> neofetch
k4ni5h@github
-------------------------
OS: Arch Linux x86_64 + macOS Monterey
Shell: zsh 5.8
Location: Panipat, IN
Hobbies: Sleeping
For more info about me visit https://k4ni5h.github.io/
Name: Kanish
Type: User
Bio: Propreitor @ SaralTech™ | Web | Mobile | FinTech | Enterprise Softwares | IIT Roorkee '20 | Lazy enough to automate anything
Twitter: k4ni5h
Location: India
> neofetch
k4ni5h@github
-------------------------
OS: Arch Linux x86_64 + macOS Monterey
Shell: zsh 5.8
Location: Panipat, IN
Hobbies: Sleeping
For more info about me visit https://k4ni5h.github.io/
a betting game. check how much you can lose ?
Build Python LLM apps in minutes ⚡️
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
Sample MLOps Workflow: Recognizing Digits with Kubeflow
Django ORM for the NoSQL object model database, firebase
The Elastic stack (ELK) powered by Docker and Compose.
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.
Is your eyes perfect? you can check here
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.
Sound source localization in reconfigurable wireless acoustic sensor networks
game in less than 2 kb on zip
Measurement methodology for advertising emissions
Mail hosting made simple
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.
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plus-minus (callbreak) multi player and single player (vs bot)
Online Examination System Using J2EE (JAVAEE) and MySQL Database.
The frontend for the Open Event API Server
Open Event Web App Generator http://opev-webgen-dev.herokuapp.com http://sched.eventyay.com
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.