from transformers import AlbertTokenizer, AlbertForSequenceClassification
from datasets import load_dataset
from transformers import Trainer, TrainingArguments

# Load dataset (ganti dengan nama dataset dan versi kamu)
dataset = load_dataset('your_username/your_dataset_name', 'your_dataset_version')

# Load tokenizer and model
tokenizer = AlbertTokenizer.from_pretrained('google/albert-base-v2')
model = AlbertForSequenceClassification.from_pretrained('google/albert-base-v2')

# Define preprocessing function
def preprocess_function(examples):
    return tokenizer(examples['text'], truncation=True, padding='max_length')

# Preprocess dataset
encoded_dataset = dataset.map(preprocess_function, batched=True)

# Define training arguments
training_args = TrainingArguments(
    output_dir='./results',
    per_device_train_batch_size=8,
    num_train_epochs=3,
    evaluation_strategy="epoch",
    save_strategy="epoch",
)

# Create trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=encoded_dataset['train'],
    eval_dataset=encoded_dataset['validation'],
)

# Train model
trainer.train()

# Save trained model
trainer.save_model('./your_private_albert_model')