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
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Download README.md from akraut/CDS_BERT_CLF: direct link, hf CLI and curl.
- Browser
- Download file 662 Bytes
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https://huggingface.co/akraut/CDS_BERT_CLF/resolve/main/README.md
- Command line
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hf download hf://akraut/CDS_BERT_CLF/README.md
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curl -L -o README.md https://huggingface.co/akraut/CDS_BERT_CLF/resolve/main/README.md
662 Bytes
| library_name: keras | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| | name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision | | |
| |----|-------------|-----|------|------|-------|-------|------------------| | |
| |Adam|0.011362014338374138|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32| | |
| ## Model Plot | |
| <details> | |
| <summary>View Model Plot</summary> | |
|  | |
| </details> |