sensor encoder project
- 1 Devlogs
- 9 Total hours
Hello! So this was a quick ML project I did comparing and contrasting two data classification methods. THE DEMO URL IS FAKEEE. You need to clone the repo!! I had no idea how to put that into a URL TwT.
Ship #1
Pending review
@smrethi_shun
on
sensor encoder project
This is my shot at Human Computer Lab’s ML challenge! It’s a compare and contrast between two data classification methods, a direct classifier and a small language model. Basically, both models read sensor data from UCI’s Human Activity Recognition Using Smartphones dataset, and learn to classify actions from the data (walking, standing, sitting, etc). The language model has to create embeddings directly from data, not turn it to language first. Then, we compare the macro-F1 scores of the direct classifier, the language model, and a trick test for the language model, purposefully switching embeddings so that way you know that the model is reliant on the embeddings only.
VERY IMPORTANT!!!! The link to try this project is fake. You have to clone the project and run it in your terminal! Instructions are on the readme!!
@smrethi_shun
on
sensor encoder project
8h 53m 27s logged
Whoaaa Ive been MIA for a hawt minute lol. Anyway surprise! Brand new project popped out of nowhere! For context, I applied to an internship which had an optional project/challenge to do. Basically, I had to test a new form of data processing. I had to classify sensor data data into categories using a small language model rather than a traditional direct classifier. So thats what I did. I compared and contrasted the two methods in terms of macro F1 score, runtime, etc. Everything is kinda one fall swoop, heres the completed project lol.
