README.md

metadata

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 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

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!")