Instructions to use jhliu/ClinicalNoteBERT-tiny-simcse_segment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jhliu/ClinicalNoteBERT-tiny-simcse_segment with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jhliu/ClinicalNoteBERT-tiny-simcse_segment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from jhliu/ClinicalNoteBERT-tiny-simcse_segment: direct link, hf CLI and curl.
- Browser
- Download file 574 Bytes
-
https://huggingface.co/jhliu/ClinicalNoteBERT-tiny-simcse_segment/resolve/main/config.json
- Command line
-
hf download hf://jhliu/ClinicalNoteBERT-tiny-simcse_segment/config.json
-
curl -L -o config.json https://huggingface.co/jhliu/ClinicalNoteBERT-tiny-simcse_segment/resolve/main/config.json
574 Bytes
| { | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 312, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1200, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 2048, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "sinusoidal_pos_embds": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.16.2", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |