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 classification/mobile_model.pkl from OKTAYBBS/DataScientst-models: direct link, hf CLI and curl.
- Browser
- Download file 4.43 MB
-
https://huggingface.co/OKTAYBBS/DataScientst-models/resolve/main/classification/mobile_model.pkl
- Command line
-
hf download hf://OKTAYBBS/DataScientst-models/classification/mobile_model.pkl
-
curl -L -o mobile_model.pkl https://huggingface.co/OKTAYBBS/DataScientst-models/resolve/main/classification/mobile_model.pkl
4.43 MB
- Xet hash:
- ccc272d262c48466e3b415e6c95190b9e0e9155d67f76c08df0619eb86e996e8
- Size of remote file:
- 4.43 MB
- SHA256:
- c60dd168fb9adade1ebeb863e5d041879357a368e6380e8907117763580406a5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.