This library assumes familiarity with (multi-source) weak supervision, if that's not the case you may want to first learn its basics in e.g. this overview slides from Stanford or this Snorkel tutorial.
That being said, these examples and notebooks will show you how to use Weasel for your own dataset, LF set, or end-model. E.g.:
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A high-level starter tutorial, with few code, many explanations and including Snorkel as a baseline (so that if you are familiar with Snorkel you can see the similarities and differences to Weasel).
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See how the whole WeaSEL pipeline works with all details, necessary steps and definitions for a new dataset & custom end-model. This notebook will probably make you learn the most about WeaSEL and how to apply it to your own problem.
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A realistic ML experiment script with all that's part of a ML pipeline, including logging to Weight&Biases, arbitrary callbacks, and eventually retrieving your fully trained end-model.
Check this notebook and/or this script out. :