Instructions to use keras/bert_base_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bert_base_multi with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/bert_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/bert_base_multi")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/bert_base_multi") - Keras
How to use keras/bert_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/bert_base_multi") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e160f69411ff341a0ea0947d5cf1b58dbcc0d64a20563f8db57376cd9304c3b
- Size of remote file:
- 712 MB
- SHA256:
- c476643c63f270d9eefc45151cfc5da2d59d4e7bdf65e687d42944e79ef2f69c
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