Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Is pretrained model available? #6

Open
hqng opened this issue Apr 10, 2024 · 2 comments
Open

Is pretrained model available? #6

hqng opened this issue Apr 10, 2024 · 2 comments

Comments

@hqng
Copy link

hqng commented Apr 10, 2024

I had read the paper. It is very interesting idea and a step forward from TabPFN.
Now I am looking at the MotherNet API and would like to try it on my internal data. However, it is quite frustrated since I cannot found any pretrained model. Am I missing something? or can you open-source a pretrained model? Thank you so much.

@amueller
Copy link
Contributor

amueller commented Apr 23, 2024

Hey! sorry for the slow response. There's an example here:

Using

from mothernet.prediction import MotherNetClassifier
from mothernet.utils import get_mn_model
model_string = "mn_d2048_H4096_L2_W32_P512_1_gpu_warm_08_25_2023_21_46_25_epoch_3940_no_optimizer.pickle"
    model_path = get_mn_model(model_string)
classifier = MotherNetClassifier(path=model_path)

yields the model from the arXiv paper.

Note that this is still a WIP and see the paper for some of the failure cases. In particular results with categorical variables depend a lot on whether OneHotEncoding is used and useful for a particular dataset.
Note that MotherNetClassifier doesn't do any ensembling and you'd need to use EnsembleMeta to get the ensemble as in the paper.

@hqng
Copy link
Author

hqng commented Apr 24, 2024

Thank you for your information. I'll try it later.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants