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Merge pull request #1214 from huggingface/new-examples

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Better examples
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docs/README.md
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@ -34,6 +34,13 @@ pip install recommonmark
## Building the documentation
Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the followig
command to generate it:
```bash
ln -s ../../examples/README.md source/examples.md
```
Once you have setup `sphinx`, you can build the documentation by running the following command in the `/docs` folder:
```bash

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docs/requirements.txt
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@ -26,3 +26,4 @@ sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.2
sphinxcontrib-serializinghtml==1.1.3
urllib3==1.25.3
sphinx-markdown-tables==0.0.9

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docs/source/conf.py
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@ -43,7 +43,8 @@ extensions = [
'sphinx.ext.coverage',
'sphinx.ext.napoleon',
'recommonmark',
'sphinx.ext.viewcode'
'sphinx.ext.viewcode',
'sphinx_markdown_tables'
]
# Add any paths that contain templates here, relative to this directory.

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docs/source/examples.rst
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examples.rst
Examples
================================================
.. list-table::
:header-rows: 1
* - Sub-section
- Description
* - `Training large models: introduction, tools and examples <#introduction>`_
- How to use gradient-accumulation, multi-gpu training, distributed training, optimize on CPU and 16-bits training to train Bert models
* - `Fine-tuning with BERT: running the examples <#fine-tuning-bert-examples>`_
- Running the examples in `examples <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples>`_\ : ``extract_classif.py``\ , ``run_bert_classifier.py``\ , ``run_bert_squad.py`` and ``run_lm_finetuning.py``
* - `Fine-tuning with OpenAI GPT, Transformer-XL, GPT-2 as well as BERT and RoBERTa <#fine-tuning>`_
- Running the examples in `examples <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples>`_\ : ``run_openai_gpt.py``\ , ``run_transfo_xl.py``, ``run_gpt2.py`` and ``run_lm_finetuning.py``
* - `Fine-tuning BERT-large on GPUs <#fine-tuning-bert-large>`_
- How to fine tune ``BERT large``
.. _introduction:
Training large models: introduction, tools and examples
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
BERT-base and BERT-large are respectively 110M and 340M parameters models and it can be difficult to fine-tune them on a single GPU with the recommended batch size for good performance (in most case a batch size of 32).
To help with fine-tuning these models, we have included several techniques that you can activate in the fine-tuning scripts `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ : gradient-accumulation, multi-gpu training, distributed training and 16-bits training . For more details on how to use these techniques you can read `the tips on training large batches in PyTorch <https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255>`_ that I published earlier this year.
Here is how to use these techniques in our scripts:
* **Gradient Accumulation**\ : Gradient accumulation can be used by supplying a integer greater than 1 to the ``--gradient_accumulation_steps`` argument. The batch at each step will be divided by this integer and gradient will be accumulated over ``gradient_accumulation_steps`` steps.
* **Multi-GPU**\ : Multi-GPU is automatically activated when several GPUs are detected and the batches are splitted over the GPUs.
* **Distributed training**\ : Distributed training can be activated by supplying an integer greater or equal to 0 to the ``--local_rank`` argument (see below).
* **16-bits training**\ : 16-bits training, also called mixed-precision training, can reduce the memory requirement of your model on the GPU by using half-precision training, basically allowing to double the batch size. If you have a recent GPU (starting from NVIDIA Volta architecture) you should see no decrease in speed. A good introduction to Mixed precision training can be found `here <https://devblogs.nvidia.com/mixed-precision-training-deep-neural-networks/>`__ and a full documentation is `here <https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html>`__. In our scripts, this option can be activated by setting the ``--fp16`` flag and you can play with loss scaling using the ``--loss_scale`` flag (see the previously linked documentation for details on loss scaling). The loss scale can be zero in which case the scale is dynamically adjusted or a positive power of two in which case the scaling is static.
To use 16-bits training and distributed training, you need to install NVIDIA's apex extension `as detailed here <https://github.com/nvidia/apex>`__. You will find more information regarding the internals of ``apex`` and how to use ``apex`` in `the doc and the associated repository <https://github.com/nvidia/apex>`_. The results of the tests performed on pytorch-BERT by the NVIDIA team (and my trials at reproducing them) can be consulted in `the relevant PR of the present repository <https://github.com/huggingface/pytorch-pretrained-BERT/pull/116>`_.
Note: To use *Distributed Training*\ , you will need to run one training script on each of your machines. This can be done for example by running the following command on each server (see `the above mentioned blog post <https://medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255>`_\ ) for more details):
.. code-block:: bash
python -m torch.distributed.launch \
--nproc_per_node=4 \
--nnodes=2 \
--node_rank=$THIS_MACHINE_INDEX \
--master_addr="192.168.1.1" \
--master_port=1234 run_bert_classifier.py \
(--arg1 --arg2 --arg3 and all other arguments of the run_classifier script)
Where ``$THIS_MACHINE_INDEX`` is an sequential index assigned to each of your machine (0, 1, 2...) and the machine with rank 0 has an IP address ``192.168.1.1`` and an open port ``1234``.
.. _fine-tuning-bert-examples:
Fine-tuning with BERT: running the examples
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We showcase several fine-tuning examples based on (and extended from) `the original implementation <https://github.com/google-research/bert/>`_\ :
* a *sequence-level classifier* on nine different GLUE tasks,
* a *token-level classifier* on the question answering dataset SQuAD, and
* a *sequence-level multiple-choice classifier* on the SWAG classification corpus.
* a *BERT language model* on another target corpus
GLUE results on dev set
~~~~~~~~~~~~~~~~~~~~~~~
We get the following results on the dev set of GLUE benchmark with an uncased BERT base
model (`bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train batch size of 24. Some of
these tasks have a small dataset and training can lead to high variance in the results between different runs.
We report the median on 5 runs (with different seeds) for each of the metrics.
.. list-table::
:header-rows: 1
* - Task
- Metric
- Result
* - CoLA
- Matthew's corr.
- 55.75
* - SST-2
- accuracy
- 92.09
* - MRPC
- F1/accuracy
- 90.48/86.27
* - STS-B
- Pearson/Spearman corr.
- 89.03/88.64
* - QQP
- accuracy/F1
- 90.92/87.72
* - MNLI
- matched acc./mismatched acc.
- 83.74/84.06
* - QNLI
- accuracy
- 91.07
* - RTE
- accuracy
- 68.59
* - WNLI
- accuracy
- 43.66
Some of these results are significantly different from the ones reported on the test set
of GLUE benchmark on the website. For QQP and WNLI, please refer to `FAQ #12 <https://gluebenchmark.com/faq>`_ on the webite.
Before running anyone 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``.
.. code-block:: shell
export GLUE_DIR=/path/to/glue
export TASK_NAME=MRPC
python run_bert_classifier.py \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/$TASK_NAME \
--bert_model bert-base-uncased \
--max_seq_length 128 \
--train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/$TASK_NAME/
where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
The dev set results will be present within the text file 'eval_results.txt' in the specified output_dir. In case of MNLI, since there are two separate dev sets, matched and mismatched, there will be a separate output folder called '/tmp/MNLI-MM/' in addition to '/tmp/MNLI/'.
The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI, CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being said, there shouldn't be any issues in running half-precision training with the remaining GLUE tasks as well, since the data processor for each task inherits from the base class DataProcessor.
MRPC
~~~~
This example code fine-tunes BERT on the Microsoft Research Paraphrase
Corpus (MRPC) corpus and runs in less than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
Before running this example you should download the
`GLUE data <https://gluebenchmark.com/tasks>`_ by running
`this script <https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e>`_
and unpack it to some directory ``$GLUE_DIR``.
.. code-block:: shell
export GLUE_DIR=/path/to/glue
python run_bert_classifier.py \
--task_name MRPC \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MRPC/ \
--bert_model bert-base-uncased \
--max_seq_length 128 \
--train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
Our test ran on a few seeds with `the original implementation hyper-parameters <https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks>`__ gave evaluation results between 84% and 88%.
**Fast run with apex and 16 bit precision: fine-tuning on MRPC in 27 seconds!**
First install apex as indicated `here <https://github.com/NVIDIA/apex>`__.
Then run
.. code-block:: shell
export GLUE_DIR=/path/to/glue
python run_bert_classifier.py \
--task_name MRPC \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MRPC/ \
--bert_model bert-base-uncased \
--max_seq_length 128 \
--train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/ \
--fp16
**Distributed training**
Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking model to reach a F1 > 92 on MRPC:
.. code-block:: bash
python -m torch.distributed.launch \
--nproc_per_node 8 run_bert_classifier.py \
--bert_model bert-large-uncased-whole-word-masking \
--task_name MRPC \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MRPC/ \
--max_seq_length 128 \
--train_batch_size 8 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
Training with these hyper-parameters gave us the following results:
.. code-block:: bash
acc = 0.8823529411764706
acc_and_f1 = 0.901702786377709
eval_loss = 0.3418912578906332
f1 = 0.9210526315789473
global_step = 174
loss = 0.07231863956341798
Here is an example on MNLI:
.. code-block:: bash
python -m torch.distributed.launch \
--nproc_per_node 8 run_bert_classifier.py \
--bert_model bert-large-uncased-whole-word-masking \
--task_name mnli \
--do_train \
--do_eval \
--do_lower_case \
--data_dir /datadrive/bert_data/glue_data//MNLI/ \
--max_seq_length 128 \
--train_batch_size 8 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir ../models/wwm-uncased-finetuned-mnli/ \
--overwrite_output_dir
.. code-block:: bash
***** Eval results *****
acc = 0.8679706601466992
eval_loss = 0.4911287787382479
global_step = 18408
loss = 0.04755385363816904
***** Eval results *****
acc = 0.8747965825874695
eval_loss = 0.45516540421714036
global_step = 18408
loss = 0.04755385363816904
This is the example of the ``bert-large-uncased-whole-word-masking-finetuned-mnli`` model
SQuAD
~~~~~
This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large) on a single tesla V100 16GB.
The data for SQuAD can be downloaded with the following links and should be saved in a ``$SQUAD_DIR`` directory.
* `train-v1.1.json <https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json>`_
* `dev-v1.1.json <https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json>`_
* `evaluate-v1.1.py <https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py>`_
.. code-block:: shell
export SQUAD_DIR=/path/to/SQUAD
python run_bert_squad.py \
--bert_model bert-base-uncased \
--do_train \
--do_predict \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2.0 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/
Training with the previous hyper-parameters gave us the following results:
.. code-block:: bash
python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json /tmp/debug_squad/predictions.json
{"f1": 88.52381567990474, "exact_match": 81.22043519394512}
**distributed training**
Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
.. code-block:: bash
python -m torch.distributed.launch --nproc_per_node=8 \
run_bert_squad.py \
--bert_model bert-large-uncased-whole-word-masking \
--do_train \
--do_predict \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ../models/wwm_uncased_finetuned_squad/ \
--train_batch_size 24 \
--gradient_accumulation_steps 12
Training with these hyper-parameters gave us the following results:
.. code-block:: bash
python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json ../models/wwm_uncased_finetuned_squad/predictions.json
{"exact_match": 86.91579943235573, "f1": 93.1532499015869}
This is the model provided as ``bert-large-uncased-whole-word-masking-finetuned-squad``.
And here is the model provided as ``bert-large-cased-whole-word-masking-finetuned-squad``\ :
.. code-block:: bash
python -m torch.distributed.launch --nproc_per_node=8 run_bert_squad.py \
--bert_model bert-large-cased-whole-word-masking \
--do_train \
--do_predict \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ../models/wwm_cased_finetuned_squad/ \
--train_batch_size 24 \
--gradient_accumulation_steps 12
Training with these hyper-parameters gave us the following results:
.. code-block:: bash
python $SQUAD_DIR/evaluate-v1.1.py $SQUAD_DIR/dev-v1.1.json ../models/wwm_uncased_finetuned_squad/predictions.json
{"exact_match": 84.18164616840113, "f1": 91.58645594850135}
SWAG
~~~~
The data for SWAG can be downloaded by cloning the following `repository <https://github.com/rowanz/swagaf>`_
.. code-block:: shell
export SWAG_DIR=/path/to/SWAG
python run_bert_swag.py \
--bert_model bert-base-uncased \
--do_train \
--do_lower_case \
--do_eval \
--data_dir $SWAG_DIR/data \
--train_batch_size 16 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--max_seq_length 80 \
--output_dir /tmp/swag_output/ \
--gradient_accumulation_steps 4
Training with the previous hyper-parameters on a single GPU gave us the following results:
.. code-block::
eval_accuracy = 0.8062081375587323
eval_loss = 0.5966546792367169
global_step = 13788
loss = 0.06423990014260186
LM Fine-tuning
~~~~~~~~~~~~~~
The data should be a text file in the same format as `sample_text.txt <./pytorch_transformers/tests/fixtures/sample_text.txt/sample_text.txt>`_ (one sentence per line, docs separated by empty line).
You can download an `exemplary training corpus <https://ext-bert-sample.obs.eu-de.otc.t-systems.com/small_wiki_sentence_corpus.txt>`_ generated from wikipedia articles and split into ~500k sentences with spaCy.
Training one epoch on this corpus takes about 1:20h on 4 x NVIDIA Tesla P100 with ``train_batch_size=200`` and ``max_seq_length=128``\ :
Thank to the work of @Rocketknight1 and @tholor there are now **several scripts** that can be used to fine-tune BERT using the pretraining objective (combination of masked-language modeling and next sentence prediction loss). These scripts are detailed in the `README <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/lm_finetuning/README.md>`_ of the `examples/lm_finetuning/ <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/lm_finetuning/>`_ folder.
.. _fine-tuning:
OpenAI GPT, Transformer-XL and GPT-2: running the examples
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
We provide three examples of scripts for OpenAI GPT, Transformer-XL, OpenAI GPT-2, BERT and RoBERTa based on (and extended from) the respective original implementations:
* fine-tuning OpenAI GPT on the ROCStories dataset
* evaluating Transformer-XL on Wikitext 103
* unconditional and conditional generation from a pre-trained OpenAI GPT-2 model
* fine-tuning GPT/GPT-2 on a causal language modeling task and BERT/RoBERTa on a masked language modeling task
Fine-tuning OpenAI GPT on the RocStories dataset
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This example code fine-tunes OpenAI GPT on the RocStories dataset.
Before running this example you should download the
`RocStories dataset <https://github.com/snigdhac/StoryComprehension_EMNLP/tree/master/Dataset/RoCStories>`_ and unpack it to some directory ``$ROC_STORIES_DIR``.
.. code-block:: shell
export ROC_STORIES_DIR=/path/to/RocStories
python run_openai_gpt.py \
--model_name openai-gpt \
--do_train \
--do_eval \
--train_dataset $ROC_STORIES_DIR/cloze_test_val__spring2016\ -\ cloze_test_ALL_val.csv \
--eval_dataset $ROC_STORIES_DIR/cloze_test_test__spring2016\ -\ cloze_test_ALL_test.csv \
--output_dir ../log \
--train_batch_size 16 \
This command runs in about 10 min on a single K-80 an gives an evaluation accuracy of about 87.7% (the authors report a median accuracy with the TensorFlow code of 85.8% and the OpenAI GPT paper reports a best single run accuracy of 86.5%).
Evaluating the pre-trained Transformer-XL on the WikiText 103 dataset
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This example code evaluate the pre-trained Transformer-XL on the WikiText 103 dataset.
This command will download a pre-processed version of the WikiText 103 dataset in which the vocabulary has been computed.
.. code-block:: shell
python run_transfo_xl.py --work_dir ../log
This command runs in about 1 min on a V100 and gives an evaluation perplexity of 18.22 on WikiText-103 (the authors report a perplexity of about 18.3 on this dataset with the TensorFlow code).
Unconditional and conditional generation from OpenAI's GPT-2 model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This example code is identical to the original unconditional and conditional generation codes.
Conditional generation:
.. code-block:: shell
python run_gpt2.py
Unconditional generation:
.. code-block:: shell
python run_gpt2.py --unconditional
The same option as in the original scripts are provided, please refer to the code of the example and the original repository of OpenAI.
Causal LM fine-tuning on GPT/GPT-2, Masked LM fine-tuning on BERT/RoBERTa
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Before running the following examples you should download the `WikiText-2 dataset <https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/>`__ and unpack it to some directory `$WIKITEXT_2_DATASET`
The following results were obtained using the `raw` WikiText-2 (no tokens were replaced before the tokenization).
This example fine-tunes GPT-2 on the WikiText-2 dataset. The loss function is a causal language modeling loss (perplexity).
.. code-block:: bash
export WIKITEXT_2_DATASET=/path/to/wikitext_dataset
python run_lm_finetuning.py
--output_dir=output
--model_type=gpt2
--model_name_or_path=gpt2
--do_train
--train_data_file=$WIKITEXT_2_DATASET/wiki.train.raw
--do_eval
--eval_data_file=$WIKITEXT_2_DATASET/wiki.test.raw
This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run.
It reaches a score of about 20 perplexity once fine-tuned on the dataset.
This example fine-tunes RoBERTa on the WikiText-2 dataset. The loss function is a masked language modeling loss (masked perplexity).
The `--mlm` flag is necessary to fine-tune BERT/RoBERTa on masked language modeling.
.. code-block:: bash
export WIKITEXT_2_DATASET=/path/to/wikitext_dataset
python run_lm_finetuning.py
--output_dir=output
--model_type=roberta
--model_name_or_path=roberta-base
--do_train
--train_data_file=$WIKITEXT_2_DATASET/wiki.train.raw
--do_eval
--eval_data_file=$WIKITEXT_2_DATASET/wiki.test.raw
--mlm
.. _fine-tuning-BERT-large:
Fine-tuning BERT-large on GPUs
------------------------------
The options we list above allow to fine-tune BERT-large rather easily on GPU(s) instead of the TPU used by the original implementation.
For example, fine-tuning BERT-large on SQuAD can be done on a server with 4 k-80 (these are pretty old now) in 18 hours. Our results are similar to the TensorFlow implementation results (actually slightly higher):
.. code-block:: bash
{"exact_match": 84.56953642384106, "f1": 91.04028647786927}
To get these results we used a combination of:
* multi-GPU training (automatically activated on a multi-GPU server),
* 2 steps of gradient accumulation and
* perform the optimization step on CPU to store Adam's averages in RAM.
Here is the full list of hyper-parameters for this run:
.. code-block:: bash
export SQUAD_DIR=/path/to/SQUAD
python ./run_bert_squad.py \
--bert_model bert-large-uncased \
--do_train \
--do_predict \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/ \
--train_batch_size 24 \
--gradient_accumulation_steps 2
If you have a recent GPU (starting from NVIDIA Volta series), you should try **16-bit fine-tuning** (FP16).
Here is an example of hyper-parameters for a FP16 run we tried:
.. code-block:: bash
export SQUAD_DIR=/path/to/SQUAD
python ./run_bert_squad.py \
--bert_model bert-large-uncased \
--do_train \
--do_predict \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/ \
--train_batch_size 24 \
--fp16 \
--loss_scale 128
The results were similar to the above FP32 results (actually slightly higher):
.. code-block:: bash
{"exact_match": 84.65468306527909, "f1": 91.238669287002}
Here is an example with the recent ``bert-large-uncased-whole-word-masking``\ :
.. code-block:: bash
python -m torch.distributed.launch --nproc_per_node=8 \
run_bert_squad.py \
--bert_model bert-large-uncased-whole-word-masking \
--do_train \
--do_predict \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/ \
--train_batch_size 24 \
--gradient_accumulation_steps 2
Fine-tuning XLNet
-----------------
STS-B
~~~~~
This example code fine-tunes XLNet on the STS-B corpus.
Before running this example you should download the
`GLUE data <https://gluebenchmark.com/tasks>`_ by running
`this script <https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e>`_
and unpack it to some directory ``$GLUE_DIR``.
.. code-block:: shell
export GLUE_DIR=/path/to/glue
python run_xlnet_classifier.py \
--task_name STS-B \
--do_train \
--do_eval \
--data_dir $GLUE_DIR/STS-B/ \
--max_seq_length 128 \
--train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
Our test ran on a few seeds with `the original implementation hyper-parameters <https://github.com/zihangdai/xlnet#1-sts-b-sentence-pair-relevance-regression-with-gpus>`__ gave evaluation results between 84% and 88%.
**Distributed training**
Here is an example using distributed training on 8 V100 GPUs to reach XXXX:
.. code-block:: bash
python -m torch.distributed.launch --nproc_per_node 8 \
run_xlnet_classifier.py \
--task_name STS-B \
--do_train \
--do_eval \
--data_dir $GLUE_DIR/STS-B/ \
--max_seq_length 128 \
--train_batch_size 8 \
--gradient_accumulation_steps 1 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
Training with these hyper-parameters gave us the following results:
.. code-block:: bash
acc = 0.8823529411764706
acc_and_f1 = 0.901702786377709
eval_loss = 0.3418912578906332
f1 = 0.9210526315789473
global_step = 174
loss = 0.07231863956341798
Here is an example on MNLI:
.. code-block:: bash
python -m torch.distributed.launch --nproc_per_node 8 run_bert_classifier.py \
--bert_model bert-large-uncased-whole-word-masking \
--task_name mnli \
--do_train \
--do_eval \
--data_dir /datadrive/bert_data/glue_data//MNLI/ \
--max_seq_length 128 \
--train_batch_size 8 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir ../models/wwm-uncased-finetuned-mnli/ \
--overwrite_output_dir
.. code-block:: bash
***** Eval results *****
acc = 0.8679706601466992
eval_loss = 0.4911287787382479
global_step = 18408
loss = 0.04755385363816904
***** Eval results *****
acc = 0.8747965825874695
eval_loss = 0.45516540421714036
global_step = 18408
loss = 0.04755385363816904
This is the example of the ``bert-large-uncased-whole-word-masking-finetuned-mnli`` model.

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# Examples
In this section a few examples are put together. All of these examples work for several models, making use of the very
similar API between the different models.
| Section | Description |
|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Language Model fine-tuning](#language-model-fine-tuning) | Fine-tuning the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. |
| [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. |
| [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. |
| [SQuAD](#squad) | Using BERT for question answering, examples with distributed training. |
## Language model fine-tuning
Based on the script [`run_lm_finetuning.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_lm_finetuning.py).
Fine-tuning the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT
to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa
are fine-tuned using a masked language modeling (MLM) loss.
Before running the following example, you should get a file that contains text on which the language model will be
fine-tuned. A good example of such text is the [WikiText-2 dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/).
We will refer to two different files: `$TRAIN_FILE`, which contains text for training, and `$TEST_FILE`, which contains
text that will be used for evaluation.
### GPT-2/GPT and causal language modeling
The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before
the tokenization). The loss here is that of causal language modeling.
```bash
export TRAIN_FILE=/path/to/dataset/wiki.train.raw
export TEST_FILE=/path/to/dataset/wiki.test.raw
python run_lm_finetuning.py \
--output_dir=output \
--model_type=gpt2 \
--model_name_or_path=gpt2 \
--do_train \
--train_data_file=$TRAIN_FILE \
--do_eval \
--eval_data_file=$TEST_FILE
```
This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches
a score of ~20 perplexity once fine-tuned on the dataset.
### RoBERTa/BERT and masked language modeling
The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different
as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their
pre-training: masked language modeling.
In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore, converge
slightly slower (over-fitting takes more epochs).
We use the `--mlm` flag so that the script may change its loss function.
```bash
export TRAIN_FILE=/path/to/dataset/wiki.train.raw
export TEST_FILE=/path/to/dataset/wiki.test.raw
python run_lm_finetuning.py \
--output_dir=output \
--model_type=roberta \
--model_name_or_path=roberta-base \
--do_train \
--train_data_file=$TRAIN_FILE \
--do_eval \
--eval_data_file=$TEST_FILE \
--mlm
```
## Language generation
Based on the script [`run_generation.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_generation.py).
Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet.
A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
can try out the different models available in the library.
Example usage:
```bash
python run_generation.py \
--model_type=gpt2 \
--model_name_or_path=gpt2
```
## GLUE
Based on the script [`run_glue.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_glue.py).
Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
Evaluation](https://gluebenchmark.com/). This script can fine-tune the following models: BERT, XLM, XLNet and RoBERTa.
GLUE is made up of a total of 9 different tasks. We get the following results on the dev set of the benchmark with an
uncased BERT base model (the checkpoint `bert-base-uncased`). All experiments ran on 8 V100 GPUs with a total train
batch size of 24. Some of these tasks have a small dataset and training can lead to high variance in the results
between different runs. We report the median on 5 runs (with different seeds) for each of the metrics.
| Task | Metric | Result |
|-------|------------------------------|-------------|
| CoLA | Matthew's corr | 55.75 |
| SST-2 | Accuracy | 92.09 |
| MRPC | F1/Accuracy | 90.48/86.27 |
| STS-B | Person/Spearman corr. | 89.03/88.64 |
| QQP | Accuracy/F1 | 90.92/87.72 |
| MNLI | Matched acc./Mismatched acc. | 83.74/84.06 |
| QNLI | Accuracy | 91.07 |
| RTE | Accuracy | 68.59 |
| WNLI | Accuracy | 43.66 |
Some of these results are significantly different from the ones reported on the test set
of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.
Before running anyone 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`.
```bash
export GLUE_DIR=/path/to/glue
export TASK_NAME=MRPC
python run_glue.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/$TASK_NAME \
--max_seq_length 128 \
--per_gpu_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/$TASK_NAME/
```
where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
The dev set results will be present within the text file `eval_results.txt` in the specified output_dir.
In case of MNLI, since there are two separate dev sets (matched and mismatched), there will be a separate
output folder called `/tmp/MNLI-MM/` in addition to `/tmp/MNLI/`.
The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI,
CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being
said, there shouldn’t be any issues in running half-precision training with the remaining GLUE tasks as well,
since the data processor for each task inherits from the base class DataProcessor.
### MRPC
#### Fine-tuning example
The following examples fine-tune BERT on the Microsoft Research Paraphrase Corpus (MRPC) corpus and runs in less
than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
Before running anyone of these GLUE tasks you should download the
[GLUE data](https://gluebenchmark.com/tasks) by running
[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
and unpack it to some directory `$GLUE_DIR`.
```bash
export GLUE_DIR=/path/to/glue
python run_glue.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--task_name MRPC \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MRPC/ \
--max_seq_length 128 \
--per_gpu_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
```
Our test ran on a few seeds with [the original implementation hyper-
parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation
results between 84% and 88%.
#### Using Apex and mixed-precision
Using Apex and 16 bit precision, the fine-tuning on MRPC only takes 27 seconds. First install
[apex](https://github.com/NVIDIA/apex), then run the following example:
```bash
export GLUE_DIR=/path/to/glue
python run_glue.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--task_name MRPC \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MRPC/ \
--max_seq_length 128 \
--per_gpu_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/ \
--fp16
```
#### Distributed training
Here is an example using distributed training on 8 V100 GPUs. The model used is the BERT whole-word-masking and it
reaches F1 > 92 on MRPC.
```bash
export GLUE_DIR=/path/to/glue
python -m torch.distributed.launch \
--nproc_per_node 8 run_glue.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--task_name MRPC \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MRPC/ \
--max_seq_length 128 \
--per_gpu_train_batch_size 8 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/
```
Training with these hyper-parameters gave us the following results:
```bash
acc = 0.8823529411764706
acc_and_f1 = 0.901702786377709
eval_loss = 0.3418912578906332
f1 = 0.9210526315789473
global_step = 174
loss = 0.07231863956341798
```
### MNLI
The following example uses the BERT-large, uncased, whole-word-masking model and fine-tunes it on the MNLI task.
```bash
export GLUE_DIR=/path/to/glue
python -m torch.distributed.launch \
--nproc_per_node 8 run_glue.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--task_name mnli \
--do_train \
--do_eval \
--do_lower_case \
--data_dir $GLUE_DIR/MNLI/ \
--max_seq_length 128 \
--per_gpu_train_batch_size 8 \
--learning_rate 2e-5 \
--num_train_epochs 3.0 \
--output_dir output_dir \
```
The results are the following:
```bash
***** Eval results *****
acc = 0.8679706601466992
eval_loss = 0.4911287787382479
global_step = 18408
loss = 0.04755385363816904
***** Eval results *****
acc = 0.8747965825874695
eval_loss = 0.45516540421714036
global_step = 18408
loss = 0.04755385363816904
```
## SQuAD
Based on the script [`run_squad.py`](https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_squad.py).
#### Fine-tuning on SQuAD
This example code fine-tunes BERT on the SQuAD dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large)
on a single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a
$SQUAD_DIR directory.
* [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
* [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
* [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
```bash
export SQUAD_DIR=/path/to/SQUAD
python run_squad.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--do_train \
--do_eval \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--per_gpu_train_batch_size 12 \
--learning_rate 3e-5 \
--num_train_epochs 2.0 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /tmp/debug_squad/
```
Training with the previously defined hyper-parameters yields the following results:
```bash
f1 = 88.52
exact_match = 81.22
```
#### Distributed training
Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
```bash
python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--do_train \
--do_eval \
--do_lower_case \
--train_file $SQUAD_DIR/train-v1.1.json \
--predict_file $SQUAD_DIR/dev-v1.1.json \
--learning_rate 3e-5 \
--num_train_epochs 2 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ../models/wwm_uncased_finetuned_squad/ \
--per_gpu_train_batch_size 24 \
--gradient_accumulation_steps 12
```
Training with the previously defined hyper-parameters yields the following results:
```bash
f1 = 93.15
exact_match = 86.91
```
This fine-tuneds model is available as a checkpoint under the reference
`bert-large-uncased-whole-word-masking-finetuned-squad`.

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@ -14,7 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for language modeling on WikiText-2 (GPT, GPT-2, BERT, RoBERTa).
Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
using a masked language modeling (MLM) loss.
"""