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75 lines
2.6 KiB
75 lines
2.6 KiB
3 years ago
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# 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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import paddle.nn.initializer as I
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from paddlenlp.transformers import BertForTokenClassification
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class BertBLSTMPunc(nn.Layer):
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def __init__(self,
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pretrained_token="bert-large-uncased",
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output_size=4,
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dropout=0.0,
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bert_size=768,
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blstm_size=128,
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num_blstm_layers=2,
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init_scale=0.1):
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super(BertBLSTMPunc, 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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self.lstm = nn.LSTM(
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input_size=bert_size,
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hidden_size=blstm_size,
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num_layers=num_blstm_layers,
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direction="bidirect",
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weight_ih_attr=paddle.ParamAttr(initializer=I.Uniform(
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low=-init_scale, high=init_scale)),
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weight_hh_attr=paddle.ParamAttr(initializer=I.Uniform(
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low=-init_scale, high=init_scale)))
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# NOTE dense*2 使用bert中间层 dense hidden_state self.bert_size
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self.dropout = nn.Dropout(dropout)
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self.fc = nn.Linear(blstm_size * 2, 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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y, (_, _) = self.lstm(x)
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# print('after lstm :', y.shape)
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y = self.fc(self.dropout(y))
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y = paddle.reshape(y, shape=[-1, self.output_size])
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# print('after fc :', y.shape)
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logit = self.softmax(y)
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# print('after softmax :', logit.shape)
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return y, logit
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if __name__ == '__main__':
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print('start model')
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model = BertBLSTMPunc()
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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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