Instructions to use pinecone/movie-recommender-user-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use pinecone/movie-recommender-user-model with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://pinecone/movie-recommender-user-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b0147fc4cd496574aa8e418bbe44885d5ce22ebfd971cc91f452f7be4a56057
- Size of remote file:
- 4.1 kB
- SHA256:
- 477fc4d2dc9093dc77886278b77404954016c658e0b27deede4aed47878d95b3
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