PyTorch CRF with N-best Decoding
Implementation of Conditional Random Fields (CRF) in PyTorch 1.0. It supports top-N most probable paths decoding.
The package is based on pytorch-crf with only the following differences
- Method
_viterbi_decodethat decodes the most probable path get optimized. Running time gets reduced to 50% or less with batch size 15+ and sequence length 20+ - The class now supports decoding top-N most probable paths through the implementation of the method
_viterbi_decode_nbest
Requirements
- Python 3 (>= 3.6)
- PyTorch (>= 1.0)
Installation
pip install pytorchcrfExamples
>>> import torch
>>> from pytorchcrf import CRF
>>> num_tags = 5 # number of tags is 5
>>> model = CRF(num_tags)
>>> 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)
# Computing log likelihood
>>> tags = torch.tensor([[2, 3], [1, 0], [3, 4]], dtype=torch.long) # (seq_length, batch_size)
>>> model(emissions, tags)
# Decoding
>>> model.decode(emissions) # decoding the best path
>>> model.decode(emissions, nbest=3) # decoding the top 3 paths
