Token Classification
Transformers
Safetensors
PyTorch
Portuguese
distilbert
ner
pii
pii-detection
de-identification
privacy
healthcare
medical
clinical
phi
portuguese
openmed
Eval Results (legacy)
Instructions to use OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1") model = AutoModelForTokenClassification.from_pretrained("OpenMed/OpenMed-PII-Portuguese-LiteClinicalU-Small-66M-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 972 Bytes
f6b3999 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
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"eval_macro_f1": 0.49701983392620164,
"eval_precision": 0.8760637474856877,
"eval_recall": 0.8747779065276169,
"eval_runtime": 1.099,
"eval_samples_per_second": 2729.742,
"eval_steps_per_second": 21.838,
"eval_weighted_f1": 0.8648568573998469,
"test_accuracy": 0.9305597290135766,
"test_f1": 0.8788265799760052,
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"test_precision": 0.8797458546412521,
"test_recall": 0.8779092244645481,
"test_runtime": 1.1662,
"test_samples_per_second": 2572.51,
"test_steps_per_second": 20.58,
"test_weighted_f1": 0.8685311149412543,
"total_flos": 934111325716480.0,
"train_loss": 1.9973642018636069,
"train_runtime": 62.4241,
"train_samples_per_second": 1153.401,
"train_steps_per_second": 18.022
} |