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Hindi, Hinglish & Regional Language NLP Models
How to evaluate Indic-language model quality, and why benchmark performance on English doesn't predict Indic-language performance.
Direct answer
A model's strong performance on English benchmarks does not predict its performance on Indic languages — evaluate a model specifically on the Indic language and use case you need, including code-switched Hindi-English (Hinglish) input if that reflects your actual users, since this is a distinct and common real-world pattern many models handle inconsistently.
Last reviewed 2026-08-29
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Why English benchmark scores don't transfer
Indic languages are generally lower-resource in most models' training data than English, and a model's overall benchmark ranking (often dominated by English-language tasks) can mask meaningfully weaker performance on a specific Indic language — check for Indic-language-specific benchmark results or run your own test rather than inferring quality from an aggregate score.
Test for code-switching specifically
Hinglish and similar code-switched patterns (mixing Hindi and English within a sentence) are extremely common in real Indian user input but underrepresented in many training sets — this is a specific, testable failure point worth checking directly rather than assuming a model that handles pure Hindi will handle mixed-language input equally well.