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[examples] Streamline doc

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# Examples
## Examples
Version 2.9 of `transformers` introduces a new `Trainer` class for PyTorch, and its equivalent `TFTrainer` for TF 2.
Version 2.9 of `transformers` introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
Here is the list of all our examples:
- **grouped by task** (all official examples work for multiple models)
@ -12,32 +12,24 @@ Here is the list of all our examples:
This is still a work-in-progress – in particular documentation is still sparse – so please **contribute improvements/pull requests.**
## Tasks built on Trainer
# The Big Table of Tasks
| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab | One-click Deploy to Azure (wip) |
|---|---|:---:|:---:|:---:|:---:|:---:|
| [`language-modeling`](./language-modeling) | Raw text | ✅ | - | - | - | - |
| [`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/blog/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) |
| [`token-classification`](./token-classification) | CoNLL NER | ✅ | ✅ | ✅ | - | - |
| [`multiple-choice`](./multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb) | - |
| [`question-answering`](./question-answering) | SQuAD | - | ✅ | - | - | - |
| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab
|---|---|:---:|:---:|:---:|:---:|
| [**`language-modeling`**](./language-modeling) | Raw text | ✅ | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
| [**`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/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)
| [**`token-classification`**](./token-classification) | CoNLL NER | ✅ | ✅ | ✅ | -
| [**`multiple-choice`**](./multiple-choice) | SWAG, RACE, ARC | ✅ | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
| [**`question-answering`**](./question-answering) | SQuAD | - | ✅ | - | -
| [**`text-generation`**](./text-generation) | - | - | - | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
| [**`distillation`**](./distillation) | All | - | - | - | -
| [**`summarization`**](./summarization) | CNN/Daily Mail | - | - | - | -
| [**`translation`**](./translation) | WMT | - | - | - | -
| [**`bertology`**](./bertology) | - | - | - | - | -
| [**`adversarial`**](./adversarial) | HANS | - | - | - | -
## Other examples and how-to's
| Section | Description |
|---|---|
| [TensorFlow 2.0 models on GLUE](./text-classification) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. |
| [Running on TPUs](#running-on-tpus) | Examples on running fine-tuning tasks on Google TPUs to accelerate workloads. |
| [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. |
| [Language Generation](./text-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. |
| [GLUE](./text-classification) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. |
| [SQuAD](./question-answering) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training. |
| [Multiple Choice](./multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. |
| [Named Entity Recognition](./token-classification) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
| [XNLI](./text-classification) | Examples running BERT/XLM on the XNLI benchmark. |
| [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.) |
<br>
## Important note
@ -52,6 +44,12 @@ pip install .
pip install -r ./examples/requirements.txt
```
## One-click Deploy to Cloud (wip)
#### Azure
[![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)
## Running on TPUs
When using Tensorflow, TPUs are supported out of the box as a `tf.distribute.Strategy`.