Clash 是一个用Go开发的 HTTP/HTTPS/SOCKS 代理工具，支持VMess, Shadowsocks, Trojan, Snell协议
The file management automation tool.
Rule engine implementation in Golang
翻译 - Golang中的规则引擎实现
🐍💯pySBD (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box.
#计算机科学#A chatbot implemented in TensorFlow based on the seq2seq model, with certain rules integrated.
A backup of Kr328/ClashForAndroid. Source: https://archive.org/details/github.com-Kr328-ClashForAndroid_-_2023-09-12_02-20-50
Marble - the real time decision engine for fraud and AML
Smart rule-based bot. For Browser & Node.
🔗 Automatically link your Obsidian notes.
#自然语言处理#A (smart) rule based NLP module to extract job skills from text
This repository contains theory and working codes of three different types of chatbots.
#计算机科学#Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranulari...
#自然语言处理#Modular, fast NLP framework, compatible with Pytorch and spaCy, offering tailored support for French clinical notes.
#计算机科学#ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based mode...
#自然语言处理#japanese sentence segmentation library for python
#自然语言处理#Odinson is a powerful and highly optimized open-source framework for rule-based information extraction. Odinson couples a simple, yet powerful pattern language that can operate over multiple represent...
#计算机科学# A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.
翻译 - 快速的Tsetlin Machine实现，采用按位运算符，带有MNIST演示。
#自然语言处理#A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format
A backup of Dreamacro/clash on 2023-08-06. Source: https://archive.org/details/github.com-Dreamacro-clash_-_2023-08-06_20-51-37
Tutorial on the Convolutional Tsetlin Machine