| import torch | |
| import torch.nn as nn | |
| from transformers import AutoModel | |
| class AbusePatternDetector(nn.Module): | |
| def __init__(self, model_name, num_labels): | |
| super().__init__() | |
| self.bert = AutoModel.from_pretrained(model_name) | |
| self.dropout = nn.Dropout(0.3) | |
| self.classifier = nn.Linear(self.bert.config.hidden_size, num_labels) | |
| def forward(self, input_ids, attention_mask): | |
| outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) | |
| pooled_output = outputs.last_hidden_state[:, 0] # Use [CLS] token | |
| pooled_output = self.dropout(pooled_output) | |
| logits = self.classifier(pooled_output) | |
| return logits | |