catala VS spaCy

Programming and law can go together tho https://github.com/CatalaLang/catala

Law is a mess, in part because its authors take shortcuts. For example, from the first example on CatalaLang's README.md:

> If the property was acquired by gift [and various conditions apply], then for the purpose of determining loss the basis shall be such fair market value. [emphasis added]

I think (and I'm not a lawyer or a tax expert) that this means that the basis of an asset can have a different value for the purpose of determining gain or determining loss. Wow, basis isn't just a number, although one might not notice this if one didn't read the six emphasized words.

But the Catala code seems to completely ignore this. Oops. I filed an issue:

https://github.com/CatalaLang/catala/issues/514

In a real use case, I imagine that substantial refactoring of the parts that consume basis might be needed when one notices that the basis is not a number.

Catals is a language trying to exactly this.

There's even a programming language for that: https://catala-lang.org/

Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.

spaCy https://spacy.io/

In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.

Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.

The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?

[0] https://spacy.io/

[1] https://prodi.gy/

spaCy

I chose spacy. Although it's not state of the art, it's very well established and stable.