# 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
from paddlenlp.layers.crf import LinearChainCrfLoss
from paddlenlp.layers.crf import ViterbiDecoder
from paddlenlp.transformers import ErnieForTokenClassification
[docs]class ErnieCrf(nn.Layer):
def __init__(self,
num_classes,
pretrained_token='ernie-1.0',
crf_lr=100,
**kwargs):
super().__init__()
self.ernie = ErnieForTokenClassification.from_pretrained(
pretrained_token, num_labels=num_classes, **kwargs)
self.num_classes = num_classes
self.crf = LinearChainCrf(
self.num_classes, crf_lr=crf_lr, with_start_stop_tag=False)
self.crf_loss = LinearChainCrfLoss(self.crf)
self.viterbi_decoder = ViterbiDecoder(
self.crf.transitions, with_start_stop_tag=False)
[docs] def forward(self,
input_ids,
token_type_ids=None,
position_ids=None,
attention_mask=None,
lengths=None,
labels=None):
logits = self.ernie(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
position_ids=position_ids)
if lengths is None:
lengths = paddle.ones(
shape=[input_ids.shape[0]],
dtype=paddle.int64) * input_ids.shape[1]
_, prediction = self.viterbi_decoder(logits, lengths)
prediction = prediction.reshape([-1])
if labels is not None:
labels = labels.reshape([input_ids.shape[0], -1])
loss = self.crf_loss(logits, lengths, labels)
avg_loss = paddle.mean(loss)
return avg_loss, prediction
else:
return prediction