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)