Conditional random field in PyTorch.

This package provides an implementation of conditional random field (CRF) in PyTorch. This implementation borrows mostly from AllenNLP CRF module with some modifications.

• Python 3.6
• PyTorch 1.0.0

You can install with pip

pip install pytorch-crf

Or, you can install from Github directly

pip install git+https://github.com/kmkurn/pytorch-crf#egg=pytorch_crf

In the examples below, we will assume that these lines have been executed

>>> import torch
>>> from torchcrf import CRF
>>> seq_length, batch_size, num_tags = 3, 2, 5
>>> emissions = torch.randn(seq_length, batch_size, num_tags)
>>> tags = torch.tensor([
...   [0, 1], [2, 4], [3, 1]
... ], dtype=torch.long)  # (seq_length, batch_size)
>>> model = CRF(num_tags)

>>> model(emissions, tags)
tensor(-12.7431, grad_fn=<SumBackward0>)

>>> mask = torch.tensor([
...   [1, 1], [1, 1], [1, 0]
... ], dtype=torch.uint8)  # (seq_length, batch_size)
>>> model(emissions, tags, mask=mask)
tensor(-10.8390, grad_fn=<SumBackward0>)

>>> model.decode(emissions)
[[3, 1, 3], [0, 1, 0]]

>>> model.decode(emissions, mask=mask)
[[3, 1, 3], [0, 1]]

See tests/test_crf.py for more examples.

MIT. See LICENSE for details.

Contributions are welcome! Please follow these instructions to install dependencies and running the tests and linter. Make a pull request to develop branch once your contribution is ready.

Make sure you setup a virtual environment with Python and PyTorch installed. Then, install all the dependencies in requirements.txt file and install this package in development mode.

pip install -r requirements.txt
pip install -e .

Simply run

ln -s ../../pre-commit.sh .git/hooks/pre-commit

Run pytest in the project root directory.

Run flake8 in the project root directory. This will also run mypy, thanks to flake8-mypy package.