Instructions to use OneScience-Group/Antibody_deep_learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TF-Keras
How to use OneScience-Group/Antibody_deep_learning with TF-Keras:
# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy) # See https://github.com/keras-team/tf-keras for more details. from huggingface_hub import from_pretrained_keras model = from_pretrained_keras("OneScience-Group/Antibody_deep_learning") - Notebooks
- Google Colab
- Kaggle
File size: 774 Bytes
fe8e241 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | args <- commandArgs(trailingOnly = FALSE)
script_arg <- grep("^--file=", args, value = TRUE)
if (length(script_arg) > 0) {
script_path <- normalizePath(sub("^--file=", "", script_arg[1]))
setwd(normalizePath(file.path(dirname(script_path), "..")))
}
library(reticulate)
use_python(Sys.getenv("RETICULATE_PYTHON"), required = TRUE)
np <- import("numpy", convert = FALSE)
encoded <- readRDS("model/GAN/seq_all_encoded.RDS")
names_encoded <- names(encoded)
for (i in seq_along(encoded)) {
out_file <- sprintf("model/GAN/seq_encoded_%02d.npz", i)
np$savez_compressed(
out_file,
x = np$array(encoded[[i]], dtype = "float32"),
name = names_encoded[[i]]
)
cat("saved", out_file, names_encoded[[i]], dim(encoded[[i]]), "\n")
}
cat("export GAN npz OK\n")
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