Instructions to use samanehs/test_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use samanehs/test_bert with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://samanehs/test_bert") - Keras
How to use samanehs/test_bert with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://samanehs/test_bert") - Notebooks
- Google Colab
- Kaggle
File size: 939 Bytes
b2ad531 | 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 | {
"module": "keras_nlp.models.bert.bert_preprocessor",
"class_name": "BertPreprocessor",
"config": {
"name": "bert_preprocessor",
"trainable": true,
"dtype": "float32",
"tokenizer": {
"module": "keras_nlp.models.bert.bert_tokenizer",
"class_name": "BertTokenizer",
"config": {
"name": "bert_tokenizer",
"trainable": true,
"dtype": "int32",
"vocabulary": null,
"sequence_length": null,
"lowercase": true,
"strip_accents": false,
"split": true,
"suffix_indicator": "##",
"oov_token": "[UNK]"
},
"registered_name": "keras_nlp>BertTokenizer"
},
"sequence_length": 512,
"truncate": "round_robin"
},
"registered_name": "keras_nlp>BertPreprocessor"
} |