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
Update README.md
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README.md
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### Model Description
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This is a part of ICFgainAI, accompanied by [manuscript]
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model: the main model file containing the trained model
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base_mat_scaler: host material input scaler used during model construction
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dopant_scaler: dopant input scaler used during model construction
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y_scaler: target value scaler to restore model output to original scale
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- **Developed by:** Chenbo Zhang (cbzhang@tongji.edu.cn), Jun Xu (15503@tongji.edu.cn)
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All rights reserved
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---
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### Model Description
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This is a part of ICFgainAI, accompanied by [manuscript]
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other parts of ICFgainAI for cross-section prediction are available at https://huggingface.co/ICFgainAI/Crosssection, accompanied by [manuscript]
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model: the main model file containing the trained model
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base_mat_scaler: host material input scaler used during model construction
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dopant_scaler: dopant input scaler used during model construction
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y_scaler: target value scaler to restore model output to original scale
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- **Developed by:** Chenbo Zhang, Tongji University (cbzhang@tongji.edu.cn), Jun Xu, Tongji University (15503@tongji.edu.cn)
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All rights reserved
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