You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
66 lines
2.4 KiB
66 lines
2.4 KiB
3 years ago
|
# 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
|
||
|
|
|
||
|
|
|
||
|
|
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_classes=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)
|
||
|
|
|
||
|
|
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
|