Easily train Natural classifiers from text files on your system
A command-line tool to easily create and train natural language models on Salesforce Einstein
Compare strings containing a mix of letters and numbers in the way a human being would in sort order.
Lightweight and performant natural sorting of arrays and collections by differentiating between unicode characters, numbers, dates, etc.
Compare strings containing a mix of letters and numbers in the way a human being would in sort order.
Compare alphanumeric strings the same way a human would, using a natural order algorithm
A command-line tool to easily create and train natural language models on Salesforce Einstein
Check if a value is a natural number
General natural language (tokenizing, stemming (English, Russian, Spanish), part-of-speech tagging, sentiment analysis, classification, inflection, phonetics, tfidf, WordNet, jaro-winkler, Levenshtein distance, Dice's Coefficient) facilities for node.
various machine learning routines for node
retext plugin to serialize prose
Compare strings in a natural order
retext plugin to parse Latin-script prose
Latin-script (natural language) parser
Get East Asian Width from a character.
Google Cloud Natural Language API client for Node.js
An AI web browsing framework focused on simplicity and extensibility.
Avatar style for DiceBear
A 4kb framework for creating sturdy frontend applications
Developer friendly Natural Language Processing ✨
Return a natural number.
<details> <summary> Netzgrafik-Editor – designed to make better decisions. </summary>
TypeScript definitions for natural-compare
ARIMA, SARIMA, SARIMAX and AutoARIMA models for time series analysis and forecasting
Used to generate natural language training examples.
A simple ruby library that let's any developer automate the training process of a Natural Language Processing Engine on API.AI, and retrieve meaning from new utterances.
I use this process to train my language model as I flesh out my constructed language. Unlike training on a natural language, it is way simpler to actually train an algorithm on a fictional language, specifically with languages you train as you build the language. Still undecided on whether to incorporate this into LearnAnswer, as they completely reshapes how I do machine learning.
Arachni is a feature-full, modular, high-performance Ruby framework aimed towards helping penetration testers and administrators evaluate the security of web applications. It is smart, it trains itself by monitoring and learning from the web application's behavior during the scan process and is able to perform meta-analysis using a number of factors in order to correctly assess the trustworthiness of results and intelligently identify (or avoid) false-positives. Unlike other scanners, it takes into account the dynamic nature of web applications, can detect changes caused while travelling through the paths of a web application’s cyclomatic complexity and is able to adjust itself accordingly. This way, attack/input vectors that would otherwise be undetectable by non-humans can be handled seamlessly. Moreover, due to its integrated browser environment, it can also audit and inspect client-side code, as well as support highly complicated web applications which make heavy use of technologies such as JavaScript, HTML5, DOM manipulation and AJAX. Finally, it is versatile enough to cover a great deal of use cases, ranging from a simple command line scanner utility, to a global high performance grid of scanners, to a Ruby library allowing for scripted audits, to a multi-user multi-scan web collaboration platform.