Instructions to use keras/multilingual_e5_small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/multilingual_e5_small with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/multilingual_e5_small", 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/multilingual_e5_small")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/multilingual_e5_small")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/multilingual_e5_small") - Keras
How to use keras/multilingual_e5_small 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/multilingual_e5_small") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f1a36c713792bc87e3de98c3edbd03319aa2c4c4d528020884ca566c19cf090
- Size of remote file:
- 471 MB
- SHA256:
- 8bd1f6ce9e07cbea1defbb72cfffd642ef2b01fe4db3db7b87d706fb3bc804d3
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