{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-stanfordaimi--stanford-deidentifier-base","slug":"stanfordaimi--stanford-deidentifier-base","name":"stanford-deidentifier-base","type":"model","url":"https://huggingface.co/StanfordAIMI/stanford-deidentifier-base","page_url":"https://unfragile.ai/stanfordaimi--stanford-deidentifier-base","categories":["model-training"],"tags":["transformers","pytorch","bert","token-classification","sequence-tagger-model","pubmedbert","uncased","radiology","biomedical","bdf-toolbox","en","dataset:radreports","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-stanfordaimi--stanford-deidentifier-base__cap_0","uri":"capability://data.processing.analysis.biomedical.entity.token.classification","name":"biomedical-entity-token-classification","description":"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.","intents":["Identify and locate all Protected Health Information tokens in radiology reports for automated de-identification","Extract specific PHI entity types (names, MRNs, dates, locations) from clinical text with token-level precision","Prepare biomedical datasets for research by removing or masking sensitive identifiers while preserving clinical content","Validate 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","Clinical data scientists preparing datasets for machine learning model training"],"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"],"requires":["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 English"],"input_types":["raw text (radiology reports, clinical narratives)","pre-tokenized sequences (optional, for advanced use cases)"],"output_types":["token-level classification labels (IOB or BIO format)","confidence scores per token","structured entity spans with start/end character offsets"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stanfordaimi--stanford-deidentifier-base__cap_1","uri":"capability://data.processing.analysis.transformer.based.sequence.tagging.inference","name":"transformer-based-sequence-tagging-inference","description":"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.","intents":["Run de-identification inference at scale on large batches of radiology reports with minimal latency","Deploy the model as a REST API endpoint for real-time PHI detection in clinical workflows","Integrate token classification into existing NLP pipelines using standard HuggingFace transformers interface","Execute 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","Teams requiring on-premises or air-gapped deployment for compliance reasons"],"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","Uncased tokenization means input preprocessing must handle case normalization, potentially losing document structure information"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","Python 3.7+","For GPU inference: CUDA 11.0+ and cuDNN 8.0+","For Azure deployment: Azure ML SDK or Azure Container Registry access"],"input_types":["raw text strings","pre-tokenized sequences with attention masks","batched sequences with dynamic padding"],"output_types":["logits tensor (batch_size × sequence_length × num_labels)","predicted class indices per token","confidence scores (softmax probabilities)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stanfordaimi--stanford-deidentifier-base__cap_2","uri":"capability://data.processing.analysis.phi.entity.boundary.detection","name":"phi-entity-boundary-detection","description":"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.","intents":["Extract exact character offsets of PHI entities for targeted masking or redaction in documents","Identify entity boundaries for downstream processing (replacement with synthetic data, hashing, or removal)","Validate de-identification by pinpointing remaining unmasked PHI in processed documents","Generate 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","Clinical NLP teams integrating de-identification into document processing workflows"],"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","No built-in handling of entity normalization (e.g., date format variations, name aliases) — returns raw extracted text","Requires careful handling of whitespace and special characters that may not align with token boundaries"],"requires":["PyTorch 1.9+","Transformers library 4.0+","Python 3.7+","Custom post-processing code to map token indices to character offsets (not provided by base model)"],"input_types":["raw clinical text","token classification predictions with BIO labels","original text and tokenizer for offset mapping"],"output_types":["entity spans (start_char, end_char, entity_type)","extracted entity text","confidence scores per entity"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stanfordaimi--stanford-deidentifier-base__cap_3","uri":"capability://data.processing.analysis.multi.label.phi.classification","name":"multi-label-phi-classification","description":"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.","intents":["Distinguish between different PHI types (names vs. dates vs. MRNs) for selective masking strategies","Rank PHI predictions by confidence to identify high-confidence vs. uncertain entity classifications","Apply 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","Clinical teams validating de-identification quality with entity-type-level metrics"],"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","No hierarchical classification — treats all entity types as independent, missing relationships between types","Confidence scores reflect model uncertainty but not ground-truth accuracy — high confidence does not guarantee correctness"],"requires":["PyTorch 1.9+","Transformers library 4.0+","Python 3.7+","Knowledge of entity type taxonomy used in training (specific PHI classes supported)"],"input_types":["raw clinical text","tokenized sequences"],"output_types":["per-token class probabilities (softmax distribution)","predicted entity type per token","confidence scores per prediction"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stanfordaimi--stanford-deidentifier-base__cap_4","uri":"capability://data.processing.analysis.batch.de.identification.processing","name":"batch-de-identification-processing","description":"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.","intents":["De-identify entire datasets of radiology reports in batch mode for research sharing or data release","Process documents longer than 512 tokens by chunking with overlap to preserve entity detection across boundaries","Generate de-identified versions of clinical documents while preserving medical content for downstream analysis","Measure 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","Compliance officers validating de-identification at scale across document collections"],"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","No built-in handling of document structure (headers, tables, formatting) — treats all text uniformly","Replacement strategy (masking vs. synthetic data) requires custom implementation — model only provides entity locations"],"requires":["PyTorch 1.9+","Transformers library 4.0+","Python 3.7+","Sufficient GPU memory for batch size (minimum 2GB for batch_size=8 on 512-token documents)","Custom post-processing code for entity replacement and document reconstruction"],"input_types":["collections of raw radiology reports (text files, CSV, database records)","variable-length documents (no length restrictions)"],"output_types":["de-identified text with PHI replaced","entity detection reports (locations and types of detected PHI)","confidence metrics per document"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stanfordaimi--stanford-deidentifier-base__cap_5","uri":"capability://data.processing.analysis.radiology.report.specific.phi.detection","name":"radiology-report-specific-phi-detection","description":"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.","intents":["De-identify radiology reports for research sharing while preserving clinical imaging findings","Extract patient identifiers and study metadata from radiology report headers for data linkage","Validate that radiology reports have been properly de-identified before sharing with external researchers","Prepare 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","Medical imaging AI teams preparing training datasets from clinical archives"],"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)","No built-in understanding of radiology-specific codes (CPT, ICD) that may contain embedded patient identifiers","Requires domain knowledge to interpret and validate results — false positives/negatives may not be obvious without clinical context"],"requires":["PyTorch 1.9+","Transformers library 4.0+","Python 3.7+","Input text must be radiology reports (chest X-rays, CT, MRI, ultrasound, etc.)","Familiarity with radiology report structure and terminology for result validation"],"input_types":["radiology reports (structured or unstructured text)","radiology report sections (impression, findings, history)"],"output_types":["detected PHI entities with locations","entity type classifications","confidence scores"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-stanfordaimi--stanford-deidentifier-base__cap_6","uri":"capability://code.generation.editing.transfer.learning.and.fine.tuning.base","name":"transfer-learning-and-fine-tuning-base","description":"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.","intents":["Adapt the model to detect custom PHI types or domain-specific entities in specialized clinical documents","Fine-tune on institution-specific radiology reports to improve detection of local PHI patterns and abbreviations","Transfer 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)","Institutions requiring de-identification in non-English languages or specialized clinical domains"],"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","No built-in active learning or data augmentation — requires manual annotation or external tools for dataset creation","Fine-tuning on small datasets (<1000 examples) risks overfitting — requires careful hyperparameter tuning and validation"],"requires":["PyTorch 1.9+","Transformers library 4.0+","Python 3.7+","GPU with minimum 8GB memory for full fine-tuning (4GB sufficient for LoRA)","Labeled training dataset with token-level entity annotations","Knowledge of HuggingFace training APIs and hyperparameter tuning"],"input_types":["labeled biomedical text with token-level entity annotations","training datasets in standard NER formats (CoNLL, BIO)"],"output_types":["fine-tuned model weights","training metrics (loss, F1, precision, recall)","validation results on held-out test sets"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"high","permissions":["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 English","PyTorch 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","Custom post-processing code to map token indices to character offsets (not provided by base model)","Knowledge of entity type taxonomy used in training (specific PHI classes supported)"],"failure_modes":["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","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","Uncased tokenization means input preprocessing must handle case normalization, potentially losing document structure information","Subword tokenization misalignment can cause off-by-one errors in character offsets if not handled carefully during reconstruction","builder identity is not verified yet","no observed match outcomes 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