@@ -6,7 +6,7 @@ A command-line interface is provided to convert original Bert/GPT/GPT-2/Transfor
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@@ -6,7 +6,7 @@ A command-line interface is provided to convert original Bert/GPT/GPT-2/Transfor
BERT
BERT
^^^^
^^^^
You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/pytorch-transformers/blob/master/pytorch_transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
You can convert any TensorFlow checkpoint for BERT (in particular `the pre-trained models released by Google <https://github.com/google-research/bert#pre-trained-models>`_\ ) in a PyTorch save file by using the `convert_tf_checkpoint_to_pytorch.py <https://github.com/huggingface/transformers/blob/master/transformers/convert_tf_checkpoint_to_pytorch.py>`_ script.
This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).
This CLI takes as input a TensorFlow checkpoint (three files starting with ``bert_model.ckpt``\ ) and the associated configuration file (\ ``bert_config.json``\ ), and creates a PyTorch model for this configuration, loads the weights from the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can be imported using ``torch.load()`` (see examples in `run_bert_extract_features.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_extract_features.py>`_\ , `run_bert_classifier.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_classifier.py>`_ and `run_bert_squad.py <https://github.com/huggingface/pytorch-pretrained-BERT/tree/master/examples/run_bert_squad.py>`_\ ).
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@@ -20,7 +20,7 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
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@@ -20,7 +20,7 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
- 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.
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.
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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@@ -12,7 +46,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
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@@ -12,7 +46,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
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.
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.
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.
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.
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/pytorch-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.
8. `DistilBERT <https://huggingface.co/transformers/model_doc/distilbert.html>`_ (from HuggingFace) released together with the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`_ by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2 <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
.. toctree::
.. toctree::
:maxdepth: 2
:maxdepth: 2
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@@ -28,6 +62,8 @@ The library currently contains PyTorch implementations, pre-trained model weight
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@@ -28,6 +62,8 @@ The library currently contains PyTorch implementations, pre-trained model weight
migration
migration
bertology
bertology
torchscript
torchscript
multilingual
benchmarks
.. toctree::
.. toctree::
:maxdepth: 2
:maxdepth: 2
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@@ -37,6 +73,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
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@@ -37,6 +73,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
main_classes/model
main_classes/model
main_classes/tokenizer
main_classes/tokenizer
main_classes/optimizer_schedules
main_classes/optimizer_schedules
main_classes/processors
.. toctree::
.. toctree::
:maxdepth: 2
:maxdepth: 2
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@@ -51,3 +88,4 @@ The library currently contains PyTorch implementations, pre-trained model weight
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@@ -51,3 +88,4 @@ The library currently contains PyTorch implementations, pre-trained model weight
An extensive test suite is included to test the library behavior and several examples. 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>`_.
An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the [tests folder](https://github.com/huggingface/transformers/tree/master/transformers/tests) and examples tests in the [examples folder](https://github.com/huggingface/transformers/tree/master/examples).
Tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
Tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
Run all the tests from the root of the cloned repository with the commands:
Run all the tests from the root of the cloned repository with the commands:
If you want to reproduce the original tokenization process of the ``OpenAI GPT`` paper, you will need to install ``ftfy`` (use version 4.4.3 if you are using Python 2) and ``SpaCy`` :
If you want to reproduce the original tokenization process of the `OpenAI GPT` paper, you will need to install `ftfy` (use version 4.4.3 if you are using Python 2) and `SpaCy`:
.. code-block:: bash
``` bash
pip install spacy ftfy==4.4.3
python -m spacy download en
```
pip install spacy ftfy==4.4.3
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).
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).
## Note on model downloads (Continuous Integration or large-scale deployments)
Note on model downloads (Continuous Integration or large-scale deployments)
If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way faster, and cheaper. Feel free to contact us privately if you need any help.
If you expect to be downloading large volumes of models (more than 1,000) from our hosted bucket (for instance through your CI setup, or a large-scale production deployment), please cache the model files on your end. It will be way faster, and cheaper. Feel free to contact us privately if you need any help.
## Do you want to run a Transformer model on a mobile device?
Do you want to run a Transformer model on a mobile device?
You should check out our [swift-coreml-transformers](https://github.com/huggingface/swift-coreml-transformers) repo.
You should check out our `swift-coreml-transformers <https://github.com/huggingface/swift-coreml-transformers>`_ repo.
It contains an example of a conversion script from a Pytorch trained Transformer model (here, ``GPT-2``) to a CoreML model that runs on iOS devices.
It also contains an implementation of BERT for Question answering.
It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.
At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch to productizing them in CoreML,
At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch to productizing them in CoreML,
or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!
or prototype a model or an app in CoreML then research its hyperparameters or architecture from PyTorch. Super exciting!
Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `pytorch-transformers`
Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `transformers`
### Models always output `tuples`
### 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 main breaking change when migrating from `pytorch-pretrained-bert` to `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/).
The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/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`.
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:
Here is a `pytorch-pretrained-bert` to `transformers` conversion example for a `BertForSequenceClassification` classification model:
```python
```python
# Let's load our model
# Let's load our model
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@@ -20,11 +20,11 @@ model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
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# If you used to have this line in pytorch-pretrained-bert:
# If you used to have this line in pytorch-pretrained-bert:
loss=model(input_ids,labels=labels)
loss=model(input_ids,labels=labels)
# Now just use this line in pytorch-transformers to extract the loss from the output tuple:
# Now just use this line in transformers to extract the loss from the output tuple:
outputs=model(input_ids,labels=labels)
outputs=model(input_ids,labels=labels)
loss=outputs[0]
loss=outputs[0]
# In pytorch-transformers you can also have access to the logits:
# In transformers you can also have access to the logits:
loss,logits=outputs[:2]
loss,logits=outputs[:2]
# And even the attention weigths if you configure the model to output them (and other outputs too, see the docstrings and documentation)
# And even the attention weigths if you configure the model to output them (and other outputs too, see the docstrings and documentation)
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@@ -96,7 +96,7 @@ for batch in train_data:
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@@ -96,7 +96,7 @@ for batch in train_data:
loss.backward()
loss.backward()
optimizer.step()
optimizer.step()
### In PyTorch-Transformers, optimizer and schedules are splitted and instantiated like this:
### In Transformers, optimizer and schedules are splitted and instantiated like this:
optimizer=AdamW(model.parameters(),lr=lr,correct_bias=False)# To reproduce BertAdam specific behavior set correct_bias=False
optimizer=AdamW(model.parameters(),lr=lr,correct_bias=False)# To reproduce BertAdam specific behavior set correct_bias=False
