Token Classification
Transformers
Safetensors
PyTorch
Spanish
roberta
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
spanish
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Spanish-FastClinical-Small-82M-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 3.0, | |
| "eval_accuracy": 0.9900848579682234, | |
| "eval_f1": 0.8850982532751092, | |
| "eval_loss": 0.02871573530137539, | |
| "eval_macro_f1": 0.8904040077313126, | |
| "eval_precision": 0.8692039667649424, | |
| "eval_recall": 0.9015846538782318, | |
| "eval_runtime": 1.6317, | |
| "eval_samples_per_second": 2034.1, | |
| "eval_steps_per_second": 15.935, | |
| "eval_weighted_f1": 0.8857914330688584, | |
| "test_accuracy": 0.9904749127984974, | |
| "test_f1": 0.8878800385197414, | |
| "test_loss": 0.030261093750596046, | |
| "test_macro_f1": 0.893313802921463, | |
| "test_precision": 0.8766639500135832, | |
| "test_recall": 0.899386845039019, | |
| "test_runtime": 1.5918, | |
| "test_samples_per_second": 2085.032, | |
| "test_steps_per_second": 16.333, | |
| "test_weighted_f1": 0.8880945974304437, | |
| "total_flos": 1363357688922112.0, | |
| "train_loss": 0.21851455562085992, | |
| "train_runtime": 81.8419, | |
| "train_samples_per_second": 973.512, | |
| "train_steps_per_second": 15.212 | |
| } |