Version 2.9 of 🤗 Transformers introduces a new Trainer class for PyTorch, and its equivalent TFTrainer for TF 2. Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.2+.

Here is the list of all our examples:

  • grouped by task (all official examples work for multiple models)

  • with information on whether they are built on top of Trainer/TFTrainer (if not, they still work, they might just lack some features),

  • whether they also include examples for pytorch-lightning, which is a great fully-featured, general-purpose training library for PyTorch,

  • links to Colab notebooks to walk through the scripts and run them easily,

  • links to Cloud deployments to be able to deploy large-scale trainings in the Cloud with little to no setup.

This is still a work-in-progress – in particular documentation is still sparse – so please contribute improvements/pull requests.

The Big Table of Tasks¶

Task Example datasets Trainer support TFTrainer support pytorch-lightning Colab
language-modeling Raw text ✅ - - Open In Colab
text-classification GLUE, XNLI ✅ ✅ ✅ Open In Colab
token-classification CoNLL NER ✅ ✅ ✅ -
multiple-choice SWAG, RACE, ARC ✅ ✅ - Open In Colab
question-answering SQuAD ✅ ✅ - -
text-generation - n/a n/a n/a Open In Colab
distillation All - - - -
summarization CNN/Daily Mail ✅ - ✅ -
translation WMT ✅ - ✅ -
bertology - - - - -
adversarial HANS ✅ - - -

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. Execute the following steps in a new virtual environment:

git clone https://github.com/huggingface/transformers
cd transformers
pip install .
pip install -r ./examples/requirements.txt

One-click Deploy to Cloud (wip)¶


Deploy to Azure

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.

In this repo, we provide a very simple launcher script named 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).

For example for run_glue:

python examples/xla_spawn.py --num_cores 8 \
	--model_name_or_path bert-base-cased \
	--task_name mnli \
	--data_dir ./data/glue_data/MNLI \
	--output_dir ./models/tpu \
	--overwrite_output_dir \
	--do_train \
	--do_eval \
	--num_train_epochs 1 \
	--save_steps 20000

Feedback and more use cases and benchmarks involving TPUs are welcome, please share with the community.

Logging & Experiment tracking¶

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

Weights & Biases¶

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

pip install wandb

Then log in the command line:

wandb login

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

import wandb

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

When using 🤗 Transformers with PyTorch Lightning, runs can be tracked through WandbLogger. Refer to related documentation & examples.


To use comet_ml, install the Python package with:

pip install comet_ml

or if in a Conda environment:

conda install -c comet_ml -c anaconda -c conda-forge comet_ml