Instructions to use samanehs/test_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use samanehs/test_bert with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://samanehs/test_bert") - Keras
How to use samanehs/test_bert with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://samanehs/test_bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 992b1965d7aa4ab3335ecf628084e48c1f51f2140a900c15e55c2269c265170f
- Size of remote file:
- 17.6 MB
- SHA256:
- 128b424b65233bcc4b995a671a93e6fc382a6409db6b280afdbb5aa0d9d50a7d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.