# coding:utf-8
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.nn as nn
from paddlenlp.layers.crf import LinearChainCrf, LinearChainCrfLoss
try:
from paddle.text import ViterbiDecoder
except Exception:
raise ImportError(
"Taskflow requires paddle version >= 2.2.0, but current paddle version is {}".format(
paddle.version.full_version
)
)
[文档]class BiGruCrf(nn.Layer):
"""The network for lexical analysis, based on two layers of BiGRU and one layer of CRF. More details see https://arxiv.org/abs/1807.01882
Args:
word_emb_dim (int): The dimension in which a word is embedded.
hidden_size (int): The number of hidden nodes in the GRU layer.
vocab_size (int): the word vocab size.
num_labels (int): the labels amount.
emb_lr (float, optional): The scaling of the learning rate of the embedding layer. Defaults to 2.0.
crf_lr (float, optional): The scaling of the learning rate of the crf layer. Defaults to 0.2.
"""
def __init__(
self, word_emb_dim, hidden_size, vocab_size, num_labels, emb_lr=2.0, crf_lr=0.2, with_start_stop_tag=True
):
super(BiGruCrf, self).__init__()
self.word_emb_dim = word_emb_dim
self.vocab_size = vocab_size
self.num_labels = num_labels
self.hidden_size = hidden_size
self.emb_lr = emb_lr
self.crf_lr = crf_lr
self.init_bound = 0.1
self.word_embedding = nn.Embedding(
num_embeddings=self.vocab_size,
embedding_dim=self.word_emb_dim,
weight_attr=paddle.ParamAttr(
learning_rate=self.emb_lr,
initializer=nn.initializer.Uniform(low=-self.init_bound, high=self.init_bound),
),
)
self.gru = nn.GRU(
input_size=self.word_emb_dim,
hidden_size=self.hidden_size,
num_layers=2,
direction="bidirectional",
weight_ih_attr=paddle.ParamAttr(
initializer=nn.initializer.Uniform(low=-self.init_bound, high=self.init_bound),
regularizer=paddle.regularizer.L2Decay(coeff=1e-4),
),
weight_hh_attr=paddle.ParamAttr(
initializer=nn.initializer.Uniform(low=-self.init_bound, high=self.init_bound),
regularizer=paddle.regularizer.L2Decay(coeff=1e-4),
),
)
self.fc = nn.Linear(
in_features=self.hidden_size * 2,
out_features=self.num_labels + 2 if with_start_stop_tag else self.num_labels,
weight_attr=paddle.ParamAttr(
initializer=nn.initializer.Uniform(low=-self.init_bound, high=self.init_bound),
regularizer=paddle.regularizer.L2Decay(coeff=1e-4),
),
)
self.crf = LinearChainCrf(self.num_labels, self.crf_lr, with_start_stop_tag)
self.crf_loss = LinearChainCrfLoss(self.crf)
self.viterbi_decoder = ViterbiDecoder(self.crf.transitions, with_start_stop_tag)
[文档] def forward(self, inputs, lengths, labels=None):
word_embed = self.word_embedding(inputs)
bigru_output, _ = self.gru(word_embed, sequence_length=lengths)
emission = self.fc(bigru_output)
if labels is not None:
loss = self.crf_loss(emission, lengths, labels)
return loss
else:
_, prediction = self.viterbi_decoder(emission, lengths)
return prediction
