Text Classification
Transformers
Safetensors
Indonesian
bert
5-emotions
sentiment-analysis
text-embeddings-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Chipan/indobert-emotion")
model = AutoModelForSequenceClassification.from_pretrained("Chipan/indobert-emotion", device_map="auto")Quick Links
Emotion Classification Model (Bahasa Indonesia)
This is a fine-tuned IndoBERT-based model for emotion classification in Bahasa Indonesia, designed to classify everyday informal or casual text into one of five emotional categories in bahasa Indonesia. The model is particularly useful for applications involving sentiment analysis, mental health monitoring, journaling platforms, or conversational AI.
🧠 Model Overview
- Base Model: indobert-base-p1 (by IndoNLU)
- Fine-tuning Task: Multi-class text classification
- Number of Classes: 5
- Model Format: safetensors
- Tokenizer: indobert-base-p1
🏷️ Emotion Classes
The model is trained to classify input text into one of the following five emotions:
| Emotion | Description |
|---|---|
| Marah | Angry, Frustation |
| Sedih | Sadness, disappointment |
| Senang | Joy, excitement |
| Stress | Anxiety, mental pressure |
| Bersyukur | Gratitude, thankfulness |
⚠️ Limitations
- Best performance on informal Indonesian (conversational, daily tone)
- May struggle with sarcasm, code-switching (mix of Indonesian-English), or domain-specific jargon
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Model tree for Chipan/indobert-emotion
Base model
indobenchmark/indobert-base-p1
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Chipan/indobert-emotion")