Instructions to use keras/bge_base_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bge_base_en with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/bge_base_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_base_en")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/bge_base_en")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/bge_base_en") - Keras
How to use keras/bge_base_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_base_en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffaccdc6e753811d6f3776c6d60550f063ee79a9b702bb7019a7bf493fb0fdcf
- Size of remote file:
- 438 MB
- SHA256:
- 4e464accaf39be6bdf3d778cb3f9cd145bc72821628f484ab852830830600abb
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