examples

Examples

Version 2.9 of transformers introduces a new Trainer class for PyTorch, and its equivalent TFTrainer for TF 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.

Tasks built on Trainer

Task	Example datasets	Trainer support	TFTrainer support	pytorch-lightning	Colab	One-click Deploy to Azure (wip)
language-modeling	Raw text	✅	-	-	-	-
text-classification	GLUE, XNLI	✅	✅	✅
token-classification	CoNLL NER	✅	✅	✅	-	-
multiple-choice	SWAG, RACE, ARC	✅	✅	-		-
question-answering	SQuAD	-	✅	-	-	-

Other examples and how-to's

Section	Description
TensorFlow 2.0 models on GLUE	Examples running BERT TensorFlow 2.0 model on the GLUE tasks.
Running on TPUs	Examples on running fine-tuning tasks on Google TPUs to accelerate workloads.
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	Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet.
GLUE	Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision.
SQuAD	Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training.
Multiple Choice	Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks.
Named Entity Recognition	Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training.
XNLI	Examples running BERT/XLM on the XNLI benchmark.
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.)

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

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 \
	examples/text-classification/run_glue.py
	--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.