Мобильная версияI'm now doing information extraction using NER. My dataset domain (mostly) in computer science. It contains label/tag: "TUJUAN", "METODE", and "TEMUAN". The problem is almost 80-90% data are labeled O which means it has no meaningful tag. The precision and recall from the model is 0, while the accuracy is about 0.78. I use IndoBERT as model for NER task.
I suspect this happens because my dataset is extremely unbalanced. At first, I want to modify the loss function based on BertForTokenClassification documentation to Dice Loss or Focal Loss as it mentioned here but I don't know how since my Python knowledge is still very weak.
class BertForTokenClassification(BertPreTrainedModel): def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.bert = BertModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION, output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC, expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT, expected_loss=_TOKEN_CLASS_EXPECTED_LOSS, ) 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], TokenClassifierOutput]: r""" labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ 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, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) My full code is here
Can I get any help how to handle my imbalance dataset based on my problems?
Источник: https://stackoverflow.com/questions/780 ... set-in-ner
