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:
# !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://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
Download deep_learning/fer_model.keras from OKTAYBBS/DataScientst-models: direct link, hf CLI and curl.
- Browser
- Download file 8.28 MB
-
https://huggingface.co/OKTAYBBS/DataScientst-models/resolve/main/deep_learning/fer_model.keras
- Command line
-
hf download hf://OKTAYBBS/DataScientst-models/deep_learning/fer_model.keras
-
curl -L -o fer_model.keras https://huggingface.co/OKTAYBBS/DataScientst-models/resolve/main/deep_learning/fer_model.keras
8.28 MB
- Xet hash:
- 4e81ced9bdb775a3d74e21163c5dd58ee9f4ac2c6649e6561f70c3332dfe5067
- Size of remote file:
- 8.28 MB
- SHA256:
- 322562c347f12cf01001b70dee68a7e6d90f9de4cc1f54c3e0e2fd8df42d5e3d
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