It has been a while since I have explored NLP with Google’s T5. So I decided to take it a step further. In this blog, I give a brief introduction to Google’s T5 and how it works hand in hand with the State of the Art Natural Language Processing library, Spark NLP.
So let’s come to our first question:
This article is for any researcher, data scientist or student who wants to build a simple, performant & accurate NLP pipeline that scales easily in a distributed environment.
The NLP Summit, a conference for the fast-growing applied natural language processing community kicked off on 6th October 2020. It was organised by the prestigious John Snow Labs. The themes of each of the four main days of the conference are Trends & Best Practices, Open Source Libraries, NLP in Healthcare, and Conversational AI.
The NLP Summit Program involved over 30 unique sessions and 40 speakers, covering the diversity of use cases, technologies, industries, and organizations where NLP is successfully applied today, from industry leaders including:
So a few weeks back, one of my friends was working on this project for object detection using OpenCV and she was supposed to give it a client-side. She struggled a lot between GET/POST requests and eventually gave up on the project.
We’ve all been there at some point, where we struggled to integrate our ML models to our apps. That was the moment when I realised that many Data Science/ Machine Learning enthusiasts might not be aware of this boon, to the industry, called Streamlit!
Streamlit is an open-source app framework that helps data scientists and machine learning engineers…
A Photographer by Day, Data Science Enthusiast by Night who Runs On Coffee ☕