How would either an architecture or a model be copyrightable?

Architectures are algorithms. If they aren't patentable and are in addition to that patented, they have no protection.

Model weights are a result of a mechanical procedure that fits a model to data, minimising some kind of error. That is not a work of human authorship.

Things that could be copyrightable are an article describing a model architecture, or a specific software implementation of a model.

As an argument why model weights are unlikely to be copyrightable consider the following parallel: we know that model output, for example, a story generated by ChatGTP based on a prompt is certainly not copyrightable, since it's not a work of human authorship, but then, how is the model? We can view the selection of training examples as something similar to a prompt and the training process as similar to the inference. I think giving copyright protection to model weights might be reasonable though, but I think it's unlikely that they have copyright protection.

As the developer of Spock (posted in another comment) -- OmegaConf is also an awesome choice and super useful. I'd suggest checking it out too!

You can go even closer to metal and use the attrs library as well (https://www.attrs.org/en/stable/)

Can you give an example of a use of reflection showing the prompt and the response?

second favorite:

https://fidelity.github.io/spock/advanced_features/Post-Hooks

Basically lets you do any validation necessary on your configs. Spock provides some basics (greater than, within bounds, etc) but it's totally up to the user via any simple asserts or validation functions a user wants to write.

go closer to the metal and use omegaconf directly.

This being my favorite hidden one:

https://fidelity.github.io/spock/advanced_features/Evolve#maintaining-cli-and-python-api-configuration-parity

Most of the cool stuff is buried in the docs under advanced features :-)

https://fidelity.github.io/spock/advanced_features/Composition

(full transparency I'm the author/maintainer/core-developer. I know the docs need a re-org to surface more of the useful features)

You might like something like this as you use azure: https://azure.microsoft.com/en-us/products/bot-services/

Microsoft is the one in charge now.

I understand semantic segmentation but it's been a while since I've tinkered in the computer vision space. Can you explain the push for panoptic segmentation and why you think it's a valuable technology? What can it accomplish that really good semantic segmentation can't?

I would check the paper, but I think they fine tune a pre trained local LM. They also created their own encodings to account for the structure of tabular data, such as the column headers, entity rows etc.

I will note though, from what I remember the table sizes were pretty small.

What license will you use for this implementation?

[removed]

[removed]

Before answering the question, I would submit that you should be thinking of keeping your models behind an api. No need to have it sitting on the client side (which is why it feels you're asking the quesiton)

And behind an API it can be as big as you'd like or can afford on your server)

From Hugging Face?

The end goal would be to add it into the products current chat in the web app, so I can't be doing that :)

There was a paper I read a few months ago (I think it was called tapas). In this paper they show how to ingest tabular data to a transformer model.

It’s going to be a lot easier to just take something like GPT-4 and feed in your data directly and ask questions.

Looks interesting, a bit more lightweight than hydra. But also misses a lot of cool features like composing multiple yaml configs

I'm leaning in this direction myself.

IANAL, but I think about the Feist Publications ruling which dealt with raw listings of facts (white pages names, addresses, phone numbers organized in the most functional format - alphabetic.) SCOTUS ruled that the raw data was not copyrightable even though it took a lot of effort to collect and compile it. It seems to be that the raw data here are the weights which would make them not copyrightable. The structural design of the model might be, and more than likely the compiled model with weights would be copyrightable.

I suspect this will work its way through the courts just in time to be rendered moot.

>I think it's worth a shot to replace LPIPS loss and adversarially train it as a discriminator

that would be very similar to this: https://openreview.net/forum?id=HZf7UbpWHuA

Looks interesting. Have you tried any of the GPT models against this benchmark?

I don't know.

One of my reviewers increased their score by 1 just today and updated their review by editing it. So don't lose hope yet :)