Token Classification
Transformers
Safetensors
PyTorch
German
bert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
german
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-German-BiomedBERT-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-German-BiomedBERT-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-German-BiomedBERT-Base-110M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1: direct link, hf CLI and curl.
- Browser
- Download file 205 Bytes
-
https://huggingface.co/OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1/resolve/0fc7fbf2b9b77a6acaa9be1845481a8a6edd317e/train_results.json
- Command line
-
hf download hf://OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1@0fc7fbf2b9b77a6acaa9be1845481a8a6edd317e/train_results.json
-
curl -L -o train_results.json https://huggingface.co/OpenMed/OpenMed-PII-German-BiomedBERT-Base-110M-v1/resolve/0fc7fbf2b9b77a6acaa9be1845481a8a6edd317e/train_results.json
205 Bytes
| { | |
| "epoch": 3.0, | |
| "total_flos": 5383209424519168.0, | |
| "train_loss": 0.19716031215915883, | |
| "train_runtime": 225.147, | |
| "train_samples_per_second": 562.965, | |
| "train_steps_per_second": 8.808 | |
| } |