Instructions to use SOTAagi2030/ClinicNote-DeID-Baseline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SOTAagi2030/ClinicNote-DeID-Baseline with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SOTAagi2030/ClinicNote-DeID-Baseline")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SOTAagi2030/ClinicNote-DeID-Baseline") model = AutoModelForTokenClassification.from_pretrained("SOTAagi2030/ClinicNote-DeID-Baseline", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SOTAagi2030/ClinicNote-DeID-Baseline: direct link, hf CLI and curl.
- Browser
- Download file 245 Bytes
-
https://huggingface.co/SOTAagi2030/ClinicNote-DeID-Baseline/resolve/main/tokenizer.json
- Command line
-
hf download hf://SOTAagi2030/ClinicNote-DeID-Baseline/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/SOTAagi2030/ClinicNote-DeID-Baseline/resolve/main/tokenizer.json
245 Bytes
| {"version":"1.0","truncation":null,"padding":null,"added_tokens":[],"normalizer":null,"pre_tokenizer":{"type":"Whitespace"},"model":{"type":"WordLevel","vocab":{"[UNK]":0,"Patient":1,"was":2,"seen":3,"in":4,"[REDACTED]":5},"unk_token":"[UNK]"}} | |