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:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://samanehs/bert_tiny_en_uncased_classifier") - Notebooks
- Google Colab
- Kaggle
This is a Bert model 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 for more information.
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