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
Commit History
-
Upload nlp/news_vectorizer.pkl with huggingface_hub
Verified signature34139b4 -
Upload nlp/news_model.pkl with huggingface_hub
Verified signature077cd9b -
Upload nlp/imdb_vectorizer.pkl with huggingface_hub
Verified signature2062322 -
Upload nlp/imdb_model.pkl with huggingface_hub
Verified signaturee5f251f -
Upload nlp/spam_vectorizer.pkl with huggingface_hub
Verified signature860ac44 -
Upload nlp/spam_model.pkl with huggingface_hub
Verified signature2c5c4d7