| """ |
| Implementation borrowed from transformers package and extended to support multiple prediction heads: |
| |
| https://github.com/huggingface/transformers/blob/master/src/transformers/models/bert/modeling_bert.py |
| """ |
|
|
| import torch |
| from torch import nn |
| from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
|
|
| import transformers |
| from transformers import BertTokenizer |
| from transformers import models |
| from transformers.modeling_outputs import SequenceClassifierOutput |
|
|
| from transformers.models.bert.configuration_bert import BertConfig |
| from transformers.models.bert.modeling_bert import ( |
| BertPreTrainedModel, |
| BERT_INPUTS_DOCSTRING, |
| _TOKENIZER_FOR_DOC, |
| _CHECKPOINT_FOR_DOC, |
| _CONFIG_FOR_DOC, |
| BertModel, |
| ) |
|
|
| from transformers.file_utils import ( |
| add_code_sample_docstrings, |
| add_start_docstrings_to_model_forward, |
| ) |
|
|
|
|
| class BertForSequenceClassification(BertPreTrainedModel): |
| def __init__(self, config, **kwargs): |
| super().__init__(transformers.PretrainedConfig()) |
| self.num_labels = kwargs.get("task_labels_map", {}) |
| self.config = config |
|
|
| self.bert = BertModel(config) |
| classifier_dropout = ( |
| config.classifier_dropout |
| if config.classifier_dropout is not None |
| else config.hidden_dropout_prob |
| ) |
| self.dropout = nn.Dropout(classifier_dropout) |
| |
| self.classifier1 = nn.Linear( |
| config.hidden_size, list(self.num_labels.values())[0] |
| ) |
| self.classifier2 = nn.Linear( |
| config.hidden_size, list(self.num_labels.values())[1] |
| ) |
|
|
| self.init_weights() |
|
|
| @add_start_docstrings_to_model_forward( |
| BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length") |
| ) |
| @add_code_sample_docstrings( |
| tokenizer_class=_TOKENIZER_FOR_DOC, |
| checkpoint=_CHECKPOINT_FOR_DOC, |
| output_type=SequenceClassifierOutput, |
| config_class=_CONFIG_FOR_DOC, |
| ) |
| def forward( |
| self, |
| input_ids=None, |
| attention_mask=None, |
| token_type_ids=None, |
| position_ids=None, |
| head_mask=None, |
| inputs_embeds=None, |
| labels=None, |
| output_attentions=None, |
| output_hidden_states=None, |
| return_dict=None, |
| task_name=None, |
| ): |
| r""" |
| labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`): |
| Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ..., |
| config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss), |
| If :obj:`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, |
| ) |
|
|
| pooled_output = outputs[1] |
|
|
| pooled_output = self.dropout(pooled_output) |
| logits = None |
| if task_name == list(self.num_labels.keys())[0]: |
| logits = self.classifier1(pooled_output) |
| elif task_name == list(self.num_labels.keys())[1]: |
| logits = self.classifier2(pooled_output) |
|
|
| loss = None |
| if labels is not None: |
| if self.config.problem_type is None: |
| if self.num_labels[task_name] == 1: |
| self.config.problem_type = "regression" |
| elif self.num_labels[task_name] > 1 and ( |
| labels.dtype == torch.long or labels.dtype == torch.int |
| ): |
| self.config.problem_type = "single_label_classification" |
| else: |
| self.config.problem_type = "multi_label_classification" |
|
|
| if self.config.problem_type == "regression": |
| loss_fct = MSELoss() |
| if self.num_labels[task_name] == 1: |
| loss = loss_fct(logits.squeeze(), labels.squeeze()) |
| else: |
| loss = loss_fct(logits, labels) |
| elif self.config.problem_type == "single_label_classification": |
| loss_fct = CrossEntropyLoss() |
| loss = loss_fct( |
| logits.view(-1, self.num_labels[task_name]), labels.view(-1) |
| ) |
| elif self.config.problem_type == "multi_label_classification": |
| loss_fct = BCEWithLogitsLoss() |
| loss = loss_fct(logits, labels) |
| if not return_dict: |
| output = (logits,) + outputs[2:] |
| return ((loss,) + output) if loss is not None else output |
|
|
| return SequenceClassifierOutput( |
| loss=loss, |
| logits=logits, |
| hidden_states=outputs.hidden_states, |
| attentions=outputs.attentions, |
| ) |
|
|
