Instructions to use keras/modern_bert_embed_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/modern_bert_embed_base with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/modern_bert_embed_base", 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/modern_bert_embed_base")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/modern_bert_embed_base")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/modern_bert_embed_base") - Keras
How to use keras/modern_bert_embed_base with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://keras/modern_bert_embed_base") - Notebooks
- Google Colab
- Kaggle
File size: 239 Bytes
f696145 | 1 2 3 4 5 6 7 8 9 10 11 | {
"keras_version": "3.15.1",
"keras_hub_version": "0.33.0.dev0",
"parameter_count": 149014272,
"date_saved": "2026-10-31@03:21:39",
"tasks": [
"MaskedLM",
"TextClassifier",
"TextEmbedder"
]
} |