Hey there!
If you're reading this, get ready because you're about to make your first submission in just a few minutes. I want to assure you that it's totally doable, even if you're new to machine learning. David's starter notebook has been incredibly helpful, and it has inspired me to simplify the process for all the machine learning enthusiasts out there who may not fully understand what we're doing but have joined this contest.
The best part? You don't need GPUs, and you don't need to grasp object-oriented programming (OOP). I've made everything super simple for you. All you need to do is click on the following link:
[Click here to access the notebook](https://colab.research.google.com/drive/1eSJzxyuTDT1UA-Xx3rwz0DwhljA8S9v9?usp=sharing)
Once all the cells have finished running, don't forget to download the submission CSV file. I've included explanations and comments throughout the notebook to make it easier for you to follow along.
Whether you're new to this or an expert, I'd love to hear your feedback and suggestions on how I can make improvements. If you have any ideas, I'll be happy to implement them and share an updated version.
If you find this notebook and discussion helpful, please consider giving it an upvote. Let's spread the knowledge and make machine learning more accessible to everyone. Peace.
LOL... Please enjoy!
I don't think ML classifiers can compete with the BERT-based examples provided by Masakhane, but they're nice examples for using sk-learn in general.