Update README.md

README.md

@@ -1,6 +1,63 @@
 ---
 license: apache-2.0
 base_model:
-- indobenchmark/indobert-base-p1
+  - indobenchmark/indobert-base-p1
 pipeline_tag: text-classification
----
+tags:
+  - indobert
+  - intent-classification
+  - jkn-kis
+  - bpjs
+  - bahasa-indonesia
+  - healthcare
+  - nlp
+language:
+  - id
+library_name: transformers
+model_type: bert
+---
+
+# 🩺 IndoBERT for Intent Classification on JKN-KIS Data
+
+This model is a fine-tuned version of [`indobenchmark/indobert-base-p1`](https://huggingface.co/indobenchmark/indobert-base-p1), adapted for **intent classification** on question data related to the **Jaminan Kesehatan Nasional – Kartu Indonesia Sehat (JKN-KIS)** program.
+
+The model is trained to recognize user intent from common questions asked in the context of **BPJS Kesehatan** services, such as registration, benefits, payment, eligibility, and more.
+
+---
+
+## 🚀 How to Use
+
+Make sure to load your model and tokenizer, and also the label encoder (`le`) if you're using `LabelEncoder` from `sklearn`.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import joblib  # only needed if you use sklearn LabelEncoder
+
+# Load model and tokenizer
+model_name = "vinapatri/intent-classification-jkn-kis"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Load the label encoder used during training
+le = joblib.load("label_encoder.pkl")
+
+def predict_intent(text):
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+    predicted_class_id = logits.argmax().item()
+    tag = le.inverse_transform([predicted_class_id])[0]
+    return tag
+
+# Example
+text = "Apa tata cara memperoleh surat keterangan tidak mampu untuk BPJS?"
+predicted_intent = predict_intent(text)
+print(f"Intent: {predicted_intent}")