hyperparameter VS spaCy

the full tutorial: https://github.com/reiase/hyperparameter/tree/master/examples/optuna

When I was working on my own configuration framework (HyperParameter, previous post), I suddenly realize that what I want is not another configuration framework with some fancy API. All I want is to change my ML experiments without modifying the code and get rid of the configuration handling codes. The right way of configuration is not writing configurable code and wasting time on different frameworks. The best solution is a tool that makes your code configurable.

github: https://github.com/reiase/hyperparameter

HyperParameter is a configuration and parameter management library for Python. HyperParameter provides the following features:

you can take a look at https://github.com/reiase/hyperparameter, a scoped, thread-safe config object that is lightweight enough. There is no need to modify too much code:

I'm developing a Hyperparameter tuning toolbox for my machine learning projects. It maps keyword arguments to hyper-parameters, for example:

I'm developing [a toolbox for managing hyper-parameters](https://github.com/reiase/hyperparameter) in my data science and machine learning projects. It provides object-style API for nested dict( which is very common for config files):

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.