Urdu Sentiment Classifier 🇵🇰
A fine-tuned bert-base-multilingual-cased model for Urdu sentiment analysis — classifying Urdu text as positive or negative.
Live Demo
Performance
| Metric | Score |
|---|---|
| Accuracy | 81.00% |
| F1 Score (weighted) | 0.8098 |
Example Predictions
from transformers import pipeline
classifier = pipeline("text-classification", model="H-Layba/urdu-sentiment-classifier")
classifier("یہ فلم بہت اچھی تھی")
# [{'label': 'positive', 'score': 0.9936}]
classifier("آج کا دن بہت برا تھا")
# [{'label': 'negative', 'score': 0.9918}]
Training Details
- Base model: bert-base-multilingual-cased
- Dataset: 50,000 Urdu movie reviews
- Epochs: 5
- Learning rate: 2e-5
- Batch size: 32 (train), 64 (eval)
- Hardware: Kaggle T4 GPU
- Mixed precision: fp16
Dataset
Trained on mirfan899/imdb_urdu_reviews — 50,000 Urdu translations of IMDB movie reviews with positive/negative sentiment labels.
Part of Urdu NLP Suite
This model is part of a larger collection of fine-tuned Urdu NLP models:
- Sentiment Classification ← this model
- Text Summarization
- Question Answering
- Urdu → English Translation