`Trainer`], necesitas hacer lo mismo cuando escribas tu propio ciclo de entrenamiento. Pero en lugar de calcular y reportar la métrica al final de cada época, esta vez acumularás todos los batches con [`add_batch`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=add_batch#datasets.Metric.add_batch) y calcularás la métrica al final.
-
-```py
->>> metric = load_metric("accuracy")
->>> model.eval()
->>> for batch in eval_dataloader:
-...     batch = {k: v.to(device) for k, v in batch.items()}
-...     with torch.no_grad():
-...         outputs = model(**batch)
-
-...     logits = outputs.logits
-...     predictions = torch.argmax(logits, dim=-1)
-...     metric.add_batch(predictions=predictions, references=batch["labels"])
-
->>> metric.compute()
-```
-
-<a id='additional-resources'></a>
-
-## Recursos adicionales
-
-Para más ejemplos de fine-tuning consulta:
-
-- [🤗 Transformers Examples](https://github.com/huggingface/transformers/tree/main/examples) incluye scripts
-  para entrenar tareas comunes de NLP en PyTorch y TensorFlow.
-
-- [🤗 Transformers Notebooks](notebooks) contiene varios notebooks sobre cómo aplicar fine-tuning a un modelo para tareas específicas en PyTorch y TensorFlow.

docs/source/examples.md

@@ -0,0 +1 @@
+../../examples/README.md

docs/source/fast_tokenizers.mdx

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Use tokenizers from 🤗 Tokenizers
+# Using tokenizers from 🤗 Tokenizers
 
 The [`PreTrainedTokenizerFast`] depends on the [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) library. The tokenizers obtained from the 🤗 Tokenizers library can be
 loaded very simply into 🤗 Transformers.

docs/source/index.mdx

@@ -35,17 +35,20 @@ Each 🤗 Transformers architecture is defined in a standalone Python module so
 
 The documentation is organized in five parts:
 
-- **GET STARTED** contains a quick tour and installation instructions to get up and running with 🤗 Transformers.
-- **TUTORIALS** are a great place to begin if you are new to our library. This section will help you gain the basic skills you need to start using 🤗 Transformers.
-- **HOW-TO GUIDES** will show you how to achieve a specific goal like fine-tuning a pretrained model for language modeling or how to create a custom model head.
-- **CONCEPTUAL GUIDES** provides more discussion and explanation of the underlying concepts and ideas behind models, tasks, and the design philosophy of 🤗 Transformers. 
-- **API** describes each class and function, grouped in:
+- **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.
+The library currently contains Jax, PyTorch and Tensorflow implementations, pretrained model weights, usage scripts and
+conversion utilities for the following models.
 
 ### Supported models
 
@@ -75,21 +78,16 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
 1. **[Data2Vec](model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
 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. **[Decision Transformer](model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[DiT](model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
 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/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
+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. **[DPT](master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
 1. **[EncoderDecoder](model_doc/encoder-decoder)** (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. **[FLAVA](model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
 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. **[GLPN](model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
 1. **[GPT](model_doc/openai-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.
@@ -115,7 +113,6 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
 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. **[Nyströmformer](model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
-1. **[OPT](master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 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.
@@ -123,11 +120,9 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
 1. **[PoolFormer](model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
 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. **[REALM](model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
+1. **[REALM](https://huggingface.co/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 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/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
-1. **[RegNet](model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
-1. **[ResNet](model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
 1. **[RoBERTa](model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: 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 [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) 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.
@@ -141,28 +136,25 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
 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. **[TAPEX](model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
 1. **[Transformer-XL](model_doc/transfo-xl)** (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. **[VAN](model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
 1. **[ViLT](model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
 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. **[ViTMAE](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
 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. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
 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. **[Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
-1. **[XGLM](model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/master/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+1. **[XGLM](https://huggingface.co/docs/master/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
 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/xlm-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. **[XLM-RoBERTa](model_doc/xlm-roberta)** (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. **[XLM-RoBERTa-XL](model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
 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.
-1. **[XLS-R](model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
-1. **[YOLOS](model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
+1. **[XLS-R](https://huggingface.co/docs/master/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
 1. **[YOSO](model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
 
 
@@ -193,25 +185,20 @@ Flax), PyTorch, and/or TensorFlow.
 |            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |        Data2VecAudio        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        Data2VecText         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|       Data2VecVision        |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|         DeBERTa-v2          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|    Decision Transformer     |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         DeBERTa-v2          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            DeiT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            DETR             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|             DPT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 | FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|            Flava            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            FNet             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|            GLPN             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
-|            GPT-J            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|            GPT-J            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -232,7 +219,6 @@ Flax), PyTorch, and/or TensorFlow.
 |        Nystromformer        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-|             OPT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          Perceiver          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           PLBart            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -242,9 +228,7 @@ Flax), PyTorch, and/or TensorFlow.
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            Realm            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|           ResNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          RoFormer           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
@@ -259,27 +243,24 @@ Flax), PyTorch, and/or TensorFlow.
 |            Swin             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|            TAPEX            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            TrOCR            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          UniSpeech          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        UniSpeechSat         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|             VAN             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            ViLT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |   Vision Encoder decoder    |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |    VisionTextDualEncoder    |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |         VisualBert          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-|           ViTMAE            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|           ViTMAE            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
 |            WavLM            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            XGLM             |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |       XLM-RoBERTa-XL        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|            YOLOS            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            YOSO             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 
 <!-- End table-->

docs/source/installation.mdx

@@ -84,7 +84,7 @@ Install 🤗 Transformers from source with the following command:
 pip install git+https://github.com/huggingface/transformers
 ```
 
-This command installs the bleeding edge `main` version rather than the latest `stable` version. The `main` version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. However, this means the `main` version may not always be stable. We strive to keep the `main` version operational, and most issues are usually resolved within a few hours or a day. If you run into a problem, please open an [Issue](https://github.com/huggingface/transformers/issues) so we can fix it even sooner!
+This command installs the bleeding edge `master` version rather than the latest `stable` version. The `master` version is useful for staying up-to-date with the latest developments. For instance, if a bug has been fixed since the last official release but a new release hasn't been rolled out yet. However, this means the `master` version may not always be stable. We strive to keep the `master` version operational, and most issues are usually resolved within a few hours or a day. If you run into a problem, please open an [Issue](https://github.com/huggingface/transformers/issues) so we can fix it even sooner!
 
 Check if 🤗 Transformers has been properly installed by running the following command:
 
@@ -96,7 +96,7 @@ python -c "from transformers import pipeline; print(pipeline('sentiment-analysis
 
 You will need an editable install if you'd like to:
 
-* Use the `main` version of the source code.
+* Use the `master` version of the source code.
 * Contribute to 🤗 Transformers and need to test changes in the code.
 
 Clone the repository and install 🤗 Transformers with the following commands:
@@ -122,7 +122,7 @@ cd ~/transformers/
 git pull
 ```
 
-Your Python environment will find the `main` version of 🤗 Transformers on the next run.
+Your Python environment will find the `master` version of 🤗 Transformers on the next run.
 
 ## Install with conda
 

docs/source/internal/file_utils.mdx

@@ -12,35 +12,35 @@ specific language governing permissions and limitations under the License.
 
 # General Utilities
 
-This page lists all of Transformers general utility functions that are found in the file `utils.py`.
+This page lists all of Transformers general utility functions that are found in the file `file_utils.py`.
 
 Most of those are only useful if you are studying the general code in the library.
 
 
 ## Enums and namedtuples
 
-[[autodoc]] utils.ExplicitEnum
+[[autodoc]] file_utils.ExplicitEnum
 
-[[autodoc]] utils.PaddingStrategy
+[[autodoc]] file_utils.PaddingStrategy
 
-[[autodoc]] utils.TensorType
+[[autodoc]] file_utils.TensorType
 
 ## Special Decorators
 
-[[autodoc]] utils.add_start_docstrings
+[[autodoc]] file_utils.add_start_docstrings
 
-[[autodoc]] utils.add_start_docstrings_to_model_forward
+[[autodoc]] file_utils.add_start_docstrings_to_model_forward
 
-[[autodoc]] utils.add_end_docstrings
+[[autodoc]] file_utils.add_end_docstrings
 
-[[autodoc]] utils.add_code_sample_docstrings
+[[autodoc]] file_utils.add_code_sample_docstrings
 
-[[autodoc]] utils.replace_return_docstrings
+[[autodoc]] file_utils.replace_return_docstrings
 
 ## Special Properties
 
-[[autodoc]] utils.cached_property
+[[autodoc]] file_utils.cached_property
 
 ## Other Utilities
 
-[[autodoc]] utils._LazyModule
+[[autodoc]] file_utils._LazyModule