Token Classification
Transformers
PyTorch
English
bert
sequence-tagger-model
pubmedbert
uncased
radiology
biomedical
bdf-toolbox
Instructions to use StanfordAIMI/stanford-deidentifier-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StanfordAIMI/stanford-deidentifier-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="StanfordAIMI/stanford-deidentifier-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/stanford-deidentifier-v2") model = AutoModelForTokenClassification.from_pretrained("StanfordAIMI/stanford-deidentifier-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "model_max_length": 512, | |
| "name_or_path": "StanfordAIMI/stanford-deidentifier-base", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/home/eprakash/.cache/huggingface/transformers/e8690d897fdcd03dc656dd85cfe99c6480274112250341c2347fceabe981ebea.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |