Instructions to use lethalantidote/phi-detector-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lethalantidote/phi-detector-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="lethalantidote/phi-detector-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("lethalantidote/phi-detector-model") model = AutoModelForTokenClassification.from_pretrained("lethalantidote/phi-detector-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,418 Bytes
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"base_model": "StanfordAIMI/stanford-deidentifier-base",
"base_model_revision": "661b9c1c717d3165512d440abc3700c386aefab6",
"bio_labels": [
"O",
"B-MEDICAL_RECORD",
"I-MEDICAL_RECORD",
"B-DIAGNOSIS",
"I-DIAGNOSIS",
"B-MEDICATION",
"I-MEDICATION"
],
"code_fingerprint": "6ca8a25e9d2aa93d901e2f15d984e1d8b497b390b48195775a5f6d85fcc3e54c",
"dataset_manifest_sha256": "77443e9234a02f1b3b9eff317662f3a93c96bdff306e86eb1d5ecbdd508bd57d",
"dependency_versions": {
"python": "3.12.13",
"torch": "2.11.0+cu128",
"transformers": "5.14.1"
},
"metrics": {
"DIAGNOSIS_f1": 1.0,
"DIAGNOSIS_precision": 1.0,
"DIAGNOSIS_recall": 1.0,
"DIAGNOSIS_support": 940.0,
"MEDICAL_RECORD_f1": 1.0,
"MEDICAL_RECORD_precision": 1.0,
"MEDICAL_RECORD_recall": 1.0,
"MEDICAL_RECORD_support": 300.0,
"MEDICATION_f1": 1.0,
"MEDICATION_precision": 1.0,
"MEDICATION_recall": 1.0,
"MEDICATION_support": 702.0,
"loss": 0.0005009130109101534,
"overall_f1": 1.0,
"overall_precision": 1.0,
"overall_recall": 1.0,
"overall_support": 1942.0,
"runtime": 1.7193,
"samples_per_second": 1129.54,
"steps_per_second": 35.48
},
"seed": 42,
"training_parameters": {
"epochs": 4.0,
"eval_batch_size": 32,
"learning_rate": 2e-05,
"max_length": 512,
"train_batch_size": 16,
"weight_decay": 0.01
}
}
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