Conditional random field in PyTorch
Conditional random field in PyTorch.
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)
Computing log likelihood
>>> model(emissions, tags) tensor(-12.7431, grad_fn=<SumBackward0>)
Computing log likelihood with mask
>>> 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]]
Decoding with mask
>>> 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 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 .
Setup pre-commit hook
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.
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