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Welcome to the Aldous wiki!
Right now, I'm using the wiki for project coordination for anyone that wants to do something.
- For people with ideas on more dimensions to capture and how to capture them.
- Requires knowledge of how to edit a JSON file and run a few terminal commands.
- Requires some understanding of how semantic matching works (embeddings, similarity, distance).
- Does NOT require knowledge of advanced code or advanced math.
- May occasionally involve community interactions.
- Open an issue or email
<timthepost@protonmail.com>to discuss before sending a PR.
- More practical, small, self-contained examples of Aldous being used would be extremly helpful at this stage.
(create
contrib/example-namein your PR). - Any example CI integrations or blog posts about how you found value with it are hugely appreciated. (create contrib/your-integration in your PR).
- Any ideas, features, feedback - y'all know how GitHub works.
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We need more unencumbered works for automated variance tests. The thing is, they must:
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Be completely out of US copyright which means published prior to January 1 1930, or produced by an entity that's exempt from protection, like official government works, AND,
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Have an established consensus on the emotional impact of the text; it has to be famous enough to have been considered well-known then and ideally still now by people in the US and Canada, AND,
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It should fit in the model's context window (try to stay under 6k tops, The Raven is as big as will fit), but also be ample enough to score above the noise threshold on at least a few sensor groups.
That's really darn picky, we know, but it's Copyright law. You can calibrate your model with anything you want, Aldous is never trained directly on any song lyrics, so use whatever you like when you do your own tunings. This is just for what we can include in the repo.
- Please spread the word!
- Star on GitHub to help it be discovered.
- Open an issue to propose any ideas (or email
<timthepost@protonmail.com>) - Take over this wiki and documentation strategy