QueryMate is an AI-powered search engine capable of understanding questions posed in natural language and extracting precise answers from a given data.
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Updated
Jun 19, 2024 - JavaScript
QueryMate is an AI-powered search engine capable of understanding questions posed in natural language and extracting precise answers from a given data.
This project focuses on building a machine learning model for spam detection in emails. Using natural language processing (NLP) techniques and classification algorithms, the goal is to classify emails as "spam" or "not spam" accurately and efficiently.
Introducing ChameleonAI, your very own, customizable roleplaying AI chatbot. Powered by Google's state-of-the-art PALM Generative AI Model, you get to talk to anything, anyone, anywhere!
NPL - Neural Parallel Language
A local open source PDF chatbot
File search rust aims to develop an efficient file search system for the Windows OS.
RE-Miner Dashboard for Visual Analytics, Review & Market Analysis
Transformers, including the T5 and MarianMT, enabled effective understanding and generating complex programming codes. Consequently, they can help us in Data Security field. Let's see how!
PROJETO DE ANÁLISE DE FAKE NEWS COM A UTILIZAÇÃO DO CORPUS FAKEBR A BIBLIOTECA NLTK EM PYTHON
The StringMetrics project implements 7 string metric algorithms: Hamming, Dice, Jaro, Jaro-Winkler, Soundex, Levenshtein, and Damerau-Levenshtein. Metrics compare strings using IMetric interface providing an approximate similarity score from 0 (no match) to 1 (exact match) useful in data cleansing, record linkage, NLP, fraud detection, etc.
A web app for interview simulation, using AI (under development). GPT based
Netflix movie recommendation system
A Go SDK for the Symbl.ai Platform
Serena is an intelligent and friendly virtual assistant like jarvis designed to simplify your daily tasks and enhance your digital experience. With a focus on natural language understanding and a sleek interface, Serena is here to assist you seamlessly.
High performance unsupervised text tokenization for Ruby
The architectural_cluster.py script performs hierarchical clustering analysis on models, inferring representative labels, calculating silhouette scores, and saving results to an Excel file. The word2vec.py script trains a Word2Vec model, computes pairwise similarity scores, and saves them in a CSV file.
This data sience proyect use NPL to recognize if a text review is positive or negative. And was trained with more than 3 million data.
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