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Merge branch 'master' of https://github.com/danai-antoniou/pytorch-transformers into add-duplicate-tokens-error
parents 2e6797cc 391db836
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.circleci/config.yml
View file @ d7395789
version: 2
jobs:
build_py3:
working_directory: ~/pytorch-transformers
build_py3_torch_and_tf:
working_directory: ~/transformers
docker:
- image: circleci/python:3.5
resource_class: xlarge
parallelism: 1
steps:
- checkout
- run: sudo pip install torch
- run: sudo pip install tensorflow==2.0.0-rc0
- run: sudo pip install --progress-bar off .
- run: sudo pip install pytest codecov pytest-cov
- run: sudo pip install tensorboardX scikit-learn
- run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
- run: python -m pytest -sv ./transformers/tests/ --cov
- run: codecov
build_py3_torch:
working_directory: ~/transformers
docker:
- image: circleci/python:3.5
resource_class: xlarge
parallelism: 1
steps:
- checkout
- run: sudo pip install torch
- run: sudo pip install --progress-bar off .
- run: sudo pip install pytest codecov pytest-cov
- run: sudo pip install tensorboardX scikit-learn
- run: python -m pytest -sv ./transformers/tests/ --cov
- run: python -m pytest -sv ./examples/
- run: codecov
build_py2:
working_directory: ~/pytorch-transformers
build_py3_tf:
working_directory: ~/transformers
docker:
- image: circleci/python:3.5
resource_class: xlarge
parallelism: 1
steps:
- checkout
- run: sudo pip install tensorflow==2.0.0-rc0
- run: sudo pip install --progress-bar off .
- run: sudo pip install pytest codecov pytest-cov
- run: sudo pip install tensorboardX scikit-learn
- run: python -m pytest -sv ./transformers/tests/ --cov
- run: codecov
build_py2_torch:
working_directory: ~/transformers
resource_class: large
parallelism: 1
docker:
- image: circleci/python:2.7
steps:
- checkout
- run: sudo pip install torch
- run: sudo pip install --progress-bar off .
- run: sudo pip install pytest codecov pytest-cov
- run: python -m pytest -sv ./transformers/tests/ --cov
- run: codecov
build_py2_tf:
working_directory: ~/transformers
resource_class: large
parallelism: 1
docker:
- image: circleci/python:2.7
steps:
- checkout
- run: sudo pip install tensorflow==2.0.0-rc0
- run: sudo pip install --progress-bar off .
- run: sudo pip install pytest codecov pytest-cov
- run: python -m pytest -sv ./pytorch_transformers/tests/ --cov
- run: python -m pytest -sv ./transformers/tests/ --cov
- run: codecov
deploy_doc:
working_directory: ~/pytorch-transformers
working_directory: ~/transformers
docker:
- image: circleci/python:3.5
steps:
......@@ -37,7 +81,6 @@ jobs:
- checkout
- run: sudo pip install --progress-bar off -r docs/requirements.txt
- run: sudo pip install --progress-bar off -r requirements.txt
- run: cd docs/source && ln -s ../../examples/README.md examples.md && cd -
- run: cd docs && make clean && make html && scp -r -oStrictHostKeyChecking=no _build/html/* $doc:$dir
workflow_filters: &workflow_filters
filters:
......@@ -48,6 +91,9 @@ workflows:
version: 2
build_and_test:
jobs:
- build_py3
- build_py2
- build_py3_torch_and_tf
- build_py3_torch
- build_py3_tf
- build_py2_torch
- build_py2_tf
- deploy_doc: *workflow_filters
\ No newline at end of file
.coveragerc
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[run]
source=pytorch_transformers
source=transformers
omit =
# skip convertion scripts from testing for now
*/convert_*
......
.github/ISSUE_TEMPLATE/migration.md
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---
name: "\U0001F4DA Migration from PyTorch-pretrained-Bert"
about: Report a problem when migrating from PyTorch-pretrained-Bert to PyTorch-Transformers
about: Report a problem when migrating from PyTorch-pretrained-Bert to Transformers
---
## 📚 Migration
......
.gitignore
View file @ d7395789
......@@ -130,5 +130,5 @@ runs
examples/runs
# data
data
/data
serialization_dir
\ No newline at end of file
README.md
View file @ d7395789
# 👾 PyTorch-Transformers
<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
[![CircleCI](https://circleci.com/gh/huggingface/pytorch-transformers.svg?style=svg)](https://circleci.com/gh/huggingface/pytorch-transformers)
PyTorch-Transformers (formerly known as `pytorch-pretrained-bert`) 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.
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://github.com/huggingface/pytorch-transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the blogpost [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.
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 |
|-|-|
| [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-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-pytorch-transformers) | Migrating your code from pytorch-pretrained-bert to pytorch-transformers |
| [Documentation](https://huggingface.co/pytorch-transformers/) | Full API documentation and more |
| [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+) and PyTorch 1.0.0+
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
PyTorch-Transformers can be installed by pip as follows:
First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
Please refere 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 pytorch-transformers
pip install transformers
```
### From source
Clone the repository and run:
Here also, you first need to install one of, or both, TensorFlow 2.0 and PyTorch.
Please refere 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, you can install from source by cloning the repository and running:
```bash
pip install [--editable] .
......@@ -49,14 +88,16 @@ pip install [--editable] .
### Tests
A series of tests 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).
A series of tests are included for the library and the example scripts. 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).
These tests can be run using `pytest` (install pytest if needed with `pip install pytest`).
Depending on which framework is installed (TensorFlow 2.0 and/or PyTorch), the irrelevant tests will be skipped. Ensure that both frameworks are installed if you want to execute all tests.
You can run the tests from the root of the cloned repository with the commands:
```bash
python -m pytest -sv ./pytorch_transformers/tests/
python -m pytest -sv ./transformers/tests/
python -m pytest -sv ./examples/
```
......@@ -66,8 +107,23 @@ You should check out our [`swift-coreml-transformers`](https://github.com/huggin
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.
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!
At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models to productizing them in CoreML, or prototype a model or an app in CoreML then research its hyperparameters or architecture from TensorFlow 2.0 and/or PyTorch. Super exciting!
## Model architectures
🤗 Transformers currently provides 8 NLU/NLG architectures:
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.
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://github.com/huggingface/transformers/tree/master/examples/distillation)** (from HuggingFace), released together with the blogpost [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.
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/transformers/examples.html).
## Online demo
......@@ -80,22 +136,25 @@ You can use it to experiment with completions generated by `GPT2Model`, `Transfo
## Quick tour
Let's do a very quick overview of PyTorch-Transformers. Detailed examples for each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the [full documentation](https://huggingface.co/pytorch-transformers/).
Let's do a very quick overview of the model architectures in 🤗 Transformers. Detailed examples for each model architecture (Bert, GPT, GPT-2, Transformer-XL, XLNet and XLM) can be found in the [full documentation](https://huggingface.co/transformers/).
```python
import torch
from pytorch_transformers import *
from transformers import *
# PyTorch-Transformers has a unified API
# for 7 transformer architectures and 30 pretrained weights.
# Transformers has a unified API
# for 8 transformer architectures and 30 pretrained weights.
# Model | Tokenizer | Pretrained weights shortcut
MODELS = [(BertModel, BertTokenizer, 'bert-base-uncased'),
(OpenAIGPTModel, OpenAIGPTTokenizer, 'openai-gpt'),
(GPT2Model, GPT2Tokenizer, 'gpt2'),
(TransfoXLModel, TransfoXLTokenizer, 'transfo-xl-wt103'),
(XLNetModel, XLNetTokenizer, 'xlnet-base-cased'),
(XLMModel, XLMTokenizer, 'xlm-mlm-enfr-1024'),
(RobertaModel, RobertaTokenizer, 'roberta-base')]
MODELS = [(BertModel, BertTokenizer, 'bert-base-uncased'),
(OpenAIGPTModel, OpenAIGPTTokenizer, 'openai-gpt'),
(GPT2Model, GPT2Tokenizer, 'gpt2'),
(TransfoXLModel, TransfoXLTokenizer, 'transfo-xl-wt103'),
(XLNetModel, XLNetTokenizer, 'xlnet-base-cased'),
(XLMModel, XLMTokenizer, 'xlm-mlm-enfr-1024'),
(DistilBertModel, DistilBertTokenizer, 'distilbert-base-uncased'),
(RobertaModel, RobertaTokenizer, 'roberta-base')]
# To use TensorFlow 2.0 versions of the models, simply prefix the class names with 'TF', e.g. `TFRobertaModel` is the TF 2.0 counterpart of the PyTorch model `RobertaModel`
# Let's encode some text in a sequence of hidden-states using each model:
for model_class, tokenizer_class, pretrained_weights in MODELS:
......@@ -121,24 +180,71 @@ for model_class in BERT_MODEL_CLASSES:
# Load pretrained model/tokenizer
model = model_class.from_pretrained('bert-base-uncased')
# Models can return full list of hidden-states & attentions weights at each layer
model = model_class.from_pretrained(pretrained_weights,
output_hidden_states=True,
output_attentions=True)
input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")])
all_hidden_states, all_attentions = model(input_ids)[-2:]
# Models can return full list of hidden-states & attentions weights at each layer
model = model_class.from_pretrained(pretrained_weights,
output_hidden_states=True,
output_attentions=True)
input_ids = torch.tensor([tokenizer.encode("Let's see all hidden-states and attentions on this text")])
all_hidden_states, all_attentions = model(input_ids)[-2:]
# Models are compatible with Torchscript
model = model_class.from_pretrained(pretrained_weights, torchscript=True)
traced_model = torch.jit.trace(model, (input_ids,))
# Models are compatible with Torchscript
model = model_class.from_pretrained(pretrained_weights, torchscript=True)
traced_model = torch.jit.trace(model, (input_ids,))
# Simple serialization for models and tokenizers
model.save_pretrained('./directory/to/save/') # save
model = model_class.from_pretrained('./directory/to/save/') # re-load
tokenizer.save_pretrained('./directory/to/save/') # save
tokenizer = tokenizer_class.from_pretrained('./directory/to/save/') # re-load
# Simple serialization for models and tokenizers
model.save_pretrained('./directory/to/save/') # save
model = model_class.from_pretrained('./directory/to/save/') # re-load
tokenizer.save_pretrained('./directory/to/save/') # save
tokenizer = BertTokenizer.from_pretrained('./directory/to/save/') # re-load
# SOTA examples for GLUE, SQUAD, text generation...
# SOTA examples for GLUE, SQUAD, text generation...
```
## Quick tour TF 2.0 training and PyTorch interoperability
Let's do a quick example of how a TensorFlow 2.0 model can be trained in 12 lines of code with 🤗 Transformers and then loaded in PyTorch for fast inspection/tests.
```python
import tensorflow as tf
import tensorflow_datasets
from transformers import *
# Load dataset, tokenizer, model from pretrained model/vocabulary
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = TFBertForSequenceClassification.from_pretrained('bert-base-cased')
data = tensorflow_datasets.load('glue/mrpc')
# Prepare dataset for GLUE as a tf.data.Dataset instance
train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, max_length=128, task='mrpc')
valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, max_length=128, task='mrpc')
train_dataset = train_dataset.shuffle(100).batch(32).repeat(2)
valid_dataset = valid_dataset.batch(64)
# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
model.compile(optimizer=optimizer, loss=loss, metrics=[metric])
# Train and evaluate using tf.keras.Model.fit()
history = model.fit(train_dataset, epochs=2, steps_per_epoch=115,
validation_data=valid_dataset, validation_steps=7)
# Load the TensorFlow model in PyTorch for inspection
model.save_pretrained('./save/')
pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
# Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
sentence_0 = "This research was consistent with his findings."
sentence_1 = "His findings were compatible with this research."
sentence_2 = "His findings were not compatible with this research."
inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
print("sentence_1 is", "a paraphrase" if pred_1 else "not a paraphrase", "of sentence_0")
print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sentence_0")
```
## Quick tour of the fine-tuning/usage scripts
......@@ -288,7 +394,7 @@ This is the model provided as `bert-large-uncased-whole-word-masking-finetuned-s
### `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.
The generation script includes the [tricks](https://github.com/rusiaaman/XLNet-gen#methodology) proposed 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 includes the [tricks](https://github.com/rusiaaman/XLNet-gen#methodology) proposed 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).
Here is how to run the script with the small version of OpenAI GPT-2 model:
......@@ -299,19 +405,32 @@ python ./examples/run_generation.py \
--model_name_or_path=gpt2 \
```
## Migrating from pytorch-pretrained-bert to pytorch-transformers
## Migrating from pytorch-transformers to transformers
Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to `transformers`.
### Positional order of some models' keywords inputs (`attention_mask`, `token_type_ids`...) changed
To be able to use Torchscript (see #1010, #1204 and #1195) the specific order of some models **keywords inputs** (`attention_mask`, `token_type_ids`...) has been changed.
If you used to call the models with keyword names for keyword arguments, e.g. `model(inputs_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)`, this should not cause any change.
Here is a quick summary of what you should take care of when migrating from `pytorch-pretrained-bert` to `pytorch-transformers`
If you used to call the models with positional inputs for keyword arguments, e.g. `model(inputs_ids, attention_mask, token_type_ids)`, you may have to double check the exact order of input arguments.
## Migrating from pytorch-pretrained-bert to 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`
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 detailed in the models' docstrings and the [documentation](https://huggingface.co/pytorch-transformers/).
The exact content of the tuples for each model is detailed 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`.
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
# Let's load our model
......@@ -320,11 +439,11 @@ model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
# If you used to have this line in pytorch-pretrained-bert:
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)
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]
# And even the attention weights if you configure the model to output them (and other outputs too, see the docstrings and documentation)
......@@ -333,13 +452,17 @@ outputs = model(input_ids, labels=labels)
loss, logits, attentions = outputs
```
### Using hidden states
By enabling the configuration option `output_hidden_states`, it was possible to retrieve the last hidden states of the encoder. In `pytorch-transformers` as well as `transformers` the return value has changed slightly: `all_hidden_states` now also includes the hidden state of the embeddings in addition to those of the encoding layers. This allows users to easily access the embeddings final state.
### Serialization
Breaking change in the `from_pretrained()`method:
Breaking change in the `from_pretrained()` method:
1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules.
2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead which can break derived model classes build based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/pytorch-transformers/pull/866) by forwarding the the model `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes.
2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead which can break derived model classes build based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/transformers/pull/866) by forwarding the the model `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes.
Also, 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 serialization method before.
......@@ -396,7 +519,7 @@ for batch in train_data:
loss.backward()
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
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps, t_total=num_total_steps) # PyTorch scheduler
### and used like this:
......@@ -411,4 +534,4 @@ for batch in train_data:
## Citation
At the moment, there is no paper associated to PyTorch-Transformers but we are working on preparing one. In the meantime, please include a mention of the library and a link to the present repository if you use this work in a published or open-source project.
At the moment, there is no paper associated with Transformers but we are working on preparing one. In the meantime, please include a mention of the library and a link to the present repository if you use this work in a published or open-source project.
docker/Dockerfile
View file @ d7395789
......@@ -2,6 +2,6 @@ FROM pytorch/pytorch:latest
RUN git clone https://github.com/NVIDIA/apex.git && cd apex && python setup.py install --cuda_ext --cpp_ext
RUN pip install pytorch_transformers
RUN pip install transformers
WORKDIR /workspace
\ No newline at end of file
docs/README.md
View file @ d7395789
......@@ -34,11 +34,11 @@ pip install recommonmark
## Building the documentation
Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the followig
Make sure that there is a symlink from the `example` file (in /examples) inside the source folder. Run the following
command to generate it:
```bash
ln -s ../../examples/README.md source/examples.md
ln -s ../../examples/README.md examples.md
```
Once you have setup `sphinx`, you can build the documentation by running the following command in the `/docs` folder:
......
docs/requirements.txt
View file @ d7395789
......@@ -26,4 +26,7 @@ sphinxcontrib-jsmath==1.0.1
sphinxcontrib-qthelp==1.0.2
sphinxcontrib-serializinghtml==1.1.3
urllib3==1.25.3
sphinx-markdown-tables==0.0.9
\ No newline at end of file
sphinx-markdown-tables==0.0.9
numpy==1.17.2
tensorflow==2.0.0rc2
torch==1.2.0
\ No newline at end of file
docs/source/_static/js/custom.js
View file @ d7395789
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const image = document.createElement("img");
image.setAttribute("src", huggingFaceLogo);
......@@ -9,14 +9,14 @@ function addIcon() {
div.style.paddingTop = '30px';
div.style.backgroundColor = '#6670FF';
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questionOrIssue.innerHTML = "Stuck? Read our <a href='https://medium.com/huggingface'>Blog posts</a> or <a href='https://github.com/huggingface/transformers'>Create an issue</a>";
customFooter.appendChild(questionOrIssue);
customFooter.classList.add("footer");
......@@ -24,10 +24,10 @@ function addCustomFooter() {
social.classList.add("footer__Social");
const imageDetails = [
{ link: "https://huggingface.co", imageLink: "http://lysand.re/icons/website.svg" },
{ link: "https://twitter.com/huggingface", imageLink: "http://lysand.re/icons/twitter.svg" },
{ link: "https://github.com/huggingface", imageLink: "http://lysand.re/icons/github.svg" },
{ link: "https://www.linkedin.com/company/huggingface/", imageLink: "http://lysand.re/icons/linkedin.svg" }
{ link: "https://huggingface.co", imageLink: "https://huggingface.co/assets/transformers-docs/website.svg" },
{ link: "https://twitter.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/twitter.svg" },
{ link: "https://github.com/huggingface", imageLink: "https://huggingface.co/assets/transformers-docs/github.svg" },
{ link: "https://www.linkedin.com/company/huggingface/", imageLink: "https://huggingface.co/assets/transformers-docs/linkedin.svg" }
];
imageDetails.forEach(imageLinks => {
......@@ -42,13 +42,38 @@ function addCustomFooter() {
});
customFooter.appendChild(social);
document.getElementsByTagName("footer")[0].appendChild(customFooter);
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docs/source/bertology.rst
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......@@ -15,4 +15,4 @@ In order to help this new field develop, we have included a few additional featu
* accessing all the attention weights for each head of BERT/GPT/GPT-2,
* retrieving heads output values and gradients to be able to compute head importance score and prune head as explained in https://arxiv.org/abs/1905.10650.
To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
To help you understand and use these features, we have added a specific example script: `bertology.py <https://github.com/huggingface/transformers/blob/master/examples/run_bertology.py>`_ while extract information and prune a model pre-trained on GLUE.
docs/source/conf.py
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......@@ -19,14 +19,14 @@ sys.path.insert(0, os.path.abspath('../..'))
# -- Project information -----------------------------------------------------
project = u'pytorch-transformers'
project = u'transformers'
copyright = u'2019, huggingface'
author = u'huggingface'
# The short X.Y version
version = u''
# The full version, including alpha/beta/rc tags
release = u'1.2.0'
release = u'2.0.0'
# -- General configuration ---------------------------------------------------
......@@ -109,7 +109,7 @@ html_static_path = ['_static']
# -- Options for HTMLHelp output ---------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = 'pytorch-transformersdoc'
htmlhelp_basename = 'transformersdoc'
# -- Options for LaTeX output ------------------------------------------------
......@@ -136,7 +136,7 @@ latex_elements = {
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'pytorch-transformers.tex', u'pytorch-transformers Documentation',
(master_doc, 'transformers.tex', u'transformers Documentation',
u'huggingface', 'manual'),
]
......@@ -146,7 +146,7 @@ latex_documents = [
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'pytorch-transformers', u'pytorch-transformers Documentation',
(master_doc, 'transformers', u'transformers Documentation',
[author], 1)
]
......@@ -157,8 +157,8 @@ man_pages = [
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'pytorch-transformers', u'pytorch-transformers Documentation',
author, 'pytorch-transformers', 'One line description of project.',
(master_doc, 'transformers', u'transformers Documentation',
author, 'transformers', 'One line description of project.',
'Miscellaneous'),
]
......
docs/source/converting_tensorflow_models.rst
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......@@ -6,7 +6,7 @@ A command-line interface is provided to convert original Bert/GPT/GPT-2/Transfor
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>`_\ ).
......@@ -20,7 +20,7 @@ Here is an example of the conversion process for a pre-trained ``BERT-Base Uncas
export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
pytorch_transformers bert \
transformers bert \
$BERT_BASE_DIR/bert_model.ckpt \
$BERT_BASE_DIR/bert_config.json \
$BERT_BASE_DIR/pytorch_model.bin
......@@ -36,7 +36,7 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT model,
export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
pytorch_transformers gpt \
transformers gpt \
$OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
$PYTORCH_DUMP_OUTPUT \
[OPENAI_GPT_CONFIG]
......@@ -50,7 +50,7 @@ Here is an example of the conversion process for a pre-trained OpenAI GPT-2 mode
export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights
pytorch_transformers gpt2 \
transformers gpt2 \
$OPENAI_GPT2_CHECKPOINT_PATH \
$PYTORCH_DUMP_OUTPUT \
[OPENAI_GPT2_CONFIG]
......@@ -64,7 +64,7 @@ Here is an example of the conversion process for a pre-trained Transformer-XL mo
export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint
pytorch_transformers transfo_xl \
transformers transfo_xl \
$TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
$PYTORCH_DUMP_OUTPUT \
[TRANSFO_XL_CONFIG]
......@@ -80,7 +80,7 @@ Here is an example of the conversion process for a pre-trained XLNet model, fine
export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
pytorch_transformers xlnet \
transformers xlnet \
$TRANSFO_XL_CHECKPOINT_PATH \
$TRANSFO_XL_CONFIG_PATH \
$PYTORCH_DUMP_OUTPUT \
......@@ -96,6 +96,6 @@ Here is an example of the conversion process for a pre-trained XLM model:
export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
pytorch_transformers xlm \
transformers xlm \
$XLM_CHECKPOINT_PATH \
$PYTORCH_DUMP_OUTPUT \
docs/source/examples.md 0 → 120000
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../../examples/README.md
\ No newline at end of file
docs/source/index.rst
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Pytorch-Transformers
Transformers
================================================================================================================================================
PyTorch-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:
This is the documentation of our repository `transformers <https://github.com/huggingface/transformers>`__.
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.
......@@ -12,7 +46,8 @@ 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.
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.
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 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
......@@ -37,6 +72,7 @@ The library currently contains PyTorch implementations, pre-trained model weight
main_classes/model
main_classes/tokenizer
main_classes/optimizer_schedules
main_classes/processors
.. toctree::
:maxdepth: 2
......
docs/source/installation.rst
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Installation
================================================
PyTorch-Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
Transformers is tested on Python 2.7 and 3.5+ (examples are tested only on python 3.5+) and PyTorch 1.1.0
With pip
^^^^^^^^
......@@ -10,7 +10,7 @@ PyTorch Transformers can be installed using pip as follows:
.. code-block:: bash
pip install pytorch-transformers
pip install transformers
From source
^^^^^^^^^^^
......@@ -19,15 +19,15 @@ To install from source, clone the repository and install with:
.. code-block:: bash
git clone https://github.com/huggingface/pytorch-transformers.git
cd pytorch-transformers
git clone https://github.com/huggingface/transformers.git
cd transformers
pip install [--editable] .
Tests
^^^^^
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`).
......@@ -35,7 +35,7 @@ Run all the tests from the root of the cloned repository with the commands:
.. code-block:: bash
python -m pytest -sv ./pytorch_transformers/tests/
python -m pytest -sv ./transformers/tests/
python -m pytest -sv ./examples/
......
docs/source/main_classes/configuration.rst
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......@@ -6,5 +6,5 @@ The base class ``PretrainedConfig`` implements the common methods for loading/sa
``PretrainedConfig``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: pytorch_transformers.PretrainedConfig
.. autoclass:: transformers.PretrainedConfig
:members:
docs/source/main_classes/model.rst
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......@@ -11,5 +11,11 @@ The base class ``PreTrainedModel`` implements the common methods for loading/sav
``PreTrainedModel``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: pytorch_transformers.PreTrainedModel
.. autoclass:: transformers.PreTrainedModel
:members:
``TFPreTrainedModel``
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFPreTrainedModel
:members:
docs/source/main_classes/optimizer_schedules.rst
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......@@ -9,7 +9,7 @@ The ``.optimization`` module provides:
``AdamW``
~~~~~~~~~~~~~~~~
.. autoclass:: pytorch_transformers.AdamW
.. autoclass:: transformers.AdamW
:members:
Schedules
......@@ -18,11 +18,11 @@ Schedules
Learning Rate Schedules
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. autoclass:: pytorch_transformers.ConstantLRSchedule
.. autoclass:: transformers.ConstantLRSchedule
:members:
.. autoclass:: pytorch_transformers.WarmupConstantSchedule
.. autoclass:: transformers.WarmupConstantSchedule
:members:
.. image:: /imgs/warmup_constant_schedule.png
......@@ -30,7 +30,7 @@ Learning Rate Schedules
:alt:
.. autoclass:: pytorch_transformers.WarmupCosineSchedule
.. autoclass:: transformers.WarmupCosineSchedule
:members:
.. image:: /imgs/warmup_cosine_schedule.png
......@@ -38,7 +38,7 @@ Learning Rate Schedules
:alt:
.. autoclass:: pytorch_transformers.WarmupCosineWithHardRestartsSchedule
.. autoclass:: transformers.WarmupCosineWithHardRestartsSchedule
:members:
.. image:: /imgs/warmup_cosine_hard_restarts_schedule.png
......@@ -47,7 +47,7 @@ Learning Rate Schedules
.. autoclass:: pytorch_transformers.WarmupLinearSchedule
.. autoclass:: transformers.WarmupLinearSchedule
:members:
.. image:: /imgs/warmup_linear_schedule.png
......
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Processors
----------------------------------------------------
This library includes processors for several traditional tasks. These processors can be used to process a dataset into
examples that can be fed to a model.
Processors
~~~~~~~~~~~~~~~~~~~~~
All processors follow the same architecture which is that of the
:class:`~transformers.data.processors.utils.DataProcessor`. The processor returns a list
of :class:`~transformers.data.processors.utils.InputExample`. These
:class:`~transformers.data.processors.utils.InputExample` can be converted to
:class:`~transformers.data.processors.utils.InputFeatures` in order to be fed to the model.
.. autoclass:: transformers.data.processors.utils.DataProcessor
:members:
.. autoclass:: transformers.data.processors.utils.InputExample
:members:
.. autoclass:: transformers.data.processors.utils.InputFeatures
:members:
GLUE
~~~~~~~~~~~~~~~~~~~~~
`General Language Understanding Evaluation (GLUE) <https://gluebenchmark.com/>`__ is a benchmark that evaluates
the performance of models across a diverse set of existing NLU tasks. It was released together with the paper
`GLUE: A multi-task benchmark and analysis platform for natural language understanding <https://openreview.net/pdf?id=rJ4km2R5t7>`__
This library hosts a total of 10 processors for the following tasks: MRPC, MNLI, MNLI (mismatched),
CoLA, SST2, STSB, QQP, QNLI, RTE and WNLI.
Those processors are:
- :class:`~transformers.data.processors.utils.MrpcProcessor`
- :class:`~transformers.data.processors.utils.MnliProcessor`
- :class:`~transformers.data.processors.utils.MnliMismatchedProcessor`
- :class:`~transformers.data.processors.utils.Sst2Processor`
- :class:`~transformers.data.processors.utils.StsbProcessor`
- :class:`~transformers.data.processors.utils.QqpProcessor`
- :class:`~transformers.data.processors.utils.QnliProcessor`
- :class:`~transformers.data.processors.utils.RteProcessor`
- :class:`~transformers.data.processors.utils.WnliProcessor`
Additionally, the following method can be used to load values from a data file and convert them to a list of
:class:`~transformers.data.processors.utils.InputExample`.
.. automethod:: transformers.data.processors.glue.glue_convert_examples_to_features
Example usage
^^^^^^^^^^^^^^^^^^^^^^^^^
An example using these processors is given in the
`run_glue.py <https://github.com/huggingface/pytorch-transformers/blob/master/examples/run_glue.py>`__ script.
\ No newline at end of file
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