Create README.md

README.md

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+---
+language: si
+license: apache-2.0
+tags:
+  - sinhala
+  - emotion-classification
+  - text-classification
+  - fine-tuned
+  - low-resource
+  - multilingual
+base_model: NLPC-UOM/SinBERT-large
+pipeline_tag: text-classification
+---
+
+# Sinhala Text Emotion Recognition Model
+
+Fine-tuned RoBERTa-style transformer for **multi-class emotion classification in Sinhala text**.  
+Detects basic emotions from Sinhala sentences/comments (e.g. social media, news).  
+Trained for 6 epochs on a Sinhala emotion dataset; validation accuracy 86% (modest performance – typical for initial fine-tuning in low-resource Sinhala NLP; suggest more epochs or Sinhala-pretrained base for better results).
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Bimsara Serasinghe
+- **Shared by:** Bimsara Serasinghe
+- **Model type:** Text Classification (fine-tuned encoder-only transformer for multi-class emotion detection)
+- **Language(s) (NLP):** Sinhala (සිංහල)
+- **License:** Apache-2.0
+- **Finetuned from model:** NLPC-UOM/SinBERT-large
+
+### Model Sources
+
+- **Repository:** https://huggingface.co/ShanukaB/SInhala_Text_Emotion_Recognition_Model
+
+## Uses
+
+### Direct Use
+
+Classify Sinhala text directly via Hugging Face `pipeline` into one of the emotion classes.
+
+### Downstream Use
+
+- Emotion-aware Sinhala chatbots & virtual assistants
+- Monitoring emotions in Sinhala social media (Facebook comments, YouTube, Twitter/X)
+- Mental health & wellbeing tools for Sinhala speakers
+- Customer support emotion detection in Sinhala
+- Academic/research projects on low-resource Sinhala affective computing
+
+### Out-of-Scope Use
+
+- High-stakes automated decisions (e.g. psychological diagnosis, legal judgments)
+- Real-time safety-critical systems without human validation
+- Non-Sinhala languages (expected very poor performance)
+  
+### Recommendations
+
+- Always pair model outputs with human review for sensitive applications (mental health, support)
+- Fine-tune longer or switch to Sinhala-specific pre-trained models (e.g. SinBERT variants if available)
+- Test on your target domain (e.g. news vs. casual chat) before deployment
+- Report dialect/code-mixed failures to improve community versions
+
+## How to Get Started with the Model
+
+```python
+from transformers import pipeline
+import joblib  # if using saved label encoder
+
+classifier = pipeline(
+    "text-classification",
+    model="YOUR_USERNAME/YOUR_MODEL_NAME",
+    tokenizer="YOUR_USERNAME/YOUR_MODEL_NAME"
+)
+
+# Optional: load label encoder if uploaded to repo
+# label_encoder = joblib.load("label_encoder.pkl")
+
+texts = [
+    "මම ගොඩක් සතුටින් ඉන්නවා! 😊",
+    "මේක බලල බයයි වෙලා... 😨",
+    "අපිට මේක ගැන කෝපයි ගොඩක්!"
+]
+
+for text in texts:
+    result = classifier(text)[0]
+    # If labels are "LABEL_0" etc., map manually or use saved encoder
+    print(f"Text: {text}")
+    print(f"→ Emotion: {result['label']} (confidence: {result['score']:.3f})\n")