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per_device instead of per_gpu/error thrown when argument unknown (#4618) 



* per_device instead of per_gpu/error thrown when argument unknown

* [docs] Restore examples.md symlink

* Correct absolute links so that symlink to the doc works correctly

* Update src/transformers/hf_argparser.py
Co-authored-by: default avatarJulien Chaumond <chaumond@gmail.com>

* Warning + reorder

* Docs

* Style

* not for squad
Co-authored-by: default avatarJulien Chaumond <chaumond@gmail.com>
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...@@ -340,8 +340,8 @@ python ./examples/text-classification/run_glue.py \ ...@@ -340,8 +340,8 @@ python ./examples/text-classification/run_glue.py \
--do_eval \ --do_eval \
--data_dir $GLUE_DIR/$TASK_NAME \ --data_dir $GLUE_DIR/$TASK_NAME \
--max_seq_length 128 \ --max_seq_length 128 \
--per_gpu_eval_batch_size=8 \ --per_device_eval_batch_size=8 \
--per_gpu_train_batch_size=8 \ --per_device_train_batch_size=8 \
--learning_rate 2e-5 \ --learning_rate 2e-5 \
--num_train_epochs 3.0 \ --num_train_epochs 3.0 \
--output_dir /tmp/$TASK_NAME/ --output_dir /tmp/$TASK_NAME/
...@@ -367,8 +367,8 @@ python ./examples/text-classification/run_glue.py \ ...@@ -367,8 +367,8 @@ python ./examples/text-classification/run_glue.py \
--data_dir=${GLUE_DIR}/STS-B \ --data_dir=${GLUE_DIR}/STS-B \
--output_dir=./proc_data/sts-b-110 \ --output_dir=./proc_data/sts-b-110 \
--max_seq_length=128 \ --max_seq_length=128 \
--per_gpu_eval_batch_size=8 \ --per_device_eval_batch_size=8 \
--per_gpu_train_batch_size=8 \ --per_device_train_batch_size=8 \
--gradient_accumulation_steps=1 \ --gradient_accumulation_steps=1 \
--max_steps=1200 \ --max_steps=1200 \
--model_name=xlnet-large-cased \ --model_name=xlnet-large-cased \
...@@ -391,8 +391,8 @@ python -m torch.distributed.launch --nproc_per_node 8 ./examples/text-classifica ...@@ -391,8 +391,8 @@ python -m torch.distributed.launch --nproc_per_node 8 ./examples/text-classifica
--do_eval \ --do_eval \
--data_dir $GLUE_DIR/MRPC/ \ --data_dir $GLUE_DIR/MRPC/ \
--max_seq_length 128 \ --max_seq_length 128 \
--per_gpu_eval_batch_size=8 \ --per_device_eval_batch_size=8 \
--per_gpu_train_batch_size=8 \ --per_device_train_batch_size=8 \
--learning_rate 2e-5 \ --learning_rate 2e-5 \
--num_train_epochs 3.0 \ --num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/ \ --output_dir /tmp/mrpc_output/ \
...@@ -428,8 +428,8 @@ python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answer ...@@ -428,8 +428,8 @@ python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answer
--max_seq_length 384 \ --max_seq_length 384 \
--doc_stride 128 \ --doc_stride 128 \
--output_dir ../models/wwm_uncased_finetuned_squad/ \ --output_dir ../models/wwm_uncased_finetuned_squad/ \
--per_gpu_eval_batch_size=3 \ --per_device_eval_batch_size=3 \
--per_gpu_train_batch_size=3 \ --per_device_train_batch_size=3 \
``` ```
Training with these hyper-parameters gave us the following results: Training with these hyper-parameters gave us the following results:
......
docs/source/examples.md deleted 100644 → 0
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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.
**Important**
To run the latest versions of the examples, you have to install from source and install some specific requirements for the examples.
Execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
pip install .
pip install -r ./examples/requirements.txt
```
| Section | Description |
|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------
| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. |
| [Running on TPUs](#running-on-tpus) | Examples on running fine-tuning tasks on Google TPUs to accelerate workloads. |
| [Language Model training](#language-model-training) | Fine-tuning (or training from scratch) 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/RoBERTa/XLNet/XLM for question answering, examples with distributed training. |
| [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. |
| [Named Entity Recognition](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
| [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
| [Adversarial evaluation of model performances](#adversarial-evaluation-of-model-performances) | Testing a model with adversarial evaluation of natural language inference on the Heuristic Analysis for NLI Systems (HANS) dataset (McCoy et al., 2019.) |
## TensorFlow 2.0 Bert models on GLUE
Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py).
Fine-tuning the library TensorFlow 2.0 Bert model for sequence classification on the MRPC task of the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/).
This script has an option for mixed precision (Automatic Mixed Precision / AMP) to run models on Tensor Cores (NVIDIA Volta/Turing GPUs) and future hardware and an option for XLA, which uses the XLA compiler to reduce model runtime.
Options are toggled using `USE_XLA` or `USE_AMP` variables in the script.
These options and the below benchmark are provided by @tlkh.
Quick benchmarks from the script (no other modifications):
| GPU | Mode | Time (2nd epoch) | Val Acc (3 runs) |
| --------- | -------- | ----------------------- | ----------------------|
| Titan V | FP32 | 41s | 0.8438/0.8281/0.8333 |
| Titan V | AMP | 26s | 0.8281/0.8568/0.8411 |
| V100 | FP32 | 35s | 0.8646/0.8359/0.8464 |
| V100 | AMP | 22s | 0.8646/0.8385/0.8411 |
| 1080 Ti | FP32 | 55s | - |
Mixed precision (AMP) reduces the training time considerably for the same hardware and hyper-parameters (same batch size was used).
## Running on TPUs
You can accelerate your workloads on Google's TPUs. For information on how to setup your TPU environment refer to this
[README](https://github.com/pytorch/xla/blob/master/README.md).
The following are some examples of running the `*_tpu.py` finetuning scripts on TPUs. All steps for data preparation are
identical to your normal GPU + Huggingface setup.
### GLUE
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`.
For running your GLUE task on MNLI dataset you can run something like the following:
```
export XRT_TPU_CONFIG="tpu_worker;0;$TPU_IP_ADDRESS:8470"
export GLUE_DIR=/path/to/glue
export TASK_NAME=MNLI
python run_glue_tpu.py \
--model_type bert \
--model_name_or_path bert-base-cased \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--data_dir $GLUE_DIR/$TASK_NAME \
--max_seq_length 128 \
--train_batch_size 32 \
--learning_rate 3e-5 \
--num_train_epochs 3.0 \
--output_dir /tmp/$TASK_NAME \
--overwrite_output_dir \
--logging_steps 50 \
--save_steps 200 \
--num_cores=8 \
--only_log_master
```
## Language model training
Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py).
Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT
to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa
are fine-tuned using a masked language modeling (MLM) loss.
Before running the following example, you should get a file that contains text on which the language model will be
trained or fine-tuned. A good example of such text is the [WikiText-2 dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/).
We will refer to two different files: `$TRAIN_FILE`, which contains text for training, and `$TEST_FILE`, which contains
text that will be used for evaluation.
### GPT-2/GPT and causal language modeling
The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before
the tokenization). The loss here is that of causal language modeling.
```bash
export TRAIN_FILE=/path/to/dataset/wiki.train.raw
export TEST_FILE=/path/to/dataset/wiki.test.raw
python run_language_modeling.py \
--output_dir=output \
--model_type=gpt2 \
--model_name_or_path=gpt2 \
--do_train \
--train_data_file=$TRAIN_FILE \
--do_eval \
--eval_data_file=$TEST_FILE
```
This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches
a score of ~20 perplexity once fine-tuned on the dataset.
### RoBERTa/BERT and masked language modeling
The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different
as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their
pre-training: masked language modeling.
In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore, converge
slightly slower (over-fitting takes more epochs).
We use the `--mlm` flag so that the script may change its loss function.
```bash
export TRAIN_FILE=/path/to/dataset/wiki.train.raw
export TEST_FILE=/path/to/dataset/wiki.test.raw
python run_language_modeling.py \
--output_dir=output \
--model_type=roberta \
--model_name_or_path=roberta-base \
--do_train \
--train_data_file=$TRAIN_FILE \
--do_eval \
--eval_data_file=$TEST_FILE \
--mlm
```
## Language generation
Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py).
Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
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/transformers/blob/master/examples/text-classification/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 single V100 GPUs with a total train
batch sizes between 16 and 64. 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 | 49.23 |
| SST-2 | Accuracy | 91.97 |
| MRPC | F1/Accuracy | 89.47/85.29 |
| STS-B | Person/Spearman corr. | 83.95/83.70 |
| QQP | Accuracy/F1 | 88.40/84.31 |
| MNLI | Matched acc./Mismatched acc. | 80.61/81.08 |
| QNLI | Accuracy | 87.46 |
| RTE | Accuracy | 61.73 |
| WNLI | Accuracy | 45.07 |
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 any one 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 \
--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/$TASK_NAME/
```
where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
The dev set results will be present within the text file `eval_results.txt` in the specified output_dir.
In case of MNLI, since there are two separate dev sets (matched and mismatched), there will be a separate
output folder called `/tmp/MNLI-MM/` in addition to `/tmp/MNLI/`.
The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI,
CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being
said, there shouldn’t be any issues in running half-precision training with the remaining GLUE tasks as well,
since the data processor for each task inherits from the base class DataProcessor.
### MRPC
#### Fine-tuning example
The following examples fine-tune BERT on the Microsoft Research Paraphrase Corpus (MRPC) corpus and runs in less
than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
Before running any one of these GLUE tasks you should download the
[GLUE data](https://gluebenchmark.com/tasks) by running
[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
and unpack it to some directory `$GLUE_DIR`.
```bash
export GLUE_DIR=/path/to/glue
python run_glue.py \
--model_name_or_path bert-base-cased \
--task_name MRPC \
--do_train \
--do_eval \
--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_name_or_path bert-base-cased \
--task_name MRPC \
--do_train \
--do_eval \
--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_name_or_path bert-base-cased \
--task_name MRPC \
--do_train \
--do_eval \
--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_name_or_path bert-base-cased \
--task_name mnli \
--do_train \
--do_eval \
--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
```
## Multiple Choice
Based on the script [`run_multiple_choice.py`]().
#### Fine-tuning on SWAG
Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
```bash
#training on 4 tesla V100(16GB) GPUS
export SWAG_DIR=/path/to/swag_data_dir
python ./examples/multiple-choice/run_multiple_choice.py \
--task_name swag \
--model_name_or_path roberta-base \
--do_train \
--do_eval \
--data_dir $SWAG_DIR \
--learning_rate 5e-5 \
--num_train_epochs 3 \
--max_seq_length 80 \
--output_dir models_bert/swag_base \
--per_gpu_eval_batch_size=16 \
--per_gpu_train_batch_size=16 \
--gradient_accumulation_steps 2 \
--overwrite_output
```
Training with the defined hyper-parameters yields the following results:
```
***** Eval results *****
eval_acc = 0.8338998300509847
eval_loss = 0.44457291918821606
```
## SQuAD
Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py).
#### Fine-tuning BERT on SQuAD1.0
This example code fine-tunes BERT on the SQuAD1.0 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)
And for SQuAD2.0, you need to download:
- [train-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json)
- [dev-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json)
- [evaluate-v2.0.py](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/)
```bash
export SQUAD_DIR=/path/to/SQUAD
python run_squad.py \
--model_type bert \
--model_name_or_path bert-base-uncased \
--do_train \
--do_eval \
--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 SQuAD1.1:
```bash
python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
--model_type bert \
--model_name_or_path bert-large-uncased-whole-word-masking \
--do_train \
--do_eval \
--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 ./examples/models/wwm_uncased_finetuned_squad/ \
--per_gpu_eval_batch_size=3 \
--per_gpu_train_batch_size=3 \
```
Training with the previously defined hyper-parameters yields the following results:
```bash
f1 = 93.15
exact_match = 86.91
```
This fine-tuned model is available as a checkpoint under the reference
`bert-large-uncased-whole-word-masking-finetuned-squad`.
#### Fine-tuning XLNet on SQuAD
This example code fine-tunes XLNet on both SQuAD1.0 and SQuAD2.0 dataset. See above to download the data for SQuAD .
##### Command for SQuAD1.0:
```bash
export SQUAD_DIR=/path/to/SQUAD
python run_squad.py \
--model_type xlnet \
--model_name_or_path xlnet-large-cased \
--do_train \
--do_eval \
--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 ./wwm_cased_finetuned_squad/ \
--per_gpu_eval_batch_size=4 \
--per_gpu_train_batch_size=4 \
--save_steps 5000
```
##### Command for SQuAD2.0:
```bash
export SQUAD_DIR=/path/to/SQUAD
python run_squad.py \
--model_type xlnet \
--model_name_or_path xlnet-large-cased \
--do_train \
--do_eval \
--version_2_with_negative \
--train_file $SQUAD_DIR/train-v2.0.json \
--predict_file $SQUAD_DIR/dev-v2.0.json \
--learning_rate 3e-5 \
--num_train_epochs 4 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ./wwm_cased_finetuned_squad/ \
--per_gpu_eval_batch_size=2 \
--per_gpu_train_batch_size=2 \
--save_steps 5000
```
Larger batch size may improve the performance while costing more memory.
##### Results for SQuAD1.0 with the previously defined hyper-parameters:
```python
{
"exact": 85.45884578997162,
"f1": 92.5974600601065,
"total": 10570,
"HasAns_exact": 85.45884578997162,
"HasAns_f1": 92.59746006010651,
"HasAns_total": 10570
}
```
##### Results for SQuAD2.0 with the previously defined hyper-parameters:
```python
{
"exact": 80.4177545691906,
"f1": 84.07154997729623,
"total": 11873,
"HasAns_exact": 76.73751686909581,
"HasAns_f1": 84.05558584352873,
"HasAns_total": 5928,
"NoAns_exact": 84.0874684608915,
"NoAns_f1": 84.0874684608915,
"NoAns_total": 5945
}
```
## XNLI
Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
#### Fine-tuning on XNLI
This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins
on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a
`$XNLI_DIR` directory.
* [XNLI 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip)
* [XNLI-MT 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-MT-1.0.zip)
```bash
export XNLI_DIR=/path/to/XNLI
python run_xnli.py \
--model_type bert \
--model_name_or_path bert-base-multilingual-cased \
--language de \
--train_language en \
--do_train \
--do_eval \
--data_dir $XNLI_DIR \
--per_gpu_train_batch_size 32 \
--learning_rate 5e-5 \
--num_train_epochs 2.0 \
--max_seq_length 128 \
--output_dir /tmp/debug_xnli/ \
--save_steps -1
```
Training with the previously defined hyper-parameters yields the following results on the **test** set:
```bash
acc = 0.7093812375249501
```
## MM-IMDb
Based on the script [`run_mmimdb.py`](https://github.com/huggingface/transformers/blob/master/examples/contrib/mm-imdb/run_mmimdb.py).
[MM-IMDb](http://lisi1.unal.edu.co/mmimdb/) is a Multimodal dataset with around 26,000 movies including images, plots and other metadata.
### Training on MM-IMDb
```
python run_mmimdb.py \
--data_dir /path/to/mmimdb/dataset/ \
--model_type bert \
--model_name_or_path bert-base-uncased \
--output_dir /path/to/save/dir/ \
--do_train \
--do_eval \
--max_seq_len 512 \
--gradient_accumulation_steps 20 \
--num_image_embeds 3 \
--num_train_epochs 100 \
--patience 5
```
## Adversarial evaluation of model performances
Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moosavi](https://github.com/ns-moosavi).
The HANS dataset can be downloaded from [this location](https://github.com/tommccoy1/hans).
This is an example of using test_hans.py:
```bash
export HANS_DIR=path-to-hans
export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
python examples/hans/test_hans.py \
--task_name hans \
--model_type $MODEL_TYPE \
--do_eval \
--data_dir $HANS_DIR \
--model_name_or_path $MODEL_PATH \
--max_seq_length 128 \
--output_dir $MODEL_PATH \
```
This will create the hans_predictions.txt file in MODEL_PATH, which can then be evaluated using hans/evaluate_heur_output.py from the HANS dataset.
The results of the BERT-base model that is trained on MNLI using batch size 8 and the random seed 42 on the HANS dataset is as follows:
```bash
Heuristic entailed results:
lexical_overlap: 0.9702
subsequence: 0.9942
constituent: 0.9962
Heuristic non-entailed results:
lexical_overlap: 0.199
subsequence: 0.0396
constituent: 0.118
```
docs/source/examples.md 0 → 120000
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../../examples/README.md
\ No newline at end of file
examples/README.md
View file @ 6a176880
...@@ -16,17 +16,17 @@ This is still a work-in-progress – in particular documentation is still sparse ...@@ -16,17 +16,17 @@ This is still a work-in-progress – in particular documentation is still sparse
| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab | Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab
|---|---|:---:|:---:|:---:|:---:| |---|---|:---:|:---:|:---:|:---:|
| [**`language-modeling`**](./language-modeling) | Raw text | ✅ | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb) | [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling) | Raw text | ✅ | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
| [**`text-classification`**](./text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb) | [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification) | GLUE, XNLI | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)
| [**`token-classification`**](./token-classification) | CoNLL NER | ✅ | ✅ | ✅ | - | [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER | ✅ | ✅ | ✅ | -
| [**`multiple-choice`**](./multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb) | [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
| [**`question-answering`**](./question-answering) | SQuAD | - | ✅ | - | - | [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering) | SQuAD | - | ✅ | - | -
| [**`text-generation`**](./text-generation) | - | - | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb) | [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation) | - | - | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
| [**`distillation`**](./distillation) | All | - | - | - | - | [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation) | All | - | - | - | -
| [**`summarization`**](./summarization) | CNN/Daily Mail | - | - | - | - | [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/summarization) | CNN/Daily Mail | - | - | - | -
| [**`translation`**](./translation) | WMT | - | - | - | - | [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/translation) | WMT | - | - | - | -
| [**`bertology`**](./bertology) | - | - | - | - | - | [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology) | - | - | - | - | -
| [**`adversarial`**](./adversarial) | HANS | - | - | - | - | [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial) | HANS | - | - | - | -
<br> <br>
...@@ -57,7 +57,7 @@ When using Tensorflow, TPUs are supported out of the box as a `tf.distribute.Str ...@@ -57,7 +57,7 @@ When using Tensorflow, TPUs are supported out of the box as a `tf.distribute.Str
When using PyTorch, we support TPUs thanks to `pytorch/xla`. For more context and information on how to setup your TPU environment refer to Google's documentation and to the When using PyTorch, we support TPUs thanks to `pytorch/xla`. For more context and information on how to setup your TPU environment refer to Google's documentation and to the
very detailed [pytorch/xla README](https://github.com/pytorch/xla/blob/master/README.md). very detailed [pytorch/xla README](https://github.com/pytorch/xla/blob/master/README.md).
In this repo, we provide a very simple launcher script named [xla_spawn.py](./xla_spawn.py) that lets you run our example scripts on multiple TPU cores without any boilerplate. In this repo, we provide a very simple launcher script named [xla_spawn.py](https://github.com/huggingface/transformers/tree/master/examples/xla_spawn.py) that lets you run our example scripts on multiple TPU cores without any boilerplate.
Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for torch.distributed). Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for torch.distributed).
For example for `run_glue`: For example for `run_glue`:
......
examples/multiple-choice/README.md
View file @ 6a176880
...@@ -19,7 +19,7 @@ python ./examples/multiple-choice/run_multiple_choice.py \ ...@@ -19,7 +19,7 @@ python ./examples/multiple-choice/run_multiple_choice.py \
--max_seq_length 80 \ --max_seq_length 80 \
--output_dir models_bert/swag_base \ --output_dir models_bert/swag_base \
--per_gpu_eval_batch_size=16 \ --per_gpu_eval_batch_size=16 \
--per_gpu_train_batch_size=16 \ --per_device_train_batch_size=16 \
--gradient_accumulation_steps 2 \ --gradient_accumulation_steps 2 \
--overwrite_output --overwrite_output
``` ```
...@@ -46,7 +46,7 @@ python ./examples/multiple-choice/run_tf_multiple_choice.py \ ...@@ -46,7 +46,7 @@ python ./examples/multiple-choice/run_tf_multiple_choice.py \
--max_seq_length 80 \ --max_seq_length 80 \
--output_dir models_bert/swag_base \ --output_dir models_bert/swag_base \
--per_gpu_eval_batch_size=16 \ --per_gpu_eval_batch_size=16 \
--per_gpu_train_batch_size=16 \ --per_device_train_batch_size=16 \
--logging-dir logs \ --logging-dir logs \
--gradient_accumulation_steps 2 \ --gradient_accumulation_steps 2 \
--overwrite_output --overwrite_output
......
examples/test_examples.py
View file @ 6a176880
...@@ -61,8 +61,8 @@ class ExamplesTests(unittest.TestCase): ...@@ -61,8 +61,8 @@ class ExamplesTests(unittest.TestCase):
--do_train --do_train
--do_eval --do_eval
--output_dir ./tests/fixtures/tests_samples/temp_dir --output_dir ./tests/fixtures/tests_samples/temp_dir
--per_gpu_train_batch_size=2 --per_device_train_batch_size=2
--per_gpu_eval_batch_size=1 --per_device_eval_batch_size=1
--learning_rate=1e-4 --learning_rate=1e-4
--max_steps=10 --max_steps=10
--warmup_steps=2 --warmup_steps=2
......
examples/text-classification/README.md
View file @ 6a176880
...@@ -68,7 +68,7 @@ python run_glue.py \ ...@@ -68,7 +68,7 @@ python run_glue.py \
--do_eval \ --do_eval \
--data_dir $GLUE_DIR/$TASK_NAME \ --data_dir $GLUE_DIR/$TASK_NAME \
--max_seq_length 128 \ --max_seq_length 128 \
--per_gpu_train_batch_size 32 \ --per_device_train_batch_size 32 \
--learning_rate 2e-5 \ --learning_rate 2e-5 \
--num_train_epochs 3.0 \ --num_train_epochs 3.0 \
--output_dir /tmp/$TASK_NAME/ --output_dir /tmp/$TASK_NAME/
...@@ -141,7 +141,7 @@ python run_glue.py \ ...@@ -141,7 +141,7 @@ python run_glue.py \
--do_eval \ --do_eval \
--data_dir $GLUE_DIR/MRPC/ \ --data_dir $GLUE_DIR/MRPC/ \
--max_seq_length 128 \ --max_seq_length 128 \
--per_gpu_train_batch_size 32 \ --per_device_train_batch_size 32 \
--learning_rate 2e-5 \ --learning_rate 2e-5 \
--num_train_epochs 3.0 \ --num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/ --output_dir /tmp/mrpc_output/
...@@ -166,7 +166,7 @@ python run_glue.py \ ...@@ -166,7 +166,7 @@ python run_glue.py \
--do_eval \ --do_eval \
--data_dir $GLUE_DIR/MRPC/ \ --data_dir $GLUE_DIR/MRPC/ \
--max_seq_length 128 \ --max_seq_length 128 \
--per_gpu_train_batch_size 32 \ --per_device_train_batch_size 32 \
--learning_rate 2e-5 \ --learning_rate 2e-5 \
--num_train_epochs 3.0 \ --num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/ \ --output_dir /tmp/mrpc_output/ \
...@@ -189,7 +189,7 @@ python -m torch.distributed.launch \ ...@@ -189,7 +189,7 @@ python -m torch.distributed.launch \
--do_eval \ --do_eval \
--data_dir $GLUE_DIR/MRPC/ \ --data_dir $GLUE_DIR/MRPC/ \
--max_seq_length 128 \ --max_seq_length 128 \
--per_gpu_train_batch_size 8 \ --per_device_train_batch_size 8 \
--learning_rate 2e-5 \ --learning_rate 2e-5 \
--num_train_epochs 3.0 \ --num_train_epochs 3.0 \
--output_dir /tmp/mrpc_output/ --output_dir /tmp/mrpc_output/
...@@ -221,7 +221,7 @@ python -m torch.distributed.launch \ ...@@ -221,7 +221,7 @@ python -m torch.distributed.launch \
--do_eval \ --do_eval \
--data_dir $GLUE_DIR/MNLI/ \ --data_dir $GLUE_DIR/MNLI/ \
--max_seq_length 128 \ --max_seq_length 128 \
--per_gpu_train_batch_size 8 \ --per_device_train_batch_size 8 \
--learning_rate 2e-5 \ --learning_rate 2e-5 \
--num_train_epochs 3.0 \ --num_train_epochs 3.0 \
--output_dir output_dir \ --output_dir output_dir \
...@@ -280,7 +280,7 @@ python run_xnli.py \ ...@@ -280,7 +280,7 @@ python run_xnli.py \
--do_train \ --do_train \
--do_eval \ --do_eval \
--data_dir $XNLI_DIR \ --data_dir $XNLI_DIR \
--per_gpu_train_batch_size 32 \ --per_device_train_batch_size 32 \
--learning_rate 5e-5 \ --learning_rate 5e-5 \
--num_train_epochs 2.0 \ --num_train_epochs 2.0 \
--max_seq_length 128 \ --max_seq_length 128 \
......
examples/token-classification/README.md
View file @ 6a176880
...@@ -69,7 +69,7 @@ python3 run_ner.py --data_dir ./ \ ...@@ -69,7 +69,7 @@ python3 run_ner.py --data_dir ./ \
--output_dir $OUTPUT_DIR \ --output_dir $OUTPUT_DIR \
--max_seq_length $MAX_LENGTH \ --max_seq_length $MAX_LENGTH \
--num_train_epochs $NUM_EPOCHS \ --num_train_epochs $NUM_EPOCHS \
--per_gpu_train_batch_size $BATCH_SIZE \ --per_device_train_batch_size $BATCH_SIZE \
--save_steps $SAVE_STEPS \ --save_steps $SAVE_STEPS \
--seed $SEED \ --seed $SEED \
--do_train \ --do_train \
...@@ -91,7 +91,7 @@ Instead of passing all parameters via commandline arguments, the `run_ner.py` sc ...@@ -91,7 +91,7 @@ Instead of passing all parameters via commandline arguments, the `run_ner.py` sc
"output_dir": "germeval-model", "output_dir": "germeval-model",
"max_seq_length": 128, "max_seq_length": 128,
"num_train_epochs": 3, "num_train_epochs": 3,
"per_gpu_train_batch_size": 32, "per_device_train_batch_size": 32,
"save_steps": 750, "save_steps": 750,
"seed": 1, "seed": 1,
"do_train": true, "do_train": true,
......
src/transformers/hf_argparser.py
View file @ 6a176880
...@@ -126,6 +126,9 @@ class HfArgumentParser(ArgumentParser): ...@@ -126,6 +126,9 @@ class HfArgumentParser(ArgumentParser):
if return_remaining_strings: if return_remaining_strings:
return (*outputs, remaining_args) return (*outputs, remaining_args)
else: else:
if remaining_args:
raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {remaining_args}")
return (*outputs,) return (*outputs,)
def parse_json_file(self, json_file: str) -> Tuple[DataClass, ...]: def parse_json_file(self, json_file: str) -> Tuple[DataClass, ...]:
......
src/transformers/trainer.py
View file @ 6a176880
...@@ -416,7 +416,7 @@ class Trainer: ...@@ -416,7 +416,7 @@ class Trainer:
logger.info("***** Running training *****") logger.info("***** Running training *****")
logger.info(" Num examples = %d", self.num_examples(train_dataloader)) logger.info(" Num examples = %d", self.num_examples(train_dataloader))
logger.info(" Num Epochs = %d", num_train_epochs) logger.info(" Num Epochs = %d", num_train_epochs)
logger.info(" Instantaneous batch size per device = %d", self.args.per_gpu_train_batch_size) logger.info(" Instantaneous batch size per device = %d", self.args.per_device_train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", total_train_batch_size) logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d", total_train_batch_size)
logger.info(" Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps) logger.info(" Gradient Accumulation steps = %d", self.args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total) logger.info(" Total optimization steps = %d", t_total)
......
src/transformers/training_args.py
View file @ 6a176880
...@@ -58,8 +58,28 @@ class TrainingArguments: ...@@ -58,8 +58,28 @@ class TrainingArguments:
default=False, metadata={"help": "Run evaluation during training at each logging step."}, default=False, metadata={"help": "Run evaluation during training at each logging step."},
) )
per_gpu_train_batch_size: int = field(default=8, metadata={"help": "Batch size per GPU/CPU for training."}) per_device_train_batch_size: int = field(
per_gpu_eval_batch_size: int = field(default=8, metadata={"help": "Batch size per GPU/CPU for evaluation."}) default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
per_gpu_train_batch_size: Optional[int] = field(
default=None,
metadata={
"help": "Deprecated, the use of `--per_device_train_batch_size` is preferred. "
"Batch size per GPU/TPU core/CPU for training."
},
)
per_gpu_eval_batch_size: Optional[int] = field(
default=None,
metadata={
"help": "Deprecated, the use of `--per_device_eval_batch_size` is preferred."
"Batch size per GPU/TPU core/CPU for evaluation."
},
)
gradient_accumulation_steps: int = field( gradient_accumulation_steps: int = field(
default=1, default=1,
metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."}, metadata={"help": "Number of updates steps to accumulate before performing a backward/update pass."},
...@@ -115,11 +135,23 @@ class TrainingArguments: ...@@ -115,11 +135,23 @@ class TrainingArguments:
@property @property
def train_batch_size(self) -> int: def train_batch_size(self) -> int:
return self.per_gpu_train_batch_size * max(1, self.n_gpu) if self.per_gpu_train_batch_size:
logger.warning(
"Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
"version. Using `--per_device_train_batch_size` is preferred."
)
per_device_batch_size = self.per_gpu_train_batch_size or self.per_device_train_batch_size
return per_device_batch_size * max(1, self.n_gpu)
@property @property
def eval_batch_size(self) -> int: def eval_batch_size(self) -> int:
return self.per_gpu_eval_batch_size * max(1, self.n_gpu) if self.per_gpu_eval_batch_size:
logger.warning(
"Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
"version. Using `--per_device_eval_batch_size` is preferred."
)
per_device_batch_size = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
return per_device_batch_size * max(1, self.n_gpu)
@cached_property @cached_property
@torch_required @torch_required
......
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