| import torch.nn as nn |
| from transformers import AutoTokenizer, AutoModel |
|
|
| class MultiLabelDeberta(nn.Module): |
| def __init__(self, num_labels): |
| super().__init__() |
| self.backbone = AutoModel.from_pretrained('microsoft/deberta-v3-base') |
| self.dropout = nn.Dropout(0.3) |
| self.classifier = nn.Linear(self.backbone.config.hidden_size, num_labels) |
|
|
| def forward(self, input_ids, attention_mask): |
| outputs = self.backbone(input_ids=input_ids, attention_mask=attention_mask) |
| pooled = outputs.last_hidden_state[:, 0] |
| pooled = self.dropout(pooled) |
| logits = self.classifier(pooled) |
| return logits |
