Conditional random fields in PyTorch.
This package provides an implementation of a conditional random fields (CRF) layer in PyTorch. The implementation borrows mostly from AllenNLP CRF module with some modifications.
.. toctree:: :maxdepth: 2
- Python 3.6
- PyTorch 1.0.0
Install with pip:
pip install pytorch-crf
Or, install from Github for the latest version:
pip install git+https://github.com/kmkurn/pytorch-crf#egg=pytorch_crf
.. currentmodule:: torchcrf
pytorch-crf exposes a single CRF class which inherits from PyTorch's nn.Module <torch.nn.Module>. This class provides an implementation of a CRF layer.
>>> import torch
>>> from torchcrf import CRF
>>> num_tags = 5 # number of tags is 5
>>> model = CRF(num_tags)Once created, you can compute the log likelihood of a sequence of tags given some emission scores.
>>> seq_length = 3 # maximum sequence length in a batch
>>> batch_size = 2 # number of samples in the batch
>>> 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(emissions, tags)
tensor(-12.7431, grad_fn=<SumBackward0>)If you have some padding in your input tensors, you can pass a mask tensor.
>>> # mask size is (seq_length, batch_size)
>>> # the last sample has length of 1
>>> mask = torch.tensor([
... [1, 1], [1, 1], [1, 0]
... ], dtype=torch.uint8)
>>> model(emissions, tags, mask=mask)
tensor(-10.8390, grad_fn=<SumBackward0>)Note that the returned value is the log likelihood so you'll need to make this value negative as your loss. By default, the log likelihood is summed over batches. For other options, consult the API documentation of CRF.forward.
To obtain the most probable sequence of tags, use the CRF.decode method.
>>> model.decode(emissions)
[[3, 1, 3], [0, 1, 0]]This method also accepts a mask tensor, see CRF.decode for details.
.. autoclass:: torchcrf.CRF :members: