transformers/examples

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:

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	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.)