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Language identification (LID) is a crucial precursor for NLP, especially formining web data. Problematically, most of the world's 7000+ languages today arenot covered by LID technologies. We address this pressing issue for Africa byintroducing AfroLID, a neural LID toolkit for $517$ African languages andvarieties. AfroLID exploits a multi-domain web dataset manually curated fromacross 14 language families utilizing five orthographic systems. When evaluatedon our blind Test set, AfroLID achieves 95.89 F_1-score. We also compareAfroLID to five existing LID tools that each cover a small number of Africanlanguages, finding it to outperform them on most languages. We further show theutility of AfroLID in the wild by testing it on the acutely under-servedTwitter domain. Finally, we offer a number of controlled case studies andperform a linguistically-motivated error analysis that allow us to bothshowcase AfroLID's powerful capabilities and limitations.

AfroLID: A Neural Language Identification Tool for African Languages