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.

πŸ“ 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

πŸ“Š 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

πŸ“₯ Example Usage

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)
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