modeling_multilabelbert.py

from transformers import BertPreTrainedModel, BertModel, AutoConfig, AutoModelForTokenClassification
import torch
import torch.nn as nn

from transformers.modeling_outputs import TokenClassifierOutput
from transformers.utils import TransformersKwargs, can_return_tuple
from transformers.processing_utils import Unpack

from .configuration_multilabelbert import MultiLabelBertConfig

from typing import Optional


class BertForMultiLabelTokenClassification(BertPreTrainedModel):
    config_class = MultiLabelBertConfig

    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()

    @can_return_tuple
    def forward(
        self,
        input_ids: torch.Tensor | None = None,
        attention_mask: torch.Tensor | None = None,
        token_type_ids: torch.Tensor | None = None,
        position_ids: torch.Tensor | None = None,
        inputs_embeds: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        special_tokens_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor] | TokenClassifierOutput:
        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            inputs_embeds=inputs_embeds,
            return_dict=True,
            **kwargs,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = nn.BCEWithLogitsLoss(reduction = 'none')
            loss = loss_fct(logits, labels)

            if special_tokens_mask is not None:
                loss = loss[special_tokens_mask != 1].mean()
            else:
                loss = loss.mean()

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

AutoModelForTokenClassification.register(MultiLabelBertConfig, BertForMultiLabelTokenClassification)
BertForMultiLabelTokenClassification.register_for_auto_class('AutoModelForTokenClassification')