Create training/train_model.py

training/train_model.py

@@ -0,0 +1,61 @@
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
+import numpy as np
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+
+model_name = "indobenchmark/indobert-base-p1"
+
+dataset = load_dataset("csv", data_files="data/eval_dataset.csv")
+
+label_map = {"negative":0, "neutral":1, "positive":2}
+
+def encode_label(example):
+    example["label"] = label_map[example["label"]]
+    return example
+
+dataset = dataset.map(encode_label)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def preprocess(example):
+    return tokenizer(example["text"], truncation=True, padding=True)
+
+dataset = dataset.map(preprocess)
+
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    preds = np.argmax(logits, axis=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
+    }
+
+training_args = TrainingArguments(
+    output_dir="./model",
+    learning_rate=2e-5,
+    per_device_train_batch_size=8,
+    num_train_epochs=3,
+    evaluation_strategy="epoch"
+)
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["train"],
+    compute_metrics=compute_metrics
+)
+
+trainer.train()
+
+# 🔥 simpan model
+trainer.save_model("model/final_model")
+tokenizer.save_pretrained("model/final_model")