File size: 2,518 Bytes
9ed19b1 63040f3 9ed19b1 63040f3 9ed19b1 63040f3 9ed19b1 63040f3 9ed19b1 63040f3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | ---
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!") |