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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 | |
| ) |