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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AQMAR Arabic NER – Full Label Set

Dataset Summary

This dataset is a reprocessed version of the AQMAR Arabic Named Entity Recognition (NER) corpus originally released by the Arabic NLP group at Carnegie Mellon University (CMU).

It preserves the full original AQMAR annotation scheme, including fine-grained miscellaneous entity categories (MIS0, MIS1, MIS2, etc.).

This version is intended for linguistic analysis and annotation studies, not as a clean leaderboard benchmark.

Source

Original data obtained from: https://www.cs.cmu.edu/~ark/ArabicNER/

The original files are in CoNLL format:

One token per line
Empty lines indicate sentence boundaries

Data Processing

Sentence boundaries are taken directly from the original files
No re-tokenization was applied
File-level train/dev/test split (20/4/4 files) to avoid sentence leakage

Label Set

Includes:

PER, LOC, ORG
Fine-grained MIS* categories
O (outside)

Some fine-grained labels are rare and may not appear in dev/test. Macro-F1 over all labels is therefore not recommended.

Intended Use

Recommended:

Fine-grained NER analysis
Annotation ontology studies
Error analysis

Not recommended:

Benchmark comparisons using macro-F1 over all labels

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