This project is an End-to-end product leveraging NLP model to assist in writing news articles by suggesting next words.
In an era of information and technology, written news is a very important mean of communication. With a high volume of articles been published every day, delivering news fast is crucial. The purpose of this project is to develop an end-to-end product to help write news articles by suggesting the next words of the text. The steps taken during this project development were the following:
Step 1: Train NLP models (recurrent and transformers neural networks) on a collection of tens of thousands of news articles to predict the next word of a text.
Step 2: Create a FLASK application to run the selected model (Backend)
Step 3: Create the Webpage where users can write articles and have the next word suggested (Frontend)
Step 4: Test locally and containerize using docker
Step 5: Deploy application using Kubernetes on GCP
Check out this video presentation for the project: https://www.youtube.com/watch?v=1wHUIBK3KbU
Initial model were trained based on Business articles from the AG's News Topic Classification Dataset available at: https://datasets.quantumstat.com
For predicting next possible candidates a recurrent neural network with LSTMs was used. Scripts are in the modelTraining directory.
The models runs in a backend of a webpage
User can pick the topic it wants to write about and the model sugests probable next words.
##Thank your interest in my project!!