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README.md
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compiled to support the development and evaluation of neural machine transliteration (NMT)
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models for Punjabi text.
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## Models:
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### <u>Gurmukhi-to-Shahmukhi Model</u>
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- **BLEU Score:** 98.1
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### <u>Shahmukhi-to-Gurmukhi Model</u>
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- **BLEU Score:** 87.7
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## Corpus Details
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- **Total Sentences:** 6.3 million
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- **Domains Covered:** Various domains including CCaligned, ccmatrix, TED, QED, OPUS, TIco,
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Wikimedia, Multicclaigned, Emille, IJCNLP, xlent, and paracrawl.
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- **Test Corpus:** FLORES-101
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## Usage
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These resources are intended to facilitate research and development in the field of Punjabi
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transliteration. They can be used to train new models or improve existing ones, enabling high-quality
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compiled to support the development and evaluation of neural machine transliteration (NMT)
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models for Punjabi text.
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## Corpus Details
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- **Total Sentences:** 6.3 million
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+
- **Domains Covered:** Various domains including CCaligned, ccmatrix, TED, QED, OPUS, TIco,
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Wikimedia, Multicclaigned, Emille, IJCNLP, xlent, and paracrawl.
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- **Test Corpus:** FLORES-101
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## Models:
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### <u>Gurmukhi-to-Shahmukhi Model</u>
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- **BLEU Score:** 98.1
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### <u>Shahmukhi-to-Gurmukhi Model</u>
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- **BLEU Score:** 87.7
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## Usage
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These resources are intended to facilitate research and development in the field of Punjabi
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transliteration. They can be used to train new models or improve existing ones, enabling high-quality
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