SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Trainer (#3800)

Browse Source 
* doc

* [tests] Add sample files for a regression task

* [HUGE] Trainer

* Feedback from @sshleifer

* Feedback from @thomwolf + logging tweak

* [file_utils] when downloading concurrently, get_from_cache will use the cached file for subsequent processes

* [glue] Use default max_seq_length of 128 like before

* [glue] move DataTrainingArguments around

* [ner] Change interface of InputExample, and align run_{tf,pl}

* Re-align the pl scripts a little bit

* ner

* [ner] Add integration test

* Fix language_modeling with API tweak

* [ci] Tweak loss target

* Don't break console output

* amp.initialize: model must be on right device before

* [multiple-choice] update for Trainer

* Re-align to 827d6d6ef0
This commit is contained in:
Julien Chaumond
2020-04-21 20:11:56 -04:00
committed by GitHub
parent eb5601b0a5
commit dd9d483d03
41 changed files with 2682 additions and 2567 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
README.md
View File

@@ -306,8 +306,9 @@ setup your environment to run the examples.
The library comprises several example scripts with SOTA performances for NLU and NLG tasks:
- `run_glue.py`: an example fine-tuning Bert, XLNet and XLM on nine different GLUE tasks (*sequence-level classification*)
- `run_squad.py`: an example fine-tuning Bert, XLNet and XLM on the question answering dataset SQuAD 2.0 (*token-level classification*)
- `run_glue.py`: an example fine-tuning sequence classification models on nine different GLUE tasks (*sequence-level classification*)
- `run_squad.py`: an example fine-tuning question answering models on the question answering dataset SQuAD 2.0 (*token-level classification*)
- `run_ner.py`: an example fine-tuning token classification models on named entity recognition (*token-level classification*)
- `run_generation.py`: an example using GPT, GPT-2, CTRL, Transformer-XL and XLNet for conditional language generation
- other model-specific examples (see the documentation).
@@ -317,7 +318,7 @@ Here are three quick usage examples for these scripts:
The [General Language Understanding Evaluation (GLUE) benchmark](https://gluebenchmark.com/) is a collection of nine sentence- or sentence-pair language understanding tasks for evaluating and analyzing natural language understanding systems.
Before running anyone of these GLUE tasks you should download the
Before running any of these GLUE tasks you should download the
[GLUE data](https://gluebenchmark.com/tasks) by running
[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
and unpack it to some directory `$GLUE_DIR`.
@@ -333,7 +334,6 @@ export GLUE_DIR=/path/to/glue
export TASK_NAME=MRPC
python ./examples/run_glue.py \
--model_type bert \
--model_name_or_path bert-base-uncased \
--task_name $TASK_NAME \
--do_train \
@@ -360,7 +360,6 @@ Parallel training is a simple way to use several GPUs (but is slower and less fl
export GLUE_DIR=/path/to/glue
python ./examples/run_glue.py \
--model_type xlnet \
--model_name_or_path xlnet-large-cased \
--do_train \
--do_eval \
@@ -386,7 +385,6 @@ This example code fine-tunes the Bert Whole Word Masking model on the Microsoft
```bash
python -m torch.distributed.launch --nproc_per_node 8 ./examples/run_glue.py \
--model_type bert \
--model_name_or_path bert-large-uncased-whole-word-masking \
--task_name MRPC \
--do_train \