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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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NL2SH-ALPACA (Alpaca-style)

This is a reformatted version of the NL2SH-ALFA dataset originally created by westenfelder/NL2SH-ALFA.

It has been converted to Alpaca-style format and prepared for instruction fine-tuning by Anshuman Jena, who acted as the converter and maintainer of this version.

{
  "instruction": "<natural language instruction>",
  "input": "",
  "output": "<bash command>"
}

Additionally, for the test split, the original bash2 (alternative command) and difficulty fields have been included in the output.

This dataset can be used to train instruction-following models for translating natural language instructions to shell commands.


Data Fields

The data fields are as follows:

  • instruction: natural language description of the shell task the model should perform. Each instruction is unique.
  • input: optional context or input for the task. In this dataset, this field is empty for all examples.
  • output: the shell command corresponding to the instruction. For the test split, alternative commands (bash2) and difficulty are appended to the output.

Data Instances

An example of "train" looks as follows:

{
    'instruction': 'Compile C code and cache compiled output (to use `ccache` on all `gcc` invocations, see the note above)',
    'input': '',
    'output': 'ccache gcc path/to/file.c'
}

Data Splits

train test
NL2SH-ALPACA 40,639 300

How to load

from datasets import load_dataset
ds = load_dataset("abandonedmonk/NL2SH-ALPACA")
print(ds["train"][0])

Citation

If you use this dataset, please cite the original work:

@misc{westenfelder2025nl2sh,
title={NL2SH-ALFA},
author={westenfelder},
year={2025},
howpublished={\url{[https://huggingface.co/datasets/westenfelder/NL2SH-ALFA}}](https://huggingface.co/datasets/westenfelder/NL2SH-ALFA}})
}

This version of the dataset was converted to Alpaca-style format and maintained by Anshuman Jena.

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