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This library has several very specific requirements (eg: numpy==1.24.4, scipy==1.10.1, pandas==1.3.5, etc.). In Python it's usually best practice for libraries to specify ranges of versions for their dependencies, because otherwise they become hard to install and lead to conflicts.
Would you be open to this? I can send a PR for this.
The text was updated successfully, but these errors were encountered:
Another good practice to reduce development creep is not to maintain version pins for common dependencies so often. I'm inclining towards changing dependencies to >= latest.compatible.version
We've also identified issues for new users due to upstream updates from Pydantic and SpaCy, as well as small improvements for imports. @rolisz I'd be happy to include your contributions from your PR while addressing these issues.
This library has several very specific requirements (eg: numpy==1.24.4, scipy==1.10.1, pandas==1.3.5, etc.). In Python it's usually best practice for libraries to specify ranges of versions for their dependencies, because otherwise they become hard to install and lead to conflicts.
Would you be open to this? I can send a PR for this.
The text was updated successfully, but these errors were encountered: