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50776af | 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 | import os
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification, Trainer, TrainingArguments
from datasets import Dataset
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix
from sklearn.utils.class_weight import compute_class_weight
# Load dataset
df = pd.read_csv("D:\Sentinel\data\sentinel_dataset_expanded.csv")
# Basic cleaning
df = df.dropna()
df["text"] = df["text"].astype(str)
print(f"Dataset size: {len(df)}")
print(f"Label distribution: {df['label'].value_counts().to_dict()}")
print(f"\nAverage text length: {df['text'].str.len().mean():.1f} characters")
# Train-test split
train_texts, val_texts, train_labels, val_labels = train_test_split(
df["text"].tolist(),
df["label"].tolist(),
test_size=0.2,
random_state=42,
stratify=df["label"].tolist() # Ensure balanced split
)
# Compute class weights for balanced training
class_weights = compute_class_weight(
class_weight='balanced',
classes=np.unique(train_labels),
y=train_labels
)
class_weights = torch.tensor(class_weights, dtype=torch.float)
print(f"\nClass weights: SAFE={class_weights[0]:.4f}, SCAM={class_weights[1]:.4f}")
print(f"Training set - SAFE: {train_labels.count(0)}, SCAM: {train_labels.count(1)}")
# Tokenizer
tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=128)
val_encodings = tokenizer(val_texts, truncation=True, padding=True, max_length=128)
train_dataset = Dataset.from_dict({
"input_ids": train_encodings["input_ids"],
"attention_mask": train_encodings["attention_mask"],
"labels": train_labels
})
val_dataset = Dataset.from_dict({
"input_ids": val_encodings["input_ids"],
"attention_mask": val_encodings["attention_mask"],
"labels": val_labels
})
# Model with class weights
model = DistilBertForSequenceClassification.from_pretrained(
"distilbert-base-uncased",
num_labels=2
)
# Custom trainer class to use class weights
class WeightedTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False, **kwargs):
labels = inputs.pop("labels")
outputs = model(**inputs)
logits = outputs.logits
loss_fct = torch.nn.CrossEntropyLoss(weight=class_weights.to(logits.device))
loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
return (loss, outputs) if return_outputs else loss
# Metrics
def compute_metrics(pred):
labels = pred.label_ids
preds = np.argmax(pred.predictions, axis=1)
precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')
acc = accuracy_score(labels, preds)
# Confusion matrix
cm = confusion_matrix(labels, preds)
print(f"\nConfusion Matrix:")
print(f" Predicted SAFE Predicted SCAM")
print(f"Actual SAFE: {cm[0][0]:14d} {cm[0][1]:14d}")
print(f"Actual SCAM: {cm[1][0]:14d} {cm[1][1]:14d}")
return {
"accuracy": acc,
"f1": f1,
"precision": precision,
"recall": recall
}
# Training args - MORE aggressive parameters
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=10, # Increased to 10 epochs
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
warmup_steps=50, # Reduced warmup
weight_decay=0.01,
learning_rate=3e-5, # Slightly higher learning rate
logging_dir="./logs",
logging_steps=5,
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="f1",
save_total_limit=2
)
trainer = WeightedTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
compute_metrics=compute_metrics
)
print("\n" + "="*60)
print("Starting training with 10 epochs...")
print("="*60)
trainer.train()
print("\n" + "="*60)
print("Final Evaluation...")
print("="*60)
eval_results = trainer.evaluate()
print(f"\nFinal Evaluation Results:")
print(f" Accuracy: {eval_results['eval_accuracy']:.4f}")
print(f" F1 Score: {eval_results['eval_f1']:.4f}")
print(f" Precision: {eval_results['eval_precision']:.4f}")
print(f" Recall: {eval_results['eval_recall']:.4f}")
script_dir = os.path.dirname(os.path.abspath(__file__))
save_path = os.path.join(script_dir, "sentinel_model")
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
print(f"\n{'='*60}")
print(f"Model training complete and saved to: {save_path}")
print("="*60)
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