Natural Language Processing

PyTorch-NLP – library of neural network layers, text processing modules and datasets

PyTorch-NLP, or torchnlp for short, is a library of neural network layers, text processing modules and datasets designed to accelerate Natural Language Processing (NLP) research.

It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping.

PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. PyTorch-NLP also provides neural network modules and metrics.

The software is released under an open source license.


  • torchnlp.datasets – introduces modules capable of downloading, caching and loading commonly used NLP datasets.
  • torchnlp.word_to_vector – introduces multiple pretrained word vectors. The package handles downloading, caching, loading, and lookup.
  • torchnlp.nn – introduces a set of torch.nn.Module commonly used in NLP.
  • torchnlp.encoders – supports encoding objects as a vector torch.Tensor and decoding a vector torch.Tensor back.
  • torchnlp.samplers – introduces a set of samplers. Samplers sample elements from a dataset. torchnlp.samplers plug into and
  • torchnlp.metrics – introduces a set of modules able to compute common NLP metrics.
  • torchnlp.utils – contains any other module or object that is useful in building out a NLP pipeline.
  • – contains modules useful for donwload and extracting datasets.

Support: Documentation
Developer: Michael Petrochuk and contributors
License: BSD 3-Clause “New” or “Revised” License

PyTorch-NLP is written in Python. Learn Python with our recommended free books and free tutorials.

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