Instructions to use K10S/IPL_Score_Predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use K10S/IPL_Score_Predictor with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://K10S/IPL_Score_Predictor") - Notebooks
- Google Colab
- Kaggle
Upload fallback_value.pkl with huggingface_hub
Browse files- fallback_value.pkl +3 -0
fallback_value.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:c3f11e4bb2a3237817f084d5e6b4847bff4053dcccd82fe89f2bb3a20c1b9c3d
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size 117
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