Update TF examples README (#12703)
* 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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Matt
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<!---
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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# Examples
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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!
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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!
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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.
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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!
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## A note on code folding
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Most of these examples have been formatted with #region blocks. In IDEs such as PyCharm and VSCode, these blocks mark
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named regions of code that can be folded for easier viewing. If you find any of these scripts overwhelming or difficult
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to follow, we highly recommend beginning with all regions folded and then examining regions one at a time!
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## The Big Table of Tasks
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Here is the list of all our examples:
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- with information on whether they are **built on top of `Keras`** (if not, they still work, they might
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just lack some features),
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- whether or not they leverage the [🤗 Datasets](https://github.com/huggingface/datasets) library.
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- links to **Colab notebooks** to walk through the scripts and run them easily,
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<!--
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Coming soon!
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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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-->
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| Task | Example datasets | Keras support | 🤗 Datasets | Colab
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|---|---|:---:|:---:|:---:|
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| **`language-modeling`** | WikiText-2 | - | - | -
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| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/multiple-choice) | SWAG | - | - | -
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| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/question-answering) | SQuAD | - | - | -
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| **`summarization`** | XSum | - | - | -
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| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) | GLUE | - | - | -
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| **`text-generation`** | n/a | - | n/a | -
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| **`token-classification`** | CoNLL NER | - | - | -
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| **`translation`** | WMT | - | - | -
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| Task | Example datasets |
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|---|---|
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| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/language-modeling) | WikiText-2
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| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/multiple-choice) | SWAG
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| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/question-answering) | SQuAD
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| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/summarization) | XSum
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| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/text-classification) | GLUE
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| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/token-classification) | CoNLL NER
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| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/tensorflow/translation) | WMT
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## Coming soon
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- **Colab notebooks** to easily run through these scripts!
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examples/tensorflow/token-classification/README.md
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examples/tensorflow/token-classification/README.md
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<!---
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# Token classification
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Fine-tuning the library models for token classification task such as Named Entity Recognition (NER), Parts-of-speech
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tagging (POS) or phrase extraction (CHUNKS). The main script `run_ner.py` leverages the [🤗 Datasets](https://github.com/huggingface/datasets) library. You can easily
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customize it to your needs if you need extra processing on your datasets.
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It will either run on a datasets hosted on our [hub](https://huggingface.co/datasets) or with your own text files for
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training and validation, you might just need to add some tweaks in the data preprocessing.
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The following example fine-tunes BERT on CoNLL-2003:
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```bash
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python run_ner.py \
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--model_name_or_path bert-base-uncased \
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--dataset_name conll2003 \
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--output_dir /tmp/test-ner
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```
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To run on your own training and validation files, use the following command:
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```bash
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python run_ner.py \
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--model_name_or_path bert-base-uncased \
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--train_file path_to_train_file \
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--validation_file path_to_validation_file \
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--output_dir /tmp/test-ner
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```
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**Note:** This script only works with models that have a fast tokenizer (backed by the [🤗 Tokenizers](https://github.com/huggingface/tokenizers) library) as it
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uses special features of those tokenizers. You can check if your favorite model has a fast tokenizer in
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[this table](https://huggingface.co/transformers/index.html#supported-frameworks).
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