Delete bert_for_sequence_classification.py

bert_for_sequence_classification.py

@@ -1,139 +0,0 @@
-import torch
-import transformers
-from torch import nn
-from torch.nn import CrossEntropyLoss
-from typing import Optional, Tuple, Union
-from transformers.modeling_outputs import SequenceClassifierOutput
-from transformers.models.bert.modeling_bert import (
-    BertPreTrainedModel,
-    BERT_INPUTS_DOCSTRING,
-    _TOKENIZER_FOR_DOC,
-    _CHECKPOINT_FOR_DOC,
-    BERT_START_DOCSTRING,
-    _CONFIG_FOR_DOC,
-    _SEQ_CLASS_EXPECTED_OUTPUT,
-    _SEQ_CLASS_EXPECTED_LOSS,
-    BertModel,
-)
-
-from transformers.file_utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings_to_model_forward,
-    add_start_docstrings
-)
-
-@add_start_docstrings(
-    """
-    Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
-    output) e.g. for GLUE tasks.
-    """,
-    BERT_START_DOCSTRING,
-)
-class BertForSequenceClassification(BertPreTrainedModel):
-    def __init__(self, config, **kwargs):
-        super().__init__(transformers.PretrainedConfig())
-        #task_labels_map={"binary_classification": 2, "label_classification": 5}
-        self.tasks = kwargs.get("tasks_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)
-        ## add task specific output heads
-        self.classifier1 = nn.Linear(
-            config.hidden_size, self.tasks[0].num_labels
-        )
-        self.classifier2 = nn.Linear(
-            config.hidden_size, self.tasks[1].num_labels
-        )
-
-        self.init_weights()
-
-    @add_start_docstrings_to_model_forward(
-    BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
-    )
-    @add_code_sample_docstrings(
-        processor_class=_TOKENIZER_FOR_DOC,
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=SequenceClassifierOutput,
-        config_class=_CONFIG_FOR_DOC,
-        expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
-        expected_loss=_SEQ_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,
-        task_ids=None,
-    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
-        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)
-    
-        unique_task_ids_list = torch.unique(task_ids).tolist()
-        loss_list = []
-        logits = None
-        for unique_task_id in unique_task_ids_list:
-            loss = None
-            task_id_filter = task_ids == unique_task_id 
-
-            if unique_task_id == 0:
-                logits = self.classifier1(pooled_output[task_id_filter])
-            elif unique_task_id == 1:
-                logits = self.classifier2(pooled_output[task_id_filter])
-
-            
-            if labels is not None: 
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.tasks[unique_task_id].num_labels), labels[task_id_filter].view(-1))
-                loss_list.append(loss)
-        
-        # logits are only used for eval. and in case of eval the batch is not multi task
-        # For training only the loss is used
-
-        if loss_list:
-            loss = torch.stack(loss_list).mean()
-        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,
-            )