fairseq
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Getting Started

  • Evaluating Pre-trained Models
  • Training a New Model
  • Advanced Training Options
  • Command-line Tools

Extending Fairseq

  • Overview
  • Tutorial: Simple LSTM
  • Tutorial: Classifying Names with a Character-Level RNN

Library Reference

  • Tasks
  • Models
  • Criterions
  • Optimizers
  • Learning Rate Schedulers
  • Data Loading and Utilities
  • Modules
fairseq
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fairseq documentation¶

Fairseq is a sequence modeling toolkit written in PyTorch that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

Getting Started

  • Evaluating Pre-trained Models
  • Training a New Model
  • Advanced Training Options
  • Command-line Tools

Extending Fairseq

  • Overview
  • Tutorial: Simple LSTM
  • Tutorial: Classifying Names with a Character-Level RNN

Library Reference

  • Tasks
    • Translation
    • Language Modeling
    • Adding new tasks
  • Models
    • Convolutional Neural Networks (CNN)
    • Long Short-Term Memory (LSTM) networks
    • Transformer (self-attention) networks
    • Adding new models
    • Incremental decoding
  • Criterions
  • Optimizers
  • Learning Rate Schedulers
  • Data Loading and Utilities
    • Datasets
    • Dictionary
    • Iterators
  • Modules

Indices and tables¶

  • Index
  • Search Page
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