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66 lines
2.3 KiB
66 lines
2.3 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 os
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import paddle
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import paddle.nn as nn
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from paddlenlp.transformers import ErnieForTokenClassification
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class ErnieLinear(nn.Layer):
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def __init__(self,
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num_classes=None,
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pretrained_token='ernie-1.0',
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cfg_path=None,
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ckpt_path=None,
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**kwargs):
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super(ErnieLinear, self).__init__()
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if cfg_path is not None and ckpt_path is not None:
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cfg_path = os.path.abspath(os.path.expanduser(cfg_path))
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ckpt_path = os.path.abspath(os.path.expanduser(ckpt_path))
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assert os.path.isfile(
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cfg_path), 'Config file is not valid: {}'.format(cfg_path)
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assert os.path.isfile(
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ckpt_path), 'Checkpoint file is not valid: {}'.format(ckpt_path)
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self.ernie = ErnieForTokenClassification.from_pretrained(
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os.path.dirname(cfg_path))
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else:
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assert isinstance(
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num_classes, int
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) and num_classes > 0, 'Argument `num_classes` must be an integer.'
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self.ernie = ErnieForTokenClassification.from_pretrained(
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pretrained_token, num_classes=num_classes, **kwargs)
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self.num_classes = self.ernie.num_classes
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self.softmax = nn.Softmax()
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def forward(self,
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input_ids,
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token_type_ids=None,
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position_ids=None,
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attention_mask=None):
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y = self.ernie(
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input_ids,
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token_type_ids=token_type_ids,
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attention_mask=attention_mask,
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position_ids=position_ids)
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y = paddle.reshape(y, shape=[-1, self.num_classes])
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logits = self.softmax(y)
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return y, logits
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