modeling_guardito.py

import torch
import torch.nn as nn
from transformers import PreTrainedModel, AutoModel
from .configuration_guardito import GuarditoConfig

class GuarditoForSequenceClassification(PreTrainedModel):
    config_class = GuarditoConfig

    def __init__(self, config):
        super().__init__(config)
        self.config = config
        
        self.backbone = AutoModel.from_pretrained(config.base_model_name)
        self.dropout = nn.Dropout(config.dropout)
        self.classifier = nn.Linear(self.backbone.config.hidden_size, 1)
        
        self.post_init()

    def forward(self, input_ids, attention_mask=None, labels=None, **kwargs):
        kwargs.pop("token_type_ids", None)
        
        outputs = self.backbone(
            input_ids=input_ids,
            attention_mask=attention_mask,
            return_dict=True,
            **kwargs
        )
        
        last_hidden_state = outputs.last_hidden_state 

        # Mean Pooling
        input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()
        sum_embeddings = torch.sum(last_hidden_state * input_mask_expanded, 1)
        sum_mask = input_mask_expanded.sum(1)
        sum_mask = torch.clamp(sum_mask, min=1e-9)
        pooled_output = sum_embeddings / sum_mask

        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            loss_fct = nn.BCEWithLogitsLoss()
            loss = loss_fct(logits.view(-1), labels.float().view(-1))

        return {
            "loss": loss,
            "logits": logits,
        }

    # Вспомогательный метод для получения вероятности и флага PII
    def predict_pii(self, input_ids, attention_mask=None, custom_threshold=None):
        outputs = self.forward(input_ids, attention_mask)
        probs = torch.sigmoid(outputs["logits"])
        
        # Если порог не передан в метод, берем из конфига
        threshold = custom_threshold if custom_threshold is not None else self.config.threshold
        
        return {
            "probs": probs,
            "is_pii": probs >= threshold
        }