Token Classification
Transformers
Safetensors
English
deberta-v2
pii
phi
healthcare
de-identification
ppsn
Instructions to use temsa/OpenMed-PPSN-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use temsa/OpenMed-PPSN-v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="temsa/OpenMed-PPSN-v4")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("temsa/OpenMed-PPSN-v4") model = AutoModelForTokenClassification.from_pretrained("temsa/OpenMed-PPSN-v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| OpenMed PPSN Extension | |
| Copyright 2026 Contributors | |
| This project includes fine-tuned/derived model artifacts from: | |
| - OpenMed/OpenMed-PII-SuperClinical-Large-434M-v1 (Hugging Face) | |
| Declared license: Apache-2.0. | |
| This project uses evaluation/training data sources including: | |
| - nvidia/Nemotron-PII (Hugging Face dataset) | |
| Declared license: CC-BY-4.0. | |
| Attribution and links: | |
| - OpenMed base model: https://huggingface.co/OpenMed/OpenMed-PII-SuperClinical-Large-434M-v1 | |
| - Nemotron-PII dataset: https://huggingface.co/datasets/nvidia/Nemotron-PII | |
| If you redistribute models or checkpoints produced here, keep this NOTICE, | |
| retain upstream license notices, and provide dataset attribution where required. | |