forked from opengaussexamples/examples
124 lines
5.1 KiB
Python
124 lines
5.1 KiB
Python
# Copyright (c) 2022 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 ElectraConfig, ElectraModel, ElectraPretrainedModel
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from transformers import ElectraConfig
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from paddlenlp.transformers import ElectraModel, ElectraPretrainedModel
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class ElectraForBinaryTokenClassification(ElectraPretrainedModel):
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"""
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Electra Model with two linear layers on top of the hidden-states output layers,
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designed for token classification tasks with nesting.
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Args:
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electra (:class:`ElectraModel`):
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An instance of ElectraModel.
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num_classes (list):
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The number of classes.
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dropout (float, optionl):
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The dropout probability for output of Electra.
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If None, use the same value as `hidden_dropout_prob' of 'ElectraModel`
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instance `electra`. Defaults to None.
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"""
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def __init__(self, config: ElectraConfig, num_classes_oth, num_classes_sym):
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super(ElectraForBinaryTokenClassification, self).__init__(config)
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self.num_classes_oth = num_classes_oth
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self.num_classes_sym = num_classes_sym
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self.electra = ElectraModel(config)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.classifier_oth = nn.Linear(config.hidden_size, self.num_classes_oth)
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self.classifier_sym = nn.Linear(config.hidden_size, self.num_classes_sym)
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def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
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sequence_output = self.electra(input_ids, token_type_ids, position_ids, attention_mask)
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sequence_output = self.dropout(sequence_output)
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logits_sym = self.classifier_sym(sequence_output)
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logits_oth = self.classifier_oth(sequence_output)
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return logits_oth, logits_sym
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class MultiHeadAttentionForSPO(nn.Layer):
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"""
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Multi-head attention layer for SPO task.
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"""
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def __init__(self, embed_dim, num_heads, scale_value=768):
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super(MultiHeadAttentionForSPO, self).__init__()
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.scale_value = scale_value**-0.5
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self.q_proj = nn.Linear(embed_dim, embed_dim * num_heads)
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self.k_proj = nn.Linear(embed_dim, embed_dim * num_heads)
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def forward(self, query, key):
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q = self.q_proj(query)
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k = self.k_proj(key)
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q = paddle.reshape(q, shape=[0, 0, self.num_heads, self.embed_dim])
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k = paddle.reshape(k, shape=[0, 0, self.num_heads, self.embed_dim])
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q = paddle.transpose(q, perm=[0, 2, 1, 3])
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k = paddle.transpose(k, perm=[0, 2, 1, 3])
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scores = paddle.matmul(q, k, transpose_y=True)
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scores = paddle.scale(scores, scale=self.scale_value)
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return scores
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class ElectraForSPO(ElectraPretrainedModel):
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"""
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Electra Model with a linear layer on top of the hidden-states output
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layers for entity recognition, and a multi-head attention layer for
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relation classification.
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Args:
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electra (:class:`ElectraModel`):
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An instance of ElectraModel.
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num_classes (int):
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The number of classes.
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dropout (float, optionl):
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The dropout probability for output of Electra.
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If None, use the same value as `hidden_dropout_prob' of 'ElectraModel`
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instance `electra`. Defaults to None.
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"""
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def __init__(self, config: ElectraConfig):
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super(ElectraForSPO, self).__init__(config)
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self.num_classes = config.num_labels
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self.electra = ElectraModel(config)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.classifier = nn.Linear(config.hidden_size, 2)
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self.span_attention = MultiHeadAttentionForSPO(config.hidden_size, config.num_labels)
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def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
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outputs = self.electra(
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input_ids, token_type_ids, position_ids, attention_mask, output_hidden_states=True, return_dict=True
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)
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sequence_outputs = outputs.last_hidden_state
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all_hidden_states = outputs.hidden_states
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sequence_outputs = self.dropout(sequence_outputs)
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ent_logits = self.classifier(sequence_outputs)
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subject_output = all_hidden_states[-2]
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cls_output = paddle.unsqueeze(sequence_outputs[:, 0, :], axis=1)
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subject_output = subject_output + cls_output
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output_size = self.num_classes + self.electra.config["hidden_size"] # noqa:F841
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rel_logits = self.span_attention(sequence_outputs, subject_output)
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return ent_logits, rel_logits
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