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---
license: apache-2.0
language:
- id
- en
base_model: MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
pipeline_tag: zero-shot-classification
tags:
- mood
- emotion
- indonesian
- nli
metrics:
- accuracy
library_name: transformers
---
# Marfin Emotion Detection Model 🎵
This model is fine-tuned from `MoritzLaurer/mDeBERTa-v3-base-mnli-xnli` for **emotion detection** tasks based on chat context, specifically optimized for **Indonesian and English**.
## 📝 Use Case
The model predicts the relationship between user chat input and emotional hypotheses. It helps detect emotions like:
- `senang` (happy)
- `sedih` (sad)
- `marah` (angry)
- `takut` (fear)
- `cinta` (love)
This is useful for:
- Emotion-based music recommendation
- Sentiment analysis in real-time chat apps
- AI-driven mood detection systems
## 📊 Training Details
- Base model: mDeBERTa-v3-base-mnli-xnli
- Fine-tuned with custom NLI-style dataset
- Metrics: **Accuracy**
## 🏷 Tags
`Zero-Shot Classification`, `Emotion`, `Mood`, `Indonesian`, `English`
## 📥 Example Usage
```python
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="MarfinF/marfin_emotion")
text = "Aku lagi sedih banget hari ini"
labels = ["senang", "sedih", "marah", "takut", "cinta"]
result = classifier(text, candidate_labels=labels)
print(result) |