Token Classification
Transformers
Safetensors
PyTorch
Portuguese
distilbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
portuguese
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 322 Bytes
f6b3999 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"backend": "tokenizers",
"cls_token": "[CLS]",
"do_lower_case": true,
"is_local": false,
"mask_token": "[MASK]",
"model_max_length": 512,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "BertTokenizer",
"unk_token": "[UNK]"
}
|