"Partial/Fuzzy Conditional random field in PyTorch."
Project description
pytorch-partial-crf
Partial/Fuzzy conditional random field in PyTorch.
How to use
Install
pip install pytorch-partial-crf
Use CRF
import torch
from pytorch_partial_crf import CRF
# Create
num_tags = 6
model = CRF(num_tags)
batch_size, sequence_length = 3, 5
emissions = torch.randn(batch_size, sequence_length, num_tags)
tags = torch.LongTensor([
[1, 2, 3, 3, 5],
[1, 3, 4, 2, 1],
[1, 0, 2, 4, 4],
])
# Computing log likelihood
model(emissions, tags)
Use partial CRF
import torch
from pytorch_partial_crf import PartialCRF
# Create
num_tags = 6
model = PartialCRF(num_tags)
batch_size, sequence_length = 3, 5
emissions = torch.randn(batch_size, sequence_length, num_tags)
# Set unknown tag to -1
tags = torch.LongTensor([
[1, 2, 3, 3, 5],
[-1, 3, 3, 2, -1],
[-1, 0, -1, -1, 4],
])
# Computing log likelihood
model(emissions, tags)
Use viterbi decode
import torch
from pytorch_partial_crf import CRF
from pytorch_partial_crf import PartialCRF
num_tags = 6
model = CRF(num_tags) # or FuzzyCRF
batch_size, sequence_length = 3, 5
emissions = torch.randn(batch_size, sequence_length, num_tags)
model.viterbi_decode(emissions)
# restricted viterbi decode
possible_tags = torch.randn(batch_size, sequence_length, num_tags)
possible_tags[possible_tags<=0] = 0
possible_tags[possible_tags>0] = 1
possible_tags = possible_tags.byte()
model.restricted_viterbi_decode(emissions, possible_tags)
License
MIT
References
The implementation is based on AllenNLP CRF module and pytorch-crf.
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