TorchCRF 1.0.0

Implementation of Conditional Random Fields in pytorch

Copyright (c) 2018 Ryuya Ikeda

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Description: # Torch CRF

Implementation of CRF (Conditional Random Fields) in PyTorch 1.0

## Requirements

• python3 (>=3.6)

• PyTorch 1.0

## Installation

$ pip install TorchCRF

## Usage

`python >>> import torch >>> from TorchCRF import CRF >>> batch_size = 2 >>> sequence_size = 3 >>> num_labels = 5 >>> mask = torch.FloatTensor([[1, 1, 1], [1, 1, 0]]) # (batch_size. sequence_size) >>> labels = torch.LongTensor([[0, 2, 3], [1, 4, 1]]) # (batch_size, sequence_size) >>> hidden = torch.randn((batch_size, sequence_size, num_labels), requires_grad=True) >>> crf = CRF(num_labels) `

### Computing log-likelihood (used where forward)

`python >>> crf.forward(hidden, labels, mask) tensor([-7.6204, -3.6124], grad_fn=<ThSubBackward>) `

### Decoding (predict labels of sequences)

`python >>> crf.viterbi_decode(hidden, mask) [[0, 2, 2], [4, 0]] `

## License

MIT

## References

• [threelittlemonkeys/lstm-crf-pytorch](https://github.com/threelittlemonkeys/lstm-crf-pytorch)

• [kmkurn/pytorch-crf](https://github.com/kmkurn/pytorch-crf)

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