Update readme.md

README.md

@@ -1,16 +1,36 @@
----
-license: apache-2.0
-language:
-- id
-- en
-metrics:
-- accuracy
-base_model:
-- MoritzLaurer/mDeBERTa-v3-base-mnli-xnli
-pipeline_tag: zero-shot-classification
-tags:
-- Mood
-- Emotion
-new_version: MarfinF/marfin_emotion
-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
+```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)