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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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from paddlenlp.layers.crf import LinearChainCrf
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from paddlenlp.layers.crf import LinearChainCrfLoss
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from paddlenlp.layers.crf import ViterbiDecoder
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from paddlenlp.transformers import ErnieForTokenClassification
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class ErnieCrf(nn.Layer):
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def __init__(self,
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num_classes,
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pretrained_token='ernie-1.0',
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crf_lr=100,
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**kwargs):
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super().__init__()
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self.ernie = ErnieForTokenClassification.from_pretrained(
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pretrained_token, num_labels=num_classes, **kwargs)
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self.num_classes = num_classes
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self.crf = LinearChainCrf(
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self.num_classes, crf_lr=crf_lr, with_start_stop_tag=False)
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self.crf_loss = LinearChainCrfLoss(self.crf)
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self.viterbi_decoder = ViterbiDecoder(
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self.crf.transitions, with_start_stop_tag=False)
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def forward(self,
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input_ids,
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token_type_ids=None,
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position_ids=None,
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attention_mask=None,
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lengths=None,
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labels=None):
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logits = self.ernie(
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input_ids,
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token_type_ids=token_type_ids,
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attention_mask=attention_mask,
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position_ids=position_ids)
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if lengths is None:
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lengths = paddle.ones(
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shape=[input_ids.shape[0]],
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dtype=paddle.int64) * input_ids.shape[1]
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_, prediction = self.viterbi_decoder(logits, lengths)
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prediction = prediction.reshape([-1])
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if labels is not None:
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labels = labels.reshape([input_ids.shape[0], -1])
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loss = self.crf_loss(logits, lengths, labels)
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avg_loss = paddle.mean(loss)
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return avg_loss, prediction
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else:
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return prediction
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