LLMs-from-scratch VS finagg

https://www.manning.com/books/build-a-large-language-model-f...

The attention mechanism we implement in this book* is specific to LLMs in terms of the text inputs, but it's fundamentally the same attention mechanism that is used in vision transformers. The only difference is that in LLMs, you turn text into tokens, and convert these tokens into vector embeddings that go into an LLM. In vision transformers, instead of regarding images as tokens, you use an image patch as a token and turn those into vector embeddings (a bit hard to explain without visuals here). In both text or vision context, it's the same attention mechanism, and it both cases it receives vector embeddings.

(*Chapter 3, already submitted last week and should be online in the MEAP soon, in the meantime the code along with the notes is also available here: https://github.com/rasbt/LLMs-from-scratch/blob/main/ch03/01...)

finagg – A Python package for aggregating and normalizing historical data from popular and free financial APIs

This is certainly a weird place to ask this question. That being said, you should explore the FRED API. Here's my project that implements most of it in Python: https://github.com/theOGognf/finagg The walkthrough shows you how to find what you're looking for

I created a Python package exactly for this. https://github.com/theOGognf/finagg. It aggregates historical fundamental data for whatever tickers you specify or from a subset of tickers. Let me know what you think

This package https://github.com/theOGognf/finagg already implements the complete SEC EDGAR REST API (disclaimer: I'm the author), and the archive-based API is in the works. I suggest you give it a go using the latest version off GitHub

I've got some time set aside to implement a (file based) SEC EDGAR API described in this issue https://github.com/theOGognf/finagg/issues/43