Create training/tuning.py

training/tuning.py

@@ -0,0 +1,31 @@
+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()