Originally posted on twitter.
Titanic is tiring 🚢
Iris is irritating 🥀
MNIST is too easy 🥱
Boston makes me queasy 🤢
California housing is not so bad 🏡
Sentiment analysis just makes me sad 🥲
Here are the datasets that I gravitate to... 🧵
What about you? 🙌
For an introduction to ML so gently 👼
The Palmer Penguins are nice and friendly 💞
It has nice illustrations too 👩🎨
Meet the Chinstrap, Adelie and Gentoo 🐧
Now if you want something a bit grander 🗽
The New York Taxi has some splendor 🚕
Does this go beyond your RAM? 🙅
Analyze it out-of-core if you can 🚀
The MNIST dataset might be passe 💾
Roman numerals are drawn another way ✍️
A bit of noise and style imbalance ⚖️
Are sure to give you a nice challenge 🏆
An important part of any ML course 👨🏫
Is to consider the model's source 📰
Even famous datasets contain mistakes ❎
Finding them can be a nice chase 🐆
Some datasets we cannot see 👀
Powering models like BERT, the friend of Ernie 📺
We can question it with clever tasks 🛠
Take a look behind the word masks 🎭
That bias is not a bug 🪳
Don't sweep it under the rug 🧹
This topic deserves a reminder 📆
These systems should be kinder 🤗
Sources
- Palmer Penguins: https://allisonhorst.github.io/palmerpenguins/articles/intro.html
- New York Taxi: https://github.com/vaexio/vaex-talks/blob/master/2019-pydata-london/PyData-London-2019-vaex-EDA-ML.ipynb
- Roman Numerals: https://https-deeplearning-ai.github.io/data-centric-comp/
- Dataset Errors: https://labelerrors.com
- Hugging Face LLM Course: https://huggingface.co/learn/llm-course/chapter1/8?fw=pt