<p align="center">
  <img src="fairseq_logo.png" width="150">
  <br />
  <br />
  <a href="https://github.com/pytorch/fairseq/blob/master/LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-blue.svg" /></a>
  <a href="https://github.com/pytorch/fairseq/releases"><img alt="Latest Release" src="https://img.shields.io/github/release/pytorch/fairseq.svg" /></a>
  <a href="https://github.com/pytorch/fairseq/actions?query=workflow:build"><img alt="Build Status" src="https://github.com/pytorch/fairseq/workflows/build/badge.svg" /></a>
  <a href="https://fairseq.readthedocs.io/en/latest/?badge=latest"><img alt="Documentation Status" src="https://readthedocs.org/projects/fairseq/badge/?version=latest" /></a>
</p>

--------------------------------------------------------------------------------

Fairseq(-py) is a sequence modeling toolkit that allows researchers and
developers to train custom models for translation, summarization, language
modeling and other text generation tasks.

### What's New:

- April 2020: [Initial model parallel support and 11B parameters unidirectional LM released](examples/megatron_11b/README.md)
- March 2020: [Byte-level BPE code released](examples/byte_level_bpe/README.md)
- February 2020: [mBART model and code released](examples/mbart/README.md)
- February 2020: [Added tutorial for back-translation](https://github.com/pytorch/fairseq/tree/master/examples/backtranslation#training-your-own-model-wmt18-english-german)
- December 2019: [fairseq 0.9.0 released](https://github.com/pytorch/fairseq/releases/tag/v0.9.0)
- November 2019: [VizSeq released (a visual analysis toolkit for evaluating fairseq models)](https://facebookresearch.github.io/vizseq/docs/getting_started/fairseq_example)
- November 2019: [CamemBERT model and code released](examples/camembert/README.md)
- November 2019: [BART model and code released](examples/bart/README.md)
- November 2019: [XLM-R models and code released](examples/xlmr/README.md)
- September 2019: [Nonautoregressive translation code released](examples/nonautoregressive_translation/README.md)
- August 2019: [WMT'19 models released](examples/wmt19/README.md)
- July 2019: fairseq relicensed under MIT license
- July 2019: [RoBERTa models and code released](examples/roberta/README.md)
- June 2019: [wav2vec models and code released](examples/wav2vec/README.md)

### Features:

Fairseq provides reference implementations of various sequence-to-sequence models, including:
- **Convolutional Neural Networks (CNN)**
  - [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md)
  - [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md)
  - [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel)
  - [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md)
  - [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)
- **LightConv and DynamicConv models**
  - [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md)
- **Long Short-Term Memory (LSTM) networks**
  - Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015)
- **Transformer (self-attention) networks**
  - Attention Is All You Need (Vaswani et al., 2017)
  - [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md)
  - [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md)
  - [Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)](examples/language_model/transformer_lm/README.md)
  - [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md)
  - [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md)
  - [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md)
  - [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md )
  - [Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)](examples/mbart/README.md)
  - [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md)
- **Non-autoregressive Transformers**
  - Non-Autoregressive Neural Machine Translation (Gu et al., 2017)
  - Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018)
  - Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019)
  - Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)
  - [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md)


**Additionally:**
- multi-GPU (distributed) training on one machine or across multiple machines
- fast generation on both CPU and GPU with multiple search algorithms implemented:
  - beam search
  - Diverse Beam Search ([Vijayakumar et al., 2016](https://arxiv.org/abs/1610.02424))
  - sampling (unconstrained, top-k and top-p/nucleus)
- large mini-batch training even on a single GPU via delayed updates
- mixed precision training (trains faster with less GPU memory on [NVIDIA tensor cores](https://developer.nvidia.com/tensor-cores))
- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers

We also provide [pre-trained models for translation and language modeling](#pre-trained-models-and-examples)
with a convenient `torch.hub` interface:
```python
en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de.single_model')
en2de.translate('Hello world', beam=5)
# 'Hallo Welt'
```
See the PyTorch Hub tutorials for [translation](https://pytorch.org/hub/pytorch_fairseq_translation/)
and [RoBERTa](https://pytorch.org/hub/pytorch_fairseq_roberta/) for more examples.

![Model](fairseq.gif)

# Requirements and Installation

* [PyTorch](http://pytorch.org/) version >= 1.4.0
* Python version >= 3.6
* For training new models, you'll also need an NVIDIA GPU and [NCCL](https://github.com/NVIDIA/nccl)
* **For faster training** install NVIDIA's [apex](https://github.com/NVIDIA/apex) library:
```bash
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--deprecated_fused_adam" --global-option="--xentropy" --global-option="--fast_multihead_attn" ./
```

To install fairseq:
```bash
pip install fairseq
```

On MacOS:
```bash
CFLAGS="-stdlib=libc++" pip install fairseq
```

If you use Docker make sure to increase the shared memory size either with
`--ipc=host` or `--shm-size` as command line options to `nvidia-docker run`.

**Installing from source**

To install fairseq from source and develop locally:
```bash
git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable .
```

# Getting Started

The [full documentation](https://fairseq.readthedocs.io/) contains instructions
for getting started, training new models and extending fairseq with new model
types and tasks.

# Pre-trained models and examples

We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below,
as well as example training and evaluation commands.

- [Translation](examples/translation/README.md): convolutional and transformer models are available
- [Language Modeling](examples/language_model/README.md): convolutional and transformer models are available
- [wav2vec](examples/wav2vec/README.md): wav2vec large model is available

We also have more detailed READMEs to reproduce results from specific papers:
- [Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)](examples/byte_level_bpe/README.md)
- [Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)](examples/joint_alignment_translation/README.md )
- [Levenshtein Transformer (Gu et al., 2019)](examples/nonautoregressive_translation/README.md)
- [Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)](examples/wmt19/README.md)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)](examples/roberta/README.md)
- [wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)](examples/wav2vec/README.md)
- [Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)](examples/translation_moe/README.md)
- [Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)](examples/pay_less_attention_paper/README.md)
- [Understanding Back-Translation at Scale (Edunov et al., 2018)](examples/backtranslation/README.md)
- [Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)](https://github.com/pytorch/fairseq/tree/classic_seqlevel)
- [Hierarchical Neural Story Generation (Fan et al., 2018)](examples/stories/README.md)
- [Scaling Neural Machine Translation (Ott et al., 2018)](examples/scaling_nmt/README.md)
- [Convolutional Sequence to Sequence Learning (Gehring et al., 2017)](examples/conv_seq2seq/README.md)
- [Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)](examples/language_model/conv_lm/README.md)

# Join the fairseq community

* Facebook page: https://www.facebook.com/groups/fairseq.users
* Google group: https://groups.google.com/forum/#!forum/fairseq-users

# License
fairseq(-py) is MIT-licensed.
The license applies to the pre-trained models as well.

# Citation

Please cite as:

```bibtex
@inproceedings{ott2019fairseq,
  title = {fairseq: A Fast, Extensible Toolkit for Sequence Modeling},
  author = {Myle Ott and Sergey Edunov and Alexei Baevski and Angela Fan and Sam Gross and Nathan Ng and David Grangier and Michael Auli},
  booktitle = {Proceedings of NAACL-HLT 2019: Demonstrations},
  year = {2019},
}
```

arxiv:1610.02424 region:us
{}
TAGS
#arxiv-1610.02424 #region-us 

<p align="center">
  <img src="fairseq_logo.png" width="150">
  <br />
  <br />
  <a href="URL alt="MIT License" src="URL /></a>
  <a href="URL alt="Latest Release" src="URL /></a>
  <a href="URL alt="Build Status" src="URL /></a>
  <a href="URL alt="Documentation Status" src="URL /></a>
</p>

--------------------------------------------------------------------------------

Fairseq(-py) is a sequence modeling toolkit that allows researchers and
developers to train custom models for translation, summarization, language
modeling and other text generation tasks.

### What's New:

- April 2020: Initial model parallel support and 11B parameters unidirectional LM released
- March 2020: Byte-level BPE code released
- February 2020: mBART model and code released
- February 2020: Added tutorial for back-translation
- December 2019: fairseq 0.9.0 released
- November 2019: VizSeq released (a visual analysis toolkit for evaluating fairseq models)
- November 2019: CamemBERT model and code released
- November 2019: BART model and code released
- November 2019: XLM-R models and code released
- September 2019: Nonautoregressive translation code released
- August 2019: WMT'19 models released
- July 2019: fairseq relicensed under MIT license
- July 2019: RoBERTa models and code released
- June 2019: wav2vec models and code released

### Features:

Fairseq provides reference implementations of various sequence-to-sequence models, including:
- Convolutional Neural Networks (CNN)
  - Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
  - Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
  - Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
  - Hierarchical Neural Story Generation (Fan et al., 2018)
  - wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
- LightConv and DynamicConv models
  - Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
- Long Short-Term Memory (LSTM) networks
  - Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015)
- Transformer (self-attention) networks
  - Attention Is All You Need (Vaswani et al., 2017)
  - Scaling Neural Machine Translation (Ott et al., 2018)
  - Understanding Back-Translation at Scale (Edunov et al., 2018)
  - Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)
  - Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
  - RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
  - Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
  - Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
  - Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)
  - Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Non-autoregressive Transformers
  - Non-Autoregressive Neural Machine Translation (Gu et al., 2017)
  - Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018)
  - Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019)
  - Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)
  - Levenshtein Transformer (Gu et al., 2019)


Additionally:
- multi-GPU (distributed) training on one machine or across multiple machines
- fast generation on both CPU and GPU with multiple search algorithms implemented:
  - beam search
  - Diverse Beam Search (Vijayakumar et al., 2016)
  - sampling (unconstrained, top-k and top-p/nucleus)
- large mini-batch training even on a single GPU via delayed updates
- mixed precision training (trains faster with less GPU memory on NVIDIA tensor cores)
- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers

We also provide pre-trained models for translation and language modeling
with a convenient 'URL' interface:

See the PyTorch Hub tutorials for translation
and RoBERTa for more examples.

!Model

# Requirements and Installation

* PyTorch version >= 1.4.0
* Python version >= 3.6
* For training new models, you'll also need an NVIDIA GPU and NCCL
* For faster training install NVIDIA's apex library:


To install fairseq:


On MacOS:


If you use Docker make sure to increase the shared memory size either with
'--ipc=host' or '--shm-size' as command line options to 'nvidia-docker run'.

Installing from source

To install fairseq from source and develop locally:


# Getting Started

The full documentation contains instructions
for getting started, training new models and extending fairseq with new model
types and tasks.

# Pre-trained models and examples

We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below,
as well as example training and evaluation commands.

- Translation: convolutional and transformer models are available
- Language Modeling: convolutional and transformer models are available
- wav2vec: wav2vec large model is available

We also have more detailed READMEs to reproduce results from specific papers:
- Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
- Levenshtein Transformer (Gu et al., 2019)
- Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
- RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
- wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
- Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
- Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
- Understanding Back-Translation at Scale (Edunov et al., 2018)
- Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
- Hierarchical Neural Story Generation (Fan et al., 2018)
- Scaling Neural Machine Translation (Ott et al., 2018)
- Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
- Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)

# Join the fairseq community

* Facebook page: URL
* Google group: URL

# License
fairseq(-py) is MIT-licensed.
The license applies to the pre-trained models as well.

Please cite as:



### What's New:

- April 2020: Initial model parallel support and 11B parameters unidirectional LM released
- March 2020: Byte-level BPE code released
- February 2020: mBART model and code released
- February 2020: Added tutorial for back-translation
- December 2019: fairseq 0.9.0 released
- November 2019: VizSeq released (a visual analysis toolkit for evaluating fairseq models)
- November 2019: CamemBERT model and code released
- November 2019: BART model and code released
- November 2019: XLM-R models and code released
- September 2019: Nonautoregressive translation code released
- August 2019: WMT'19 models released
- July 2019: fairseq relicensed under MIT license
- July 2019: RoBERTa models and code released
- June 2019: wav2vec models and code released ### Features:

Fairseq provides reference implementations of various sequence-to-sequence models, including:
- Convolutional Neural Networks (CNN)
  - Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
  - Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
  - Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
  - Hierarchical Neural Story Generation (Fan et al., 2018)
  - wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
- LightConv and DynamicConv models
  - Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
- Long Short-Term Memory (LSTM) networks
  - Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015)
- Transformer (self-attention) networks
  - Attention Is All You Need (Vaswani et al., 2017)
  - Scaling Neural Machine Translation (Ott et al., 2018)
  - Understanding Back-Translation at Scale (Edunov et al., 2018)
  - Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)
  - Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
  - RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
  - Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
  - Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
  - Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)
  - Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Non-autoregressive Transformers
  - Non-Autoregressive Neural Machine Translation (Gu et al., 2017)
  - Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018)
  - Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019)
  - Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)
  - Levenshtein Transformer (Gu et al., 2019)


Additionally:
- multi-GPU (distributed) training on one machine or across multiple machines
- fast generation on both CPU and GPU with multiple search algorithms implemented:
  - beam search
  - Diverse Beam Search (Vijayakumar et al., 2016)
  - sampling (unconstrained, top-k and top-p/nucleus)
- large mini-batch training even on a single GPU via delayed updates
- mixed precision training (trains faster with less GPU memory on NVIDIA tensor cores)
- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers

We also provide pre-trained models for translation and language modeling
with a convenient 'URL' interface:

See the PyTorch Hub tutorials for translation
and RoBERTa for more examples.

!Model # Requirements and Installation

* PyTorch version >= 1.4.0
* Python version >= 3.6
* For training new models, you'll also need an NVIDIA GPU and NCCL
* For faster training install NVIDIA's apex library:


To install fairseq:


On MacOS:


If you use Docker make sure to increase the shared memory size either with
'--ipc=host' or '--shm-size' as command line options to 'nvidia-docker run'.

Installing from source

To install fairseq from source and develop locally: # Getting Started

The full documentation contains instructions
for getting started, training new models and extending fairseq with new model
types and tasks. # Pre-trained models and examples

We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below,
as well as example training and evaluation commands.

- Translation: convolutional and transformer models are available
- Language Modeling: convolutional and transformer models are available
- wav2vec: wav2vec large model is available

We also have more detailed READMEs to reproduce results from specific papers:
- Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
- Levenshtein Transformer (Gu et al., 2019)
- Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
- RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
- wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
- Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
- Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
- Understanding Back-Translation at Scale (Edunov et al., 2018)
- Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
- Hierarchical Neural Story Generation (Fan et al., 2018)
- Scaling Neural Machine Translation (Ott et al., 2018)
- Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
- Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017) # Join the fairseq community

* Facebook page: URL
* Google group: URL # License
fairseq(-py) is MIT-licensed.
The license applies to the pre-trained models as well.

Please cite as:
TAGS
#arxiv-1610.02424 #region-us 
 ### What's New:

- April 2020: Initial model parallel support and 11B parameters unidirectional LM released
- March 2020: Byte-level BPE code released
- February 2020: mBART model and code released
- February 2020: Added tutorial for back-translation
- December 2019: fairseq 0.9.0 released
- November 2019: VizSeq released (a visual analysis toolkit for evaluating fairseq models)
- November 2019: CamemBERT model and code released
- November 2019: BART model and code released
- November 2019: XLM-R models and code released
- September 2019: Nonautoregressive translation code released
- August 2019: WMT'19 models released
- July 2019: fairseq relicensed under MIT license
- July 2019: RoBERTa models and code released
- June 2019: wav2vec models and code released ### Features:

Fairseq provides reference implementations of various sequence-to-sequence models, including:
- Convolutional Neural Networks (CNN)
  - Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
  - Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
  - Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
  - Hierarchical Neural Story Generation (Fan et al., 2018)
  - wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
- LightConv and DynamicConv models
  - Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
- Long Short-Term Memory (LSTM) networks
  - Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015)
- Transformer (self-attention) networks
  - Attention Is All You Need (Vaswani et al., 2017)
  - Scaling Neural Machine Translation (Ott et al., 2018)
  - Understanding Back-Translation at Scale (Edunov et al., 2018)
  - Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)
  - Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
  - RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
  - Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
  - Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
  - Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)
  - Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Non-autoregressive Transformers
  - Non-Autoregressive Neural Machine Translation (Gu et al., 2017)
  - Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018)
  - Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019)
  - Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)
  - Levenshtein Transformer (Gu et al., 2019)


Additionally:
- multi-GPU (distributed) training on one machine or across multiple machines
- fast generation on both CPU and GPU with multiple search algorithms implemented:
  - beam search
  - Diverse Beam Search (Vijayakumar et al., 2016)
  - sampling (unconstrained, top-k and top-p/nucleus)
- large mini-batch training even on a single GPU via delayed updates
- mixed precision training (trains faster with less GPU memory on NVIDIA tensor cores)
- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers

We also provide pre-trained models for translation and language modeling
with a convenient 'URL' interface:

See the PyTorch Hub tutorials for translation
and RoBERTa for more examples.

!Model # Requirements and Installation

* PyTorch version >= 1.4.0
* Python version >= 3.6
* For training new models, you'll also need an NVIDIA GPU and NCCL
* For faster training install NVIDIA's apex library:


To install fairseq:


On MacOS:


If you use Docker make sure to increase the shared memory size either with
'--ipc=host' or '--shm-size' as command line options to 'nvidia-docker run'.

Installing from source

To install fairseq from source and develop locally: # Getting Started

The full documentation contains instructions
for getting started, training new models and extending fairseq with new model
types and tasks. # Pre-trained models and examples

We provide pre-trained models and pre-processed, binarized test sets for several tasks listed below,
as well as example training and evaluation commands.

- Translation: convolutional and transformer models are available
- Language Modeling: convolutional and transformer models are available
- wav2vec: wav2vec large model is available

We also have more detailed READMEs to reproduce results from specific papers:
- Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
- Levenshtein Transformer (Gu et al., 2019)
- Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
- RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
- wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
- Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
- Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
- Understanding Back-Translation at Scale (Edunov et al., 2018)
- Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
- Hierarchical Neural Story Generation (Fan et al., 2018)
- Scaling Neural Machine Translation (Ott et al., 2018)
- Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
- Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017) # Join the fairseq community

* Facebook page: URL
* Google group: URL # License
fairseq(-py) is MIT-licensed.
The license applies to the pre-trained models as well.

Please cite as:
passage: TAGS
#arxiv-1610.02424 #region-us 
### What's New:

- April 2020: Initial model parallel support and 11B parameters unidirectional LM released
- March 2020: Byte-level BPE code released
- February 2020: mBART model and code released
- February 2020: Added tutorial for back-translation
- December 2019: fairseq 0.9.0 released
- November 2019: VizSeq released (a visual analysis toolkit for evaluating fairseq models)
- November 2019: CamemBERT model and code released
- November 2019: BART model and code released
- November 2019: XLM-R models and code released
- September 2019: Nonautoregressive translation code released
- August 2019: WMT'19 models released
- July 2019: fairseq relicensed under MIT license
- July 2019: RoBERTa models and code released
- June 2019: wav2vec models and code released passage: ### Features:

Fairseq provides reference implementations of various sequence-to-sequence models, including:
- Convolutional Neural Networks (CNN)
  - Language Modeling with Gated Convolutional Networks (Dauphin et al., 2017)
  - Convolutional Sequence to Sequence Learning (Gehring et al., 2017)
  - Classical Structured Prediction Losses for Sequence to Sequence Learning (Edunov et al., 2018)
  - Hierarchical Neural Story Generation (Fan et al., 2018)
  - wav2vec: Unsupervised Pre-training for Speech Recognition (Schneider et al., 2019)
- LightConv and DynamicConv models
  - Pay Less Attention with Lightweight and Dynamic Convolutions (Wu et al., 2019)
- Long Short-Term Memory (LSTM) networks
  - Effective Approaches to Attention-based Neural Machine Translation (Luong et al., 2015)
- Transformer (self-attention) networks
  - Attention Is All You Need (Vaswani et al., 2017)
  - Scaling Neural Machine Translation (Ott et al., 2018)
  - Understanding Back-Translation at Scale (Edunov et al., 2018)
  - Adaptive Input Representations for Neural Language Modeling (Baevski and Auli, 2018)
  - Mixture Models for Diverse Machine Translation: Tricks of the Trade (Shen et al., 2019)
  - RoBERTa: A Robustly Optimized BERT Pretraining Approach (Liu et al., 2019)
  - Facebook FAIR's WMT19 News Translation Task Submission (Ng et al., 2019)
  - Jointly Learning to Align and Translate with Transformer Models (Garg et al., 2019)
  - Multilingual Denoising Pre-training for Neural Machine Translation (Liu et at., 2020)
  - Neural Machine Translation with Byte-Level Subwords (Wang et al., 2020)
- Non-autoregressive Transformers
  - Non-Autoregressive Neural Machine Translation (Gu et al., 2017)
  - Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (Lee et al. 2018)
  - Insertion Transformer: Flexible Sequence Generation via Insertion Operations (Stern et al. 2019)
  - Mask-Predict: Parallel Decoding of Conditional Masked Language Models (Ghazvininejad et al., 2019)
  - Levenshtein Transformer (Gu et al., 2019)


Additionally:
- multi-GPU (distributed) training on one machine or across multiple machines
- fast generation on both CPU and GPU with multiple search algorithms implemented:
  - beam search
  - Diverse Beam Search (Vijayakumar et al., 2016)
  - sampling (unconstrained, top-k and top-p/nucleus)
- large mini-batch training even on a single GPU via delayed updates
- mixed precision training (trains faster with less GPU memory on NVIDIA tensor cores)
- extensible: easily register new models, criterions, tasks, optimizers and learning rate schedulers

We also provide pre-trained models for translation and language modeling
with a convenient 'URL' interface:

See the PyTorch Hub tutorials for translation
and RoBERTa for more examples.

!Model# Requirements and Installation

* PyTorch version >= 1.4.0
* Python version >= 3.6
* For training new models, you'll also need an NVIDIA GPU and NCCL
* For faster training install NVIDIA's apex library:


To install fairseq:


On MacOS:


If you use Docker make sure to increase the shared memory size either with
'--ipc=host' or '--shm-size' as command line options to 'nvidia-docker run'.

Installing from source

To install fairseq from source and develop locally:# Getting Started

The full documentation contains instructions
for getting started, training new models and extending fairseq with new model
types and tasks.