PyTorch-Transformers is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP).
PyTorch-Transformers is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP).
The library currently contains PyTorch implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
The library currently contains PyTorch implementations, pre-trained model weights, usage scripts and conversion utilities for the following models:
-**[Google's BERT model](https://github.com/google-research/bert)** 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.
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.
-**[OpenAI's GPT model](https://github.com/openai/finetune-transformer-lm)** 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.
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.
-**[OpenAI's GPT-2 model](https://blog.openai.com/better-language-models/)** released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
3.**[GPT-2](https://blog.openai.com/better-language-models/)**(from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-**[Google/CMU's Transformer-XL model](https://github.com/kimiyoung/transformer-xl)** released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
4.**[Transformer-XL](https://github.com/kimiyoung/transformer-xl)**(from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-**[Google/CMU's XLNet model](https://github.com/zihangdai/xlnet/)** released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
5.**[XLNet](https://github.com/zihangdai/xlnet/)**(from Google/CMU) released with the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-**[Facebook's XLM model](https://github.com/facebookresearch/XLM/)** released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
6.**[XLM](https://github.com/facebookresearch/XLM/)**(from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](#documentation).
These implementations have been tested on several datasets (see the example scripts) and should match the performances of the original implementations (e.g. ~93 F1 on SQuAD for BERT Whole-Word-Masking, ~88 F1 on RocStories for OpenAI GPT, ~18.3 perplexity on WikiText 103 for Transformer-XL, ~0.916 Peason R coefficient on STS-B for XLNet). You can find more details on the performances in the Examples section of the [documentation](https://huggingface.co/pytorch-transformers/examples.html).
| Section | Description |
| Section | Description |
|-|-|
|-|-|
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@@ -21,7 +21,7 @@ These implementations have been tested on several datasets (see the example scri
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| [Quick tour: Fine-tuning/usage scripts](#quick-tour-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
| [Quick tour: Fine-tuning/usage scripts](#quick-tour-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
| [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-pytorch-transformers) | Migrating your code from pytorch-pretrained-bert to pytorch-transformers |
| [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-pytorch-transformers) | Migrating your code from pytorch-pretrained-bert to pytorch-transformers |
| [Documentation](#documentation) | Full API documentation and more |
| [Documentation](https://huggingface.co/pytorch-transformers/) | Full API documentation and more |
## Installation
## Installation
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@@ -202,13 +202,14 @@ Examples for each model class of each model architecture (Bert, GPT, GPT-2, Tran
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The library comprises several example scripts with SOTA performances for NLU and NLG tasks:
The library comprises several example scripts with SOTA performances for NLU and NLG tasks:
- fine-tuning Bert/XLNet/XLM with a *sequence-level classifier* on nine different GLUE tasks,
-`run_glue.py`: an example fine-tuning Bert, XLNet and XLM on nine different GLUE tasks (*sequence-level classification*)
- fine-tuning Bert/XLNet/XLM with a *token-level classifier* on the question answering dataset SQuAD 2.0, and
-`run_squad.py`: an example fine-tuning Bert, XLNet and XLM on the question answering dataset SQuAD 2.0 (*token-level classification*)
- using GPT/GPT-2/Transformer-XL and XLNet for conditional language generation.
-`run_generation.py`: an example using GPT, GPT-2, Transformer-XL and XLNet for conditional language generation
- other model-specific examples (see the documentation).
Here are three quick usage examples for these scripts:
Here are three quick usage examples for these scripts:
### Fine-tuning for sequence classification: GLUE tasks examples
### `run_glue.py`: Fine-tuning on GLUE tasks for sequence classification
The [General Language Understanding Evaluation (GLUE) benchmark](https://gluebenchmark.com/) is a collection of nine sentence- or sentence-pair language understanding tasks for evaluating and analyzing natural language understanding systems.
The [General Language Understanding Evaluation (GLUE) benchmark](https://gluebenchmark.com/) is a collection of nine sentence- or sentence-pair language understanding tasks for evaluating and analyzing natural language understanding systems.
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@@ -302,7 +303,7 @@ Training with these hyper-parameters gave us the following results:
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@@ -302,7 +303,7 @@ Training with these hyper-parameters gave us the following results:
loss = 0.07231863956341798
loss = 0.07231863956341798
```
```
### Fine-tuning for question-answering: SQuAD example
### `run_squad.py`: Fine-tuning on SQuAD for question-answering
This example code fine-tunes BERT on the SQuAD dataset using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
This example code fine-tunes BERT on the SQuAD dataset using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD:
This is the model provided as `bert-large-uncased-whole-word-masking-finetuned-squad`.
This is the model provided as `bert-large-uncased-whole-word-masking-finetuned-squad`.
### Conditional generation: Text generation with GPT, GPT-2, Transformer-XL and XLNet
### `run_generation.py`: Text generation with GPT, GPT-2, Transformer-XL and XLNet
A conditional generation script is also included to generate text from a prompt.
A conditional generation script is also included to generate text from a prompt.
The generation script include the [tricks](https://github.com/rusiaaman/XLNet-gen#methodology) proposed by by Aman Rusia to get high quality generation with memory models like Transformer-XL and XLNet (include a predefined text to make short inputs longer).
The generation script include the [tricks](https://github.com/rusiaaman/XLNet-gen#methodology) proposed by by Aman Rusia to get high quality generation with memory models like Transformer-XL and XLNet (include a predefined text to make short inputs longer).
A command-line interface is provided to convert a TensorFlow checkpoint in a PyTorch dump of the ``BertForPreTraining`` class (for BERT) or NumPy checkpoint in a PyTorch dump of the ``OpenAIGPTModel`` class (for OpenAI GPT).
A command-line interface is provided to convert a TensorFlow checkpoint in a PyTorch dump of the ``BertForPreTraining`` class (for BERT) or NumPy checkpoint in a PyTorch dump of the ``OpenAIGPTModel`` class (for OpenAI GPT).
PyTorch-Transformers is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP).
The library currently contains PyTorch 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.
3. `GPT-2 <https://blog.openai.com/better-language-models>`_ (from OpenAI) released with the paper `Language Models are Unsupervised Multitask Learners <https://blog.openai.com/better-language-models>`_ by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
4. `Transformer-XL <https://github.com/kimiyoung/transformer-xl>`_ (from Google/CMU) released with the paper `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_ by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
5. `XLNet <https://github.com/zihangdai/xlnet>`_ (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_ by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
6. `XLM <https://github.com/facebookresearch/XLM>`_ (from Facebook) released together with the paper `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_ by Guillaume Lample and Alexis Conneau.
This repository contains op-for-op PyTorch reimplementations, pre-trained models and fine-tuning examples for:
* `Google's BERT model <https://github.com/google-research/bert>`__\ ,
* `OpenAI's GPT model <https://github.com/openai/finetune-transformer-lm>`__\ ,
* `Google/CMU's Transformer-XL model <https://github.com/kimiyoung/transformer-xl>`__\ , and
* `OpenAI's GPT-2 model <https://blog.openai.com/better-language-models/>`__.
These implementations have been tested on several datasets (see the examples) and should match the performances of the associated TensorFlow implementations (e.g. ~91 F1 on SQuAD for BERT, ~88 F1 on RocStories for OpenAI GPT and ~18.3 perplexity on WikiText 103 for the Transformer-XL). You can find more details in the `Examples <./examples.html>`__ section.
Here are some information on these models:
**BERT** was released together 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.
This PyTorch implementation of BERT is provided with `Google's pre-trained models <https://github.com/google-research/bert>`__\ , examples, notebooks and a command-line interface to load any pre-trained TensorFlow checkpoint for BERT is also provided.
**OpenAI GPT** was released together 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.
This PyTorch implementation of OpenAI GPT is an adaptation of the `PyTorch implementation by HuggingFace <https://github.com/huggingface/pytorch-openai-transformer-lm>`__ and is provided with `OpenAI's pre-trained model <https://github.com/openai/finetune-transformer-lm>`__ and a command-line interface that was used to convert the pre-trained NumPy checkpoint in PyTorch.
**Google/CMU's Transformer-XL** was released together with the paper `Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <http://arxiv.org/abs/1901.02860>`__ by Zihang Dai\*, Zhilin Yang\* , Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
This PyTorch implementation of Transformer-XL is an adaptation of the original `PyTorch implementation <https://github.com/kimiyoung/transformer-xl>`__ which has been slightly modified to match the performances of the TensorFlow implementation and allow to re-use the pretrained weights. A command-line interface is provided to convert TensorFlow checkpoints in PyTorch models.
**OpenAI GPT-2** was released together with the paper `Language Models are Unsupervised Multitask Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford\*, Jeffrey Wu\* , Rewon Child, David Luan, Dario Amodei\*\* and Ilya Sutskever\*\*.
This PyTorch implementation of OpenAI GPT-2 is an adaptation of the `OpenAI's implementation <https://github.com/openai/gpt-2>`__ and is provided with `OpenAI's pre-trained model <https://github.com/openai/gpt-2>`__ and a command-line interface that was used to convert the TensorFlow checkpoint in PyTorch.
**Facebook Research's XLM** was released together with the paper `Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
This PyTorch implementation of XLM is an adaptation of the original `PyTorch implementation <https://github.com/facebookresearch/XLM>`__.
**Google's XLNet** was released together with the paper `XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang\*, Zihang Dai\*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov and Quoc V. Le.
This PyTorch implementation of XLM is an adaptation of the `Tensorflow implementation <https://github.com/zihangdai/xlnet>`__.
Content
-------
.. list-table::
:header-rows: 1
* - Section
- Description
* - `Installation <./installation.html>`__
- How to install the package
* - `Philosphy <./philosophy.html>`__
- The philosophy behind this package
* - `Usage <./usage.html>`__
- Quickstart examples
* - `Examples <./examples.html>`__
- Detailed examples on how to fine-tune Bert
* - `Notebooks <./notebooks.html>`__
- Introduction on the provided Jupyter Notebooks
* - `TPU <./tpu.html>`__
- Notes on TPU support and pretraining scripts
* - `Command-line interface <./cli.html>`__
- Convert a TensorFlow checkpoint in a PyTorch dump
* - `Migration <./migration.html>`__
- Migrating from ``pytorch_pretrained_BERT`` (v0.6) to ``pytorch_transformers`` (v1.0)
* - `Bertology <./bertology.html>`__
- Exploring the internals of the pretrained models.
* - `TorchScript <./torchscript.html>`__
- Convert a model to TorchScript for use in other programming languages
This package comprises the following classes that can be imported in Python and are detailed in the `documentation <./model_doc/overview.html>`__ section of this package:
*
Eight **Bert** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_bert.py <./_modules/pytorch_transformers/modeling_bert.html>`__ file):
* `BertModel <./model_doc/bert.html#pytorch_transformers.BertModel>`__ - raw BERT Transformer model (\ **fully pre-trained**\ ),
* `BertForMaskedLM <./model_doc/bert.html#pytorch_transformers.BertForMaskedLM>`__ - BERT Transformer with the pre-trained masked language modeling head on top (\ **fully pre-trained**\ ),
* `BertForNextSentencePrediction <./model_doc/bert.html#pytorch_transformers.BertForNextSentencePrediction>`__ - BERT Transformer with the pre-trained next sentence prediction classifier on top (\ **fully pre-trained**\ ),
* `BertForPreTraining <./model_doc/bert.html#pytorch_transformers.BertForPreTraining>`__ - BERT Transformer with masked language modeling head and next sentence prediction classifier on top (\ **fully pre-trained**\ ),
* `BertForSequenceClassification <./model_doc/bert.html#pytorch_transformers.BertForSequenceClassification>`__ - BERT Transformer with a sequence classification head on top (BERT Transformer is **pre-trained**\ , the sequence classification head **is only initialized and has to be trained**\ ),
* `BertForMultipleChoice <./model_doc/bert.html#pytorch_transformers.BertForMultipleChoice>`__ - BERT Transformer with a multiple choice head on top (used for task like Swag) (BERT Transformer is **pre-trained**\ , the multiple choice classification head **is only initialized and has to be trained**\ ),
* `BertForTokenClassification <./model_doc/bert.html#pytorch_transformers.BertForTokenClassification>`__ - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**\ , the token classification head **is only initialized and has to be trained**\ ),
* `BertForQuestionAnswering <./model_doc/bert.html#pytorch_transformers.BertForQuestionAnswering>`__ - BERT Transformer with a token classification head on top (BERT Transformer is **pre-trained**\ , the token classification head **is only initialized and has to be trained**\ ).
*
Three **OpenAI GPT** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_openai.py <./_modules/pytorch_transformers/modeling_openai.html>`__ file):
* `OpenAIGPTModel <./model_doc/gpt.html#pytorch_transformers.OpenAIGPTModel>`__ - raw OpenAI GPT Transformer model (\ **fully pre-trained**\ ),
* `OpenAIGPTLMHeadModel <./model_doc/gpt.html#pytorch_transformers.OpenAIGPTLMHeadModel>`__ - OpenAI GPT Transformer with the tied language modeling head on top (\ **fully pre-trained**\ ),
* `OpenAIGPTDoubleHeadsModel <./model_doc/gpt.html#pytorch_transformers.OpenAIGPTDoubleHeadsModel>`__ - OpenAI GPT Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT Transformer is **pre-trained**\ , the multiple choice classification head **is only initialized and has to be trained**\ ),
*
Two **Transformer-XL** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_transfo_xl.py <./_modules/pytorch_transformers/modeling_transfo_xl.html>`__ file):
* `TransfoXLModel <./model_doc/transformerxl.html#pytorch_transformers.TransfoXLModel>`__ - Transformer-XL model which outputs the last hidden state and memory cells (\ **fully pre-trained**\ ),
* `TransfoXLLMHeadModel <./model_doc/transformerxl.html#pytorch_transformers.TransfoXLLMHeadModel>`__ - Transformer-XL with the tied adaptive softmax head on top for language modeling which outputs the logits/loss and memory cells (\ **fully pre-trained**\ ),
*
Three **OpenAI GPT-2** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_gpt2.py <./_modules/pytorch_transformers/modeling_gpt2.html>`__ file):
* `GPT2Model <./model_doc/gpt2.html#pytorch_transformers.GPT2Model>`__ - raw OpenAI GPT-2 Transformer model (\ **fully pre-trained**\ ),
* `GPT2LMHeadModel <./model_doc/gpt2.html#pytorch_transformers.GPT2LMHeadModel>`__ - OpenAI GPT-2 Transformer with the tied language modeling head on top (\ **fully pre-trained**\ ),
* `GPT2DoubleHeadsModel <./model_doc/gpt2.html#pytorch_transformers.GPT2DoubleHeadsModel>`__ - OpenAI GPT-2 Transformer with the tied language modeling head and a multiple choice classification head on top (OpenAI GPT-2 Transformer is **pre-trained**\ , the multiple choice classification head **is only initialized and has to be trained**\ ),
*
Four **XLM** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_xlm.py <./_modules/pytorch_transformers/modeling_xlm.html>`__ file):
* `XLMModel <./model_doc/xlm.html#pytorch_transformers.XLMModel>`__ - raw XLM Transformer model (\ **fully pre-trained**\ ),
* `XLMWithLMHeadModel <./model_doc/xlm.html#pytorch_transformers.XLMWithLMHeadModel>`__ - XLM Transformer with the tied language modeling head on top (\ **fully pre-trained**\ ),
* `XLMForSequenceClassification <./model_doc/xlm.html#pytorch_transformers.XLMForSequenceClassification>`__ - XLM Transformer with a sequence classification head on top (XLM Transformer is **pre-trained**\ , the sequence classification head **is only initialized and has to be trained**\ ),
* `XLMForQuestionAnswering <./model_doc/xlm.html#pytorch_transformers.XLMForQuestionAnswering>`__ - XLM Transformer with a token classification head on top (XLM Transformer is **pre-trained**\ , the token classification head **is only initialized and has to be trained**\ )
*
Four **XLNet** PyTorch models (\ ``torch.nn.Module``\ ) with pre-trained weights (in the `modeling_xlnet.py <./_modules/pytorch_transformers/modeling_xlnet.html>`__ file):
* `XLNetModel <./model_doc/xlnet.html#pytorch_transformers.XLNetModel>`__ - raw XLNet Transformer model (\ **fully pre-trained**\ ),
* `XLNetLMHeadModel <./model_doc/xlnet.html#pytorch_transformers.XLNetLMHeadModel>`__ - XLNet Transformer with the tied language modeling head on top (\ **fully pre-trained**\ ),
* `XLNetForSequenceClassification <./model_doc/xlnet.html#pytorch_transformers.XLNetForSequenceClassification>`__ - XLNet Transformer with a sequence classification head on top (XLM Transformer is **pre-trained**\ , the sequence classification head **is only initialized and has to be trained**\ ),
* `XLNetForQuestionAnswering <./model_doc/xlnet.html#pytorch_transformers.XLNetForQuestionAnswering>`__ - XLNet Transformer with a token classification head on top (XLNet Transformer is **pre-trained**\ , the token classification head **is only initialized and has to be trained**\ )
TODO Lysandre filled: I filled in XLM and XLNet. I didn't do the Tokenizers because I don't know the current philosophy behind them.
*
Tokenizers for **BERT** (using word-piece) (in the `tokenization_bert.py <./_modules/pytorch_transformers/tokenization_bert.html>`__ file):
* ``BertTokenizer`` - perform end-to-end tokenization, i.e. basic tokenization followed by WordPiece tokenization.
*
Tokenizer for **OpenAI GPT** (using Byte-Pair-Encoding) (in the `tokenization_openai.py <./_modules/pytorch_transformers/tokenization_openai.html>`__ file):
Tokenizer for **OpenAI GPT-2** (using byte-level Byte-Pair-Encoding) (in the `tokenization_gpt2.py <./_modules/pytorch_transformers/tokenization_gpt2.html>`__ file):
Tokenizer for **Transformer-XL** (word tokens ordered by frequency for adaptive softmax) (in the `tokenization_transfo_xl.py <./_modules/pytorch_transformers/tokenization_transfo_xl.html>`__ file):
* ``OpenAIGPTTokenizer`` - perform word tokenization and can order words by frequency in a corpus for use in an adaptive softmax.
*
Tokenizer for **XLNet** (SentencePiece based tokenizer) (in the `tokenization_xlnet.py <./_modules/pytorch_transformers/tokenization_xlnet.html>`__ file):
* ``BertConfig`` - Configuration class to store the configuration of a ``BertModel`` with utilities to read and write from JSON configuration files.
* ``OpenAIGPTConfig`` - Configuration class to store the configuration of a ``OpenAIGPTModel`` with utilities to read and write from JSON configuration files.
* ``GPT2Config`` - Configuration class to store the configuration of a ``GPT2Model`` with utilities to read and write from JSON configuration files.
* ``TransfoXLConfig`` - Configuration class to store the configuration of a ``TransfoXLModel`` with utilities to read and write from JSON configuration files.
* ``XLMConfig`` - Configuration class to store the configuration of a ``XLMModel`` with utilities to read and write from JSON configuration files.
* ``XLNetConfig`` - Configuration class to store the configuration of a ``XLNetModel`` with utilities to read and write from JSON configuration files.
The repository further comprises:
*
Five examples on how to use **BERT** (in the `examples folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples>`__\ ):
* `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_bert_extract_features.py>`__ - Show how to extract hidden states from an instance of ``BertModel``\ ,
* `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_bert_classifier.py>`__ - Show how to fine-tune an instance of ``BertForSequenceClassification`` on GLUE's MRPC task,
* `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_bert_squad.py>`__ - Show how to fine-tune an instance of ``BertForQuestionAnswering`` on SQuAD v1.0 and SQuAD v2.0 tasks.
* `run_swag.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_swag.py>`__ - Show how to fine-tune an instance of ``BertForMultipleChoice`` on Swag task.
* `simple_lm_finetuning.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/lm_finetuning/simple_lm_finetuning.py>`__ - Show how to fine-tune an instance of ``BertForPretraining`` on a target text corpus.
*
One example on how to use **OpenAI GPT** (in the `examples folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples>`__\ ):
* `run_openai_gpt.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_openai_gpt.py>`__ - Show how to fine-tune an instance of ``OpenGPTDoubleHeadsModel`` on the RocStories task.
*
One example on how to use **Transformer-XL** (in the `examples folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples>`__\ ):
* `run_transfo_xl.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_transfo_xl.py>`__ - Show how to load and evaluate a pre-trained model of ``TransfoXLLMHeadModel`` on WikiText 103.
*
One example on how to use **OpenAI GPT-2** in the unconditional and interactive mode (in the `examples folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples>`__\ ):
* `run_gpt2.py <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/examples/run_gpt2.py>`__ - Show how to use OpenAI GPT-2 an instance of ``GPT2LMHeadModel`` to generate text (same as the original OpenAI GPT-2 examples).
These examples are detailed in the `Examples <#examples>`__ section of this readme.
*
Three notebooks that were used to check that the TensorFlow and PyTorch models behave identically (in the `notebooks folder <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks>`__\ ):
* `Comparing-TF-and-PT-models.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing-TF-and-PT-models.ipynb>`__ - Compare the hidden states predicted by ``BertModel``\ ,
* `Comparing-TF-and-PT-models-SQuAD.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing-TF-and-PT-models-SQuAD.ipynb>`__ - Compare the spans predicted by ``BertForQuestionAnswering`` instances,
* `Comparing-TF-and-PT-models-MLM-NSP.ipynb <https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/notebooks/Comparing-TF-and-PT-models-MLM-NSP.ipynb>`__ - Compare the predictions of the ``BertForPretraining`` instances.
These notebooks are detailed in the `Notebooks <#notebooks>`__ section of this readme.
*
A command-line interface to convert TensorFlow checkpoints (BERT, Transformer-XL) or NumPy checkpoint (OpenAI) in a PyTorch save of the associated PyTorch model:
This CLI is detailed in the `Command-line interface <#Command-line-interface>`__ section of this readme.
@@ -6,43 +6,47 @@ This repo was tested on Python 2.7 and 3.5+ (examples are tested only on python
...
@@ -6,43 +6,47 @@ This repo was tested on Python 2.7 and 3.5+ (examples are tested only on python
With pip
With pip
^^^^^^^^
^^^^^^^^
PyTorch pretrained bert can be installed by pip as follows:
PyTorch pretrained bert can be installed with pip as follows:
.. code-block:: bash
.. code-block:: bash
pip install pytorch-pretrained-bert
pip install pytorch-transformers
If you want to reproduce the original tokenization process of the ``OpenAI GPT`` paper, you will need to install ``ftfy`` (limit to version 4.4.3 if you are using Python 2) and ``SpaCy`` :
.. code-block:: bash
pip install spacy ftfy==4.4.3
python -m spacy download en
If you don't install ``ftfy`` and ``SpaCy``\ , the ``OpenAI GPT`` tokenizer will default to tokenize using BERT's ``BasicTokenizer`` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
Here also, if you want to reproduce the original tokenization process of the ``OpenAI GPT`` model, you will need to install ``ftfy`` (limit to version 4.4.3 if you are using Python 2) and ``SpaCy`` :
Tests
^^^^^
An extensive test suite is included for the library and the example scripts. Library tests can be found in the `tests folder <https://github.com/huggingface/pytorch-transformers/tree/master/pytorch_transformers/tests>`_ and examples tests in the `examples folder <https://github.com/huggingface/pytorch-transformers/tree/master/examples>`_.
These tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
You can run the tests from the root of the cloned repository with the commands:
Again, if you don't install ``ftfy`` and ``SpaCy``\ , the ``OpenAI GPT`` tokenizer will default to tokenize using BERT's ``BasicTokenizer`` followed by Byte-Pair Encoding (which should be fine for most usage).
A series of tests is included in the `tests folder <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/tests>`_ and can be run using ``pytest`` (install pytest if needed: ``pip install pytest``\ ).
OpenAI GPT original tokenization workflow
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
You can run the tests with the command:
If you want to reproduce the original tokenization process of the ``OpenAI GPT`` paper, you will need to install ``ftfy`` (limit to version 4.4.3 if you are using Python 2) and ``SpaCy`` :
.. code-block:: bash
.. code-block:: bash
python -m pytest -sv tests/
pip install spacy ftfy==4.4.3
python -m spacy download en
If you don't install ``ftfy`` and ``SpaCy``\ , the ``OpenAI GPT`` tokenizer will default to tokenize using BERT's ``BasicTokenizer`` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `pytorch-transformers`
### Models always output `tuples`
The main breaking change when migrating from `pytorch-pretrained-bert` to `pytorch-transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/pytorch-transformers/).
In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
Here is a `pytorch-pretrained-bert` to `pytorch-transformers` conversion example for a `BertForSequenceClassification` classification model:
While not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other seralization method before.
### Optimizers: BertAdam & OpenAIAdam are now AdamW, schedules are standard PyTorch schedules
The two optimizers previously included, `BertAdam` and `OpenAIAdam`, have been replaced by a single `AdamW` optimizer.
The new optimizer `AdamW` matches PyTorch `Adam` optimizer API.
The schedules are now standard [PyTorch learning rate schedulers](https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate) and not part of the optimizer anymore.
Here is a conversion examples from `BertAdam` with a linear warmup and decay schedule to `AdamW` and the same schedule:
Here is a quick-start example using `GPT2Tokenizer` and `GPT2LMHeadModel` class with OpenAI's pre-trained model to predict the next token from a text prompt.
First let's prepare a tokenized input from our text string using `GPT2Tokenizer`
assertpredicted_text=='Who was Jim Henson? Jim Henson was a man'
```
Examples for each model class of each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the [documentation](#documentation).
Hereisaquick-startexampleusing``BertTokenizer``\,``BertModel``and``BertForMaskedLM``classwithGoogleAI's pre-trained ``Bert base uncased`` model. See the `doc section <./model_doc/overview.html>`_ below for all the details on these classes.
First let'sprepareatokenizedinputwith``BertTokenizer``
Hereisaquick-startexampleusing``OpenAIGPTTokenizer``\,``OpenAIGPTModel``and``OpenAIGPTLMHeadModel``classwithOpenAI's pre-trained model. See the `doc section <./model_doc/overview.html>`_ for all the details on these classes.
First let'sprepareatokenizedinputwith``OpenAIGPTTokenizer``
Here is a quick-start example using ``GPT2Tokenizer``\ , ``GPT2Model`` and ``GPT2LMHeadModel`` class with OpenAI'spre-trainedmodel.Seethe`docsection<./model_doc/overview.html>`_forallthedetailsontheseclasses.
Firstlet's prepare a tokenized input with ``GPT2Tokenizer``
.. code-block:: python
import torch
from pytorch_transformers import GPT2Tokenizer, GPT2Model, GPT2LMHeadModel
# OPTIONAL: if you want to have more information on what'shappening,activatetheloggerasfollows
importlogging
logging.basicConfig(level=logging.INFO)
#Loadpre-trainedmodeltokenizer(vocabulary)
tokenizer=GPT2Tokenizer.from_pretrained('gpt2')
#Encodesomeinputs
text_1="Who was Jim Henson ?"
text_2="Jim Henson was a puppeteer"
indexed_tokens_1=tokenizer.encode(text_1)
indexed_tokens_2=tokenizer.encode(text_2)
#ConvertinputstoPyTorchtensors
tokens_tensor_1=torch.tensor([indexed_tokens_1])
tokens_tensor_2=torch.tensor([indexed_tokens_2])
Let's see how to use ``GPT2Model`` to get hidden states
.. code-block:: python
# Load pre-trained model (weights)
model = GPT2Model.from_pretrained('gpt2')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor_1 = tokens_tensor_1.to('cuda')
tokens_tensor_2 = tokens_tensor_2.to('cuda')
model.to('cuda')
# Predict hidden states features for each layer
with torch.no_grad():
hidden_states_1, past = model(tokens_tensor_1)
# past can be used to reuse precomputed hidden state in a subsequent predictions
# (see beam-search examples in the run_gpt2.py example).
hidden_states_2, past = model(tokens_tensor_2, past=past)
And how to use ``GPT2LMHeadModel``
.. code-block:: python
# Load pre-trained model (weights)
model = GPT2LMHeadModel.from_pretrained('gpt2')
model.eval()
# If you have a GPU, put everything on cuda
tokens_tensor_1 = tokens_tensor_1.to('cuda')
tokens_tensor_2 = tokens_tensor_2.to('cuda')
model.to('cuda')
# Predict all tokens
with torch.no_grad():
predictions_1, past = model(tokens_tensor_1)
# past can be used to reuse precomputed hidden state in a subsequent predictions
# (see beam-search examples in the run_gpt2.py example).
predictions_2, past = model(tokens_tensor_2, past=past)
