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PyTorch-NLP v0.4.0 Release Notes

Release Date: 2019-04-03 // over 4 years ago

  • ⚡️ Major updates

    • Rewrote encoders to better support more generic encoders like a LabelEncoder. Furthermore, added broad support for batch_encode, batch_decode and enforce_reversible.
    • 🔧 Rearchitected default reserved tokens to ensure configurability while still providing the convenience of good defaults.

    • ➕ Added support to collate sequences with torch.utils.data.dataloader.DataLoader. For example:

      from functools import partialfrom torchnlp.utils import collate_tensorsfrom torchnlp.encoders.text import stack_and_pad_tensors collate_fn = partial(collate_tensors, stack_tensors=stack_and_pad_tensors) torch.utils.data.dataloader.DataLoader(*args, collate_fn=collate_fn, **kwargs)

    • ➕ Added doctest support ensuring the documented examples are tested.

    • ✂ Removed SRU support, it's too heavy of a module to support. Please use https://github.com/taolei87/sru instead. Happy to accept a PR with a better tested and documented SRU module!

    • ⚡️ Update version requirements to support Python 3.6 and 3.7, dropping support for Python 3.5.

    • ⚡️ Updated version requirements to support PyTorch 1.0+.

    • 🔀 Merged #66 reducing the memory requirements for pre-trained word vectors by 2x.

    ⚡️ Minor Updates

    • Formatted the code base with YAPF.
    • 🛠 Fixed pandas and collections warnings.

    • ➕ Added invariant assertion to Encoder via enforce_reversible. For example:

      encoder = Encoder().enforce_reversible()

    Ensuring Encoder.decode(Encoder.encode(object)) == object

    • 🛠 Fixed the accuracy metric for PyTorch 1.0.