0ae96ff8a7
* Created using Colaboratory * [examples] reorganize files * remove run_tpu_glue.py as superseded by TPU support in Trainer * Bugfix: int, not tuple * move files around
30 lines
2.3 KiB
Markdown
30 lines
2.3 KiB
Markdown
# Examples
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In this section a few examples are put together. All of these examples work for several models, making use of the very
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similar API between the different models.
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**Important**
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To run the latest versions of the examples, you have to install from source and install some specific requirements for the examples.
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Execute the following steps in a new virtual environment:
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```bash
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git clone https://github.com/huggingface/transformers
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cd transformers
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pip install .
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pip install -r ./examples/requirements.txt
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```
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| Section | Description |
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|----------------------------|-----------------------------------------------------
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| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. |
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| [Running on TPUs](#running-on-tpus) | Examples on running fine-tuning tasks on Google TPUs to accelerate workloads. |
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| [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. |
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| [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. |
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| [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. |
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| [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training. |
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| [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. |
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| [Named Entity Recognition](https://github.com/huggingface/transformers/tree/master/examples/ner) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
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| [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
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| [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.) |
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