Instructions to use sarthakpatell/ASLL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sarthakpatell/ASLL with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://sarthakpatell/ASLL") - Notebooks
- Google Colab
- Kaggle
Download class_names.npy from sarthakpatell/ASLL: direct link, hf CLI and curl.
- Browser
- Download file 940 Bytes
-
https://huggingface.co/sarthakpatell/ASLL/resolve/main/class_names.npy
- Command line
-
hf download hf://sarthakpatell/ASLL/class_names.npy
-
curl -L -o class_names.npy https://huggingface.co/sarthakpatell/ASLL/resolve/main/class_names.npy
940 Bytes
- Xet hash:
- 6099aa58986a65e78826ff0c1bfb40ff2f9d18d933dab9972ca5a62716b0f83e
- Size of remote file:
- 940 Bytes
- SHA256:
- dcd5dd14b81322e3811825e0a77c0b649ad8d0be4ba76cce632ba8f13fb8f37c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.