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
Download config.json from shqkel-code/bert-base-nsme: direct link, hf CLI and curl.
- Browser
- Download file 612 Bytes
-
https://huggingface.co/shqkel-code/bert-base-nsme/resolve/main/config.json
- Command line
-
hf download hf://shqkel-code/bert-base-nsme/config.json
-
curl -L -o config.json https://huggingface.co/shqkel-code/bert-base-nsme/resolve/main/config.json
612 Bytes
| { | |
| "_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 | |
| } | |