wehub-resource-sync
70 lines
2.9 KiB
Python
70 lines
2.9 KiB
Python
# Copyright (c) 2020 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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class SentenceTransformer(nn.Layer):
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def __init__(self, pretrained_model, dropout=None):
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super().__init__()
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self.ptm = pretrained_model
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self.dropout = nn.Dropout(dropout if dropout is not None else 0.1)
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# num_labels = 2 (similar or dissimilar)
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self.classifier = nn.Linear(self.ptm.config["hidden_size"] * 3, 2)
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def forward(
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self,
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query_input_ids,
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title_input_ids,
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query_token_type_ids=None,
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query_position_ids=None,
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query_attention_mask=None,
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title_token_type_ids=None,
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title_position_ids=None,
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title_attention_mask=None,
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):
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query_token_embedding, _ = self.ptm(
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query_input_ids, query_token_type_ids, query_position_ids, query_attention_mask
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)
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query_token_embedding = self.dropout(query_token_embedding)
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query_attention_mask = paddle.unsqueeze(
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(query_input_ids != self.ptm.pad_token_id).astype(self.ptm.pooler.dense.weight.dtype), axis=2
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)
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# Set token embeddings to 0 for padding tokens
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query_token_embedding = query_token_embedding * query_attention_mask
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query_sum_embedding = paddle.sum(query_token_embedding, axis=1)
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query_sum_mask = paddle.sum(query_attention_mask, axis=1)
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query_mean = query_sum_embedding / query_sum_mask
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title_token_embedding, _ = self.ptm(
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title_input_ids, title_token_type_ids, title_position_ids, title_attention_mask
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)
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title_token_embedding = self.dropout(title_token_embedding)
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title_attention_mask = paddle.unsqueeze(
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(title_input_ids != self.ptm.pad_token_id).astype(self.ptm.pooler.dense.weight.dtype), axis=2
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)
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# Set token embeddings to 0 for padding tokens
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title_token_embedding = title_token_embedding * title_attention_mask
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title_sum_embedding = paddle.sum(title_token_embedding, axis=1)
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title_sum_mask = paddle.sum(title_attention_mask, axis=1)
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title_mean = title_sum_embedding / title_sum_mask
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sub = paddle.abs(paddle.subtract(query_mean, title_mean))
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projection = paddle.concat([query_mean, title_mean, sub], axis=-1)
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logits = self.classifier(projection)
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return logits
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