Reduce package size

Hi and thanks for this great library.

I am encountering a common problem with ML libraries using KeyBERT - namely, the package is very large due to its dependencies. E.g. `torch` is a gigantic library (used in the `sentence-transformers` li), `scikit-learn` is very large etc. This makes it very difficult to use this library in a serverless context due to cloud function size limitations and cold start issues.

I would like to suggest working to reduce the package size. This can be done by making some dependencies optional and adding guards against them.

The issue names no files, tests, or entry points. Start by locating the package dependency declarations and reviewing how torch, sentence-transformers, and scikit-learn are required; done means optional dependencies can be omitted without breaking supported functionality and the package is smaller for serverless deployments.

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