| --- |
| 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 |
| base_model: |
| - indobenchmark/indobert-base-p1 |
| --- |
| |
| # 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!") |
