Well done!
vluz
joined 1 year ago
That's wonderful to know! Thank you again.
I'll follow your instructions, this implementation is exactly what I was looking for.
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?
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
While designing a similar classifier, I've considered the idea of giving it the whole thread as "context" of sorts.
Not just the parent comment, the whole thread up to original post.
I've abandoned the idea.
A comment must stand on it's own, and it would put limits on results, the way I was planning to do it.
I might be very wrong, your insight into this would be very helpful.
My original idea was to go recursively trough the thread and test each comment individually.
Then I would influence the actual comment results with the combined results of it's parents.
No context during inference, just one comment at a time.
For example consider thread OP->C1->C2->C3.
My current model takes milliseconds per test with little resources used.
It would be ok up to very large threads but would contain a limit to save on answer time.
I want to determine if Comment 3 is toxic in the context of C2, C1, and OP.
Test C3, test C2, test C1, test OP. Save results.
My current model gives answer in several fields ("toxic", "severe toxic", "obscene", "threat", "insult", and "identity hate")
The idea was to then combine the results of each into a final result for C3.
How to combine? Haven't figure it out but it would be results manipulation instead of inference/context, etc.
Edit: Is there any way you can point me at examples difficult to classify? It would be a nice real world test to my stuff.
Current iteration of model is very new and has not been tested in the wild.
Don't trust brave, never will.
Just pip install mscandy -U
If at all true this would be world-changing news.
Erasmus is a semester up to max of one year.
It is impossible that condition came from eating normal food here, compared to a lifetime somewhere else.
Take my grand aunt with 102 years of age as an example, she would be a walking pot of mercury by now.
She ate fish all her life and due to location, way more fish than meat.
What about me at 50 years old and not even an hint of poisoning. I eat more fish than meat.
How does that work?
Here are the numbers for heavy metal poisoning for 2022, ordered by rank and Country:
https://epi.yale.edu/epi-results/2022/component/hmt
I'm very sorry for your friend and wish the best without reservation, but her condition was not from eating fish during a semester in Portugal.
HateLLM will be a smash. /s