pytorch-crf
Conditional random field in PyTorch.
Description
This package provides an implementation of conditional random field (CRF) in PyTorch. This implementation borrows mostly from AllenNLP CRF module with some modifications.
Requirements
- Python 3.6
- PyTorch 0.3.0
Installation
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
Examples
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.autograd.Variable(torch.randn(seq_length, batch_size, num_tags), requires_grad=True)
>>> tags = torch.autograd.Variable(torch.LongTensor([[0, 1], [2, 4], [3, 1]])) # (seq_length, batch_size)
>>> model = CRF(num_tags)Computing log likelihood
>>> model(emissions, tags)
Variable containing:
-10.0635
[torch.FloatTensor of size 1]Computing log likelihood with mask
>>> mask = torch.autograd.Variable(torch.ByteTensor([[1, 1], [1, 1], [1, 0]])) # (seq_length, batch_size)
>>> model(emissions, tags, mask=mask)
Variable containing:
-8.4981
[torch.FloatTensor of size 1]Decoding
>>> model.decode(emissions)
[[3, 1, 3], [0, 1, 0]]Decoding with mask
>>> model.decode(emissions, mask=mask)
[[3, 1, 3], [0, 1]]See tests/test_crf.py for more examples.
License
MIT. See LICENSE for details.
Contributing
Contributions are welcome! Please follow these instructions to setup dependencies and running the tests and linter. Make a pull request once your contribution is ready.
Installing dependencies
Make sure you setup a virtual environment with Python 3.6 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 .
Running tests
Run pytest in the project root directory.
Running linter
Run flake8 in the project root directory. This will also run mypy, thanks to flake8-mypy package.