Glot500 Corpus

A dataset of natural language data collected by putting together more than 150 existing mono-lingual and multilingual datasets together and crawling known multilingual websites. The focus of this dataset is on 500 extremely low-resource languages. (More Languages still to be uploaded here)

This dataset is used to train the Glot500 model.

• Homepage: homepage
• Repository: github
• Paper: acl, arxiv

This dataset has the identical data format as the Taxi1500 Raw Data dataset, so that both datasets can be used in parallel seamlessly.

Parts of the original Glot500 dataset cannot be published publicly. Please fill out [thi form]{https://docs.google.com/forms/d/1FHto_4wWYvEF3lz7DDo3P8wQqfS3WhpYfAu5vM95-qU/viewform?edit_requested=true} to get access to these parts.

Usage

Replace nbl_Latn with your specific language.

from datasets import load_dataset
dataset = load_dataset('cis-lmu/Glot500', 'nbl_Latn', split='train')
print(dataset['train'][0]) # First row of nbl_Latn

License

We don't own any part of the data. The original source of each sentence of the data is indicated in dataset field.

To see the copyright license of the original datasets visit here.

We license the actual packaging, the metadata and the annotations of these data under the cc0-1.0.

If you are a website/dataset owner and do not want your data to be included in this corpra, please send us an email at glot500@cis.lmu.de.

Ethical Considerations

1. Biases: The text corpus may reflect the perspectives, opinions, or demographics of its sources or creators. It is important for users to critically evaluate the text in context especially for news sources and social medias.

2. Representativeness: While we have aimed for diversity and inclusivity, the text corpus may not fully represent all native speakers. Users should be mindful of any potential underrepresentation.

3. Ethics: We acknowledge that the collection and use of text data can have ethical implications. We have strived to handle the data responsibly, but we encourage users to consider the broader ethical implications of their own research or applications.

Citation

If you use any part of this code and data in your research, please cite it using the following BibTeX entry.

@inproceedings{imanigooghari-etal-2023-glot500,
 title = "Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages",
 author = {ImaniGooghari, Ayyoob and
 Lin, Peiqin and
 Kargaran, Amir Hossein and
 Severini, Silvia and
 Jalili Sabet, Masoud and
 Kassner, Nora and
 Ma, Chunlan and
 Schmid, Helmut and
 Martins, Andr{\'e} and
 Yvon, Fran{\c{c}}ois and
 Sch{\"u}tze, Hinrich},
 editor = "Rogers, Anna and
 Boyd-Graber, Jordan and
 Okazaki, Naoaki",
 booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
 month = jul,
 year = "2023",
 address = "Toronto, Canada",
 publisher = "Association for Computational Linguistics",
 url = "https://aclanthology.org/2023.acl-long.61",
 doi = "10.18653/v1/2023.acl-long.61",
 pages = "1082--1117",
 abstract = "The NLP community has mainly focused on scaling Large Language Models (LLMs) vertically, i.e., making them better for about 100 languages. We instead scale LLMs horizontally: we create, through continued pretraining, Glot500-m, an LLM that covers 511 predominantly low-resource languages. An important part of this effort is to collect and clean Glot500-c, a corpus that covers these 511 languages and allows us to train Glot500-m. We evaluate Glot500-m on five diverse tasks across these languages. We observe large improvements for both high-resource and low-resource languages compared to an XLM-R baseline. Our analysis shows that no single factor explains the quality of multilingual LLM representations. Rather, a combination of factors determines quality including corpus size, script, {``}help{''} from related languages and the total capacity of the model. Our work addresses an important goal of NLP research: we should notlimit NLP to a small fraction of the world{'}s languages and instead strive to support as many languages as possible to bring the benefits of NLP technology to all languages and cultures. Code, data and models are available at \url{https://github.com/cisnlp/Glot500}.",
}