k4ni5h
Propreitor @ SaralTech™ | Web | Mobile | FinTech | Enterprise Softwares | IIT Roorkee '20 | Lazy enough to automate anything
Scope3 | RTBAnalyticaIndia
Pinned Repositories
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's Repositories
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