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