deep learning based sequence labeling tools

Tokenization, Stemming, Lemmatization, Bag of words, TF-IDF

Course offered by Udemy . Created and taught by Ankit Mistry, Vijay Gadhave, Data Science & Machine Learning Academy.

A restaurant management web application in which the system assigns a unique token to each order and notifies the customer when the order is complete.

A simple spam classifier using Naive Bayes and Natural Language Processing

Welcome to the Zenith Token Application, where managing your Zenith tokens is a seamless experience. This application empowers you to effortlessly check your token balance and execute secure transactions by sending Zenith tokens to other addresses.

This project utilizes a machine learning model where consumer brand data is employed. Initially, a preliminary model is developed, followed by a refined model using a process called 'fine-tuning' to improve results. Additionally, a comprehensive testing suite has been created to validate accuracy and reliability of the model's predictions.

C Compiler in PHP

The project aims to build a search engine for EncyclEarthpedia by retrieving and processing content from Wikipedia articles, despite the unavailability of their database and API. Key tasks include retrieving Wikipedia content, cleaning and processing text data, tokenizing the content, counting token frequency, and visualizing the mostfrequenttokens

External contract to add supplementary check to the CMTAT

An Nft drop website of 100 sex positions

XFT's tokenized luxury watch marketplace.

First Dapp (for BlockchainChallenge)

Includes the basic Information retrieval techniques demonstrated with python. (tokenization, isolated word correction, context sensitive word correction, Stemming, and Lemmatization )

here you can see how to tokenize the data: 1) how to use the useful libraries for nlp 2) how to clean data 3)how to make normalization and removing stop words 4) how to make lemmatization and stemming

Cryptoeconomics research group's repository

NLP Spam Classifier Model to separate out Spam messages from legitimate messages.

UAT es un tokenizer aritmético básico con funciones para determinar errores, separar tokens por tipos y preparar strings para conversiones a infix, etc.