model.py

import torch
import torch.nn as nn
from transformers import AutoModel

MODEL_ID = "Omartificial-Intelligence-Space/SA-BERT-V1"

class EOUClassifier(nn.Module):
    def __init__(self, model_id=MODEL_ID, num_labels=2, use_class_weights=True, pooling="cls"):
        super().__init__()
        self.num_labels = num_labels
        self.pooling = pooling  # "cls" or "mean"

        # Load encoder
        self.bert = AutoModel.from_pretrained(model_id)

        self.dropout = nn.Dropout(0.15)
        self.layer_1 = nn.Linear(768, 384)
        self.act = nn.GELU()
        self.layer_2 = nn.Linear(384, num_labels)

        
        self.loss_fn = nn.CrossEntropyLoss()


    def forward(self, input_ids, attention_mask, labels=None):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)

        if self.pooling == "cls":
            pooled = outputs.last_hidden_state[:, 0]  # [CLS]
        else:
            # Mean pooling
            hidden = outputs.last_hidden_state
            mask = attention_mask.unsqueeze(-1)
            pooled = (hidden * mask).sum(dim=1) / mask.sum(dim=1)

        x = self.dropout(pooled)
        x = self.layer_1(x)
        x = self.act(x)
        x = self.dropout(x)
        logits = self.layer_2(x)

        if labels is not None:
            loss = self.loss_fn(logits, labels)
            return {"loss": loss, "logits": logits}

        return {"logits": logits}