sensor encoder project

@Shuflduf returned your project · about 1 month ago

This needs changes before we can consider it shipped. Review the Shipwright feedback, make updates, then ship again.

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You still haven't uploaded the right files to the Github release for the demo. A user should be able to test your project without needing to clone the repository. Consider using a tool like Nuitka or Pyinstaller to turn your project into binaries to add to the Github release.

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@mathias returned your project · about 1 month ago

This needs changes before we can consider it shipped. Review the Shipwright feedback, make updates, then ship again.

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Hi smrethi_shun! Your project look great but you need to have a Demo URL that show your project, you can make a GitHub Release with a built version of your project
You can follow this guide about shipping a Desktop app -> https://ship.a.shipwrights.dev/docs/desktop-apps/linux

If you have any questions, ask them in #ask-the-shipwrights

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Ship #1 Changes requested

@smrethi_shun on sensor encoder project · about 2 months ago

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!!

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@smrethi_shun on sensor encoder project · about 2 months ago

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.