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from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, MSELoss
from transformers.modeling_outputs import ModelOutput
from transformers.models.deberta_v2.modeling_deberta_v2 import (
ContextPooler, DebertaV2Model, DebertaV2PreTrainedModel, StableDropout)
@dataclass
class SequenceClassifierOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
binary_logits: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
attentions: Optional[Tuple[torch.FloatTensor, ...]] = None
class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.deberta = DebertaV2Model(config)
self.pooler = ContextPooler(config)
output_dim = self.pooler.output_dim
self.binary_classifier = nn.Linear(output_dim, 1)
self.regressor = nn.Linear(output_dim, 1)
drop_out = getattr(config, "cls_dropout", None)
drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
self.dropout = StableDropout(drop_out)
self.post_init()
def freeze_embeddings(self) -> None:
"""Frezees the embedding layer."""
for param in self.deberta.embeddings.parameters():
param.requires_grad = False
def get_input_embeddings(self):
return self.deberta.get_input_embeddings()
def set_input_embeddings(self, new_embeddings):
self.deberta.set_input_embeddings(new_embeddings)
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,
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, 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.deberta(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
encoder_layer = outputs[0]
pooled_output = self.pooler(encoder_layer)
pooled_output = self.dropout(pooled_output)
binary_logits = self.binary_classifier(pooled_output)
logits = self.regressor(pooled_output)
loss = None
if labels is not None:
regression_loss_fct = MSELoss()
regression_loss = regression_loss_fct(logits.squeeze(), labels.squeeze().float())
binary_loss_fct = BCEWithLogitsLoss()
# We use as binary labels all texts with a score above 3!
binary_labels = (labels >= 3).float()
classification_loss = binary_loss_fct(binary_logits.squeeze(), binary_labels.squeeze())
loss = regression_loss + classification_loss
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss, logits=logits, binary_logits=binary_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
) |