Instructions to use keras/multilingual_e5_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/multilingual_e5_large with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/multilingual_e5_large", 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_large")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/multilingual_e5_large")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/multilingual_e5_large") - Keras
How to use keras/multilingual_e5_large 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_large") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a01b4906d92eff17f980dfeaccfbc0a1d493a2861b7088b44eeee0338d8a17e9
- Size of remote file:
- 2.24 GB
- SHA256:
- cc08da200820c94c1e9b93830461d9673ad2c3e59cec08281534548965396704
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