Instructions to use samanehs/bert_tiny_en_uncased_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use samanehs/bert_tiny_en_uncased_classifier with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://samanehs/bert_tiny_en_uncased_classifier") - Keras
How to use samanehs/bert_tiny_en_uncased_classifier 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/bert_tiny_en_uncased_classifier") - Notebooks
- Google Colab
- Kaggle
File size: 454 Bytes
c69bf79 e695b48 c69bf79 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"module": "keras_nlp.src.models.bert.bert_backbone",
"class_name": "BertBackbone",
"config": {
"name": "bert_backbone",
"trainable": true,
"vocabulary_size": 30522,
"num_layers": 2,
"num_heads": 2,
"hidden_dim": 128,
"intermediate_dim": 512,
"dropout": 0.1,
"max_sequence_length": 512,
"num_segments": 2
},
"registered_name": "keras_nlp>BertBackbone"
} |