Upload 2 files

models/adversarial.py

@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2022/1/7 11:02 上午
+# @Author  : JianingWang
+# @File    : adversarial.py
+import torch
+
+
+class FGM:
+    def __init__(self, model):
+        self.model = model
+        self.backup = {}
+
+    def attack(self, epsilon=1., emb_name="word_embeddings"):
+        # emb_name这个参数要换成你模型中embedding的参数名
+        for name, param in self.model.named_parameters():
+            if param.requires_grad and emb_name in name:
+                self.backup[name] = param.data.clone()
+                norm = torch.norm(param.grad)
+                if norm != 0:
+                    r_at = epsilon * param.grad / norm
+                    param.data.add_(r_at)
+
+    def restore(self, emb_name="word_embeddings"):
+        # emb_name这个参数要换成你模型中embedding的参数名
+        for name, param in self.model.named_parameters():
+            if param.requires_grad and emb_name in name:
+                assert name in self.backup
+                param.data = self.backup[name]
+        self.backup = {}

models/semeval7.py

@@ -0,0 +1,93 @@
+# -*- 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
+        )