---
dataset_info:
- config_name: portuguese
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 21307799396
    num_examples: 2000000
  download_size: 7981082403
  dataset_size: 21307799396
- config_name: bengali
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 13222913829
    num_examples: 2000000
  download_size: 3804453185
  dataset_size: 13222913829
- config_name: code
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 6854288242
    num_examples: 975000
  download_size: 2194755063
  dataset_size: 6854288242
- config_name: english
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 15670768606
    num_examples: 2000000
  download_size: 5700592325
  dataset_size: 15670768606
- config_name: hindi
  features:
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 22579216927
    num_examples: 2000000
  download_size: 6194810350
  dataset_size: 22579216927
configs:
- config_name: portuguese
  data_files:
  - split: train
    path: portuguese/train-*
- config_name: bengali
  data_files:
  - split: train
    path: bengali/train-*
- config_name: code
  data_files:
  - split: train
    path: code/train-*
- config_name: english
  default: true
  data_files:
  - split: train
    path: english/train-*
- config_name: hindi
  data_files:
  - split: train
    path: hindi/train-*
language:
- hi
- bn
- en
- pt
license: other
task_categories:
- text-generation
tags:
- tokenizer
- tokenization
- english
- code
- bengali
- hindi
- portuguese
pretty_name: Polygl0t tokenizers
size_categories:
- 1M<n<10M
---
# Polygl0t Tokenizers

## Table of Contents

- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Subsets and Splits](#subsets-and-splits)
- [Dataset Creation](#dataset-creation)
  - [Source Data](#source-data)
- [Additional Information](#additional-information)
  - [Dataset Maintainers](#dataset-maintainers)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Acknowledgments](#acknowledgments)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://huggingface.co/datasets/Polygl0t/tokenizers
- **Repository:** https://huggingface.co/datasets/Polygl0t/tokenizers
- **Point of Contact:** [Polyg0t](mailto:kluge@uni-bonn.de)

### 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).

<details>
<summary><b>All programming languages</b></summary>
 <code>
  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
 </code>
</details>

## 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

```json
{
  "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`](hindi_test.txt)) are for testing/evaluation purposes.

### Dataset Creation

### Source Data

- **Bengali:** The Bengali text samples were sourced from [Polygl0t/gigakriya-v1](https://huggingface.co/datasets/Polygl0t/gigakriya-v1).
- **English:** The English text samples were sourced from [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu).
- **Hindi:** The Hindi text samples were sourced from [Polygl0t/gigalekh-v1](https://huggingface.co/datasets/Polygl0t/gigalekh-v1).
- **Portuguese:** The Portuguese text samples were sourced from [Polygl0t/gigaverbo-v2](https://huggingface.co/datasets/Polygl0t/gigaverbo-v2).
- **Code:** The code samples were sourced from [bigcode/starcoderdata](https://huggingface.co/datasets/bigcode/starcoderdata).

## Additional Information

### Dataset Maintainers

- [Nicholas Kluge Corrêa](mailto:kluge@uni-bonn.de).
- [Shiza Fatimah](mailto:shizafatimah15@gmail.com).
- [Aniket Sen](mailto:sen@hiskp.uni-bonn.de).

### 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

```latex
@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](https://www.hpc.uni-bonn.de/en/systems/marvin) hosted by [University of Bonn](https://www.uni-bonn.de/en) along with the support provided by its High Performance Computing & Analytics Lab.


### Contributions

If you want to contribute, contact us at [polyglot@uni-bonn.de](mailto:polyglot@uni-bonn.de)!