Instructions to use keras/deberta_v3_base_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/deberta_v3_base_multi with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/deberta_v3_base_multi", 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/deberta_v3_base_multi")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/deberta_v3_base_multi") - Keras
How to use keras/deberta_v3_base_multi 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/deberta_v3_base_multi") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9293f988bcd523448e6c89e54684dc61bd284933358e48bc863812655969dacb
- Size of remote file:
- 1.11 GB
- SHA256:
- d5b9c7ada5df2cf274b4889911841e5d3bec34ddc39fea2d6b223d4cc5343d0c
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