omneity-labs/lid-benchmark

LID Benchmark

Comprehensive evaluation of 17 language identification models across 8 diverse benchmarks.

Built by Omneity Labs.

Subsets

Config	Description	Rows
results_summary	One row per model × benchmark × scope with aggregate metrics	~136
results_aggregate	Detailed aggregate metrics per model × benchmark × scope	~816
results_per_language	Per-language accuracy for every model × benchmark × scope	~57k
results_speed	Inference speed (samples/sec) per model × benchmark	~136
model_languages	Supported language codes declared by each model	~4.7k
results_individual	Every individual prediction (model × benchmark × sample)	~28M

Models

gherbal-v1, gherbal-v2, gherbal-v3, gherbal-v4, nllb-lid, openlid-v1, openlid-v2, hplt-openlid-v3, fastlid-176, glotlid, franc, franc-all, franc-min, cld2, langdetect, langid, py3langid

Benchmarks

Benchmark	Source
flores-devtest	openlanguagedata/flores_plus (devtest split)
flores-dev	openlanguagedata/flores_plus (dev split)
madar	Madar
gherbal-multi	sawalni-ai/gherbal-multi
atlasia-lid	atlasia/Arabic-LID-Leaderboard
wili-2018	wili_2018
commonlid	commoncrawl/CommonLID
bouquet	facebook/bouquet

Methodology

All predictions are normalized to ISO 639-3 + Script (ISO 15924) codes using babelcode. Metrics: accuracy, macro-F1, weighted-F1, precision, recall — computed under multiple scopes (full, self, v1–v4).

Interactive App

Explore results interactively: LID Benchmark Leaderboard

Citation

If you use this benchmark data in your research, please reference:

• Omneity Labs LID Benchmark: https://huggingface.co/datasets/omneity-labs/lid-benchmark
• Gherbal model: https://www.omneitylabs.com/models/gherbal
• Evaluation benchmarks: See individual benchmark datasets linked above.

Author

• Omar Kamali
• Omneity Labs

License

The evaluation results in this dataset are released under Apache 2.0. The underlying benchmark datasets retain their original licenses.