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Examples readme.md (#4215)
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‎docs/source/usage.rst

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of sequence classification is the GLUE dataset, which is entirely based on that task. If you would like to fine-tune
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a model on a GLUE sequence classification task, you may leverage the
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`run_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_glue.py>`_ or
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`run_tf_glue.py <https://github.com/huggingface/transformers/tree/master/examples/run_tf_glue.py>`_ scripts.
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`run_tf_glue.py <https://github.com/huggingface/transformers/tree/master/examples/text-classification/run_tf_glue.py>`_ scripts.
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Here is an example using the pipelines do to sentiment analysis: identifying if a sequence is positive or negative.
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It leverages a fine-tuned model on sst2, which is a GLUE task.

‎examples/README.md

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# 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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Version 2.9 of `transformers` introduces a new `Trainer` class for PyTorch, and its equivalent `TFTrainer` for TF 2.
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Here is the list of all our examples:
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- **grouped by task** (all official examples work for multiple models)
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- with information on whether they are **built on top of `Trainer`/`TFTrainer`** (if not, they still work, they might just lack some features),
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- links to **Colab notebooks** to walk through the scripts and run them easily,
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- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
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This is still a work-in-progress – in particular documentation is still sparse – so please **contribute improvements/pull requests.**
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## Tasks built on Trainer
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| Task | Example datasets | Trainer support | TFTrainer support | Colab | One-click Deploy to Azure (wip) |
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|---|---|:---:|:---:|:---:|:---:|
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| [`language-modeling`](./language-modeling) | Raw text | ✅ | - | - | - |
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| [`text-classification`](./text-classification) | GLUE, XNLI | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/transformers/blob/master/notebooks/trainer/01_text_classification.ipynb) | [![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2FAzure%2Fazure-quickstart-templates%2Fmaster%2F101-storage-account-create%2Fazuredeploy.json) |
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| [`token-classification`](./token-classification) | CoNLL NER | ✅ | ✅ | - | - |
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| [`multiple-choice`](./multiple-choice) | SWAG, RACE, ARC | ✅ | - | - | - |
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## Other examples and how-to's
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| Section | Description |
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|---|---|
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| [TensorFlow 2.0 models on GLUE](./text-classification) | 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-modeling) | 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](./text-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. |
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| [GLUE](./text-classification) | 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](./question-answering) | 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](./token-classification) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
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| [XNLI](./text-classification) | Examples running BERT/XLM on the XNLI benchmark. |
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| [Adversarial evaluation of model performances](./adversarial) | 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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## Important note
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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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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.
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Execute the following steps in a new virtual environment:
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```bash
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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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## Running on TPUs
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Documentation to come.

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