Token Classification
Transformers
Safetensors
PyTorch
Italian
bert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
italian
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Italian PII detection model OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1
11a61ab verified Download eval_results.json from OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1: direct link, hf CLI and curl.
- Browser
- Download file 404 Bytes
-
https://huggingface.co/OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1/resolve/main/eval_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/OpenMed/OpenMed-PII-Italian-BioClinicalBERT-Base-110M-v1/resolve/main/eval_results.json
404 Bytes
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9895407633164784, | |
| "eval_f1": 0.9268506943271277, | |
| "eval_loss": 0.0316462479531765, | |
| "eval_macro_f1": 0.9092147648514213, | |
| "eval_precision": 0.9213407671122456, | |
| "eval_recall": 0.9324269204622706, | |
| "eval_runtime": 3.9888, | |
| "eval_samples_per_second": 1246.48, | |
| "eval_steps_per_second": 19.555, | |
| "eval_weighted_f1": 0.9246379209865742 | |
| } |