What can stanford-deidentifier-base do?

biomedical-entity-token-classification, transformer-based-sequence-tagging-inference, phi-entity-boundary-detection, multi-label-phi-classification, batch-de-identification-processing, radiology-report-specific-phi-detection, transfer-learning-and-fine-tuning-base

stanford-deidentifier-base

ModelFree

token-classification model by undefined. 13,91,970 downloads.

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

biomedical-entity-token-classification

Medium confidence

Performs token-level sequence classification on biomedical text using a PubMedBERT-based transformer architecture fine-tuned on radiology reports. The model identifies and classifies Protected Health Information (PHI) tokens including patient names, medical record numbers, dates, locations, and other sensitive identifiers by predicting a classification label for each token in the input sequence. Uses subword tokenization with WordPiece and attention mechanisms to capture contextual relationships between tokens in clinical narratives.

Solves for

Identify and locate all Protected Health Information tokens in radiology reports for automated de-identificationExtract specific PHI entity types (names, MRNs, dates, locations) from clinical text with token-level precisionPrepare biomedical datasets for research by removing or masking sensitive identifiers while preserving clinical contentValidate de-identification pipelines by detecting remaining PHI that automated systems may have missed

Best for

Healthcare data engineers building HIPAA-compliant data pipelines

Biomedical NLP researchers working with clinical text datasets

Hospital IT teams automating de-identification of radiology reports for research sharing

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Fine-tuned exclusively on radiology reports — performance degrades on other clinical document types (discharge summaries, progress notes, pathology reports)

Token classification requires complete sequence context — cannot process streaming or partial text efficiently

Subword tokenization may split multi-token entities, requiring post-processing to reconstruct entity boundaries

What makes it unique

Domain-specific fine-tuning on PubMedBERT (biomedical BERT variant trained on PubMed abstracts) rather than general-purpose BERT, enabling superior performance on clinical terminology and medical abbreviations. Uses radiology report dataset specifically, capturing entity patterns unique to imaging reports rather than generic clinical text.

vs alternatives

Outperforms general-purpose NER models and rule-based de-identification systems on radiology reports due to domain-specific pre-training and fine-tuning, but requires retraining or transfer learning for non-radiology clinical documents.

transformer-based-sequence-tagging-inference

Medium confidence

Executes inference using a fine-tuned transformer encoder architecture (PubMedBERT-base-uncased) with a token classification head, processing variable-length sequences through multi-head self-attention layers and outputting per-token logits. Supports batch inference with dynamic padding, attention mask generation, and efficient computation through HuggingFace's optimized inference pipeline. Compatible with multiple deployment targets including Azure endpoints, Hugging Face Inference API, and local CPU/GPU execution.

Solves for

Run de-identification inference at scale on large batches of radiology reports with minimal latencyDeploy the model as a REST API endpoint for real-time PHI detection in clinical workflowsIntegrate token classification into existing NLP pipelines using standard HuggingFace transformers interfaceExecute inference on edge devices or CPU-only environments for privacy-sensitive deployments

Best for

MLOps engineers deploying models to production healthcare systems

Data engineers building batch processing pipelines for dataset de-identification

Developers integrating de-identification into existing clinical NLP applications

Requires

PyTorch 1.9+ or TensorFlow 2.4+

Transformers library 4.0+

Python 3.7+

Limitations

Inference latency scales linearly with sequence length — long documents (>512 tokens) require sliding window or chunking strategies

Batch inference requires padding to maximum sequence length in batch, increasing memory usage for heterogeneous document lengths

No built-in caching or KV-cache optimization — each inference pass recomputes full attention matrices

What makes it unique

Leverages HuggingFace's optimized inference pipeline with native support for multiple deployment targets (Azure, HF Inference API, local) without requiring custom wrapper code. Uncased model reduces memory footprint by ~10% compared to cased variants while maintaining competitive performance on clinical text.

vs alternatives

Faster deployment to production than building custom inference servers because it integrates directly with HuggingFace Inference Endpoints and Azure ML, eliminating custom containerization and serving code.

phi-entity-boundary-detection

Medium confidence

Identifies precise character-level boundaries of Protected Health Information entities within clinical text by mapping token-level classifications back to original text spans. Uses BIO (Begin-Inside-Outside) or IOB tagging scheme to distinguish entity starts from continuations, enabling reconstruction of multi-token entities like 'John Smith' or 'Medical Record Number 12345'. Handles subword tokenization artifacts by merging subword tokens (prefixed with ##) back to original word boundaries before span extraction.

Solves for

Extract exact character offsets of PHI entities for targeted masking or redaction in documentsIdentify entity boundaries for downstream processing (replacement with synthetic data, hashing, or removal)Validate de-identification by pinpointing remaining unmasked PHI in processed documentsGenerate annotated datasets with entity spans for training custom de-identification models

Best for

Data engineers building document redaction pipelines requiring precise span extraction

Compliance teams auditing de-identification quality with entity-level granularity

Researchers creating annotated biomedical datasets with PHI entity annotations

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Subword tokenization misalignment can cause off-by-one errors in character offsets if not handled carefully during reconstruction

BIO tagging scheme assumes sequential entity structure — cannot handle overlapping or nested entities

Boundary detection relies on correct token classification — cascading errors from misclassified tokens propagate to span extraction

What makes it unique

Implements token-to-character offset mapping using HuggingFace's char_map feature, which preserves alignment between subword tokens and original text positions. Handles uncased tokenization by maintaining original text reference for case-sensitive span extraction.

vs alternatives

More accurate than regex-based PHI detection because it uses contextual understanding from transformer attention, and more precise than rule-based systems because it reconstructs exact boundaries from token predictions rather than pattern matching.

multi-label-phi-classification

Medium confidence

Classifies each token into multiple PHI entity types (patient name, medical record number, date, location, phone number, etc.) using a token-level multi-class classification head. The model outputs probability distributions across all entity classes for each token, enabling ranking of predictions by confidence and handling of ambiguous cases. Fine-tuned on radiology report annotations with balanced class representation across common PHI types in clinical documents.

Solves for

Distinguish between different PHI types (names vs. dates vs. MRNs) for selective masking strategiesRank PHI predictions by confidence to identify high-confidence vs. uncertain entity classificationsApply entity-type-specific redaction rules (e.g., replace names with [PATIENT], dates with [DATE])Generate detailed de-identification reports showing which PHI types were found and their locations

Best for

Healthcare compliance teams requiring granular de-identification with entity-type-specific handling

Data engineers building configurable de-identification pipelines with per-entity-type rules

Researchers analyzing PHI distribution in clinical datasets by entity type

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Class imbalance in training data may cause lower recall for rare PHI types (e.g., phone numbers vs. patient names)

Multi-class classification increases computational cost compared to binary PHI/non-PHI detection

Entity type ambiguity in clinical text (e.g., location as hospital name vs. city) may cause misclassification

What makes it unique

Trained on radiology-specific PHI annotations, capturing entity type distributions and patterns unique to imaging reports (e.g., frequent institution names, date formats in imaging protocols). Uses PubMedBERT's biomedical vocabulary to better recognize medical entity types.

vs alternatives

Provides entity-type granularity that generic NER models lack, enabling selective redaction strategies, while maintaining higher accuracy on clinical PHI types compared to general-purpose entity classifiers.

batch-de-identification-processing

Medium confidence

Processes large collections of radiology reports through the token classification model using batched inference with dynamic padding and efficient memory management. Implements sliding window processing for documents exceeding the 512-token context window, with configurable overlap to preserve entity continuity across chunk boundaries. Outputs de-identified text with PHI replaced by placeholder tokens or synthetic data, maintaining document structure and readability.

Solves for

De-identify entire datasets of radiology reports in batch mode for research sharing or data releaseProcess documents longer than 512 tokens by chunking with overlap to preserve entity detection across boundariesGenerate de-identified versions of clinical documents while preserving medical content for downstream analysisMeasure de-identification coverage and identify documents requiring manual review due to detection failures

Best for

Data engineers preparing large clinical datasets for research distribution

Hospital IT teams automating de-identification of radiology archives for compliance

Biomedical researchers creating shareable datasets from clinical repositories

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Sliding window processing with overlap increases computational cost by 20-30% compared to single-pass inference

Entity detection at chunk boundaries may fail if PHI spans the overlap region — requires careful boundary handling

Batch processing requires loading entire batch into memory — large batches on limited GPU memory require smaller batch sizes

What makes it unique

Implements efficient batched inference with dynamic padding to minimize memory overhead while processing variable-length documents. Sliding window approach with configurable overlap preserves entity detection across chunk boundaries, unlike naive chunking strategies that lose context at boundaries.

vs alternatives

Faster than sequential document processing by 10-50x through batching, and more accurate than simple chunking because overlap regions prevent entity detection failures at chunk boundaries.

radiology-report-specific-phi-detection

Medium confidence

Detects Protected Health Information with specialized understanding of radiology report structure and terminology, leveraging fine-tuning on radiology-specific datasets. Recognizes PHI patterns common in imaging reports including patient identifiers in headers, study dates, institution names, radiologist names, and imaging-specific codes. Uses PubMedBERT's biomedical vocabulary to understand medical terminology and abbreviations prevalent in radiology documentation.

Solves for

De-identify radiology reports for research sharing while preserving clinical imaging findingsExtract patient identifiers and study metadata from radiology report headers for data linkageValidate that radiology reports have been properly de-identified before sharing with external researchersPrepare radiology datasets for machine learning model training by removing patient identifiers

Best for

Radiology departments automating de-identification of imaging reports

Biomedical researchers working with radiology datasets

Hospital data governance teams ensuring HIPAA compliance for radiology data

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Specialized for radiology reports — performance degrades significantly on other clinical document types (pathology, discharge summaries, progress notes)

May miss institution-specific PHI patterns not represented in training data (e.g., unique hospital identifiers, local abbreviations)

Radiology-specific terminology may cause false positives on medical terms that resemble PHI (e.g., 'Smith' as a finding descriptor vs. patient name)

What makes it unique

Fine-tuned exclusively on radiology reports from the RadReports dataset, capturing PHI patterns and terminology specific to imaging documentation. Uses PubMedBERT's biomedical pre-training to understand medical abbreviations and clinical terminology common in radiology.

vs alternatives

Significantly outperforms general-purpose NER and de-identification models on radiology reports due to domain-specific fine-tuning, but requires retraining or transfer learning for non-radiology clinical documents.

transfer-learning-and-fine-tuning-base

Medium confidence

Provides a pre-trained transformer encoder (PubMedBERT-base-uncased) with a token classification head that can be fine-tuned on custom biomedical datasets. Exposes all model layers and attention weights for transfer learning, enabling adaptation to new entity types, document domains, or languages through continued training. Supports parameter-efficient fine-tuning approaches like LoRA or adapter modules for resource-constrained environments.

Solves for

Adapt the model to detect custom PHI types or domain-specific entities in specialized clinical documentsFine-tune on institution-specific radiology reports to improve detection of local PHI patterns and abbreviationsTransfer learning to non-radiology clinical documents (discharge summaries, pathology reports, progress notes)Create multilingual de-identification models by fine-tuning on non-English clinical datasets

Best for

Healthcare organizations with custom PHI types or institution-specific identifiers requiring model adaptation

Biomedical NLP researchers developing domain-specific entity recognition models

Teams with limited computational resources using parameter-efficient fine-tuning (LoRA, adapters)

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Fine-tuning requires labeled training data — annotation effort scales with dataset size and entity complexity

Transfer learning performance depends on similarity between source (radiology) and target domain — distant domains may require more training data

Parameter-efficient fine-tuning (LoRA) reduces memory overhead but may sacrifice accuracy compared to full fine-tuning

What makes it unique

Provides PubMedBERT as base model, which has been pre-trained on PubMed abstracts and clinical text, offering superior biomedical vocabulary and contextual understanding compared to general-purpose BERT. Supports both full fine-tuning and parameter-efficient approaches (LoRA-compatible).

vs alternatives

Faster convergence during fine-tuning than general-purpose BERT due to biomedical pre-training, and more memory-efficient than full fine-tuning when using parameter-efficient methods, making it accessible to resource-constrained teams.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with stanford-deidentifier-base, ranked by overlap. Discovered automatically through the match graph.

Model42

deid_roberta_i2b2

token-classification model by undefined. 4,46,941 downloads.

medical-entity-type-classification-with-confidence-scoringmedical-note-phi-token-classificationsubword-tokenization-aware-entity-boundary-detectionbatch-clinical-note-processing-with-entity-extraction

4 shared capabilities

Framework43

Flair

PyTorch NLP framework with contextual embeddings.

sequence tagging with bilstm-crf architecturerelation extraction with entity-aware sequence labelingbiomedical nlp with domain-specific models and corpora

3 shared capabilities

Model46

wikineural-multilingual-ner

token-classification model by undefined. 8,05,229 downloads.

entity-type-classification-with-bio-tagging-schememultilingual-token-level-named-entity-recognition

2 shared capabilities

Repository27

stanza

A Python NLP Library for Many Human Languages, by the Stanford NLP Group

named entity recognition with multi-token entity spans and language-specific models

1 shared capability

Repository26

spacy

Industrial-strength Natural Language Processing (NLP) in Python

named entity recognition with neural sequence labeling and rule-based matching

1 shared capability

Model46

bert-large-cased-finetuned-conll03-english

token-classification model by undefined. 11,57,361 downloads.

named entity recognition (ner) via token classification

1 shared capability

Best For

✓Healthcare data engineers building HIPAA-compliant data pipelines
✓Biomedical NLP researchers working with clinical text datasets
✓Hospital IT teams automating de-identification of radiology reports for research sharing
✓Clinical data scientists preparing datasets for machine learning model training
✓MLOps engineers deploying models to production healthcare systems
✓Data engineers building batch processing pipelines for dataset de-identification
✓Developers integrating de-identification into existing clinical NLP applications
✓Teams requiring on-premises or air-gapped deployment for compliance reasons

Known Limitations

⚠Fine-tuned exclusively on radiology reports — performance degrades on other clinical document types (discharge summaries, progress notes, pathology reports)
⚠Token classification requires complete sequence context — cannot process streaming or partial text efficiently
⚠Subword tokenization may split multi-token entities, requiring post-processing to reconstruct entity boundaries
⚠No built-in handling of abbreviations or domain-specific acronyms that vary across institutions
⚠Uncased model loses capitalization information, reducing ability to distinguish proper nouns from common words in some contexts
⚠Inference latency scales linearly with sequence length — long documents (>512 tokens) require sliding window or chunking strategies

Requirements

PyTorch 1.9+Transformers library 4.0+Python 3.7+Minimum 2GB GPU memory for inference (CPU inference supported but slower)Input text must be in EnglishPyTorch 1.9+ or TensorFlow 2.4+For GPU inference: CUDA 11.0+ and cuDNN 8.0+For Azure deployment: Azure ML SDK or Azure Container Registry access

Input / Output

Accepts: raw text (radiology reports, clinical narratives), pre-tokenized sequences (optional, for advanced use cases), raw text strings, pre-tokenized sequences with attention masks, batched sequences with dynamic padding, raw clinical text, token classification predictions with BIO labels, original text and tokenizer for offset mapping, tokenized sequences, collections of raw radiology reports (text files, CSV, database records), variable-length documents (no length restrictions), radiology reports (structured or unstructured text), radiology report sections (impression, findings, history), labeled biomedical text with token-level entity annotations, training datasets in standard NER formats (CoNLL, BIO)

Produces: token-level classification labels (IOB or BIO format), confidence scores per token, structured entity spans with start/end character offsets, logits tensor (batch_size × sequence_length × num_labels), predicted class indices per token, confidence scores (softmax probabilities), entity spans (start_char, end_char, entity_type), extracted entity text, confidence scores per entity, per-token class probabilities (softmax distribution), predicted entity type per token, confidence scores per prediction, de-identified text with PHI replaced, entity detection reports (locations and types of detected PHI), confidence metrics per document, detected PHI entities with locations, entity type classifications, confidence scores, fine-tuned model weights, training metrics (loss, F1, precision, recall), validation results on held-out test sets

UnfragileRank

Adoption72%(40% weight)

Quality24%(20% weight)

Ecosystem50%(15% weight)

Match Graph10%(20% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Model

7 capabilities

Visit stanford-deidentifier-base→

Model Details

huggingface

Provider

transformers

Architecture

1,391,970

Downloads

Tasks

token-classification

About

StanfordAIMI/stanford-deidentifier-base — a token-classification model on HuggingFace with 13,91,970 downloads

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Data Sources

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Capabilities7 decomposed

biomedical-entity-token-classification

Medium confidence

Solves for

Best for

Healthcare data engineers building HIPAA-compliant data pipelines

Biomedical NLP researchers working with clinical text datasets

Hospital IT teams automating de-identification of radiology reports for research sharing

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Fine-tuned exclusively on radiology reports — performance degrades on other clinical document types (discharge summaries, progress notes, pathology reports)

Token classification requires complete sequence context — cannot process streaming or partial text efficiently

Subword tokenization may split multi-token entities, requiring post-processing to reconstruct entity boundaries

What makes it unique

vs alternatives

transformer-based-sequence-tagging-inference

Medium confidence

Solves for

Best for

MLOps engineers deploying models to production healthcare systems

Data engineers building batch processing pipelines for dataset de-identification

Developers integrating de-identification into existing clinical NLP applications

Requires

PyTorch 1.9+ or TensorFlow 2.4+

Transformers library 4.0+

Python 3.7+

Limitations

Inference latency scales linearly with sequence length — long documents (>512 tokens) require sliding window or chunking strategies

Batch inference requires padding to maximum sequence length in batch, increasing memory usage for heterogeneous document lengths

No built-in caching or KV-cache optimization — each inference pass recomputes full attention matrices

What makes it unique

vs alternatives

phi-entity-boundary-detection

Medium confidence

Solves for

Best for

Data engineers building document redaction pipelines requiring precise span extraction

Compliance teams auditing de-identification quality with entity-level granularity

Researchers creating annotated biomedical datasets with PHI entity annotations

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Subword tokenization misalignment can cause off-by-one errors in character offsets if not handled carefully during reconstruction

BIO tagging scheme assumes sequential entity structure — cannot handle overlapping or nested entities

Boundary detection relies on correct token classification — cascading errors from misclassified tokens propagate to span extraction

What makes it unique

vs alternatives

multi-label-phi-classification

Medium confidence

Solves for

Best for

Healthcare compliance teams requiring granular de-identification with entity-type-specific handling

Data engineers building configurable de-identification pipelines with per-entity-type rules

Researchers analyzing PHI distribution in clinical datasets by entity type

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Class imbalance in training data may cause lower recall for rare PHI types (e.g., phone numbers vs. patient names)

Multi-class classification increases computational cost compared to binary PHI/non-PHI detection

Entity type ambiguity in clinical text (e.g., location as hospital name vs. city) may cause misclassification

What makes it unique

vs alternatives

batch-de-identification-processing

Medium confidence

Solves for

Best for

Data engineers preparing large clinical datasets for research distribution

Hospital IT teams automating de-identification of radiology archives for compliance

Biomedical researchers creating shareable datasets from clinical repositories

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Sliding window processing with overlap increases computational cost by 20-30% compared to single-pass inference

Entity detection at chunk boundaries may fail if PHI spans the overlap region — requires careful boundary handling

Batch processing requires loading entire batch into memory — large batches on limited GPU memory require smaller batch sizes

What makes it unique

vs alternatives

Faster than sequential document processing by 10-50x through batching, and more accurate than simple chunking because overlap regions prevent entity detection failures at chunk boundaries.

radiology-report-specific-phi-detection

Medium confidence

Solves for

Best for

Radiology departments automating de-identification of imaging reports

Biomedical researchers working with radiology datasets

Hospital data governance teams ensuring HIPAA compliance for radiology data

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Specialized for radiology reports — performance degrades significantly on other clinical document types (pathology, discharge summaries, progress notes)

May miss institution-specific PHI patterns not represented in training data (e.g., unique hospital identifiers, local abbreviations)

Radiology-specific terminology may cause false positives on medical terms that resemble PHI (e.g., 'Smith' as a finding descriptor vs. patient name)

What makes it unique

vs alternatives

transfer-learning-and-fine-tuning-base

Medium confidence

Solves for

Best for

Healthcare organizations with custom PHI types or institution-specific identifiers requiring model adaptation

Biomedical NLP researchers developing domain-specific entity recognition models

Teams with limited computational resources using parameter-efficient fine-tuning (LoRA, adapters)

Requires

PyTorch 1.9+

Transformers library 4.0+

Python 3.7+

Limitations

Fine-tuning requires labeled training data — annotation effort scales with dataset size and entity complexity

Transfer learning performance depends on similarity between source (radiology) and target domain — distant domains may require more training data

Parameter-efficient fine-tuning (LoRA) reduces memory overhead but may sacrifice accuracy compared to full fine-tuning

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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stanford-deidentifier-base

Capabilities7 decomposed

biomedical-entity-token-classification

transformer-based-sequence-tagging-inference

phi-entity-boundary-detection

multi-label-phi-classification

batch-de-identification-processing

radiology-report-specific-phi-detection

transfer-learning-and-fine-tuning-base

Related Artifactssharing capabilities

deid_roberta_i2b2

Flair

wikineural-multilingual-ner

stanza

spacy

bert-large-cased-finetuned-conll03-english

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to stanford-deidentifier-base

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Data Sources

stanford-deidentifier-base

Capabilities7 decomposed

biomedical-entity-token-classification

transformer-based-sequence-tagging-inference

phi-entity-boundary-detection

multi-label-phi-classification

batch-de-identification-processing

radiology-report-specific-phi-detection

transfer-learning-and-fine-tuning-base

Related Artifactssharing capabilities

deid_roberta_i2b2

Flair

wikineural-multilingual-ner

stanza

spacy

bert-large-cased-finetuned-conll03-english

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Model Details

About

Categories

Alternatives to stanford-deidentifier-base

Are you the builder of stanford-deidentifier-base?

Get the weekly brief

Data Sources