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| # -*- coding: utf-8 -*- | |
| # @Time : 2022/1/28 5:38 下午 | |
| # @Author : JianingWang | |
| # @File : semeval7.py | |
| import torch | |
| from torch import nn | |
| from torch.nn import CrossEntropyLoss, MSELoss | |
| from transformers.activations import ACT2FN | |
| from transformers.modeling_outputs import SequenceClassifierOutput | |
| from transformers.models.deberta_v2.modeling_deberta_v2 import ContextPooler, DebertaV2Model, DebertaV2PreTrainedModel, StableDropout | |
| class DebertaV2ForSemEval7MultiTask(DebertaV2PreTrainedModel): | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.deberta = DebertaV2Model(config) | |
| self.pooler = ContextPooler(config) | |
| output_dim = self.pooler.output_dim | |
| self.num_labels = 3 | |
| self.dense = nn.Linear(config.pooler_hidden_size*2, config.pooler_hidden_size) | |
| self.classifier = nn.Linear(output_dim, self.num_labels) | |
| self.regression = nn.Linear(output_dim, 1) | |
| drop_out = getattr(config, "cls_dropout", None) | |
| drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out | |
| self.dropout = StableDropout(drop_out) | |
| self.post_init() | |
| def get_input_embeddings(self): | |
| return self.deberta.get_input_embeddings() | |
| def set_input_embeddings(self, new_embeddings): | |
| self.deberta.set_input_embeddings(new_embeddings) | |
| def forward( | |
| self, | |
| input_ids=None, | |
| attention_mask=None, | |
| token_type_ids=None, | |
| position_ids=None, | |
| inputs_embeds=None, | |
| labels=None, | |
| output_attentions=None, | |
| output_hidden_states=None, | |
| return_dict=None, | |
| score=None | |
| ): | |
| r""" | |
| labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): | |
| Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., | |
| config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If | |
| `config.num_labels > 1` a classification loss is computed (Cross-Entropy). | |
| """ | |
| return_dict = return_dict if return_dict is not None else self.config.use_return_dict | |
| outputs = self.deberta( | |
| input_ids, | |
| token_type_ids=token_type_ids, | |
| attention_mask=attention_mask, | |
| position_ids=position_ids, | |
| inputs_embeds=inputs_embeds, | |
| output_attentions=output_attentions, | |
| output_hidden_states=output_hidden_states, | |
| return_dict=return_dict, | |
| ) | |
| w = torch.logical_and(input_ids >= min(self.config.start_token_ids), input_ids <= max(self.config.start_token_ids)) | |
| start_index = w.nonzero()[:, 1].view(-1, 2) | |
| # <start_entity> + <end_entity> 进分类 | |
| pooler_output = torch.cat([torch.cat([x[y[0], :], x[y[1], :]]).unsqueeze(0) for x, y in zip(outputs.last_hidden_state, start_index)]) | |
| # [CLS] + <start_entity> + <end_entity> 进分类 | |
| # pooler_output = torch.cat([torch.cat([z, x[y[0], :], x[y[1], :]]).unsqueeze(0) | |
| # for x, y, z in zip(outputs.last_hidden_state, start_index, outputs.last_hidden_state[:, 0])]) | |
| context_token = self.dropout(pooler_output) | |
| pooled_output = self.dense(context_token) | |
| pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output) | |
| pooled_output = self.dropout(pooled_output) | |
| re_logits = self.regression(pooled_output) | |
| cls_logits = self.classifier(pooled_output) | |
| loss = None | |
| if labels is not None: | |
| re_loss_func = MSELoss() | |
| re_loss = re_loss_func(re_logits.squeeze(), score.squeeze()) | |
| cls_loss_func = CrossEntropyLoss() | |
| cls_loss = cls_loss_func(cls_logits.view(-1, self.num_labels), labels.view(-1)) | |
| loss = re_loss + cls_loss | |
| return SequenceClassifierOutput( | |
| loss=loss, logits=torch.cat((cls_logits, re_logits), 1), hidden_states=outputs.hidden_states, attentions=outputs.attentions | |
| ) | |