Instructions to use shqkel-code/bert-base-nsme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shqkel-code/bert-base-nsme 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://shqkel-code/bert-base-nsme") - Notebooks
- Google Colab
- Kaggle
Download transformer_model.keras from shqkel-code/bert-base-nsme: direct link, hf CLI and curl.
- Browser
- Download file 123 MB
-
https://huggingface.co/shqkel-code/bert-base-nsme/resolve/main/transformer_model.keras
- Command line
-
hf download hf://shqkel-code/bert-base-nsme/transformer_model.keras
-
curl -L -o transformer_model.keras https://huggingface.co/shqkel-code/bert-base-nsme/resolve/main/transformer_model.keras
123 MB
- Xet hash:
- 23a543db85d070d9ea47e904b669b1bbf2ff9cdf6e9b22b521f931b4837c3d09
- Size of remote file:
- 123 MB
- SHA256:
- ecb9ef1606393a863a0d44694717a71dcfaec379b21ae902bf971d65daae35a4
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