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docs/source/index.rst
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Transformers
================================================================================================================================================
Transformers is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP).
🤗 Transformers (formerly known as `pytorch-transformers` and `pytorch-pretrained-bert`) provides 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.
The library currently contains PyTorch implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
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
Contents
---------------------------------
The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
1. `BERT <https://github.com/google-research/bert>`_ (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_ by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
2. `GPT <https://github.com/openai/finetune-transformer-lm>`_ (from OpenAI) released with the paper `Improving Language Understanding by Generative Pre-Training <https://blog.openai.com/language-unsupervised>`_ by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
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7. `RoBERTa <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`_ (from Facebook), released together with the paper a `Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together with the blog post `Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`_ by Victor Sanh, Lysandre Debut and Thomas Wolf.
.. toctree::
:maxdepth: 2
:caption: Notes

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docs/source/pretrained_models.rst
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``bert-large-uncased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
| | | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD |
| | | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__). |
| | | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__). |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``bert-large-cased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters |
| | | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD |
| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| | ``bert-base-cased-finetuned-mrpc`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
| | | | The ``bert-base-cased`` model fine-tuned on MRPC |
| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
| GPT | ``openai-gpt`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
| | | | OpenAI GPT English model |