Commiting project

DataProcessing/clickbait_classifier.py

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+import torch
+from transformers import BertModel, BertConfig, PretrainedConfig, PreTrainedModel, AutoModel, AutoConfig
+from typing import List, Optional, Tuple, Union
+from transformers.modeling_outputs import TokenClassifierOutput,SequenceClassifierOutput
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss, BCELoss
+import torch.nn as nn
+# from modeling_mpnet import MPNetModel, MPnetConfig
+
+class ClickbaitConfig(PretrainedConfig):
+    def __init__(
+        self,
+        model_type: str = "bert",
+        pretrained_model: str = "bert-base-uncased",
+        num_labels: int = 1,
+        dropout: float = 0.1,
+        inner_dim1: int = 256,
+        inner_dim2: int = 32, 
+        max_length: int = 512,
+        load_pretrained: bool = True,
+        freeze_bert: bool = True,
+        **kwargs
+    ):
+        super(ClickbaitConfig, self).__init__(num_labels=num_labels, **kwargs)
+        self.model_type = model_type
+        self.pretrained_model = pretrained_model
+        self.dropout = dropout
+        self.inner_dim1 = inner_dim1
+        self.inner_dim2 = inner_dim2
+        self.max_length = max_length
+        self.load_pretrained = load_pretrained
+        self.freeze_bert = freeze_bert
+
+
+class BertClickbaitClassifier(PreTrainedModel):
+    """
+      Taken and extended from BertforSequenceClassification : https://github.com/huggingface/transformers/blob/v4.19.2/src/transformers/models/bert/modeling_bert.py#L1508
+    """
+    config_class = ClickbaitConfig
+    def __init__(self, config: ClickbaitConfig):
+        super(BertClickbaitClassifier, self).__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+        # self.bert_config = BertConfig.from_pretrained(config.pretrained_model)
+        self.bert_config = AutoConfig.from_pretrained(config.pretrained_model)
+
+        # self.bert = BertModel(self.bert_config)
+        self.bert = AutoModel.from_pretrained(config.pretrained_model, config=self.bert_config)
+        # self.bert = SentenceTransformer(config.pretrained_model, config=self.bert_config)
+        # self.bert = MPNetModel(config.pretrained_model, config=self.bert_config)
+        if config.load_pretrained:
+            print("Load pretrained weights from {}".format(config.pretrained_model))
+            self.bert = self.bert.from_pretrained(config.pretrained_model)
+        if config.freeze_bert:
+            print("Freeze weights in the BERT model. Just the classifier will be trained")
+            for param in self.bert.parameters():
+                param.requires_grad = False
+
+        self.linear_1 = nn.Linear(self.bert.config.hidden_size, config.inner_dim1)
+        self.dropout_1 = nn.Dropout(config.dropout) 
+        self.relu_1 = nn.ReLU()
+        self.dropout_2 = nn.Dropout(config.dropout)
+        self.linear_2 = nn.Linear(config.inner_dim1, config.inner_dim2)
+        self.relu_2 = nn.ReLU()
+        self.dropout_3 = nn.Dropout(config.dropout)
+        self.classifier = nn.Linear(config.inner_dim2, config.num_labels)
+        self.sigmoid = nn.Sigmoid()
+
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        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.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        output = outputs[0][:,0,:]
+
+        x = self.dropout_1(output)
+        x = self.linear_1(x)
+        x = self.relu_1(x)
+        x = self.dropout_2(x)
+        x = self.linear_2(x)
+        x = self.relu_2(x)
+        x = self.dropout_3(x)
+
+        logits = self.classifier(x)
+        logits = self.sigmoid(logits)
+
+        loss = None
+        if labels is not None:
+            loss_fct = BCELoss(weight=WEIGHT)
+            labels = 1.0*labels
+            loss = loss_fct(logits.view(-1), labels.view(-1))
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits
+        )