Open Source License Identification Library is an experimental code, that use Scikit-learn to implement a Multinomial Naive Bayes classifier trained with SPDX data to identify Open Source Licenses. This should be consider as a proof of concept for identify Open Source licenses using Machine Learning.

This is an experimental project, please don't use it for production. For a more robust implementation, please check the project Askalono https://github.com/jpeddicord/askalono

You can use OSLiLi in your terminal as command line, please install the oslili-cli package:

$ pip3 install oslili-cli
$ oslili-cli LICENSE
License: MIT (0.89 probability)
Copyright: ('2021', '(c)  Andrew Barrier')

In order to use the library, you need to import and use identify_license or identify_copyright.

import argparse
from oslili import LicenseAndCopyrightIdentifier


def main():
    msg = 'Identify open source license and copyright statements'
    parser = argparse.ArgumentParser(description=msg)
    parser.add_argument('file_path', help='Path to the file to analyze')
    args = parser.parse_args()
    file_path = args.file_path

    with open(args.file_path, 'r') as f:
        text = f.read()

    identifier = LicenseAndCopyrightIdentifier()
    license_spdx_code, license_proba = identifier.identify_license(text)
    print(f'License: {license_spdx_code} ({license_proba:.2f} probability)')
    year_range, statement = identifier.identify_copyright(text)
    if statement:
        if None not in statement:
            print(f'Copyright: {statement}')


if __name__ == '__main__':
    main()

This tool does not provide legal advice; I'm not a lawyer.

The code is an experimental implementation to match your input to a database of similar license texts and tell you if it's a close match. Refrain from relying on the accuracy of the output of this tool.

Remember: The tool can't tell you if a license works for your project or use case. Please should seek independent legal advice for any licensing questions.

License data is sourced directly from SPDX: https://github.com/spdx/license-list-data

Contributions are very welcome! See CONTRIBUTING for more info.

This library is licensed under the Apache 2.0 License.