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Update TF examples README (#12703)

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* Update Transformers README, rename token_classification example to token-classification to be consistent with the others

* Update examples/tensorflow/README.md

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add README for TF token classification

* Update examples/tensorflow/token-classification/README.md

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update examples/tensorflow/token-classification/README.md

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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<!---
Copyright 2020 The HuggingFace Team. All rights reserved.
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
@@ -15,29 +15,30 @@ limitations under the License.
# Examples
This folder contains actively maintained examples of use of 🤗 Transformers using the TensorFlow backend, organized along NLP tasks. It is under construction so we thank you for your patience!
This folder contains actively maintained examples of use of 🤗 Transformers organized into different NLP tasks. All examples in this folder are **TensorFlow** examples, and are written using native Keras rather than classes like `TFTrainer`, which we now consider deprecated. If you've previously only used 🤗 Transformers via `TFTrainer`, we highly recommend taking a look at the new style - we think it's a big improvement!
Files containing `tf` such as `run_tf_glue.py` are the old-style files that will be rewritten very soon! Files without this such as `run_text_classification.py` are the newer ones. This message will be removed when the revamp is complete.
In addition, all scripts here now support the [🤗 Datasets](https://github.com/huggingface/datasets) library - you can grab entire datasets just by changing one command-line argument!
## A note on code folding
Most of these examples have been formatted with #region blocks. In IDEs such as PyCharm and VSCode, these blocks mark
named regions of code that can be folded for easier viewing. If you find any of these scripts overwhelming or difficult
to follow, we highly recommend beginning with all regions folded and then examining regions one at a time!
## The Big Table of Tasks
Here is the list of all our examples:
- with information on whether they are **built on top of `Keras`** (if not, they still work, they might
just lack some features),
- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
- links to **Colab notebooks** to walk through the scripts and run them easily,
<!--
Coming soon!
- links to **Cloud deployments** to be able to deploy large-scale trainings in the Cloud with little to no setup.
-->
| Task | Example datasets | Keras support | 🤗 Datasets | Colab
|---|---|:---:|:---:|:---:|
| **`language-modeling`** | WikiText-2 | - | - | -
| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/multiple-choice) | SWAG | - | - | -
| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/question-answering) | SQuAD | - | - | -
| **`summarization`** | XSum | - | - | -
| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) | GLUE | - | - | -
| **`text-generation`** | n/a | - | n/a | -
| **`token-classification`** | CoNLL NER | - | - | -
| **`translation`** | WMT | - | - | -
| Task | Example datasets |
|---|---|
| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/language-modeling) | WikiText-2
| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/multiple-choice) | SWAG
| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/question-answering) | SQuAD
| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) | XSum
| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) | GLUE
| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/token-classification) | CoNLL NER
| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/translation) | WMT
## Coming soon
- **Colab notebooks** to easily run through these scripts!