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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
)
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