---
license: cc-by-4.0
language: 
- ace
- ban
- bbc
- bew
- bjn
- bts
- btx
- bug
- cnh
- hil
- iba
- ilo
- kac
- lus
- mad
- mak
- meo
- min
- pag
- pam
- shn
- tet
- war
pretty_name: Gatitos
task_categories: 
- machine-translation
tags: 
- machine-translation
---

The GATITOS (Google's Additional Translations Into Tail-languages: Often Short) dataset is a high-quality, multi-way parallel dataset of tokens and short phrases.
This dataset consists in 4,000 English segments (4,500 tokens) that have been translated into each of 173 languages, 170 of which are low-resource, 23 are spoken in Southeast Asia.
This dataset contains primarily short segments: 93% single tokens, and only 23 sentences (0.6%) have over 5 tokens.
As such it is best thought of as a multilingual lexicon, rather than a parallel training corpus.
The source text is frequent words in the English Language, along with some common phrases and short sentences.
Care has been taken to ensure that they include good coverage of numbers, months, days of the week, swadesh words, and names of the languages themselves (including the endonym).


## Languages

ace, ban, bbc, bew, bjn, bts, btx, bug, cnh, hil, iba, ilo, kac, lus, mad, mak, meo, min, pag, pam, shn, tet, war

## Supported Tasks

Machine Translation

## Dataset Usage
### Using `datasets` library
```
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/gatitos", trust_remote_code=True)
```
### Using `seacrowd` library
```import seacrowd as sc
# Load the dataset using the default config
dset = sc.load_dataset("gatitos", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("gatitos"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
```

More details on how to load the `seacrowd` library can be found [here](https://github.com/SEACrowd/seacrowd-datahub?tab=readme-ov-file#how-to-use).


## Dataset Homepage

[https://github.com/google-research/url-nlp/blob/main/gatitos/README.md](https://github.com/google-research/url-nlp/blob/main/gatitos/README.md)

## Dataset Version

Source: 1.0.0. SEACrowd: 2024.06.20.

## Dataset License

Creative Commons Attribution 4.0 (cc-by-4.0)

## Citation

If you are using the **Gatitos** dataloader in your work, please cite the following:
```
@misc{jones2023bilex,
      title={Bilex Rx: Lexical Data Augmentation for Massively Multilingual Machine Translation},
      author={Alex Jones and Isaac Caswell and Ishank Saxena and Orhan Firat},
      year={2023},
      eprint={2303.15265},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
    }
}


@article{lovenia2024seacrowd,
    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, 
    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
    year={2024},
    eprint={2406.10118},
    journal={arXiv preprint arXiv: 2406.10118}
}

```