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/text_robot_model.pkl from OKTAYBBS/DataScientst-models: direct link, hf CLI and curl.
- Browser
- Download file 1.22 kB
-
https://huggingface.co/OKTAYBBS/DataScientst-models/resolve/main/deep_learning/text_robot_model.pkl
- Command line
-
hf download hf://OKTAYBBS/DataScientst-models/deep_learning/text_robot_model.pkl
-
curl -L -o text_robot_model.pkl https://huggingface.co/OKTAYBBS/DataScientst-models/resolve/main/deep_learning/text_robot_model.pkl
1.22 kB
- Xet hash:
- 9e339624aa4c7e5a57228f74227f65777e0b2a03f206c44e341066ace652149e
- Size of remote file:
- 1.22 kB
- SHA256:
- 7c895025b9713eef08928dd3f8c4018a922521fbc97efcc14ba6d826f8c084d0
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