Fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Fairseq(-py) is a sequence modeling toolkit written in Python and developed at Facebook’s AI Research. This toolkit allows AI researchers and developers to train customized models for translation, summarization, language modeling, and other text generation tasks. It provides reference implementations of various sequence-to-sequence models, including Long Short-Term Memory (LSTM) networks and a novel convolutional neural network (CNN). This can increase the speed for generating translations than comparable recurrent neural network (RNN) models.

Features

Fairseq toolkit provides reference implementations of various sequence-to-sequence models, including:

  • Convolutional Neural Networks (CNN)
  • LightConv and DynamicConv models
  • Long Short-Term Memory (LSTM) networks
  • Transformer (self-attention) networks
  • Non-autoregressive Transformers
  • multi-GPU (distributed) training on one machine or across multiple machines

Github: https://github.com/pytorch/fairseq

Installation: https://ai.facebook.com/tools/fairseq/

Paper: https://arxiv.org/pdf/1904.01038.pdf

Video: https://www.youtube.com/watch?v=OtgDdWtHvto

Installation

Requirements

Install PyTorch.

Install fairseq-py.

pip install fairseq

On MacOS:

CFLAGS="-stdlib=libc++" pip install fairseq

Full Documentation: https://fairseq.readthedocs.io/en/latest/

https://www.youtube.com/watch?v=OtgDdWtHvto

Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.

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