Automated Text Classification (relevant labeled data and moded definitions)

[RohitRathore1]

In reference of issue no #57

Uber Ludwig

Completing the plan I described in issue #57 using Deep Learning generally requires writing advanced Python code. Fortunately, Uber has released a super valuable tool called Ludwig that makes it possible to build and use predictive models with incredible ease. We will run Ludwig from within Google Colaboratory in order to use their free GPU runtime. Training Deep Learning models without using GPUs can be the difference between waiting a few minutes to waiting hours.

Automated Text Classification

In order to build predictive models, we need relevant labeled data and moded definitions. So, you will practice with a simple text classification model straight from the Ludwig examples. We are going to use a labeled dataset of BBC articles organized by category. This article should give you a sense of the level of coding we won’t have to do because we are using Ludwig.

[SubhradeepSS]

@TeAmP0is0N I am a beginner in deep learning.I have worked on a text summarization project during JGEC Winter of Code. I am interested to work on the issue.Will it be ok for a beginner like me to proceed upon the issue?

[RohitRathore1]

@SubhradeepSS Yeah, you can proceed with it. The issue is open for all. Go ahead.

[deep1401]

Hi, would you be open to using Zero Shot Learning for this task? I could make a notebook to demonstrate on certain datasets you'll suggest using the Huggingface models.