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hlt-mt / FBK-fairseq

Repository containing the open source code of works published at the FBK MT unit.
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readme

FBK-fairseq

This repository contains the open source code by the MT unit of FBK. Dedicated README for each work can be found in the fbk_works directory.

2024

2021

If using this repository, please acknowledge the related paper(s) citing them. Bibtex citations are available for each work in the dedicated README file.

Installation

To install the repository, do:

pip install -e .
pip install -r speech_requirements.txt  # required for speech translation

Below, there is the original Fairseq README file.



MIT License Latest Release Build Status Documentation Status

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.

We provide reference implementations of various sequence modeling papers:

List of implemented papers

* **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/README.adaptive_inputs.md) + [Lexically constrained decoding with dynamic beam allocation (Post & Vilar, 2018)](examples/constrained_decoding/README.md) + [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context (Dai et al., 2019)](examples/truncated_bptt/README.md) + [Adaptive Attention Span in Transformers (Sukhbaatar et al., 2019)](examples/adaptive_span/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) + [Unsupervised Quality Estimation for Neural Machine Translation (Fomicheva et al., 2020)](examples/unsupervised_quality_estimation/README.md) + [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations (Baevski et al., 2020)](examples/wav2vec/README.md) + [Generating Medical Reports from Patient-Doctor Conversations Using Sequence-to-Sequence Models (Enarvi et al., 2020)](examples/pointer_generator/README.md) + [Linformer: Self-Attention with Linear Complexity (Wang et al., 2020)](examples/linformer/README.md) + [Cross-lingual Retrieval for Iterative Self-Supervised Training (Tran et al., 2020)](examples/criss/README.md) + [Deep Transformers with Latent Depth (Li et al., 2020)](examples/latent_depth/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) * **Finetuning** + [Better Fine-Tuning by Reducing Representational Collapse (Aghajanyan et al. 2020)](examples/rxf/README.md)

What's New:

Previous updates

* May 2020: [Follow fairseq on Twitter](https://twitter.com/fairseq) * April 2020: [Monotonic Multihead Attention code released](examples/simultaneous_translation/README.md) * April 2020: [Quant-Noise code released](examples/quant_noise/README.md) * 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:

We also provide pre-trained models for translation and language modeling with a convenient torch.hub interface:

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 and RoBERTa for more examples.

Requirements and Installation

git clone https://github.com/pytorch/fairseq
cd fairseq
pip install --editable ./

# on MacOS:
# CFLAGS="-stdlib=libc++" pip install --editable ./

# to install the latest stable release (0.10.0)
# pip install fairseq==0.10.0
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" ./

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.

We also have more detailed READMEs to reproduce results from specific papers:

Join the fairseq community

License

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

Citation

Please cite as:

@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},
}