{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-obi--deid_roberta_i2b2","slug":"obi--deid_roberta_i2b2","name":"deid_roberta_i2b2","type":"model","url":"https://huggingface.co/obi/deid_roberta_i2b2","page_url":"https://unfragile.ai/obi--deid_roberta_i2b2","categories":["model-training"],"tags":["transformers","pytorch","safetensors","roberta","token-classification","deidentification","medical notes","ehr","phi","en","dataset:I2B2","arxiv:1907.11692","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-obi--deid_roberta_i2b2__cap_0","uri":"capability://data.processing.analysis.medical.note.phi.token.classification","name":"medical-note-phi-token-classification","description":"Identifies and classifies Protected Health Information (PHI) tokens in clinical notes using a fine-tuned RoBERTa transformer model trained on the I2B2 2014 de-identification challenge dataset. The model performs sequence labeling via token-level classification, outputting BIO (Begin-Inside-Outside) tags for 8 PHI entity types (PATIENT, DOCTOR, HOSPITAL, DATE, LOCATION, ORGANIZATION, CONTACT, AGE). Uses HuggingFace transformers library with PyTorch backend for inference, supporting batch processing and token probability scores for confidence-based filtering.","intents":["automatically detect and mask PHI tokens in EHR notes before data sharing or research use","audit clinical documentation for compliance with HIPAA de-identification standards","extract structured PHI entities from unstructured medical text for data governance workflows","build de-identification pipelines that preserve clinical meaning while removing identifiers"],"best_for":["healthcare data engineers building HIPAA-compliant data pipelines","clinical NLP researchers working with real patient notes","compliance teams automating PHI detection in EHR exports","organizations implementing automated de-identification before data sharing"],"limitations":["Trained exclusively on English clinical notes from I2B2 2014 dataset — performance degrades on non-English text or notes from different medical domains/institutions","Token-level classification requires pre-tokenization alignment; subword tokenization (WordPiece) may split medical terms, reducing entity boundary precision","No contextual reasoning — cannot distinguish between identical tokens that are PHI in one context but not another (e.g., 'John' as patient name vs. common medication name)","Batch inference latency ~50-200ms per note depending on length; not optimized for real-time streaming de-identification","Model size ~355M parameters; requires GPU for production throughput or CPU inference becomes bottleneck (>500ms per note on CPU)"],"requires":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0)","HuggingFace model hub access (internet connection for first download, ~1.4GB disk space)","GPU recommended for batch processing (NVIDIA CUDA 11.0+ or compatible)"],"input_types":["raw text (clinical notes, discharge summaries, progress notes)","pre-tokenized text (if using custom tokenization)","batch text arrays (for efficient multi-note processing)"],"output_types":["BIO token tags (B-PATIENT, I-PATIENT, B-DATE, I-DATE, etc.)","token-level confidence scores (logits converted to probabilities)","structured entity spans with character offsets for downstream masking/redaction"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-obi--deid_roberta_i2b2__cap_1","uri":"capability://data.processing.analysis.batch.clinical.note.processing.with.entity.extraction","name":"batch-clinical-note-processing-with-entity-extraction","description":"Processes multiple clinical notes in parallel batches through the token classifier, aggregating token-level predictions into structured entity spans with character offsets and confidence scores. Implements efficient batching via HuggingFace pipeline abstraction, which handles tokenization, padding, and attention mask generation automatically. Outputs entity-level results (not token-level) with start/end character positions for direct integration with text masking or redaction workflows, supporting variable-length documents without manual padding.","intents":["de-identify large EHR datasets (1000s of notes) in parallel without manual per-note processing","extract PHI entities with exact character offsets for surgical redaction (replacing only identified spans)","generate confidence-filtered entity lists for manual review workflows (flag low-confidence predictions)","integrate de-identification as a preprocessing step in clinical NLP pipelines"],"best_for":["data engineering teams processing bulk EHR exports for research or data sharing","clinical NLP platforms requiring automated entity extraction before downstream tasks","compliance automation tools that need structured PHI entity data for audit trails"],"limitations":["Batch processing requires loading entire batch into GPU memory; large notes or high batch sizes cause OOM errors on standard GPUs (<24GB VRAM)","Entity aggregation from token predictions requires post-processing logic; consecutive tokens of same entity type must be merged, adding ~5-10ms per note","Character offset mapping assumes consistent tokenization between input text and model tokenization; special characters or encoding mismatches can cause offset drift","No built-in handling of overlapping entities or entity type conflicts; if model predicts overlapping PHI spans, user must implement conflict resolution"],"requires":["Python 3.7+","transformers library (>=4.0.0) with pipeline support","PyTorch or TensorFlow backend","GPU with >=8GB VRAM for batch_size>=32 (CPU inference possible but slow)","Post-processing code to convert token predictions to entity spans"],"input_types":["list of clinical note strings (variable length, 100-5000 tokens typical)","batch configuration (batch_size, truncation strategy for notes >512 tokens)"],"output_types":["structured entity objects with fields: entity_type, start_char, end_char, confidence_score, token_count","de-identified text (if masking applied post-extraction)","entity statistics (count by type, confidence distribution)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-obi--deid_roberta_i2b2__cap_2","uri":"capability://data.processing.analysis.medical.entity.type.classification.with.confidence.scoring","name":"medical-entity-type-classification-with-confidence-scoring","description":"Classifies each token into one of 8 medical PHI entity types (PATIENT, DOCTOR, HOSPITAL, DATE, LOCATION, ORGANIZATION, CONTACT, AGE) or non-entity (O tag), with per-token logit scores converted to probability distributions. The model outputs softmax probabilities across all 17 possible tags (8 entity types × 2 for BIO prefix + 1 O tag), enabling confidence-based filtering and uncertainty quantification. Supports threshold-based entity filtering (e.g., only accept predictions with >0.9 confidence) for precision-recall tuning in downstream workflows.","intents":["distinguish between different PHI types for selective de-identification (e.g., mask only dates, keep patient names for internal use)","identify low-confidence predictions for manual review or escalation to human annotators","tune de-identification aggressiveness via confidence thresholds (high threshold = fewer false positives, more false negatives)","generate confidence metrics for data quality reporting and model performance monitoring"],"best_for":["compliance teams needing granular control over which PHI types are redacted","clinical research platforms requiring manual review workflows for uncertain predictions","organizations building confidence-based SLAs for automated de-identification"],"limitations":["Confidence scores reflect model uncertainty, not ground-truth accuracy; high confidence does not guarantee correctness, especially on out-of-distribution clinical notes","Entity type confusion common between similar categories (e.g., LOCATION vs. ORGANIZATION, DOCTOR vs. PATIENT in ambiguous contexts); confidence scores don't resolve semantic ambiguity","No calibration applied to raw logits; softmax probabilities may be overconfident on rare entity types (AGE, CONTACT) due to training data imbalance","Threshold tuning requires labeled validation set; optimal threshold varies by entity type and clinical domain, requiring per-domain calibration"],"requires":["Python 3.7+","transformers library with logits output support","numpy for probability computation","labeled validation data for threshold calibration (optional but recommended)"],"input_types":["tokenized clinical text","confidence threshold parameter (float 0.0-1.0)"],"output_types":["entity type predictions (8 classes)","confidence scores (0.0-1.0 per token)","filtered entity lists (above threshold only)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-obi--deid_roberta_i2b2__cap_3","uri":"capability://data.processing.analysis.subword.tokenization.aware.entity.boundary.detection","name":"subword-tokenization-aware-entity-boundary-detection","description":"Handles RoBERTa's WordPiece subword tokenization (splitting medical terms like 'pneumonia' into multiple tokens) by tracking BIO tags across subword boundaries and reconstructing entity spans at the character level. The model predicts BIO tags for each subword token; post-processing logic merges consecutive I- (Inside) tags into single entities and maps token positions back to character offsets in the original text. This enables accurate entity boundary detection even when medical terminology is split across multiple subword tokens.","intents":["accurately identify entity boundaries in clinical text with complex medical terminology","map token-level predictions back to original character positions for precise text masking","handle edge cases where entity names span multiple subword tokens without losing entity boundaries","preserve original text formatting and spacing when applying de-identification masks"],"best_for":["clinical NLP systems requiring character-level precision for text redaction","de-identification pipelines that must preserve document formatting and metadata","research projects analyzing entity boundary accuracy in medical NER"],"limitations":["Subword tokenization introduces ambiguity at entity boundaries; consecutive I- tags may represent continuation of same entity or separate entities, requiring heuristic merging","Character offset mapping assumes consistent encoding between input text and tokenizer output; UTF-8 encoding issues or special characters can cause offset drift","BIO tag sequence validation not built-in; invalid sequences (e.g., I-PATIENT without preceding B-PATIENT) are not automatically corrected, requiring downstream validation","Performance degrades on non-English text or text with special characters (medical abbreviations, symbols) that tokenize unpredictably"],"requires":["Python 3.7+","transformers library with tokenizer access","custom post-processing code for token-to-character mapping (not provided in base model)","understanding of BIO tagging scheme and subword tokenization"],"input_types":["raw clinical text (string)","token predictions with BIO tags","tokenizer object (for offset mapping)"],"output_types":["entity spans with character offsets (start, end)","merged entity sequences (consecutive I- tags combined)","original text with entity boundaries marked"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-obi--deid_roberta_i2b2__cap_4","uri":"capability://data.processing.analysis.i2b2.domain.specific.medical.terminology.recognition","name":"i2b2-domain-specific-medical-terminology-recognition","description":"Recognizes medical entities and PHI patterns specific to the I2B2 2014 de-identification challenge dataset, including clinical abbreviations, medical codes, date formats, and institutional naming conventions from the training corpus. The model has learned patterns from 1,010 annotated clinical notes covering diverse medical specialties (cardiology, oncology, etc.), enabling recognition of domain-specific entity variations (e.g., 'Dr. Smith' vs. 'SMITH, JOHN' as doctor names, date formats like '01/15/2020' vs. 'January 15, 2020'). This domain specificity comes from fine-tuning on medical text rather than general-purpose corpora.","intents":["recognize PHI entities in clinical notes from similar institutions/EHR systems as I2B2 training data","handle medical abbreviations and clinical shorthand (e.g., 'pt' for patient, 'DOB' for date of birth)","identify date formats and temporal expressions common in medical documentation","extract institutional identifiers and organizational names from clinical notes"],"best_for":["healthcare organizations using EHR systems similar to I2B2 source institutions","clinical NLP projects working with English-language medical notes","research teams analyzing de-identification performance on medical text"],"limitations":["Performance degrades significantly on clinical notes from different institutions, EHR systems, or medical domains not represented in I2B2 training data","No transfer learning to other languages; model is English-only and cannot be easily adapted to non-English clinical notes","Training data from 2014; may not recognize modern clinical abbreviations, new medication names, or contemporary institutional naming conventions","Limited to 8 entity types defined in I2B2; cannot recognize other PHI types (e.g., medical record numbers, insurance IDs) not in training set","No domain adaptation mechanism; retraining required to improve performance on out-of-domain clinical notes"],"requires":["English-language clinical notes","notes from similar medical institutions/EHR systems as I2B2 training data (for optimal performance)","understanding of I2B2 entity type definitions and annotation guidelines"],"input_types":["English clinical notes (discharge summaries, progress notes, consultation notes)"],"output_types":["I2B2-defined PHI entities (PATIENT, DOCTOR, HOSPITAL, DATE, LOCATION, ORGANIZATION, CONTACT, AGE)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-obi--deid_roberta_i2b2__cap_5","uri":"capability://tool.use.integration.huggingface.transformers.ecosystem.integration","name":"huggingface-transformers-ecosystem-integration","description":"Integrates seamlessly with HuggingFace Transformers library, enabling one-line model loading via `AutoModelForTokenClassification.from_pretrained('obi/deid_roberta_i2b2')` and inference via the pipeline API. Supports standard Transformers features: automatic tokenization, batch processing, device management (CPU/GPU/TPU), mixed-precision inference (fp16), and model quantization. Model weights stored in safetensors format (secure, fast deserialization) on HuggingFace Model Hub, with no custom loading code required. Compatible with Hugging Face Inference API endpoints for serverless deployment.","intents":["quickly integrate medical de-identification into existing HuggingFace-based NLP pipelines","deploy the model to HuggingFace Inference API endpoints without custom server code","use standard Transformers utilities for model optimization (quantization, distillation, pruning)","leverage HuggingFace ecosystem tools (Datasets, Accelerate, TRL) for fine-tuning or evaluation"],"best_for":["teams already using HuggingFace Transformers for other NLP tasks","developers building multi-model NLP pipelines with standardized interfaces","organizations deploying models via HuggingFace Inference API or Spaces"],"limitations":["Requires HuggingFace Transformers library (adds ~500MB dependency); not suitable for minimal/embedded deployments","Model Hub download requires internet connection on first use (~1.4GB); offline deployment requires pre-downloading model weights","Transformers library updates may introduce breaking changes; version pinning required for reproducibility","HuggingFace Inference API has rate limits and latency SLAs; not suitable for real-time, high-throughput applications","No built-in model monitoring or versioning; requires external tools for production model management"],"requires":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.3.0)","HuggingFace account (optional, for Inference API)","internet connection for first model download"],"input_types":["text strings (via pipeline API)","tokenized inputs (via model.forward() directly)"],"output_types":["token classification predictions (via pipeline)","raw logits (via model.forward())"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-obi--deid_roberta_i2b2__cap_6","uri":"capability://tool.use.integration.pytorch.safetensors.model.serialization","name":"pytorch-safetensors-model-serialization","description":"Model weights serialized in safetensors format (secure, fast binary format) rather than pickle, enabling safe deserialization without arbitrary code execution risk. Safetensors format supports lazy loading (loading only required layers), fast weight initialization, and cross-framework compatibility (PyTorch, TensorFlow, JAX). Model Hub provides both safetensors and PyTorch pickle formats; safetensors is recommended for production deployments due to security and performance benefits.","intents":["safely load pre-trained model weights without code execution vulnerabilities","reduce model loading latency via lazy loading and optimized binary format","enable cross-framework model usage (PyTorch model usable in TensorFlow/JAX pipelines)","audit model weights for tampering or supply-chain attacks"],"best_for":["production systems requiring secure model loading (healthcare, finance)","organizations with strict security policies against pickle deserialization","teams building multi-framework ML pipelines"],"limitations":["Safetensors format less mature than pickle; some edge cases or custom layer types may not serialize correctly","Requires safetensors library (small dependency, ~10MB); adds minor overhead to model loading","No built-in encryption; safetensors files are readable binary; requires additional encryption for sensitive deployments","Cross-framework compatibility requires framework-specific adapters; not all PyTorch features translate to TensorFlow"],"requires":["safetensors library (>=0.3.0)","PyTorch (>=1.9.0) for PyTorch backend"],"input_types":["safetensors model file (binary)"],"output_types":["loaded PyTorch model (nn.Module)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-obi--deid_roberta_i2b2__cap_7","uri":"capability://tool.use.integration.mit.licensed.open.source.model.distribution","name":"mit-licensed-open-source-model-distribution","description":"Model released under MIT license on HuggingFace Model Hub, enabling unrestricted commercial and research use, modification, and redistribution. Open-source weights and architecture allow inspection, fine-tuning, and integration into proprietary systems without licensing restrictions. Model card includes training details, evaluation metrics, and usage guidelines for transparency and reproducibility.","intents":["use the model in commercial healthcare products without licensing fees or restrictions","fine-tune the model on proprietary clinical data for domain adaptation","inspect model architecture and weights for research or security auditing","redistribute the model as part of open-source or proprietary software"],"best_for":["commercial healthcare companies building de-identification products","academic researchers studying medical NLP and de-identification","open-source projects requiring permissive licensing"],"limitations":["MIT license provides no warranty or liability protection; users assume all responsibility for model performance and safety","No commercial support or SLA; issues must be resolved via community or internal resources","Model performance not guaranteed on proprietary clinical data; fine-tuning may be required for production use","No restrictions on misuse; model could be used for privacy violations if deployed without proper safeguards"],"requires":["understanding of MIT license terms","responsibility for model governance and compliance in production deployments"],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0)","HuggingFace model hub access (internet connection for first download, ~1.4GB disk space)","GPU recommended for batch processing (NVIDIA CUDA 11.0+ or compatible)","transformers library (>=4.0.0) with pipeline support","PyTorch or TensorFlow backend","GPU with >=8GB VRAM for batch_size>=32 (CPU inference possible but slow)","Post-processing code to convert token predictions to entity spans","transformers library with logits output support"],"failure_modes":["Trained exclusively on English clinical notes from I2B2 2014 dataset — performance degrades on non-English text or notes from different medical domains/institutions","Token-level classification requires pre-tokenization alignment; subword tokenization (WordPiece) may split medical terms, reducing entity boundary precision","No contextual reasoning — cannot distinguish between identical tokens that are PHI in one context but not another (e.g., 'John' as patient name vs. common medication name)","Batch inference latency ~50-200ms per note depending on length; not optimized for real-time streaming de-identification","Model size ~355M parameters; requires GPU for production throughput or CPU inference becomes bottleneck (>500ms per note on CPU)","Batch processing requires loading entire batch into GPU memory; large notes or high batch sizes cause OOM errors on standard GPUs (<24GB VRAM)","Entity aggregation from token predictions requires post-processing logic; consecutive tokens of same entity type must be merged, adding ~5-10ms per note","Character offset mapping assumes consistent tokenization between input text and model tokenization; special characters or encoding mismatches can cause offset drift","No built-in handling of overlapping entities or entity type conflicts; if model predicts overlapping PHI spans, user must implement conflict resolution","Confidence scores reflect model uncertainty, not ground-truth accuracy; high confidence does not guarantee correctness, especially on out-of-distribution clinical notes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6291939476072453,"quality":0.26,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:23:01.785Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":454159,"model_likes":38}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=obi--deid_roberta_i2b2","compare_url":"https://unfragile.ai/compare?artifact=obi--deid_roberta_i2b2"}},"signature":"Ae8rFd/SnOjo2nKnnoGRdjRrEHOcDVuCnLNzGEpF74vap5hb/X1GfzDqo2fuQQyBKZfOTeacn6G+2OgrYTEaAg==","signedAt":"2026-06-22T01:10:40.676Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/obi--deid_roberta_i2b2","artifact":"https://unfragile.ai/obi--deid_roberta_i2b2","verify":"https://unfragile.ai/api/v1/verify?slug=obi--deid_roberta_i2b2","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}