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mahwizzzz / urdu_text_correction
Model's Last Updated: March 21 2025
text-generation
Introduction of urdu_text_correction
Model Details of urdu_text_correction
Urdu Text Correction Model
This model fine-tunes facebook/mbart-large-50 for automatic correction of Urdu text, addressing spelling mistakes, grammatical errors, and improving text fluency.
Overview
The Urdu Text Correction model is designed to automatically detect and correct errors in Urdu text, making it valuable for content editors, publishers, educational institutions, and applications requiring high-quality Urdu text.
Performance Metrics
The model achieves the following results on our evaluation set:
| Metric | Score |
|---|---|
| BLEU | 0.6996 |
| METEOR | 0.8296 |
| WER (Word Error Rate) | 0.1795 |
| CER (Character Error Rate) | 0.0761 |
| ROUGE-1 | 0.2025 |
| ROUGE-2 | 0.0699 |
| ROUGE-L | 0.2023 |
| Exact Match | 0.1096 |
| Generation Length | 28.4033 |
| Loss | 0.4305 |
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("mahwizzzz/urdu_text_correction")
model = AutoModelForSeq2SeqLM.from_pretrained("mahwizzzz/urdu_text_correction")
# Example text with errors
incorrect_text = "یہہ ایک اچھی بات ہے"
# Tokenize and generate correction
inputs = tokenizer(incorrect_text, return_tensors="pt", max_length=128, truncation=True)
outputs = model.generate(**inputs, max_length=128)
corrected_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Original: {incorrect_text}")
print(f"Corrected: {corrected_text}")
Model Description
This is an encoder-decoder model based on mBART-large-50, specifically fine-tuned on Urdu text correction pairs. The model learns to transform incorrect Urdu text into its corrected form, addressing issues such as:
- Spelling mistakes
- Grammar errors
- Word order issues
- Missing diacritics
- Punctuation errors
Training Details
Data
The model was trained on a dataset of incorrect-correct Urdu text pairs.
Hyperparameters
The model was trained with the following hyperparameters:
- Learning rate: 3e-05
- Batch size: 128 (32 per device with gradient accumulation of 4)
- Optimizer: AdamW with betas=(0.9, 0.999)
- LR scheduler: Cosine with 500 warmup steps
- Training epochs: 3
- Mixed precision: Native AMP
Limitations
The model may not perform well on highly domain-specific text (technical, medical, etc.) Very long texts may need to be split into smaller chunks due to the model's maximum sequence length The model may sometimes over-correct dialectal variations or stylistic choices Performance is dependent on the quality and diversity of the training data
Future Work
Expanding the training dataset with more diverse text sources Domain adaptation for specific use cases (legal, medical, etc.) Performance optimization for faster inference Improved handling of complex grammatical structures
Citation
If you use this model in your research or project, please cite:
@model{urdu_text_correction,
title = {Urdu Text Correction Model}
author = {Mahwiz Khalil},
year = {2025},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/mahwizzzz/urdu_text_correction}}
}
Runs of mahwizzzz urdu_text_correction on huggingface.co
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More Information About urdu_text_correction huggingface.co Model
Url of urdu_text_correction
urdu_text_correction huggingface.co Url
Provider of urdu_text_correction huggingface.co
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