Instructions to use shqkel-code/bert-base-nsme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shqkel-code/bert-base-nsme 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://shqkel-code/bert-base-nsme") - Notebooks
- Google Colab
- Kaggle
File size: 612 Bytes
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"_name_or_path": "klue/bert-base",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.46.3",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 32000
}
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