*Hello ISMites*,
Through this contest, lets us indulge in solving a real life problem, which is affecting a lot of internet users.  Clearly this is a NLP task, but lets change it a bit, we will apply NLP to **Hindi** Language this time. and lets see how much we could Improve NLP for Hindi language and NLP culture in ISM. 

A kind request to the participant would be treating this contest more as a **learning opportunity**, Please do start discussion in 'Discussion' section. If you have good tutorial you want to share, or have a idea you want to discuss or want feedback, please start a discussion for the same. 

*I would be regularly posting baseline solution, starter codes and tutorial for newbies.*

**Please take a look at the Rules section.**

# ClickBait Detection For Hindi Language
## what is ClickBait?
   [Clickbait](https://en.wikipedia.org/wiki/Clickbait), a form of false advertisement, uses hyperlink text or a thumbnail link that is designed to attract attention and to entice users to follow that link and read, view, or listen to the linked piece of online content, with a defining characteristic of being deceptive, typically sensationalized or misleading.

## why it is necessary?
Click-bait waste a lot of productive time of people. These headlines are written in a very catchy manner such that people are tempted to click these links and they don’t contain any relevant information, hence making it necessary to warn a user about click-bait.

## Task

Train A binary text classifier to detect clickbait in Hindi Languages. Dataset provided is headlines of news corpus in Hindi Language, with label of '0' and '1'.
   - '1' Indicating clickbait
   - '0' Indicating non- clickbait.

## Evaluation
   For evauation we will be using F1-score matric. you would be scored initially on Public data which will be 35% of original test data. Final result would be on total test data and will be made public after contest ends.

## code submission
 ***each participant is required to submit working notebook as kaggle kernel, which should have runtime less than 6 hrs.*** you can use pretrained models available publicly, fine tune them, but all main script (i.e model declaration, weight loader, inference notebook) should be submitted and all of them should be working and producing same result