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
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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.
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