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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
-
-
- class PointwiseMatching(nn.Layer):
- def __init__(self, pretrained_model, dropout=None):
- 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)
-
- def forward(self,
- input_ids,
- token_type_ids=None,
- position_ids=None,
- attention_mask=None):
-
- _, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids,
- attention_mask)
-
- cls_embedding = self.dropout(cls_embedding)
- logits = self.classifier(cls_embedding)
-
- return logits
-
-
- class PairwiseMatching(nn.Layer):
- def __init__(self, pretrained_model, dropout=None, margin=0.1):
- super().__init__()
- self.ptm = pretrained_model
- self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
- self.margin = margin
-
- # hidden_size -> 1, calculate similarity
- self.similarity = nn.Linear(self.ptm.config["hidden_size"], 1)
-
- def predict(self,
- input_ids,
- token_type_ids=None,
- position_ids=None,
- attention_mask=None):
-
- _, cls_embedding = self.ptm(input_ids, token_type_ids, position_ids,
- attention_mask)
-
- cls_embedding = self.dropout(cls_embedding)
- sim_score = self.similarity(cls_embedding)
- sim_score = F.sigmoid(sim_score)
-
- return sim_score
-
- def forward(self,
- pos_input_ids,
- neg_input_ids,
- pos_token_type_ids=None,
- neg_token_type_ids=None,
- pos_position_ids=None,
- neg_position_ids=None,
- pos_attention_mask=None,
- neg_attention_mask=None):
-
- _, pos_cls_embedding = self.ptm(pos_input_ids, pos_token_type_ids,
- pos_position_ids, pos_attention_mask)
-
- _, neg_cls_embedding = self.ptm(neg_input_ids, neg_token_type_ids,
- neg_position_ids, neg_attention_mask)
-
- pos_embedding = self.dropout(pos_cls_embedding)
- neg_embedding = self.dropout(neg_cls_embedding)
-
- pos_sim = self.similarity(pos_embedding)
- neg_sim = self.similarity(neg_embedding)
-
- pos_sim = F.sigmoid(pos_sim)
- neg_sim = F.sigmoid(neg_sim)
-
- labels = paddle.full(
- shape=[pos_cls_embedding.shape[0]], fill_value=1.0, dtype='float32')
-
- loss = F.margin_ranking_loss(
- pos_sim, neg_sim, labels, margin=self.margin)
-
- return loss
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