Delete models.py with huggingface_hub

models.py

@@ -1,227 +0,0 @@
-"""
-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]