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:
# !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://pinecone/movie-recommender-user-model") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from pinecone/movie-recommender-user-model: direct link, hf CLI and curl.
- Browser
- Download file 33.3 kB
-
https://huggingface.co/pinecone/movie-recommender-user-model/resolve/main/saved_model.pb
- Command line
-
hf download hf://pinecone/movie-recommender-user-model/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/pinecone/movie-recommender-user-model/resolve/main/saved_model.pb
33.3 kB
- Xet hash:
- 7b5f6aa2b136283c2c44d0dfc2253303aed412b8da3059f1da670251489038fa
- Size of remote file:
- 33.3 kB
- SHA256:
- 72066e205cb0936df0d7e245c7afe8f7aa724f650c2ebcb3994bc903312c1e36
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