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paper2code

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The JWT signature verification failed. Check the signing key and the algorithm.
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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📄 Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

Paper2Code Benchmark

Dataset Description

The Paper2Code Benchmark is designed to evaluate the ability to reproduce methods and experiments described in scientific papers.
We collected 90 papers from ICML 2024, NeurIPS 2024, and ICLR 2024, selecting only those with publicly available GitHub repositories.
To ensure manageable complexity, we filtered for repositories with fewer than 70,000 tokens.

Using a model-based evaluation, we selected the top 30 papers from each conference based on repository quality.

For more details, refer to Section 4.1 "Paper2Code Benchmark" of the paper.

Uses 

from datasets import load_dataset

dataset = load_dataset("iaminju/paper2code", split="test")

For access to the benchmark files (including pdf files), please refer to the Paper2Code data directory in our GitHub repository.

Dataset Structure 

Dataset({
    features: ['paper', 'source', 'repo_name', 'repo_url', 'paper_json', 'paper_cleaned_json', 'conference'],
    num_rows: 90
})
  • paper: Title of the paper.
  • source: Presentation type — oral or poster.
  • repo_name: Name of the repository provided by the original authors.
  • repo_url: URL of the repository provided by the original authors.
  • paper_json: Parsed JSON version of the paper. We use s2orc-doc2json for this conversion.
  • paper_cleaned_json: Preprocessed version of the paper used by PaperCoder.
  • conference: The conference where the paper was accepted - icml2024, iclr2024 or nips2024

Citation 

@article{seo2025paper2code,
  title={Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning},
  author={Seo, Minju and Baek, Jinheon and Lee, Seongyun and Hwang, Sung Ju},
  year={2025},
  url={https://arxiv.org/pdf/2504.17192}
}
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