Hi all, author here! My submission fell of the new page after 5 minutes sometime this morning and really glad to see it get re-posted.
AMA!
AMA!
What are your larger plans here? Do you have an interface in mind for searching or organizing things?
I think an amazing eCFR search experiment would be transformer vectors in a graph, using the hierarchy, citations, and references as edges to (sub)paragraph and section nodes - perhaps even using a modified HNSW somehow. The graph that exists there now isn't leveraged enough.
Per this dataset itself, I already output to Vespa formatted JSON (as noted in https://github.com/maxdotio/ecfr-prepare )...and the resulting vectors from the inference get appended to the original JSON doc as a field.
I have a Vespa schema hat I need to upload (that doesnt include the vector field yet but can be added using the Vespa vector search walkthroughs). It's been a busy day but I'll quickly try to find a place to put it for now :)
--EDIT-- Pushed the schema to the above repo, and some bash. You'll need Docker and to follow the Vespa MSMARCO instructions first at https://docs.vespa.ai/en/tutorials/text-search-semantic.html to get used to the engine.
My experience has been with 14 CFR and 21 CFR. I would love to see any tool you come up with in the future and would be happy to give you feedback.
As but one example: contracts often include the phrase "a reasonable effort".
Here's a definition: "Reasonable Efforts means, with respect to a given goal, the efforts that a reasonable person in the position of the promisor would use so as to achieve that goal as expeditiously as possible"
Try defining that in an algorithm!
But perhaps better tools can be built to make the process more efficient, less biased, and less redundant.
What are your favorite ways to do sentence and paragraph embeddedings and is there a framework you like where you can tune to custom data? Do you find fine tuning your embedding model helpful?