Instructions to use keras/bge_base_zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bge_base_zh with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/bge_base_zh", 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/bge_base_zh")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/bge_base_zh")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/bge_base_zh") - Keras
How to use keras/bge_base_zh 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/bge_base_zh") - Notebooks
- Google Colab
- Kaggle
File size: 239 Bytes
610e8e4 | 1 2 3 4 5 6 7 8 9 10 11 | {
"keras_version": "3.13.2",
"keras_hub_version": "0.30.0.dev0",
"parameter_count": 102267648,
"date_saved": "2026-07-04@10:13:27",
"tasks": [
"MaskedLM",
"TextClassifier",
"TextEmbedder"
]
} |