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import torch.nn as nn
from transformers import AutoModel
class CustomMPRNAForSequenceClassification(nn.Module):
def __init__(self, base_model, num_labels):
super().__init__()
self.base_model = base_model
self.num_labels = num_labels
self.classifier = nn.Linear(base_model.config.hidden_size, num_labels)
self.dropout = nn.Dropout(0.1)
def forward(self, input_ids, attention_mask=None, labels=None):
outputs = self.base_model(input_ids=input_ids, attention_mask=attention_mask)
pooled_output = outputs[0][:, 0, :]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
return {"logits": logits, "loss": loss} if loss is not None else {"logits": logits}
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