Instructions to use SLPG/Punjabi_Gurmukhi_to_Shahmukhi_Transliteration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Fairseq
How to use SLPG/Punjabi_Gurmukhi_to_Shahmukhi_Transliteration with Fairseq:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "SLPG/Punjabi_Gurmukhi_to_Shahmukhi_Transliteration" ) - Notebooks
- Google Colab
- Kaggle
Punjabi Gurmukhi to Shahmukhi Transliteration System
Our supervised Punjabi transliteration systems built using unsupervised corpus are bidirectional NMT systems which effectively convert text between Gurmukhi and Shahmukhi scripts. The Gurmukhi-to-Shahmukhi model achieves a 98.1 BLEU score and 99.5% word-level accuracy, while the Shahmukhi-to-Gurmukhi model scores 87.7 BLEU.
Corpus Details
- Total Sentences: 6.3 million
- Domains Covered: Various domains including CCaligned, ccmatrix, TED, QED, OPUS, TIco, Wikimedia, Multicclaigned, Emille, IJCNLP, xlent, and paracrawl.
- Test Corpus: FLORES-101
Model Details
- **BLEU Score:** 98.1
- **Word-level Accuracy:** 99.5%
- **Character Error Rate (CER):** 99.1%
You may also explore our Shahmukhi-to-Gurmukhi Model with BLEU Score of 87.7 here.
Usage
These resources are intended to facilitate research and development in the field of Punjabi transliteration. They can be used to train new models or improve existing ones, enabling high-quality transliteration between Gurmukhi and Shahmukhi scripts.
Citation
If you use our model, kindly cite our paper:
@article{Shehzadi2024,
title={Unsupervised Punjabi Corpus and Neural Machine Transliteration
System},
author={Shehzadi Ambreen, Sadaf Abdul Rauf, MG Abbas Malik and Muhammad Imran }, journal={Heliyon},
year={2024},
note={Under review}
}