Time Series Forecasting
Joblib
Keras
PyTorch
generic
demand-forecasting
supply-chain
gru
lstm
lightgbm
xgboost
random-forest
mixture-of-experts
Instructions to use AbdullahImran/Saudi-Supply-Demand-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use AbdullahImran/Saudi-Supply-Demand-Models 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://AbdullahImran/Saudi-Supply-Demand-Models") - Notebooks
- Google Colab
- Kaggle
Download dl_multi_horizon_cv_out/history_fold4.joblib from AbdullahImran/Saudi-Supply-Demand-Models: direct link, hf CLI and curl.
- Browser
- Download file 516 Bytes
-
https://huggingface.co/AbdullahImran/Saudi-Supply-Demand-Models/resolve/main/dl_multi_horizon_cv_out/history_fold4.joblib
- Command line
-
hf download hf://AbdullahImran/Saudi-Supply-Demand-Models/dl_multi_horizon_cv_out/history_fold4.joblib
-
curl -L -o history_fold4.joblib https://huggingface.co/AbdullahImran/Saudi-Supply-Demand-Models/resolve/main/dl_multi_horizon_cv_out/history_fold4.joblib
516 Bytes
- Xet hash:
- 073451c5a6741f388121b0e5c4def9998fc8120a0ebe02455b9db50d6a10b414
- Size of remote file:
- 516 Bytes
- SHA256:
- d33777d3806a584eaf35662636e76e00925d9e496662add7941f4c158344d346
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