<commit_before><p align="center">
    <br>
    <img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
    <br>
<p>
<p align="center">
    <a href="https://circleci.com/gh/huggingface/transformers">
        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
    </a>
    <a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
    </a>
    <a href="https://huggingface.co/transformers/index.html">
        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
    </a>
    <a href="https://github.com/huggingface/transformers/releases">
        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
    </a>
</p>

<h3 align="center">
<p>State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
</h3>

🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.

### Features

- As easy to use as pytorch-transformers
- As powerful and concise as Keras
- High performance on NLU and NLG tasks
- Low barrier to entry for educators and practitioners

State-of-the-art NLP for everyone
- Deep learning researchers
- Hands-on practitioners
- AI/ML/NLP teachers and educators

Lower compute costs, smaller carbon footprint
- Researchers can share trained models instead of always retraining
- Practitioners can reduce compute time and production costs
- 8 architectures with over 30 pretrained models, some in more than 100 languages

Choose the right framework for every part of a model's lifetime
- Train state-of-the-art models in 3 lines of code
- Deep interoperability between TensorFlow 2.0 and PyTorch models
- Move a single model between TF2.0/PyTorch frameworks at will
- Seamlessly pick the right framework for training, evaluation, production


| Section | Description |
|-|-|
| [Installation](#installation) | How to install the package |
| [Model architectures](#model-architectures) | Architectures (with pretrained weights) |
| [Online demo](#online-demo) | Experimenting with this repo’s text generation capabilities |
| [Quick tour: Usage](#quick-tour) | Tokenizers & models usage: Bert and GPT-2 |
| [Quick tour: TF 2.0 and PyTorch ](#Quick-tour-TF-20-training-and-PyTorch-interoperability) | Train a TF 2.0 model in 10 lines of code, load it in PyTorch |
| [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
| [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers |
| [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers |
| [Documentation](https://huggingface.co/transformers/) | Full API documentation and more |

## Installation

This repo is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+), PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1

### With pip

First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.

When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:

```bash
pip install transformers
```<commit_msg>fix pytorch-transformers migration description in README<commit_after><p align="center">
    <br>
    <img src="https://raw.githubusercontent.com/huggingface/transformers/master/docs/source/imgs/transformers_logo_name.png" width="400"/>
    <br>
<p>
<p align="center">
    <a href="https://circleci.com/gh/huggingface/transformers">
        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/master">
    </a>
    <a href="https://github.com/huggingface/transformers/blob/master/LICENSE">
        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
    </a>
    <a href="https://huggingface.co/transformers/index.html">
        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/transformers/index.html.svg?down_color=red&down_message=offline&up_message=online">
    </a>
    <a href="https://github.com/huggingface/transformers/releases">
        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
    </a>
</p>

<h3 align="center">
<p>State-of-the-art Natural Language Processing for TensorFlow 2.0 and PyTorch
</h3>

🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.

### Features

- As easy to use as pytorch-transformers
- As powerful and concise as Keras
- High performance on NLU and NLG tasks
- Low barrier to entry for educators and practitioners

State-of-the-art NLP for everyone
- Deep learning researchers
- Hands-on practitioners
- AI/ML/NLP teachers and educators

Lower compute costs, smaller carbon footprint
- Researchers can share trained models instead of always retraining
- Practitioners can reduce compute time and production costs
- 8 architectures with over 30 pretrained models, some in more than 100 languages

Choose the right framework for every part of a model's lifetime
- Train state-of-the-art models in 3 lines of code
- Deep interoperability between TensorFlow 2.0 and PyTorch models
- Move a single model between TF2.0/PyTorch frameworks at will
- Seamlessly pick the right framework for training, evaluation, production


| Section | Description |
|-|-|
| [Installation](#installation) | How to install the package |
| [Model architectures](#model-architectures) | Architectures (with pretrained weights) |
| [Online demo](#online-demo) | Experimenting with this repo’s text generation capabilities |
| [Quick tour: Usage](#quick-tour) | Tokenizers & models usage: Bert and GPT-2 |
| [Quick tour: TF 2.0 and PyTorch ](#Quick-tour-TF-20-training-and-PyTorch-interoperability) | Train a TF 2.0 model in 10 lines of code, load it in PyTorch |
| [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
| [Migrating from pytorch-transformers to transformers](#Migrating-from-pytorch-transformers-to-transformers) | Migrating your code from pytorch-transformers to transformers |
| [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-transformers) | Migrating your code from pytorch-pretrained-bert to transformers |
| [Documentation](https://huggingface.co/transformers/) | Full API documentation and more |

## Installation

This repo is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+), PyTorch 1.0.0+ and TensorFlow 2.0.0-rc1

### With pip

First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.

When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:

```bash
pip install transformers
```
{'fraction_non_alphanumeric': 0.11269276393831554, 'fraction_numerical': 0.012811387900355872, 'mean_word_length': 4.719810040705563, 'pattern_counts': {'":': 0, '<': 47, '<?xml version=': 0, '>': 47, 'https://': 24, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '29888', 'n_tokens_mistral': 2715, 'n_tokens_neox': 2558, 'n_words': 775}