Instructions to use keras/bge_m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bge_m3 with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/bge_m3", 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_m3")import keras_hub # Create a TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/bge_m3")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/bge_m3") - Keras
How to use keras/bge_m3 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_m3") - Notebooks
- Google Colab
- Kaggle
File size: 1,458 Bytes
3551359 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | {
"module": "keras_hub.src.models.xlm_roberta.xlm_roberta_text_embedder_preprocessor",
"class_name": "XLMRobertaTextEmbedderPreprocessor",
"config": {
"name": "xlm_roberta_text_embedder_preprocessor",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "float32"
},
"registered_name": null
},
"tokenizer": {
"module": "keras_hub.src.models.xlm_roberta.xlm_roberta_tokenizer",
"class_name": "XLMRobertaTokenizer",
"config": {
"name": "xlm_roberta_tokenizer",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "int32"
},
"registered_name": null
},
"config_file": "tokenizer.json",
"proto": null,
"sequence_length": null,
"add_bos": false,
"add_eos": false
},
"registered_name": "keras_hub>XLMRobertaTokenizer"
},
"config_file": "preprocessor.json",
"sequence_length": 8192,
"truncate": "round_robin"
},
"registered_name": "keras_hub>XLMRobertaTextEmbedderPreprocessor"
} |