transformers/examples/README.md

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# Examples

This folder contains actively maintained examples of use of 🤗 Transformers organized along NLP tasks. If you are looking for an example that used to
be in this folder, it may have moved to our [research projects](https://github.com/huggingface/transformers/tree/master/examples/research_projects) subfolder (which contains frozen snapshots of research projects).

## Important note

**Important**

To make sure you can successfully run the latest versions of the example scripts, you have to **install the library from source** and install some example-specific requirements. To do this, execute the following steps in a new virtual environment:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
pip install .
```
Then cd in the example folder of your choice and run
```bash
pip install -r requirements.txt
```

Alternatively, you can run the version of the examples as they were for your current version of Transformers via (for instance with v3.5.1):
```bash
git checkout tags/v3.5.1
```

## The Big Table of Tasks

Here is the list of all our examples:
- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might
  just lack some features),
- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
- links to **Colab notebooks** to walk through the scripts and run them easily,
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Coming soon!
- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
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| Task | Example datasets | Trainer support | TFTrainer support | 🤗 Datasets | Colab
|---|---|:---:|:---:|:---:|:---:|
| [**`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)
| [**`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`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb)
| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                     | CNN/Daily Mail  | ✅  | - | - | -
| [**`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/notebooks/blob/master/examples/text_classification.ipynb)
| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | - | [![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)
| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb)
| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/seq2seq)                       | WMT             | ✅  | - | - | -


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## Distributed training and mixed precision

All the PyTorch scripts mentioned above work out of the box with distributed training and mixed precision, thanks to
the [Trainer API](https://huggingface.co/transformers/main_classes/trainer.html). To launch one of them on _n_ GPUS,
use the following command:

```bash
python -m torch.distributed.launch \
    --nproc_per_node number_of_gpu_you_have path_to_script.py \
	--all_arguments_of_the_script 
```

As an example, here is how you would fine-tune the BERT large model (with whole word masking) on the text
classification MNLI task using the `run_glue` script, with 8 GPUs:

```bash
python -m torch.distributed.launch \
    --nproc_per_node 8 text-classification/run_glue.py \
    --model_name_or_path bert-large-uncased-whole-word-masking \
    --task_name mnli \
    --do_train \
    --do_eval \
    --max_seq_length 128 \
    --per_device_train_batch_size 8 \
    --learning_rate 2e-5 \
    --num_train_epochs 3.0 \
    --output_dir /tmp/mnli_output/
```

If you have a GPU with mixed precision capabilities (architecture Pascal or more recent), you can use mixed precision
training with PyTorch 1.6.0 or latest, or by installing the [Apex](https://github.com/NVIDIA/apex) library for previous
versions. Just add the flag `--fp16` to your command launching one of the scripts mentioned above!

Using mixed precision training usually results in 2x-speedup for training with the same final results (as shown in
[this table](https://github.com/huggingface/transformers/tree/master/examples/text-classification#mixed-precision-training)
for text classification).

## Running on TPUs

When using Tensorflow, TPUs are supported out of the box as a `tf.distribute.Strategy`.

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).

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`):

```bash
python xla_spawn.py --num_cores num_tpu_you_have \
    path_to_script.py \
	--all_arguments_of_the_script 
```

As an example, here is how you would fine-tune the BERT large model (with whole word masking) on the text
classification MNLI task using the `run_glue` script, with 8 TPUs:

```bash
python xla_spawn.py --num_cores 8 \
    text-classification/run_glue.py \
    --model_name_or_path bert-large-uncased-whole-word-masking \
    --task_name mnli \
    --do_train \
    --do_eval \
    --max_seq_length 128 \
    --per_device_train_batch_size 8 \
    --learning_rate 2e-5 \
    --num_train_epochs 3.0 \
    --output_dir /tmp/mnli_output/
```

## Logging & Experiment tracking

You can easily log and monitor your runs code. The following are currently supported:

* [TensorBoard](https://www.tensorflow.org/tensorboard)
* [Weights & Biases](https://docs.wandb.ai/integrations/huggingface)
* [Comet ML](https://www.comet.ml/docs/python-sdk/huggingface/)

### Weights & Biases

To use Weights & Biases, install the wandb package with:

```bash
pip install wandb
```

Then log in the command line:

```bash
wandb login
```

If you are in Jupyter or Colab, you should login with:

```python
import wandb
wandb.login()
```

To enable logging to W&B, include `"wandb"` in the `report_to` of your `TrainingArguments` or script. Or just pass along `--report_to all` if you have `wandb` installed.

Whenever you use `Trainer` or `TFTrainer` classes, your losses, evaluation metrics, model topology and gradients (for `Trainer` only) will automatically be logged.

Advanced configuration is possible by setting environment variables:

<table>
  <thead>
    <tr>
      <th style="text-align:left">Environment Variables</th>
      <th style="text-align:left">Options</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="text-align:left">WANDB_LOG_MODEL</td>
      <td style="text-align:left">Log the model as artifact at the end of training (<b>false</b> by default)</td>
    </tr>
    <tr>
      <td style="text-align:left">WANDB_WATCH</td>
      <td style="text-align:left">
        <ul>
          <li><b>gradients</b> (default): Log histograms of the gradients</li>
          <li><b>all</b>: Log histograms of gradients and parameters</li>
          <li><b>false</b>: No gradient or parameter logging</li>
        </ul>
      </td>
    </tr>
    <tr>
      <td style="text-align:left">WANDB_PROJECT</td>
      <td style="text-align:left">Organize runs by project</td>
    </tr>
  </tbody>
</table>

Set run names with `run_name` argument present in scripts or as part of `TrainingArguments`.

Additional configuration options are available through generic [wandb environment variables](https://docs.wandb.com/library/environment-variables).

Refer to related [documentation & examples](https://docs.wandb.ai/integrations/huggingface).

### Comet.ml

To use `comet_ml`, install the Python package with:

```bash
pip install comet_ml
```

or if in a Conda environment:

```bash
conda install -c comet_ml -c anaconda -c conda-forge comet_ml
```