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README.md

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+<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},
+}
+```

wmt14_en2de_cased_bin.zip

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