Token Classification
Transformers
Safetensors
PyTorch
English
roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
hipaa
openmed
Eval Results (legacy)
Instructions to use EdgeAIMed/EdgeAIMed-PII-FastClinical-Small-82M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdgeAIMed/EdgeAIMed-PII-FastClinical-Small-82M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EdgeAIMed/EdgeAIMed-PII-FastClinical-Small-82M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EdgeAIMed/EdgeAIMed-PII-FastClinical-Small-82M-v1") model = AutoModelForTokenClassification.from_pretrained("EdgeAIMed/EdgeAIMed-PII-FastClinical-Small-82M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 319 Bytes
8d973e0 | 1 2 3 4 5 6 7 8 9 10 11 | {
"epoch": 3.0,
"eval_accuracy": 0.9944782542656068,
"eval_f1": 0.9575298969573839,
"eval_loss": 0.02151305042207241,
"eval_precision": 0.9602049530315969,
"eval_recall": 0.954869704469355,
"eval_runtime": 11.4312,
"eval_samples_per_second": 437.399,
"eval_steps_per_second": 6.911
} |