example VS spaCy

The Slack use case highlighted on the front page would be better if Slack markup was an available syntax. Slack is not really Markdown, so composing messages in Heynote won't have high fidelity.

One thing that often trips me up is that links and code formatting cannot coexist:

    [`method_name`](https://github.com/example/example/blob/main/src/foo.rs)

Gem::Specification.new do |s| s.name = 'example' s.version = '0.1.0' s.licenses = ['MIT'] s.summary = "This is an example!" s.description = "Much longer explanation of the example!" s.authors = ["Ruby Coder"] s.email = '[email protected]' s.files = ["lib/example.rb"] s.homepage = 'https://rubygems.org/gems/example' s.metadata = { "source_code_uri" => "https://github.com/example/example" } end

Git allows checking out only a limited set of changes with the --depth flag:

    git clone --branch v1.0 --depth 1 https://github.com/example/example.git

Hello, so I attempt to git clone like this git clone https://github.com/example/example.git

build: 'https://github.com/example/example.git#main'

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.