Instructions to use sujal7102003/recommendation-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use sujal7102003/recommendation-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://sujal7102003/recommendation-models") - Notebooks
- Google Colab
- Kaggle
Download xgboost.joblib from sujal7102003/recommendation-models: direct link, hf CLI and curl.
- Browser
- Download file 88.8 kB
-
https://huggingface.co/sujal7102003/recommendation-models/resolve/main/xgboost.joblib
- Command line
-
hf download hf://sujal7102003/recommendation-models/xgboost.joblib
-
curl -L -o xgboost.joblib https://huggingface.co/sujal7102003/recommendation-models/resolve/main/xgboost.joblib
88.8 kB
- Xet hash:
- 6ad3dbda1d07d1b13ad7ba8aa691e93d105cc421eaa546c8fe85a63f10f658dc
- Size of remote file:
- 88.8 kB
- SHA256:
- 3c4bc0d4820334362b7233ff246b913068446f276e1298792a65ba4e3be7a226
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