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
| library_name: keras-hub | |
| pipeline_tag: text-classification | |
| This is a [`Bert` model](https://keras.io/api/keras_nlp/models/bert) uploaded using the KerasNLP library. | |
| This model is related to a `Classifier` task. | |
| Model 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 | |
| This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information. | |