E-motionAssistant/Tamil_Emotion_Recognition_Model

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

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")