training/tuning.py

from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoTokenizer
from datasets import load_dataset

model_name = "indobenchmark/indobert-base-p1"

dataset = load_dataset("csv", data_files="data/eval_dataset.csv")

tokenizer = AutoTokenizer.from_pretrained(model_name)

def preprocess(examples):
    return tokenizer(examples["text"], truncation=True, padding=True)

dataset = dataset.map(preprocess, batched=True)

model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)

training_args = TrainingArguments(
    output_dir="./results",
    learning_rate=2e-5,   # 🔥 tuning parameter
    per_device_train_batch_size=8,
    num_train_epochs=3,
    weight_decay=0.01
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"]
)

trainer.train()