Cheetah

This is the repository accompanying our ACL 2024 paper Cheetah: Natural Language Generation for 517 African Languages. In this paper, we develop Cheetah, a massively multilingual NLG language model for African languages. Cheetah supports 517 African languages and language varieties, allowing us to address the scarcity of NLG resources and provide a solution to foster linguistic diversity.

We demonstrate the effectiveness of Cheetah through comprehensive evaluations across seven generation downstream tasks. In five of the seven tasks, Cheetah significantly outperforms other models, showcasing its remarkable performance for generating coherent and contextually appropriate text in a wide range of African languages. We additionally conduct a detailed human evaluation to delve deeper into the linguistic capabilities of Cheetah. The introduction of Cheetah has far-reaching benefits for linguistic diversity. By leveraging pretrained models and adapting them to specific languages, our approach facilitates the development of practical NLG applications for African communities. The findings of this study contribute to advancing NLP research in low-resource settings, enabling greater accessibility and inclusion for African languages in a rapidly expanding digital landscape.

For more details, please read the [paper](https://arxiv.org/pdf/2401.01053.pdf)

3. How to use Cheetah model

Below is an example for using Cheetah predict masked tokens.

from transformers import T5Tokenizer, AutoModelForSeq2SeqLM

tokenizer = T5Tokenizer.from_pretrained("UBC-NLP/cheetah-base")
model = AutoModelForSeq2SeqLM.from_pretrained("UBC-NLP/cheetah-base")

yor_prompt="ìròyìn kan nípa owó ìjọba <extra_id_0> kan"

input_ids = tokenizer(yor_prompt, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print("Tokenized input:", tokenizer.tokenize(yor_prompt))
print("Decoded output:", tokenizer.decode(outputs[0], skip_special_tokens=True))

Output:

Tokenized input: ['▁ìròyìn', '▁kan', '▁nípa', '▁owó', '▁ìjọba', '<extra_id_0>', '▁kan']
Decoded output:  ìpínlẹ̀

4. Ethics

Cheetah aligns with Afrocentric NLP where the needs of African people is put into consideration when developing technology. We believe Cheetah will not only be useful to speakers of the languages supported, but also researchers of African languages such as anthropologists and linguists. We discuss below some use cases for Cheetah and offer a number of broad impacts.

• Cheetah aims to address the lack of access to technology in about 90% of the world's languages, which automatically discriminates against native speakers of those languages. More precisely, it does so by focusing on Africa. To the best of our knowledge, Cheetah is the first massively multilingual PLM developed for African languages and language varieties. A model with knowledge of 517 African languages, is by far the largest to date for African NLP.
• Cheetah enables improved access of important information to the African community in Indigenous African languages. This is especially beneficial for people who may not be fluent in other languages. This will potentially connect more people globally.
• Cheetah affords opportunities for language preservation for many African languages. To the best of our knowledge, Cheetah consists of languages that have not been used for any NLP task until now. We believe that it can help encourage continued use of these languages in several domains, as well as trigger future development of language technologies for many of these languages.
• Cheetah Although LMs are useful for a wide range of applications, they can also be misused. Cheetah is developed using publicly available datasets that may carry biases. Although we strive to perform analyses and diagnostic case studies to probe performance of our models, our investigations are by no means comprehensive nor guarantee absence of bias in the data. In particular, we do not have access to native speakers of most of the languages covered. This hinders our ability to investigate samples from each (or at least the majority) of the languages.

Supported languages

Please refer to supported-languages

Citation

If you use the pre-trained model (Cheetah) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows:

@inproceedings{adebara-etal-2024-cheetah,
    title = "Cheetah: Natural Language Generation for 517 {A}frican Languages",
    author = "Adebara, Ife  and
      Elmadany, AbdelRahim  and
      Abdul-Mageed, Muhammad",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.691",
    pages = "12798--12823",
}

Acknowledgments

We gratefully acknowledges support from Canada Research Chairs (CRC), the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN-2018-04267), the Social Sciences and Humanities Research Council of Canada (SSHRC; 435-2018-0576; 895-2020-1004; 895-2021-1008), Canadian Foundation for Innovation (CFI; 37771), Digital Research Alliance of Canada, UBC ARC-Sockeye, Advanced Micro Devices, Inc. (AMD), and Google. Any opinions, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of CRC, NSERC, SSHRC, CFI, the Alliance, AMD, Google, or UBC ARC-Sockeye.