Welcome to pytorch-partial-crf’s documentation!¶
Getting started¶
This package provides an implementation of a Partial/Fuzzy CRF layer for learning incompleted tag sequences, and a linear-chain CRF layer for learning tag sequences.
import torch
from pytorch_partial_crf import PartialCRF
num_tags = 6 # number of tags is 6
model = PartialCRF(num_tags)
Computing log likelihood¶
batch_size = 3
sequence_length = 5
emissions = torch.randn(batch_size, sequence_length, num_tags)
# Set to -1 if it is unknown tag
tags = torch.LongTensor([
[1, 2, 3, 3, 5],
[-1, 3, -1, 2, 1],
[1, 0, -1, 4, -1],
]) # (seq_length, batch_size)
model(emissions, tags) # Computing log likelihood
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)