visolex
/

bartpho-hsd

+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import (
+    AutoModel, AutoConfig, AutoTokenizer,
+    T5ForConditionalGeneration, T5Config,
+    AutoModelForSequenceClassification,
+    PreTrainedModel, PretrainedConfig
+)
+from transformers.modeling_utils import (
+    load_state_dict,
+    WEIGHTS_NAME,
+    SAFE_WEIGHTS_NAME,
+    SAFE_WEIGHTS_INDEX_NAME,
+    WEIGHTS_INDEX_NAME
+)
+from transformers.utils import (
+    is_safetensors_available,
+    is_torch_available,
+    logging,
+    EntryNotFoundError,
+    PushToHubMixin
+)
+import os
+import json
+import numpy as np
+logger = logging.get_logger(__name__)
+class BaseHateSpeechModel(nn.Module):
+    """Base class cho tất cả các mô hình hate speech detection"""
+    def __init__(self, model_name: str, num_labels: int = 3):
+        super().__init__()
+        self.num_labels = num_labels
+        self.model_name = model_name
+    def forward(self, input_ids, attention_mask, labels=None):
+        raise NotImplementedError
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        """
+        Load model từ pretrained checkpoint.
+        Transformers sẽ tự động load state_dict sau khi khởi tạo model.
+        """
+        # Extract config từ kwargs (transformers sẽ pass config vào đây)
+        config = kwargs.pop("config", None)
+        # Load config nếu chưa có
+        if config is None:
+            try:
+                config = AutoConfig.from_pretrained(pretrained_model_name_or_path)
+            except Exception:
+                config = {}
+        # Get num_labels từ config hoặc kwargs
+        num_labels = kwargs.pop("num_labels", None)
+        if num_labels is None:
+            if hasattr(config, "num_labels"):
+                num_labels = config.num_labels
+            elif isinstance(config, dict) and "num_labels" in config:
+                num_labels = config["num_labels"]
+            else:
+                num_labels = 3
+        # Lấy base model name từ config
+        base_model_name = None
+        if hasattr(config, "_name_or_path"):
+            base_model_name = config._name_or_path
+        elif isinstance(config, dict) and "_name_or_path" in config:
+            base_model_name = config["_name_or_path"]
+        # Khởi tạo model với base model name
+        if base_model_name:
+            model = cls(model_name=base_model_name, num_labels=num_labels, **kwargs)
+        else:
+            # Fallback: dùng default model_name từ class
+            model = cls(num_labels=num_labels, **kwargs)
+        return model
+class PhoBERTV2Model(BaseHateSpeechModel):
+    """PhoBERT-V2 cho hate speech detection"""
+    def __init__(self, model_name: str = "vinai/phobert-base-v2", num_labels: int = 3):
+        super().__init__(model_name, num_labels)
+        self.config = AutoConfig.from_pretrained(model_name, ignore_mismatched_sizes=True)
+        self.encoder = AutoModel.from_pretrained(model_name, config=self.config, ignore_mismatched_sizes=True)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.config.hidden_size, num_labels)
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.pooler_output
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class BartPhoModel(BaseHateSpeechModel):
+    """BART Pho cho hate speech detection"""
+    def __init__(self, model_name: str = "vinai/bartpho-syllable-base", num_labels: int = 3):
+        super().__init__(model_name, num_labels)
+        self.config = AutoConfig.from_pretrained(model_name, ignore_mismatched_sizes=True)
+        self.encoder = AutoModel.from_pretrained(model_name, config=self.config, ignore_mismatched_sizes=True)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.config.d_model, num_labels)
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        # Sử dụng hidden state của token cuối cùng
+        last_hidden_states = outputs.last_hidden_state
+        pooled_output = last_hidden_states.mean(dim=1)  # Mean pooling
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class ViSoBERTModel(BaseHateSpeechModel):
+    """ViSoBERT cho hate speech detection"""
+    def __init__(self, model_name: str = "uitnlp/visobert", num_labels: int = 3):
+        super().__init__(model_name, num_labels)
+        self.config = AutoConfig.from_pretrained(model_name, ignore_mismatched_sizes=True)
+        self.encoder = AutoModel.from_pretrained(model_name, config=self.config, ignore_mismatched_sizes=True)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.config.hidden_size, num_labels)
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        # Kiểm tra xem có pooler_output không, nếu không thì dùng last_hidden_state
+        if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None:
+            pooled_output = outputs.pooler_output
+        else:
+            # Fallback: sử dụng mean pooling của last_hidden_state
+            pooled_output = outputs.last_hidden_state.mean(dim=1)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class PhoBERTV1Model(BaseHateSpeechModel):
+    """PhoBERT-V1 cho hate speech detection"""
+    def __init__(self, model_name: str = "vinai/phobert-base", num_labels: int = 3):
+        super().__init__(model_name, num_labels)
+        self.config = AutoConfig.from_pretrained(model_name, ignore_mismatched_sizes=True)
+        self.encoder = AutoModel.from_pretrained(model_name, config=self.config, ignore_mismatched_sizes=True)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.config.hidden_size, num_labels)
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        # Một số encoder không có pooler_output
+        if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None:
+            pooled_output = outputs.pooler_output
+        else:
+            pooled_output = outputs.last_hidden_state.mean(dim=1)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class MBERTModel(BaseHateSpeechModel):
+    """mBERT (bert-base-multilingual-cased) cho hate speech detection"""
+    def __init__(self, model_name: str = "bert-base-multilingual-cased", num_labels: int = 3):
+        super().__init__(model_name, num_labels)
+        self.config = AutoConfig.from_pretrained(model_name, ignore_mismatched_sizes=True)
+        self.encoder = AutoModel.from_pretrained(model_name, config=self.config, ignore_mismatched_sizes=True)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.config.hidden_size, num_labels)
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None:
+            pooled_output = outputs.pooler_output
+        else:
+            pooled_output = outputs.last_hidden_state.mean(dim=1)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class SPhoBERTModel(BaseHateSpeechModel):
+    """SPhoBERT (biến thể PhoBERT syllable-level) cho hate speech detection"""
+    def __init__(self, model_name: str = "vinai/phobert-base", num_labels: int = 3):
+        super().__init__(model_name, num_labels)
+        self.config = AutoConfig.from_pretrained(model_name, ignore_mismatched_sizes=True)
+        self.encoder = AutoModel.from_pretrained(model_name, config=self.config, ignore_mismatched_sizes=True)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.config.hidden_size, num_labels)
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        if hasattr(outputs, 'pooler_output') and outputs.pooler_output is not None:
+            pooled_output = outputs.pooler_output
+        else:
+            pooled_output = outputs.last_hidden_state.mean(dim=1)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class ViHateT5Model(BaseHateSpeechModel):
+    """ViHateT5 cho hate speech detection"""
+    def __init__(self, model_name: str = "VietAI/vit5-base", num_labels: int = 3):
+        super().__init__(model_name, num_labels)
+        self.config = T5Config.from_pretrained(model_name)
+        self.encoder = T5ForConditionalGeneration.from_pretrained(model_name, config=self.config)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.config.d_model, num_labels)
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        # Sử dụng hidden state của token cuối cùng
+        last_hidden_states = outputs.last_hidden_state
+        pooled_output = last_hidden_states.mean(dim=1)  # Mean pooling
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class XLMRModel(BaseHateSpeechModel):
+    """XLM-R Large cho hate speech detection"""
+    def __init__(self, model_name: str = "xlm-roberta-large", num_labels: int = 3):
+        super().__init__(model_name, num_labels)
+        self.config = AutoConfig.from_pretrained(model_name, ignore_mismatched_sizes=True)
+        self.encoder = AutoModel.from_pretrained(model_name, config=self.config, ignore_mismatched_sizes=True)
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(self.config.hidden_size, num_labels)
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.pooler_output
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class RoBERTaGRUModel(BaseHateSpeechModel):
+    """RoBERTa + GRU Hybrid model"""
+    def __init__(self, model_name: str = "vinai/phobert-base-v2", num_labels: int = 3, hidden_size: int = 256):
+        super().__init__(model_name, num_labels)
+        self.config = AutoConfig.from_pretrained(model_name, ignore_mismatched_sizes=True)
+        self.encoder = AutoModel.from_pretrained(model_name, config=self.config, ignore_mismatched_sizes=True)
+        self.gru = nn.GRU(
+            input_size=self.config.hidden_size,
+            hidden_size=hidden_size,
+            num_layers=2,
+            batch_first=True,
+            dropout=0.1,
+            bidirectional=True
+        )
+        self.dropout = nn.Dropout(0.1)
+        self.classifier = nn.Linear(hidden_size * 2, num_labels)  # *2 for bidirectional
+    def forward(self, input_ids, attention_mask, labels=None):
+        outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
+        hidden_states = outputs.last_hidden_state  # [batch_size, seq_len, hidden_size]
+        # GRU processing
+        gru_output, _ = self.gru(hidden_states)  # [batch_size, seq_len, hidden_size*2]
+        # Global average pooling
+        pooled_output = gru_output.mean(dim=1)  # [batch_size, hidden_size*2]
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class TextCNNModel(BaseHateSpeechModel):
+    """TextCNN cho hate speech detection"""
+    def __init__(self, vocab_size: int, embedding_dim: int = 128, num_labels: int = 3,
+                 num_filters: int = 100, filter_sizes: list = [3, 4, 5], dropout: float = 0.5):
+        super().__init__("textcnn", num_labels)
+        self.embedding = nn.Embedding(vocab_size, embedding_dim)
+        self.convs = nn.ModuleList([
+            nn.Conv2d(1, num_filters, (filter_size, embedding_dim))
+            for filter_size in filter_sizes
+        ])
+        self.dropout = nn.Dropout(dropout)
+        self.classifier = nn.Linear(num_filters * len(filter_sizes), num_labels)
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        """Override để detect vocab_size từ state_dict hoặc checkpoint file"""
+        # Get vocab_size từ kwargs hoặc config
+        vocab_size = kwargs.pop("vocab_size", None)
+        config = kwargs.pop("config", None)
+        # Nếu chưa có vocab_size, thử detect từ checkpoint file
+        if vocab_size is None:
+            import os
+            state_dict = None
+            # Try to load state_dict từ local path để detect vocab_size
+            if os.path.isdir(pretrained_model_name_or_path):
+                if os.path.isfile(os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)):
+                    try:
+                        from safetensors.torch import load_file
+                        state_dict = load_file(os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME))
+                    except Exception:
+                        pass
+                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
+                    try:
+                        state_dict = torch.load(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME), map_location="cpu")
+                    except Exception:
+                        pass
+            # Detect vocab_size từ embedding.weight
+            if state_dict is not None and "embedding.weight" in state_dict:
+                vocab_size = state_dict["embedding.weight"].shape[0]
+            else:
+                vocab_size = 30000  # Default
+        # Get num_labels
+        num_labels = kwargs.pop("num_labels", None)
+        if num_labels is None:
+            if config and hasattr(config, "num_labels"):
+                num_labels = config.num_labels
+            elif config and isinstance(config, dict) and "num_labels" in config:
+                num_labels = config["num_labels"]
+            else:
+                num_labels = 3
+        # Khởi tạo model
+        model = cls(vocab_size=vocab_size, num_labels=num_labels, **kwargs)
+        return model
+    def forward(self, input_ids, attention_mask, labels=None):
+        # Embedding
+        embedded = self.embedding(input_ids)  # [batch_size, seq_len, embedding_dim]
+        # Add channel dimension for Conv2d
+        embedded = embedded.unsqueeze(1)  # [batch_size, 1, seq_len, embedding_dim]
+        # Convolutional layers
+        conv_outputs = []
+        for conv in self.convs:
+            conv_out = F.relu(conv(embedded))  # [batch_size, num_filters, seq_len', 1]
+            conv_out = conv_out.squeeze(3)  # [batch_size, num_filters, seq_len']
+            pooled = F.max_pool1d(conv_out, conv_out.size(2))  # [batch_size, num_filters, 1]
+            pooled = pooled.squeeze(2)  # [batch_size, num_filters]
+            conv_outputs.append(pooled)
+        # Concatenate all conv outputs
+        concatenated = torch.cat(conv_outputs, dim=1)  # [batch_size, num_filters * len(filter_sizes)]
+        # Classification
+        concatenated = self.dropout(concatenated)
+        logits = self.classifier(concatenated)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class BiLSTMModel(BaseHateSpeechModel):
+    """BiLSTM cho hate speech detection"""
+    def __init__(self, vocab_size: int, embedding_dim: int = 128, hidden_size: int = 256,
+                 num_labels: int = 3, num_layers: int = 2, dropout: float = 0.5):
+        super().__init__("bilstm", num_labels)
+        self.embedding = nn.Embedding(vocab_size, embedding_dim)
+        self.lstm = nn.LSTM(
+            input_size=embedding_dim,
+            hidden_size=hidden_size,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=dropout if num_layers > 1 else 0,
+            bidirectional=True
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.classifier = nn.Linear(hidden_size * 2, num_labels)  # *2 for bidirectional
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        """Override để detect vocab_size từ state_dict hoặc checkpoint file"""
+        # Get vocab_size từ kwargs hoặc config
+        vocab_size = kwargs.pop("vocab_size", None)
+        config = kwargs.pop("config", None)
+        # Nếu chưa có vocab_size, thử detect từ checkpoint file
+        if vocab_size is None:
+            import os
+            state_dict = None
+            # Try to load state_dict từ local path để detect vocab_size
+            if os.path.isdir(pretrained_model_name_or_path):
+                if os.path.isfile(os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)):
+                    try:
+                        from safetensors.torch import load_file
+                        state_dict = load_file(os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME))
+                    except Exception:
+                        pass
+                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
+                    try:
+                        state_dict = torch.load(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME), map_location="cpu")
+                    except Exception:
+                        pass
+            # Detect vocab_size từ embedding.weight
+            if state_dict is not None and "embedding.weight" in state_dict:
+                vocab_size = state_dict["embedding.weight"].shape[0]
+            else:
+                vocab_size = 30000  # Default
+        # Get num_labels
+        num_labels = kwargs.pop("num_labels", None)
+        if num_labels is None:
+            if config and hasattr(config, "num_labels"):
+                num_labels = config.num_labels
+            elif config and isinstance(config, dict) and "num_labels" in config:
+                num_labels = config["num_labels"]
+            else:
+                num_labels = 3
+        # Khởi tạo model
+        model = cls(vocab_size=vocab_size, num_labels=num_labels, **kwargs)
+        return model
+    def forward(self, input_ids, attention_mask, labels=None):
+        # Embedding
+        embedded = self.embedding(input_ids)  # [batch_size, seq_len, embedding_dim]
+        # BiLSTM
+        lstm_output, (hidden, cell) = self.lstm(embedded)  # [batch_size, seq_len, hidden_size*2]
+        # Global average pooling (có thể thay bằng max pooling hoặc last hidden state)
+        # Option 1: Global average pooling
+        pooled_output = lstm_output.mean(dim=1)  # [batch_size, hidden_size*2]
+        # Option 2: Last hidden state (uncomment if preferred)
+        # pooled_output = lstm_output[:, -1, :]  # [batch_size, hidden_size*2]
+        # Option 3: Max pooling (uncomment if preferred)
+        # pooled_output = torch.max(lstm_output, dim=1)[0]  # [batch_size, hidden_size*2]
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss_fn = nn.CrossEntropyLoss()
+            loss = loss_fn(logits, labels)
+        return {"loss": loss, "logits": logits}
+class EnsembleModel(BaseHateSpeechModel):
+    """Ensemble model kết hợp các mô hình deep learning"""
+    def __init__(self, models: list, num_labels: int = 3, weights: list = None):
+        super().__init__("ensemble", num_labels)
+        self.models = nn.ModuleList(models)
+        self.num_models = len(models)
+        self.weights = weights if weights else [1.0] * self.num_models
+        self.weights = torch.tensor(self.weights, dtype=torch.float32)
+        self.weights = self.weights / self.weights.sum()  # Normalize weights
+    def forward(self, input_ids, attention_mask, labels=None):
+        all_logits = []
+        total_loss = 0
+        for i, model in enumerate(self.models):
+            model_output = model(input_ids, attention_mask, labels)
+            all_logits.append(model_output["logits"])
+            if model_output["loss"] is not None:
+                total_loss += self.weights[i] * model_output["loss"]
+        # Weighted average of logits
+        ensemble_logits = torch.zeros_like(all_logits[0])
+        for i, logits in enumerate(all_logits):
+            ensemble_logits += self.weights[i] * logits
+        return {
+            "loss": total_loss if total_loss > 0 else None,
+            "logits": ensemble_logits
+        }
+def get_model(model_name: str, num_labels: int = 3, **kwargs):
+    """
+    Factory function để tạo model dựa trên tên
+    Args:
+        model_name: Tên model ("phobert-v2", "bartpho", "visobert", "vihate-t5",
+                   "xlm-r", "roberta-gru", "textcnn", "bilstm", "bilstm-crf", "ensemble")
+        num_labels: Số lượng nhãn (3 cho hate speech: CLEAN, OFFENSIVE, HATE)
+        **kwargs: Các tham số bổ sung cho model
+    Returns:
+        Model instance
+    """
+    model_mapping = {
+        "phobert-v1": PhoBERTV1Model,
+        "phobert-v2": PhoBERTV2Model,
+        "bartpho": BartPhoModel,
+        "visobert": ViSoBERTModel,
+        "vihate-t5": ViHateT5Model,
+        "xlm-r": XLMRModel,
+        "mbert": MBERTModel,
+        "sphobert": SPhoBERTModel,
+        "roberta-gru": RoBERTaGRUModel,
+        "textcnn": TextCNNModel,
+        "bilstm": BiLSTMModel,
+        "ensemble": EnsembleModel
+    }
+    if model_name not in model_mapping:
+        raise ValueError(f"Unknown model: {model_name}. Available models: {list(model_mapping.keys())}")
+    model_class = model_mapping[model_name]
+    return model_class(num_labels=num_labels, **kwargs)