Tabular Classification
Keras
Scikit-learn
English
tensorflow
random-forest
cnn
clustering
nlp
computer-vision
recommendation-system
time-series
streamlit
Instructions to use OKTAYBBS/DataScientst-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use OKTAYBBS/DataScientst-models with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://OKTAYBBS/DataScientst-models") - Scikit-learn
How to use OKTAYBBS/DataScientst-models with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("OKTAYBBS/DataScientst-models", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92fc90fdbb5514db7dbfad994f3f0e84cc9dd80160fcb27b3ce3ba88b221e324
- Size of remote file:
- 39.3 MB
- SHA256:
- 746ebfaca9bfb1bdee915c5a016a0f12a20a550438d41d3e889e8f3796d9b6b6
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