Instructions to use ICFgainAI/Lifetime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ICFgainAI/Lifetime 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/Lifetime") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ICFgainAI/Lifetime: direct link, hf CLI and curl.
- Browser
- Download file 648 Bytes
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https://huggingface.co/ICFgainAI/Lifetime/resolve/main/README.md
- Command line
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hf download hf://ICFgainAI/Lifetime/README.md
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curl -L -o README.md https://huggingface.co/ICFgainAI/Lifetime/resolve/main/README.md
648 Bytes
metadata
license: cc-by-nc-nd-4.0
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/Section
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)
All rights reserved