pytorch-partial-crf
Partial/Fuzzy conditional random field in PyTorch.
Document: https://pytorch-partial-crf.readthedocs.io/
How to use
Install
pip install pytorch-partial-crfUse 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 negative 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 negative log likelihood
model(emissions, tags)Use Marginal CRF
import torch
from pytorch_partial_crf import MarginalCRF
# Create
num_tags = 6
model = MarginalCRF(num_tags)
batch_size, sequence_length = 3, 5
emissions = torch.randn(batch_size, sequence_length, num_tags)
# Set probability tags
marginal_tags = torch.Tensor([
[
[0.2, 0.2, 0.2, 0.1, 0.1, 0.2],
[0.8, 0.0, 0.0, 0.1, 0.1, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
[0.3, 0.0, 0.0, 0.1, 0.6, 0.0],
],
[
[0.2, 0.2, 0.2, 0.1, 0.1, 0.2],
[0.8, 0.0, 0.0, 0.1, 0.1, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
[0.3, 0.0, 0.0, 0.1, 0.6, 0.0],
],
[
[0.2, 0.2, 0.2, 0.1, 0.1, 0.2],
[0.8, 0.0, 0.0, 0.1, 0.1, 0.0],
[0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
[0.3, 0.0, 0.0, 0.1, 0.6, 0.0],
],
])
# Computing negative log likelihood
model(emissions, marginal_tags)Decoding
Viterbi decode
model.viterbi_decode(emissions)
Restricted viterbi decode
possible_tags = torch.randn(batch_size, sequence_length, num_tags)
possible_tags[possible_tags <= 0] = 0 # `0` express that can not pass.
possible_tags[possible_tags > 0] = 1 # `1` express that can pass.
possible_tags = possible_tags.byte()
model.restricted_viterbi_decode(emissions, possible_tags)Marginal probabilities
model.marginal_probabilities(emissions)Contributing
We welcome contributions! Please post your requests and comments on Issue.
License
MIT
References
The implementation is based on AllenNLP CRF module and pytorch-crf.