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64 lines
2.2 KiB
64 lines
2.2 KiB
# 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.transformers import BertForTokenClassification
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class BertLinearPunc(nn.Layer):
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def __init__(self,
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pretrained_token="bert-base-uncased",
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output_size=4,
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dropout=0.2,
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bert_size=768,
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hiddensize=1568):
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super(BertLinearPunc, self).__init__()
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self.output_size = output_size
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self.bert = BertForTokenClassification.from_pretrained(
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pretrained_token, num_classes=bert_size)
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# self.bert_vocab_size = vocab_size
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# self.bn = nn.BatchNorm1d(segment_size*self.bert_vocab_size)
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# self.fc = nn.Linear(segment_size*self.bert_vocab_size, output_size)
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# NOTE dense*2 使用bert中间层 dense hidden_state self.bert_size
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self.dropout1 = nn.Dropout(dropout)
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self.fc1 = nn.Linear(bert_size, hiddensize)
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self.dropout2 = nn.Dropout(dropout)
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self.relu = nn.ReLU()
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self.fc2 = nn.Linear(hiddensize, output_size)
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self.softmax = nn.Softmax()
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def forward(self, x):
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# print('input :', x.shape)
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x = self.bert(x) #[0]
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# print('after bert :', x.shape)
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x = self.fc1(self.dropout1(x))
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x = self.fc2(self.relu(self.dropout2(x)))
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x = paddle.reshape(x, shape=[-1, self.output_size])
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# print('after fc :', x.shape)
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logit = self.softmax(x)
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# print('after softmax :', logit.shape)
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return x, logit
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if __name__ == '__main__':
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print('start model')
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model = BertLinearPunc()
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x = paddle.randint(low=0, high=40, shape=[2, 5])
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print(x)
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y, logit = model(x)
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