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Conditional random field in PyTorch

Project Description

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.

NOTE: This software is still in alpha version; every minor version change introduces backward incompatibility.

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)
>>> # Initialize model parameters
... for p in model.parameters():
...    _ = torch.nn.init.uniform(p, -1, 1)
...
>>>

Forward computation

>>> model(emissions, tags)
Variable containing:
-10.0635
[torch.FloatTensor of size 1]

Forward computation 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, 0]]

See tests/test_crf.py for more examples.

License

MIT. See LICENSE.txt 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.

Release History

Release History

This version
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0.4.0

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0.3.1

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0.3.0

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0.2.0

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0.1.1

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0.1.0

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pytorch_crf-0.4.0-py3-none-any.whl (11.1 kB) Copy SHA256 Checksum SHA256 py3 Wheel Dec 7, 2017

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