Instructions to use ICFgainAI/Section with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ICFgainAI/Section 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://ICFgainAI/Section") - Notebooks
- Google Colab
- Kaggle
Model Description
This is a part of ICFgainAI, published at https://doi.org/10.1002/lpor.71253
other parts of ICFgainAI are available at https://huggingface.co/ICFgainAI/Lifetime
model: the main model file containing the trained model
base_mat_scaler: host material input scaler used during model construction
dopant_scaler: dopant input scaler used during model construction
y_scaler: target value scaler to restore model output to original scale
- Developed by: Chenbo Zhang, Tongji University (cbzhang@tongji.edu.cn), Jun Xu, Tongji University (15503@tongji.edu.cn)
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# !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://ICFgainAI/Section")