OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report
Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages. In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise. We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks. During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are inadequate. We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages. OpenLID-v3 is available on https://huggingface.co/HPLT/OpenLID-v3.
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Mariia Fedorova
claimed_verified
2026-02-17T15:53:31.964Z
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Maria F
MariaFjodorowa
user
MariaFjodorowa
6993581c50fb2c0be4783be9
Nikolay Arefyev
6993581c50fb2c0be4783bea
Maja Buljan
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Jindřich Helcl
6993581c50fb2c0be4783bec
Stephan Oepen
6993581c50fb2c0be4783bed
Egil Rønningstad
6993581c50fb2c0be4783bee
Yves Scherrer
OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report
Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages. In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise. We call this extended system OpenLID-v3 and evaluate it against GlotLID on multiple benchmarks. During development, we focus on three groups of closely related languages (Bosnian, Croatian, and Serbian; Romance varieties of Northern Italy and Southern France; and Scandinavian languages) and contribute new evaluation datasets where existing ones are inadequate. We find that ensemble approaches improve precision but also substantially reduce coverage for low-resource languages. OpenLID-v3 is available on https://huggingface.co/HPLT/OpenLID-v3.
OpenLID-v3 improves language identification accuracy for closely related languages and low-resource variants through enhanced training data, cluster merging, and noise detection mechanisms.
language identification multilingual datasets web data OpenLID GlotLID language variant clusters noise detection ensemble approaches low-resource languages