Instructions to use akraut/CDS_BERT_CLF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use akraut/CDS_BERT_CLF 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://akraut/CDS_BERT_CLF") - Notebooks
- Google Colab
- Kaggle
Download saved_model.pb from akraut/CDS_BERT_CLF: direct link, hf CLI and curl.
- Browser
- Download file 7.52 MB
-
https://huggingface.co/akraut/CDS_BERT_CLF/resolve/refs%2Fpr%2F1/saved_model.pb
- Command line
-
hf download hf://akraut/CDS_BERT_CLF@refs/pr/1/saved_model.pb
-
curl -L -o saved_model.pb https://huggingface.co/akraut/CDS_BERT_CLF/resolve/refs%2Fpr%2F1/saved_model.pb
7.52 MB
- Xet hash:
- 49a87f559f81df0c5ed436e7580653f0fcc0a1e36e6bdc5edeeffc5f1785cef2
- Size of remote file:
- 7.52 MB
- SHA256:
- 0d1f8b4af8bcf53c8b5822f13124977bf73394505e481d67d641dbd5a02056eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.