Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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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

License

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

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