Instructions to use keras/bge_m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bge_m3 with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/bge_m3", 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_m3")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/bge_m3")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/bge_m3") - Keras
How to use keras/bge_m3 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_m3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a988b8aa2ddab99e65057d8e015f4bd2499df5e2e8ac5de2e67e4d0d9650e09d
- Size of remote file:
- 2.27 GB
- SHA256:
- f3482cddc622ecda8872f5cb78bf121cac63bdbf910d4bd7b9f2ec6d80c1f189
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