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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Sinhala Perplexity Test Dataset

Dataset Description

This dataset contains 500 sentence pairs in Sinhala, provided in two scripts: Unicode Sinhala and Romanized Sinhala. It is intended for evaluating perplexity and benchmarking language models on Sinhala text.

This dataset was introduced and used in the following benchmark study:

Rajapakse, M., & Weerasinghe, R. (2025). Script Sensitivity: Benchmarking Language Models on Unicode, Romanized and Mixed-Script Sinhala. arXiv:2601.14958v2. https://arxiv.org/abs/2601.14958v2

If you use this dataset, please cite the above paper.

Citation

@article{rajapakse2026comprehensive,
  title={A Comprehensive Benchmark of Language Models on Unicode and Romanized Sinhala},
  author={Rajapakse, Minuri and Weerasinghe, Ruvan},
  journal={arXiv preprint arXiv:2601.14958},
  year={2026}
}

Dataset Structure

The dataset consists of two fields:

sinhala_unicode: Sinhala sentences written in Unicode Sinhala script.
sinhala_romanized: Corresponding sentences transliterated into Romanized Sinhala.

Each entry in the dataset corresponds to one pair of sentences.

How to Use the Dataset

You can easily load the dataset using the datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Minuri/sinhala-perplexity-test-dataset")

# Access the first example
example = dataset['data'][0]
print("Unicode Sentence:", example['sinhala_unicode'])
print("Romanized Sentence:", example['sinhala_romanized'])

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Paper for Minuri/sinhala-perplexity-test-dataset

Script Sensitivity: Benchmarking Language Models on Unicode, Romanized and Mixed-Script Sinhala

Paper • 2601.14958 • Published Mar 21