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- # Copyright (c) 2020 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 SentenceTransformer(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"] * 3, 2)
-
- def forward(self,
- query_input_ids,
- title_input_ids,
- query_token_type_ids=None,
- query_position_ids=None,
- query_attention_mask=None,
- title_token_type_ids=None,
- title_position_ids=None,
- title_attention_mask=None):
- query_token_embedding, _ = self.ptm(
- query_input_ids, query_token_type_ids, query_position_ids,
- query_attention_mask)
- query_token_embedding = self.dropout(query_token_embedding)
- query_attention_mask = paddle.unsqueeze(
- (query_input_ids != self.ptm.pad_token_id
- ).astype(self.ptm.pooler.dense.weight.dtype),
- axis=2)
- # Set token embeddings to 0 for padding tokens
- query_token_embedding = query_token_embedding * query_attention_mask
- query_sum_embedding = paddle.sum(query_token_embedding, axis=1)
- query_sum_mask = paddle.sum(query_attention_mask, axis=1)
- query_mean = query_sum_embedding / query_sum_mask
-
- title_token_embedding, _ = self.ptm(
- title_input_ids, title_token_type_ids, title_position_ids,
- title_attention_mask)
- title_token_embedding = self.dropout(title_token_embedding)
- title_attention_mask = paddle.unsqueeze(
- (title_input_ids != self.ptm.pad_token_id
- ).astype(self.ptm.pooler.dense.weight.dtype),
- axis=2)
- # Set token embeddings to 0 for padding tokens
- title_token_embedding = title_token_embedding * title_attention_mask
- title_sum_embedding = paddle.sum(title_token_embedding, axis=1)
- title_sum_mask = paddle.sum(title_attention_mask, axis=1)
- title_mean = title_sum_embedding / title_sum_mask
-
- sub = paddle.abs(paddle.subtract(query_mean, title_mean))
- projection = paddle.concat([query_mean, title_mean, sub], axis=-1)
-
- logits = self.classifier(projection)
-
- return logits
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