Instructions to use keras/bge_small_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bge_small_en with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/bge_small_en", num_classes=2, ) # Fine-tune text_classifier.fit(x=["Thilling adventure!", "Total snoozefest."], y=[1, 0]) # Classify text text_classifier.predict(["Not my cup of tea."])import keras_hub # Create a MaskedLM model task = keras_hub.models.MaskedLM.from_preset("hf://keras/bge_small_en")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/bge_small_en")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/bge_small_en") - Keras
How to use keras/bge_small_en with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/bge_small_en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45ab1e520e9e39517ce7fca0bca8f43979a562673d98b08624729ea8e4cab494
- Size of remote file:
- 134 MB
- SHA256:
- 09767d428efc770fe11a838520616981f1a680d24ea622e7d034ff16200f3c44
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