Create README.md

README.md

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+---
+license: mit
+tags:
+  - indobert
+  - emotion-classification
+  - text-classification
+  - indonesian
+  - torch
+language:
+  - id
+datasets:
+  - PRDECT-ID
+model-index:
+  - name: IndoBERT Emotion Classification (5-Class)
+    results:
+      - task:
+          type: text-classification
+          name: Emotion Classification
+        dataset:
+          name: PRDECT-ID
+          type: text
+          description: >
+            A dataset of Indonesian product reviews labeled with five emotion categories:
+            love, happiness, anger, fear, and sadness.
+        metrics:
+          - name: Accuracy
+            type: accuracy
+            value: 0.7167
+          - name: F1 Score
+            type: f1
+            value: 0.7125
+          - name: Precision
+            type: precision
+            value: 0.7179
+          - name: Recall
+            type: recall
+            value: 0.7167
+---
+
+# IndoBERT Emotion Classification (5-Class)
+
+Model ini merupakan hasil *fine-tuning* dari [`indobenchmark/indobert-base-p1`](https://huggingface.co/indobenchmark/indobert-base-p1) untuk tugas klasifikasi emosi dalam Bahasa Indonesia, dengan 5 label emosi: `love`, `happiness`, `anger`, `fear`, dan `sadness`.
+
+## 🧠 Dataset
+
+Model ini dilatih menggunakan **PRDECT-ID Dataset**, yaitu kumpulan ulasan produk berbahasa Indonesia dari e-commerce Tokopedia, yang sudah dianotasi dengan label emosi oleh ahli psikologi klinis.
+
+- 29 kategori produk
+- Anotasi emosi oleh tim profesional
+- Setiap entri memiliki 1 label emosi
+
+## 🛠 Fine-tuning Details
+
+- **Base model**: `indobenchmark/indobert-base-p1`
+- **Training epochs**: 5 dari total 10 (early stopping dengan `load_best_model_at_end=True`)
+- **Batch size**: 8
+- **Learning rate**: 2e-5
+- **Weight decay**: 0.05
+- **Validation strategy**: per epoch
+- **Evaluation metric**: `eval_accuracy` (dengan `greater_is_better=True`)
+- **Cross-validation**: Stratified K-Fold (n_splits=5)
+
+### Eval Results (Best Model @ Epoch 3)
+
+| Metric      | Value   |
+|-------------|---------|
+| Accuracy    | 0.7167  |
+| F1 Score    | 0.7125  |
+| Precision   | 0.7179  |
+| Recall      | 0.7167  |
+| Eval Loss   | 0.7614  |
+
+## 🚀 How to Use
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
+
+model = AutoModelForSequenceClassification.from_pretrained("galennolan/indobert-b-p1-indoemotion-5class")
+tokenizer = AutoTokenizer.from_pretrained("galennolan/indobert-b-p1-indoemotion-5class")
+
+emotion_classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+emotion_classifier("Produk ini bikin aku senang banget!")