Datasets:

Polygl0t
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tokenizers

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Shipment & Transport-Sea, Air, Rail, Road, Pipeline The mode of transportation is an important consideration when planning the shipment process. Besides the costs, the urgency of the shipment, the value of the goods being shipped as well as the size and weight of the goods need to be evaluated when determining the form...
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Wednesday, October 13, 2010 SEEDS OF CHANGE- JEN CULLERTON JOHNSON "Those trees you are cutting down today were not planted by you but by those who came before. You must plant trees that will benefit the community to come, like a seedling with sun, good soil, and abundant rain, the roots of our future will bury themsel...
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Polygl0t Tokenizers

Dataset Summary

This dataset contains several subsets for training multilingual tokenizers. Every subset possesses a collection of curated text samples in different languages.

Supported Tasks and Leaderboards

This dataset can be used for the task of text generation, specifically for training and evaluating tokenizers in multiple languages.

Languages

Hindi, Bengali, English, Portuguese, and Code (a mixture of 36 programming languages).

All programming languages


  fortran, jupyter, cpp, solidity, python, cmake, assembly, ruby, perl, lua, typescript, c, java, html, powershell, php, haskell, shell, scala, sql, visual_basic, ada, julia, markdown, batchfile, rust, cuda, json, kotlin, go, r, javascript, pascal, yaml, css, c_sharp

Dataset Structure

Data Instances

The dataset consists of the following features:

text: a string of text in the respective language of the subset.

Data Fields

{
  "text": "Olá, como vai você?"
}

Subsets and Splits

The dataset includes the following subsets:

Portuguese: This subset contains 2,000,000 text samples in Portuguese.
Hindi: This subset contains 2,000,000 text samples in Hindi.
Bengali: This subset contains 2,000,000 text samples in Bengali
English: This subset contains 2,000,000 text samples in English.
Code: This subset contains 975,000 text samples in various programming languages.

The txt files (e.g., hindi_test.txt) are for testing/evaluation purposes.

Dataset Creation

Source Data

Bengali: The Bengali text samples were sourced from Polygl0t/gigakriya-v1.
English: The English text samples were sourced from HuggingFaceFW/fineweb-edu.
Hindi: The Hindi text samples were sourced from Polygl0t/gigalekh-v1.
Portuguese: The Portuguese text samples were sourced from Polygl0t/gigaverbo-v2.
Code: The code samples were sourced from bigcode/starcoderdata.

Additional Information

Dataset Maintainers

Licensing Information

Please refer to the individual licenses of the source datasets used to create this corpus, as listed in the "Source Data" section above. The combined dataset does not have a single unified license, and users should ensure compliance with the terms of each source dataset when utilizing this corpus.

Citation Information

@misc{correa2026tucano2cool,
      title={{Tucano 2 Cool: Better Open Source LLMs for Portuguese}}, 
      author={Nicholas Kluge Corr{\^e}a and Aniket Sen and Shiza Fatimah and Sophia Falk and Lennard Landgraf and Julia Kastner and Lucie Flek},
      year={2026},
      eprint={2603.03543},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.03543}, 
}

@misc{shiza2026lilmoo,
      title={{Raising Bars, Not Parameters: LilMoo Compact Language Model for Hindi}}, 
      author={Shiza Fatimah and Aniket Sen and Sophia Falk and Florian Mai and Lucie Flek and Nicholas Kluge Corr{\^e}a},
      year={2026},
      eprint={2603.03508},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.03508}, 
}

@misc{fatimah2026liltii,
  title={{LilTii: A 0.6B Bengali Language Model that Outperforms Qwen}},
  author={Shiza Fatimah and Aniket Sen and Sophia Falk and Florian Mai and Lucie Flek and Nicholas Kluge Corr{\^e}a},
  year={2026},
  howpublished={\url{https://hf.co/blog/Polygl0t/liltii}}
}

Acknowledgments

Polyglot is a project funded by the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the State of North Rhine-Westphalia (MWK) as part of TRA Sustainable Futures (University of Bonn) and the Excellence Strategy of the federal and state governments.

We also gratefully acknowledge the granted access to the Marvin cluster hosted by University of Bonn along with the support provided by its High Performance Computing & Analytics Lab.