Oof, pop-culture references are hard and I had not considered that at all.
Thanks for the examples, I'll have a think on how to deal with those.
My only insight is one you already had.
Test at least the comment before, and then use the output to dampen or amplify the final result.
Sorry for being no help at all.
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My project is very basic but I'll post it here for any insight you might get out of it.
I teach Python in a variety of settings and this is part of a class.
The data used is from Kaggle: https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/
The original data came from Wikipedia toxic comments dataset.
There is code too from several users, very helpful for some insight into the problem.
Data is dirty and needs clean up so I've done so and posted result on HF here:
https://huggingface.co/datasets/vluz/Tox
Model is a very basic TensorFlow implementation intended for teaching TF basics.
https://github.com/vluz/ToxTest
Some of the helper scripts are very wonky, need fixing before I present this in class.
Here are my weights after 30 epochs:
https://huggingface.co/vluz/toxmodel30
And here is it running on a HF space:
https://huggingface.co/spaces/vluz/Tox
Absolutely stellar write up. Thank you!
I have a couple of questions.
Imagine I have a powerful consumer gpu card to trow at this solution, 4090ti for the sake of example.
- How many containers can share one physical card, taking into account total vram memory will not be exceeded?
- How does one virtual gpu look like in the container? Can I run standard stuff like PyTorch, Tensorflow, and CUDA stuff in general?