train.py

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
from datasets import load_dataset
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support

def compute_metrics(pred):
    labels = pred.label_ids
    preds = pred.predictions.argmax(-1)
    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
    acc = accuracy_score(labels, preds)
    return {
        'accuracy': acc,
        'f1': f1,
        'precision': precision,
        'recall': recall
    }

class ArabicDialectTrainer:
    def __init__(self, model_name="CAMeL-Lab/bert-base-arabic-camelbert-msa"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        # 18 فئة للهجات العربية المختلفة
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=18)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)
        
        # تعريف تصنيف اللهجات
        self.dialect_mapping = {
            0: 'OM',  # عُمان
            1: 'SD',  # السودان
            2: 'SA',  # السعودية
            3: 'KW',  # الكويت
            4: 'QA',  # قطر
            5: 'LB',  # لبنان
            6: 'JO',  # الأردن
            7: 'SY',  # سوريا
            8: 'IQ',  # العراق
            9: 'MA',  # المغرب
            10: 'EG', # مصر
            11: 'PL', # فلسطين
            12: 'YE', # اليمن
            13: 'BH', # البحرين
            14: 'DZ', # الجزائر
            15: 'AE', # الإمارات
            16: 'TN', # تونس
            17: 'LY'  # ليبيا
        }

    def tokenize_data(self, examples):
        return self.tokenizer(
            examples['text'],
            padding='max_length',
            truncation=True,
            max_length=128
        )

    def prepare_dataset(self, dataset):
        tokenized_dataset = dataset.map(self.tokenize_data, batched=True)
        tokenized_dataset = tokenized_dataset.remove_columns(['text', 'id'])
        tokenized_dataset = tokenized_dataset.rename_column('label', 'labels')
        tokenized_dataset.set_format('torch')
        return tokenized_dataset

    def train(self, train_dataset, eval_dataset=None, output_dir="./trained_model", num_train_epochs=3):
        print("تهيئة معلمات التدريب...")
        training_args = TrainingArguments(
            output_dir=output_dir,
            num_train_epochs=num_train_epochs,
            per_device_train_batch_size=32,
            per_device_eval_batch_size=32,
            warmup_steps=500,
            weight_decay=0.01,
            logging_dir='./logs',
            logging_steps=100,
            evaluation_strategy="epoch" if eval_dataset else "no",
            save_strategy="epoch",
            load_best_model_at_end=True if eval_dataset else False,
            metric_for_best_model="f1" if eval_dataset else None,
        )

        trainer = Trainer(
            model=self.model,
            args=training_args,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            compute_metrics=compute_metrics,
        )

        print("بدء التدريب...")
        trainer.train()
        
        if eval_dataset:
            print("تقييم النموذج...")
            results = trainer.evaluate()
            print(f"نتائج التقييم: {results}")

        print("حفظ النموذج...")
        self.model.save_pretrained(output_dir)
        self.tokenizer.save_pretrained(output_dir)
        print("تم حفظ النموذج بنجاح!")

def main():
    print("تحميل مجموعة البيانات...")
    dataset = load_dataset("Abdelrahman-Rezk/Arabic_Dialect_Identification")
    
    trainer = ArabicDialectTrainer()
    
    print("تجهيز البيانات للتدريب...")
    train_dataset = trainer.prepare_dataset(dataset['train'])
    eval_dataset = trainer.prepare_dataset(dataset['validation'])
    
    print("بدء عملية التدريب...")
    trainer.train(train_dataset, eval_dataset)

if __name__ == "__main__":
    main()