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- # 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
- import paddle.nn.functional as F
-
- import paddlenlp as ppnlp
-
-
- class QuestionMatching(nn.Layer):
- def __init__(self, pretrained_model, dropout=None, rdrop_coef=0.0):
- super().__init__()
- self.ptm = pretrained_model
- self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
-
- # num_labels = 2 (similar or dissimilar)
- self.classifier = nn.Linear(self.ptm.config["hidden_size"], 2)
- self.rdrop_coef = rdrop_coef
- self.rdrop_loss = ppnlp.losses.RDropLoss()
-
- def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None, do_evaluate=False):
-
- _, cls_embedding1 = self.ptm(input_ids, token_type_ids, position_ids, attention_mask)
- cls_embedding1 = self.dropout(cls_embedding1)
- logits1 = self.classifier(cls_embedding1)
-
- # For more information about R-drop please refer to this paper: https://arxiv.org/abs/2106.14448
- # Original implementation please refer to this code: https://github.com/dropreg/R-Drop
- if self.rdrop_coef > 0 and not do_evaluate:
- _, cls_embedding2 = self.ptm(input_ids, token_type_ids, position_ids, attention_mask)
- cls_embedding2 = self.dropout(cls_embedding2)
- logits2 = self.classifier(cls_embedding2)
- kl_loss = self.rdrop_loss(logits1, logits2)
- else:
- kl_loss = 0.0
-
- return logits1, kl_loss
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