I'm with Olaverse Lab, so this is my own project and a feedback request. We make multilingual text APIs for language identification, diacritic restoration, classification, question generation and structured extraction. Language ID covers 608 languages; African languages are an important focus alongside English and other languages. For anyone adding language detection to a workflow, here's a small test checklist that doesn't require using our API: Separate one-word messages from longer paragraphs. Include names, URLs, numbers and text with missing diacritics. Keep mixed-language messages in their own test bucket rather than assuming one label tells the whole story. Record mistakes by language and message length, not just an overall accuracy figure. Decide what happens when a result is wrong: human review, asking the user, or a safe fallback. We offer free allowances on RapidAPI, with limits depending on the API. I'm not claiming the checklist is a benchmark or that our models pass every case. Which failure case would make language detection unusable in your product? Site if you want to look: https://www.olaverselab.com   submitted by   /u/ScientistOrdinary235 [link]   [comments]