Recent comments in /f/MachineLearning
harharveryfunny t1_je9t0b4 wrote
Just try it ! Yes - they do very well.
You don't even need to ask them to translate - just give them a foreign language source and ask questions about it, or ask for a summary !
viertys OP t1_je9srha wrote
Reply to comment by deep-yearning in [D] Improvements/alternatives to U-net for medical images segmentation? by viertys
All images have cavities in them and in general the cavities make up 5-10% of the image.
Here is an example: https://imgur.com/a/z0yeH0C The mask on the left is the ground truth and the mask on the right is the predicted one.
​
I'm currently using Kaggle and I can't use very large batch sizes. My batch size is 4 now. Is there an alternative to Kaggle that you would suggest?
elbiot t1_je9s53t wrote
Reply to comment by LetGoAndBeReal in [D] The best way to train an LLM on company data by jaxolingo
Your claim that prompting can achieve what fine tuning can't contradicts the documentation for openai that you posted that said fine tuning can do whatever prompting can without the length limit
MysteryInc152 t1_je9s41k wrote
Bilingual LLMs are much better translators than traditional SOTA.
https://github.com/ogkalu2/Human-parity-on-machine-translations
[deleted] t1_je9rxe3 wrote
Reply to [D] Simple Questions Thread by AutoModerator
[deleted]
MysteryInc152 t1_je9rwv5 wrote
Reply to comment by ZestyData in [D] Can large language models be applied to language translation? by matthkamis
He's talking about unsupervised predict the next token GPTs. That's definitely not how Google Translate and the like work.
And GPT like models far outperform traditional SOTA translators
https://github.com/ogkalu2/Human-parity-on-machine-translations
[deleted] t1_je9rb1f wrote
Reply to comment by learn-deeply in [D] Training a 65b LLaMA model by Business-Lead2679
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3z3ki3l t1_je9qt86 wrote
Reply to comment by TheAdvisorZabeth in [R] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention by floppy_llama
If this isn’t copypasta, you’re having a manic episode. See a doctor, please.
deep-yearning t1_je9qqrf wrote
Reply to comment by viertys in [D] Improvements/alternatives to U-net for medical images segmentation? by viertys
Accuracy is not a good metric here given the large number of true negative pixels you will get.
How large is the typical region you are trying to segment (in pixels)? If you've already done data augmentation I would also try to generate images if you can. Use a larger batch size, try different optimizers and a learning rate scheduler. How many images do not have cavities in them?
JigglyWiener t1_je9qp60 wrote
Reply to comment by ML4Bratwurst in [D] What do you think about all this hype for ChatGPT? by Dear-Vehicle-3215
Standard Gartner Hype Cycle. Influencers found out, so did every 50-60 year old professional I've ever met. They keep asking me how to make money off this, and my only answer is "buy Nvidia" lol.
Dry_Bag_2485 t1_je9qo78 wrote
Reply to comment by matthkamis in [D] Can large language models be applied to language translation? by matthkamis
Don’t ask questions on Reddit if you can’t handle certain pixels depicting letters in an order you find offensive😂 The ML Reddits are really going downhill
Dry_Bag_2485 t1_je9qfyi wrote
Reply to comment by matthkamis in [D] Can large language models be applied to language translation? by matthkamis
Try DeepL. Or openais translation endpoints, there’s a lot of options other than google
roybatty553 t1_je9ptxe wrote
Reply to comment by ZestyData in [D] Can large language models be applied to language translation? by matthkamis
matthkamis is right. You can (and did) pack a lot of condescension into a single ‘Uh..’ . The rest of your reply was well-informed and helpful (for me too; thank you) but your opener helped no one.
matthkamis OP t1_je9pn5x wrote
Reply to comment by spiky_sugar in [D] Can large language models be applied to language translation? by matthkamis
Thank you
spiky_sugar t1_je9phxc wrote
Tiny_Arugula_5648 t1_je9oyfo wrote
Not sure why no one is calling this out but there is no indication a LLM is going to be useful here.. you have tabular data, unless it's unstructured text held in there it's not goung to be useful, pick the right model for the job..
saintshing t1_je9okpn wrote
Reply to comment by saintshing in [R] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention by floppy_llama
Apparently some people managed to reconstruct images from brain activitiy using stable diffusion technique. I wonder how it would apply to animals.
matthkamis OP t1_je9ojio wrote
Reply to comment by ZestyData in [D] Can large language models be applied to language translation? by matthkamis
The assuming isn’t the passive aggressive part. It’s the leading your response with “uh..” it reads kinda like “well actually…”
RemindMeBot t1_je9o9gp wrote
Reply to comment by tedmobsky in [D] The best way to train an LLM on company data by jaxolingo
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fletchertyler914 t1_je9o94n wrote
I just found this a few days ago and actually used it as a prototype base to learn the ropes, so thanks op! I ended up gutting the ingest flow in favor of an additional upload api route to make it more flexible, but overall it was a good example/guide to follow. Nice work.
Celmeno t1_je9o7z6 wrote
ChatGPT is a tool. GPT-5 might be the end of humanity. We do not have done enough alignment.
tedmobsky t1_je9o66u wrote
!Remindme 3 days
JigglyWiener t1_je9o2b6 wrote
Reply to comment by statmlsn in [D] What do you think about all this hype for ChatGPT? by Dear-Vehicle-3215
Tushy
viertys OP t1_je9nno8 wrote
Reply to comment by BrotherAmazing in [D] Improvements/alternatives to U-net for medical images segmentation? by viertys
I didn't mention it in the post, but I'm using the albumentations module. I rotate, shift, rotate, blur, horizontal flip, downscale and use gauss noise. I get around 400 images after doing this. Is there anything you would suggest?
I have an accuracy of 98.50 and I have dice of around 0.30-0.65 in each image
And yes, the images are grayscale and they are cropped around the teeth area, so only that part of the X-ray remains.
deep-yearning t1_je9te4j wrote
Reply to comment by viertys in [D] Improvements/alternatives to U-net for medical images segmentation? by viertys
Train locally on your own machine if you have a GPU, or try using google colab if you don't. Google Colab has V100 which should fit larger batch sizes.
To be honest, given how limited the data set is and how small some of the segmentation masks are, I am not sure other architectures will be able to do any better than U-Net.
I would also try the nnU-Net which should give state-of-the-art performance, and so will give you a good idea of what's possible with the dataset that you have: https://github.com/MIC-DKFZ/nnUNet