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  - parallel-corpus
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  - document-alignment
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  ---
 
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  # Pralekha
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  <div style="display: flex; gap: 10px;">
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  <img src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey" alt="License: CC BY 4.0">
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  </a>
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  </div>
 
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  PRALEKHA is a large-scale benchmark for evaluating document-level alignment techniques. It includes 2M+ documents, covering 11 Indic languages and English, with a balanced mix of aligned and unaligned pairs.
 
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  ---
 
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  ## Dataset Description
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- PRALEKHA covers 12 languages—Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu, Urdu, and English. It includes a mixture of high- and medium-resource languages, covering 11 different scripts. The dataset spans two broad domains: **news bulletins** and **podcast scripts**, offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.
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  The dataset has a **1:2 ratio of aligned to unaligned document pairs**, making it ideal for benchmarking cross-lingual document alignment techniques.
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  - Haiyue Song ([[email protected]](mailto:[email protected]))
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  - Mohammed Safi Ur Rahman Khan ([[email protected]](mailto:[email protected]))
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- Please get in touch with us for any copyright concerns.
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-
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- ---
 
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  - parallel-corpus
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  - document-alignment
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  ---
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+
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  # Pralekha
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  <div style="display: flex; gap: 10px;">
 
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  <img src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey" alt="License: CC BY 4.0">
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  </a>
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  </div>
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+
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  PRALEKHA is a large-scale benchmark for evaluating document-level alignment techniques. It includes 2M+ documents, covering 11 Indic languages and English, with a balanced mix of aligned and unaligned pairs.
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+
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  ---
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+
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  ## Dataset Description
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+ PRALEKHA covers 12 languages—Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Tamil, Telugu, Urdu, and English. It includes a mixture of high- and medium-resource languages, covering 11 different scripts. The dataset spans two broad domains: **news bulletins** and **podcast scripts**, offering both written and spoken forms of data. All the data is human-written or human-verified, ensuring high quality.
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  The dataset has a **1:2 ratio of aligned to unaligned document pairs**, making it ideal for benchmarking cross-lingual document alignment techniques.
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  - Haiyue Song ([[email protected]](mailto:[email protected]))
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  - Mohammed Safi Ur Rahman Khan ([[email protected]](mailto:[email protected]))
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+ Please get in touch with us for any copyright concerns.