File size: 8,700 Bytes
5f742b3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
"""
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] |