README.md

metadata

language:
  - ur
license: apache-2.0
tags:
  - text-classification
  - sentiment-analysis
  - urdu
  - bert
  - fine-tuned
  - nlp
datasets:
  - mirfan899/imdb_urdu_reviews
metrics:
  - accuracy
  - f1
model-index:
  - name: urdu-sentiment-classifier
    results:
      - task:
          type: text-classification
        dataset:
          name: IMDB Urdu Reviews
          type: mirfan899/imdb_urdu_reviews
        metrics:
          - type: accuracy
            value: 0.81
          - type: f1
            value: 0.8098

Urdu Sentiment Classifier 🇵🇰

A fine-tuned bert-base-multilingual-cased model for Urdu sentiment analysis — classifying Urdu text as positive or negative.

Live Demo

Try it on HuggingFace Spaces

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