Create README.md

README.md

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+---
+language: ta
+license: apache-2.0
+tags:
+  - tamil
+  - emotion-classification
+  - text-classification
+  - fine-tuned
+  - multilingual
+base_model: jusgowiturs/autotrain-tamil_emotion_11_tamilbert-2710380899
+pipeline_tag: text-classification
+---
+
+# Tamil Text Emotion Recognition Model
+
+Fine-tuned Tamil language model for **11-class emotion classification** in Tamil text.  
+Detects: Ambiguous, Anger, Anticipation, Disgust, Fear, Joy, Love, Neutral, Sadness, Surprise, Trust.  
+Achieves ~94.5% accuracy on validation set after 6 epochs of fine-tuning.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Shanuka B Serasinghe
+- **Shared by:** Shanuka B Serasinghe
+- **Model type:** Text Classification (fine-tuned transformer for multi-class emotion detection)
+- **Language(s) (NLP):** Tamil (தமிழ்)
+- **License:** Apache-2.0
+- **Finetuned from model:** jusgowiturs/autotrain-tamil_emotion_11_tamilbert-2710380899 (AutoTrain-generated Tamil-BERT style checkpoint)
+
+### Model Sources
+
+- **Repository:** https://huggingface.co/ShanukaB/Tamil_Emotion_Recognition_Model
+
+
+## Uses
+
+### Direct Use
+
+Direct inference with Hugging Face `pipeline` for classifying Tamil sentences/comments into one of 11 emotions.
+
+### Downstream Use
+
+- Building emotion-aware Tamil chatbots
+- Tamil social media sentiment & emotion monitoring
+- Mental health & emotional wellbeing applications in Tamil
+- Customer support systems with emotion detection
+- Further research/fine-tuning in low-resource Tamil NLP
+
+### Out-of-Scope Use
+
+- High-stakes automated decisions (e.g. mental health diagnosis, hiring, legal)
+- Real-time safety-critical systems without human oversight
+- Non-Tamil languages (performance will be very poor)
+
+## Bias, Risks, and Limitations
+
+- Best performance on short-to-medium informal/colloquial Tamil text (social media style)
+- Heavy code-mixing (Tamil + English) reduces accuracy
+- Sarcasm, irony, subtle emotions, strong dialects, or very formal/literary Tamil may be misclassified
+- Potential biases from training data (e.g. over-representation of certain topics/styles in emotion datasets)
+- Not robust to adversarial inputs or out-of-distribution text
+
+### Recommendations
+
+- Always combine model predictions with human review in sensitive use-cases
+- Test thoroughly on your specific domain/dialect before deployment
+- Report issues (especially dialect or code-mixed failures) to improve future versions
+
+## How to Get Started with the Model
+
+```python
+from transformers import pipeline
+
+classifier = pipeline(
+    "text-classification",
+    model="YOUR_USERNAME/YOUR_MODEL_NAME",
+    tokenizer="YOUR_USERNAME/YOUR_MODEL_NAME"
+)
+
+texts = [
+    "இது ரொம்ப அழகா இருக்கு! 🥰🥰",
+    "என்னடா இது… மிகவும் கோபமா வருது",
+    "யாரும் இல்லாம தனிமையா ஃபீல் பண்றேன் 😔",
+    "அடேங்கப்பா! இது எப்படி சாத்தியமா? 😲"
+]
+
+for text in texts:
+    result = classifier(text)[0]
+    print(f"Text: {text}")
+    print(f"→ {result['label']} (confidence: {result['score']:.3f})\n")