Instructions to use keras/bge_large_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bge_large_en with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/bge_large_en", 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_large_en")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/bge_large_en")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/bge_large_en") - Keras
How to use keras/bge_large_en 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_large_en") - Notebooks
- Google Colab
- Kaggle
| { | |
| "module": "keras_hub.src.models.bert.bert_backbone", | |
| "class_name": "BertBackbone", | |
| "config": { | |
| "name": "bert_backbone", | |
| "trainable": true, | |
| "dtype": { | |
| "module": "keras", | |
| "class_name": "DTypePolicy", | |
| "config": { | |
| "name": "float32" | |
| }, | |
| "registered_name": null | |
| }, | |
| "vocabulary_size": 30522, | |
| "num_layers": 24, | |
| "num_heads": 16, | |
| "hidden_dim": 1024, | |
| "intermediate_dim": 4096, | |
| "dropout": 0.1, | |
| "max_sequence_length": 512, | |
| "num_segments": 2 | |
| }, | |
| "registered_name": "keras_hub>BertBackbone" | |
| } |