README.md

metadata

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

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)