SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification
Paper • 2605.03301 • Published
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tide2-sentry-clinical-ner
A DeBERTa-v3-large token-classification model for clinical PHI/PII de-identification of English clinical notes. It is the distilled DeBERTa v3 student released with the SHIELD paper (Posada et al., 2026, arXiv:2605.03301), built for on-premise, enterprise-scale de-identification behind a hospital firewall.
Gated & Research Use Only. Access is gated behind Stanford Medicine's Research Use Agreement and individual registration on Redivis (see License & access). This model is not is not a substitute for human review.
Register and accept the Research Use Agreement on the Redivis page to access the model: https://stanford.redivis.com/datasets/0m5h-7vks05xhs
Model description & intended use
- Architecture:
DebertaV2ForTokenClassification(deberta-v2model type), fine-tuned frommicrosoft/deberta-v3-large(1024 hidden / 24 layers / 128,100 vocab, SentencePiece tokenizer). Max sequence length 512 tokens. - Task: token classification (NER) with a BIO tagging scheme.
- Intended use: first-pass detection of protected health information (PHI/PII) in English clinical free text, as one stage of a de-identification pipeline (e.g. TIDE 2.0 ). The model outputs entity spans; downstream anonymization (surrogate replacement, FPE, date jitter) is handled separately by TIDE 2.0 .
Entity / label set
The model covers the 9 canonical SHIELD PHI categories plus an OTHER
catch-all (10 entity types → 21 BIO labels including O):
AGE, DATE, DOCTOR, HOSPITAL, ID, LOCATION, PATIENT, PHONE, WEB,
OTHER.
Full label list: O, and B-/I- for each of
AGE, DATE, DOCTOR, HOSPITAL, ID, LOCATION, OTHER, PATIENT, PHONE, WEB.
Usage
This is a gated repository. Authenticate first with a Hugging Face token that has been granted access:
pip install transformers sentencepiece torch
hf auth login # or set the HF_TOKEN environment variable
With transformers
from transformers import pipeline
ner = pipeline(
"token-classification",
model="stanford-med-hdr/tide2-sentry-clinical-ner",
aggregation_strategy="simple",
)
text = "Patient John Doe seen by Dr. Smith on 5/3/2021 at Stanford Hospital."
for span in ner(text):
print(span["entity_group"], ":", span["word"])
# Expect PATIENT / DOCTOR / DATE / HOSPITAL spans.
With TIDE 2.0
TIDE 2.0 resolves a transformer model by name and falls back to
snapshot_download(repo_id=...), so you can point its transformer recognizer at
stanford-med-hdr/tide2-sentry-clinical-ner. Because the repo is gated, export a
token with access (HF_TOKEN) before running the pipeline. See the
tide2-core notebook to run it on a google colab instance.
Training data & procedure
This model is a distilled student; it was NOT trained on the SHIELD gold set. Per the paper (§2.3), SHIELD's 1,381 gold-annotated notes are held out entirely as an independent evaluation set. The student was trained via a three-stage teacher→student distillation on a silver-standard corpus of ~13,000 unlabeled STARR-OMOP clinical notes labeled by the teacher model Gemini 2.5 Flash, using token-level cross-entropy over BIO tags. No SHIELD gold-standard annotations were included in the training data.
- Training-data source: STARR-OMOP (Stanford Medicine Research Data Repository,
158B tokens of clinical text). Notes were selected via set-cover diversity sampling across six axes: age, sex, race, ethnicity, note type, and note length.
- Hyperparameters: 10 epochs, learning rate 1e-5, cosine schedule, batch size 15, AdamW (fused), bf16 mixed precision, weight decay 1e-6, seed 42. The deployed checkpoint was selected to maximize recall, the appropriate objective for de-identification safety.
Evaluation
Primary — SHIELD gold benchmark (span-level)
On the SHIELD gold evaluation set (1,381 real STARR-OMOP notes; 10,229 gold PHI spans across 9 categories), span-level with an 80%-overlap match criterion (paper §3.3):
| Metric | Value |
|---|---|
| Micro-averaged precision | 0.89 |
| Micro-averaged recall | 0.88 |
This is the best-performing distilled student in the paper, with the most uniform coverage across all 9 PHI categories. It trails the Gemini 2.5 Flash teacher on macro recall (0.81 vs 0.90). The paper also reports cross-dataset evaluation on i2b2 2014 and AIMI: universal structured PHI (dates, phones, IDs, web) transfers well, while institution-specific entities transfer less well.
Limitations & bias
- English clinical text only. Performance on other languages or non-clinical text is not evaluated.
- 512-token context window. Longer notes must be chunked; span accuracy near chunk boundaries can degrade.
- Not a complete de-identification solution. Use as one stage alongside human review and complementary recognizers; residual PHI is possible.
- Research Use Only.
License & access
This model is distributed under Stanford Medicine's Data Research Use Agreement
(the model DRUA), included in this repository as LICENSE:
non-commercial research use only, no redistribution, no sharing of the download
link, and no re-identification attempts.
Access is gated with manual approval. To obtain access:
- Register and accept the Research Use Agreement on the Redivis page: https://stanford.redivis.com/datasets/0m5h-7vks05xhs
- Request access on this Hugging Face repository and complete the gated form.
- A stanford-med-hdr maintainer grants access after verifying your Redivis registration. Each user must register individually.
References / citation
- Paper: Jose D. Posada, David Love, Somalee Datta, Priya Desai. SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification. Stanford Medicine, 2026. arXiv:2605.03301. https://arxiv.org/abs/2605.03301
- SHIELD evaluation dataset: https://stanford.redivis.com/datasets/ee4n-6gv2f84et
- TIDE 2.0 Deid Framework: https://github.com/susom/tide2-core
@article{posada2026shield,
title = {SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language
Models for Enterprise-Scale De-identification},
author = {Posada, Jose D. and Love, David and Datta, Somalee and Desai, Priya},
journal = {arXiv preprint arXiv:2605.03301},
year = {2026},
url = {https://arxiv.org/abs/2605.03301}
}
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