Marfin Emotion Detection Model π΅
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.
π 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
π Training Details
- Base model: mDeBERTa-v3-base-mnli-xnli
- Fine-tuned with custom NLI-style dataset
- Metrics: Accuracy
π· Tags
Zero-Shot Classification
, Emotion
, Mood
, Indonesian
, English
π₯ 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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Base model
MoritzLaurer/mDeBERTa-v3-base-mnli-xnli