k4ni5h

Betting-Ace

a betting game. check how much you can lose ?

chainlit

Build Python LLM apps in minutes ⚡️

Chess-Playing-Bot

Illusion-Click

Is your eyes perfect? you can check here

linguipedia

movie_review

open-event-webapp

Open Event Web App Generator http://opev-webgen-dev.herokuapp.com http://sched.eventyay.com

ReactionBot2.0

Su-Tic-Tac-Toe

titanic

k4ni5h/movie_review

k4ni5h/open-event-webapp

Open Event Web App Generator http://opev-webgen-dev.herokuapp.com http://sched.eventyay.com

k4ni5h/chainlit

Build Python LLM apps in minutes ⚡️

k4ni5h/Illusion-Click

Is your eyes perfect? you can check here

k4ni5h/digits-recognizer-kubeflow

Sample MLOps Workflow: Recognizing Digits with Kubeflow

k4ni5h/django-firebase-orm

Django ORM for the NoSQL object model database, firebase

k4ni5h/docker-elk

The Elastic stack (ELK) powered by Docker and Compose.

k4ni5h/Extractive-Text-Summerization

Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.

k4ni5h/geohash2kml

k4ni5h/hacko

k4ni5h/javamvc

k4ni5h/k4ni5h

k4ni5h/lama-cleaner

Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.

k4ni5h/methodology

Measurement methodology for advertising emissions

k4ni5h/modoboa

Mail hosting made simple

k4ni5h/nlp-in-practice

Starter code to solve real world text data problems. Includes: Gensim Word2Vec, phrase embeddings, Text Classification with Logistic Regression, word count with pyspark, simple text preprocessing, pre-trained embeddings and more.

k4ni5h/onikan

plus-minus (callbreak) multi player and single player (vs bot)

k4ni5h/OnlineExam_Servlet_Jsp

Online Examination System Using J2EE (JAVAEE) and MySQL Database.

k4ni5h/open-event-frontend

The frontend for the Open Event API Server

k4ni5h/pattern

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

k4ni5h/python-cloudflare

Python wrapper for the Cloudflare Client API v4

k4ni5h/RAKE

A python implementation of the Rapid Automatic Keyword Extraction

k4ni5h/Real-Time-Voice-Cloning

Clone a voice in 5 seconds to generate arbitrary speech in real-time

k4ni5h/redux

Predictable state container for JavaScript apps

k4ni5h/screenshot-capture

Screenshot Capture / Browser Extension

k4ni5h/sqlova

k4ni5h/tensorflow_makefile

k4ni5h/Tinder-App-Flutter

Tinder inspired application created using Flutter and Firebase.

k4ni5h/ud-annotatrix

k4ni5h/visual-chatgpt

VisualChatGPT