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models.py

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+"""
+Module định nghĩa các mô hình cho spam review detection
+"""
+
+import torch
+import torch.nn as nn
+from transformers import AutoModel, AutoConfig, AutoModelForSequenceClassification
+from .custom_models import TextCNN, BiLSTM, RoBERTaGRU, SPhoBERT
+
+class TransformerForSpamDetection(nn.Module):
+    """
+    Base transformer model cho spam review detection
+    """
+    def __init__(self, model_name: str, num_labels: int):
+        super().__init__()
+        config = AutoConfig.from_pretrained(model_name, num_labels=num_labels)
+        self.encoder = AutoModel.from_pretrained(model_name, config=config)
+        self.classifier = nn.Linear(config.hidden_size, num_labels)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, input_ids, attention_mask, labels=None, **kwargs):
+        # Filter out arguments that BertModel doesn't expect
+        filtered_kwargs = {k: v for k, v in kwargs.items() 
+                          if k not in ['num_items_in_batch', 'position_ids']}
+        
+        # Pass filtered arguments to encoder (including token_type_ids for BERT)
+        out = self.encoder(input_ids=input_ids, attention_mask=attention_mask, **filtered_kwargs)
+        pooled = out.last_hidden_state[:, 0]  # CLS token
+        pooled = self.dropout(pooled)
+        logits = self.classifier(pooled)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+
+class ViT5ForSpamDetection(nn.Module):
+    """
+    ViT5 model cho spam review detection - sử dụng encoder-only approach
+    """
+    def __init__(self, model_name: str, num_labels: int):
+        super().__init__()
+        from transformers import T5EncoderModel, T5Config
+        
+        # Load T5 encoder only
+        config = T5Config.from_pretrained(model_name)
+        self.t5_encoder = T5EncoderModel.from_pretrained(model_name, config=config)
+        
+        # Classification head
+        self.classifier = nn.Linear(config.d_model, num_labels)
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, input_ids, attention_mask, labels=None, **kwargs):
+        # Filter out arguments that T5EncoderModel doesn't expect
+        filtered_kwargs = {k: v for k, v in kwargs.items() 
+                          if k not in ['num_items_in_batch', 'position_ids']}
+        
+        # Chỉ sử dụng encoder của T5
+        encoder_outputs = self.t5_encoder(input_ids=input_ids, attention_mask=attention_mask, **filtered_kwargs)
+        
+        # Lấy pooled representation (first token)
+        pooled = encoder_outputs.last_hidden_state[:, 0]
+        pooled = self.dropout(pooled)
+        logits = self.classifier(pooled)
+        
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        
+        return {"loss": loss, "logits": logits}
+
+def get_model(model_name: str, num_labels: int, vocab_size: int = None):
+    """
+    Factory function để tạo model dựa trên tên model
+    
+    Args:
+        model_name: Tên model (phobert-v2, textcnn, bilstm, etc.)
+        num_labels: Số lượng classes
+        vocab_size: Kích thước vocabulary (chỉ cần cho BiLSTM-CRF)
+    
+    Returns:
+        Model instance
+    """
+    # Mapping từ model name đến base model
+    model_mapping = {
+        "phobert-v1": "vinai/phobert-base",
+        "phobert-v2": "vinai/phobert-base-v2",
+        "bartpho": "vinai/bartpho-syllable",
+        "visobert": "uitnlp/visobert",
+        "xlm-r": "xlm-roberta-large",
+        "mbert": "bert-base-multilingual-cased",
+        "vit5": "VietAI/vit5-base"
+    }
+    
+    if model_name == "vit5":
+        # Sử dụng ViT5ForSpamDetection cho T5 model
+        base_model_name = model_mapping[model_name]
+        return ViT5ForSpamDetection(base_model_name, num_labels)
+    elif model_name in model_mapping:
+        # Sử dụng standard transformer model
+        base_model_name = model_mapping[model_name]
+        return TransformerForSpamDetection(base_model_name, num_labels)
+    
+    elif model_name == "textcnn":
+        # TextCNN custom model
+        base_model_name = "vinai/phobert-base-v2"  # Sử dụng PhoBERT embeddings
+        return TextCNN(base_model_name, num_labels)
+    
+    elif model_name == "bilstm":
+        # BiLSTM custom model
+        base_model_name = "vinai/phobert-base-v2"
+        return BiLSTM(base_model_name, num_labels)
+    
+    elif model_name == "roberta-gru":
+        # RoBERTa-GRU hybrid model
+        base_model_name = "vinai/phobert-base-v2"
+        return RoBERTaGRU(base_model_name, num_labels)
+    
+    elif model_name == "sphobert":
+        # SPhoBERT fusion model
+        base_model_name = "vinai/phobert-base-v2"
+        return SPhoBERT(base_model_name, num_labels)
+    
+    elif model_name == "bilstm-crf":
+        # BiLSTM-CRF model (placeholder implementation)
+        # Trong thực tế cần implement CRF layer
+        base_model_name = "vinai/phobert-base-v2"
+        return BiLSTM(base_model_name, num_labels)
+    
+    else:
+        raise ValueError(f"Unknown model name: {model_name}. Available models: {list(model_mapping.keys()) + ['textcnn', 'bilstm', 'roberta-gru', 'sphobert', 'bilstm-crf']}")
+
+def get_model_config(model_name: str):
+    """
+    Lấy cấu hình cho model
+    
+    Args:
+        model_name: Tên model
+    
+    Returns:
+        Dict chứa cấu hình model
+    """
+    configs = {
+        "phobert-v1": {
+            "model_name": "vinai/phobert-base",
+            "description": "PhoBERT v1 - Pre-trained BERT for Vietnamese",
+            "max_length": 256,
+            "learning_rate": 5e-5
+        },
+        "phobert-v2": {
+            "model_name": "vinai/phobert-base-v2",
+            "description": "PhoBERT v2 - Improved PhoBERT for Vietnamese", 
+            "max_length": 256,
+            "learning_rate": 5e-5
+        },
+        "bartpho": {
+            "model_name": "vinai/bartpho-syllable",
+            "description": "BART Pho - Vietnamese BART model",
+            "max_length": 256,
+            "learning_rate": 5e-5
+        },
+        "visobert": {
+            "model_name": "uitnlp/visobert",
+            "description": "ViSoBERT - Vietnamese Social BERT",
+            "max_length": 256,
+            "learning_rate": 5e-5
+        },
+        "xlm-r": {
+            "model_name": "xlm-roberta-large",
+            "description": "XLM-RoBERTa Large - Multilingual model",
+            "max_length": 256,
+            "learning_rate": 3e-5
+        },
+        "mbert": {
+            "model_name": "bert-base-multilingual-cased",
+            "description": "mBERT - Multilingual BERT model",
+            "max_length": 256,
+            "learning_rate": 5e-5
+        },
+        "vit5": {
+            "model_name": "VietAI/vit5-base",
+            "description": "ViT5 - Vietnamese T5",
+            "max_length": 256,
+            "learning_rate": 5e-5
+        },
+        "textcnn": {
+            "model_name": "vinai/phobert-base-v2",
+            "description": "TextCNN - Convolutional Neural Network for text",
+            "max_length": 256,
+            "learning_rate": 1e-3,
+            "custom_model": True
+        },
+        "bilstm": {
+            "model_name": "vinai/phobert-base-v2",
+            "description": "BiLSTM - Bidirectional LSTM for text classification",
+            "max_length": 256,
+            "learning_rate": 1e-3,
+            "custom_model": True
+        },
+        "roberta-gru": {
+            "model_name": "vinai/phobert-base-v2",
+            "description": "RoBERTa-GRU - Hybrid RoBERTa + GRU model",
+            "max_length": 256,
+            "learning_rate": 5e-5,
+            "custom_model": True
+        },
+        "sphobert": {
+            "model_name": "vinai/phobert-base-v2",
+            "description": "SPhoBERT - PhoBERT + SentenceBERT embedding fusion",
+            "max_length": 256,
+            "learning_rate": 5e-5,
+            "custom_model": True
+        },
+        "bilstm-crf": {
+            "model_name": "vinai/phobert-base-v2",
+            "description": "BiLSTM-CRF - Bidirectional LSTM with CRF",
+            "max_length": 256,
+            "learning_rate": 1e-3,
+            "custom_model": True
+        }
+    }
+    
+    if model_name not in configs:
+        raise ValueError(f"Model {model_name} not found. Available models: {list(configs.keys())}")
+    
+    return configs[model_name]