TorchCRF 1.0.5

An Implementation of Conditional Random Fields in pytorch

Torch CRF

Implementation of CRF (Conditional Random Fields) in PyTorch

Requirements

• python3 (>=3.6)
• PyTorch (>=1.0)

Installation

`$ pip install TorchCRF`

Usage

```python
>>> import torch
>>> from TorchCRF import CRF
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> batch_size = 2
>>> sequence_size = 3
>>> num_labels = 5
>>> mask = torch.ByteTensor([[1, 1, 1], [1, 1, 0]]).to(device) # (batch_size. sequence_size)
>>> labels = torch.LongTensor([[0, 2, 3], [1, 4, 1]]).to(device)  # (batch_size, sequence_size)
>>> hidden = torch.randn((batch_size, sequence_size, num_labels), requires_grad=True).to(device)
>>> crf = CRF(num_labels)
```

Computing log-likelihood (used where forward)

```python
>>> crf.forward(hidden, labels, mask)
tensor([-7.6204, -3.6124], device='cuda:0', 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
• kmkurn/pytorch-crf