Upload PII detection model OpenMed-PII-ClinicalLongformer-Base-149M-v1

README.md

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+---
+language:
+  - en
+license: apache-2.0
+base_model: yikuan8/Clinical-Longformer
+tags:
+  - token-classification
+  - ner
+  - pii
+  - pii-detection
+  - de-identification
+  - privacy
+  - healthcare
+  - medical
+  - clinical
+  - phi
+  - hipaa
+  - pytorch
+  - transformers
+  - openmed
+datasets:
+  - nvidia/Nemotron-PII
+pipeline_tag: token-classification
+library_name: transformers
+metrics:
+  - f1
+  - precision
+  - recall
+model-index:
+  - name: OpenMed-PII-ClinicalLongformer-149M-v1
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        dataset:
+          name: nvidia/Nemotron-PII (test_strat)
+          type: nvidia/Nemotron-PII
+          split: test
+        metrics:
+          - type: f1
+            value: 0.9533
+            name: F1 (micro)
+          - type: precision
+            value: 0.9572
+            name: Precision
+          - type: recall
+            value: 0.9494
+            name: Recall
+widget:
+  - text: "Dr. Sarah Johnson (SSN: 123-45-6789) can be reached at sarah.johnson@hospital.org or 555-123-4567. She lives at 123 Oak Street, Boston, MA 02108."
+    example_title: Clinical Note with PII
+---
+
+# OpenMed-PII-ClinicalLongformer-149M-v1
+
+**PII Detection Model** | 149M Parameters | Open Source
+
+[![F1 Score](https://img.shields.io/badge/F1-95.33%25-brightgreen)]() [![Precision](https://img.shields.io/badge/Precision-95.72%25-blue)]() [![Recall](https://img.shields.io/badge/Recall-94.94%25-orange)]()
+
+## Model Description
+
+**OpenMed-PII-ClinicalLongformer-149M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection** in text. This model identifies and classifies **54 types of sensitive information** including names, addresses, SSNs, medical record numbers, and more.
+
+### Key Features
+
+- **High Accuracy**: Achieves strong F1 scores across diverse PII categories
+- **Comprehensive Coverage**: Detects 50+ entity types spanning personal, financial, medical, and contact information
+- **Privacy-Focused**: Designed for de-identification and compliance with HIPAA, GDPR, and other privacy regulations
+- **Production-Ready**: Optimized for real-world text processing pipelines
+
+## Performance
+
+Evaluated on a stratified 2,000-sample test set from NVIDIA Nemotron-PII:
+
+| Metric | Score |
+|:---|:---:|
+| **Micro F1** | **0.9533** |
+| Precision | 0.9572 |
+| Recall | 0.9494 |
+| Macro F1 | 0.9528 |
+| Weighted F1 | 0.9515 |
+| Accuracy | 0.9943 |
+
+### Top 10 PII Models
+
+| Rank | Model | F1 | Precision | Recall |
+|:---:|:---|:---:|:---:|:---:|
+| 1 | [OpenMed-PII-SuperClinical-Large-434M-v1](https://huggingface.co/openmed/OpenMed-PII-SuperClinical-Large-434M-v1) | 0.9608 | 0.9685 | 0.9532 |
+| 2 | [OpenMed-PII-BigMed-Large-560M-v1](https://huggingface.co/openmed/OpenMed-PII-BigMed-Large-560M-v1) | 0.9604 | 0.9644 | 0.9565 |
+| 3 | [OpenMed-PII-EuroMed-210M-v1](https://huggingface.co/openmed/OpenMed-PII-EuroMed-210M-v1) | 0.9600 | 0.9681 | 0.9521 |
+| 4 | [OpenMed-PII-SnowflakeMed-568M-v1](https://huggingface.co/openmed/OpenMed-PII-SnowflakeMed-568M-v1) | 0.9594 | 0.9640 | 0.9548 |
+| 5 | [OpenMed-PII-SuperMedical-Large-355M-v1](https://huggingface.co/openmed/OpenMed-PII-SuperMedical-Large-355M-v1) | 0.9592 | 0.9632 | 0.9553 |
+| 6 | [OpenMed-PII-ClinicalBGE-568M-v1](https://huggingface.co/openmed/OpenMed-PII-ClinicalBGE-568M-v1) | 0.9587 | 0.9636 | 0.9538 |
+| 7 | [OpenMed-PII-mClinicalE5-Large-560M-v1](https://huggingface.co/openmed/OpenMed-PII-mClinicalE5-Large-560M-v1) | 0.9582 | 0.9631 | 0.9533 |
+| 8 | [OpenMed-PII-ModernMed-Large-395M-v1](https://huggingface.co/openmed/OpenMed-PII-ModernMed-Large-395M-v1) | 0.9579 | 0.9639 | 0.9520 |
+| 9 | [OpenMed-PII-BioClinicalModern-Large-395M-v1](https://huggingface.co/openmed/OpenMed-PII-BioClinicalModern-Large-395M-v1) | 0.9579 | 0.9656 | 0.9502 |
+| 10 | [OpenMed-PII-ClinicalE5-Large-335M-v1](https://huggingface.co/openmed/OpenMed-PII-ClinicalE5-Large-335M-v1) | 0.9577 | 0.9604 | 0.9550 |
+
+### Best Performing Entities
+
+| Entity | F1 | Precision | Recall | Support |
+|:---|:---:|:---:|:---:|:---:|
+| `coordinate` | 0.997 | 0.994 | 1.000 | 174 |
+| `license_plate` | 0.995 | 0.990 | 1.000 | 97 |
+| `email` | 0.994 | 0.995 | 0.993 | 825 |
+| `employee_id` | 0.994 | 0.994 | 0.994 | 157 |
+| `ipv4` | 0.993 | 0.993 | 0.993 | 135 |
+
+### Challenging Entities
+
+These entity types have lower performance and may benefit from additional post-processing:
+
+| Entity | F1 | Precision | Recall | Support |
+|:---|:---:|:---:|:---:|:---:|
+| `education_level` | 0.870 | 0.886 | 0.854 | 192 |
+| `fax_number` | 0.853 | 0.818 | 0.892 | 111 |
+| `time` | 0.826 | 0.881 | 0.777 | 488 |
+| `unique_id` | 0.769 | 0.882 | 0.682 | 44 |
+| `occupation` | 0.646 | 0.734 | 0.576 | 772 |
+
+## Supported Entity Types
+
+This model detects **54 PII entity types** organized into categories:
+
+<details>
+<summary><strong>Identifiers</strong> (16 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `account_number` | Account Number |
+| `api_key` | Api Key |
+| `bank_routing_number` | Bank Routing Number |
+| `certificate_license_number` | Certificate License Number |
+| `credit_debit_card` | Credit Debit Card |
+| `cvv` | Cvv |
+| `employee_id` | Employee Id |
+| `health_plan_beneficiary_number` | Health Plan Beneficiary Number |
+| `mac_address` | Mac Address |
+| `medical_record_number` | Medical Record Number |
+| ... | *and 6 more* |
+
+</details>
+
+<details>
+<summary><strong>Personal Info</strong> (14 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `age` | Age |
+| `biometric_identifier` | Biometric Identifier |
+| `blood_type` | Blood Type |
+| `date_of_birth` | Date Of Birth |
+| `education_level` | Education Level |
+| `first_name` | First Name |
+| `last_name` | Last Name |
+| `gender` | Gender |
+| `language` | Language |
+| `occupation` | Occupation |
+| ... | *and 4 more* |
+
+</details>
+
+<details>
+<summary><strong>Contact Info</strong> (4 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `email` | Email |
+| `phone_number` | Phone Number |
+| `fax_number` | Fax Number |
+| `url` | Url |
+
+</details>
+
+<details>
+<summary><strong>Location</strong> (6 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `city` | City |
+| `coordinate` | Coordinate |
+| `country` | Country |
+| `county` | County |
+| `state` | State |
+| `street_address` | Street Address |
+
+</details>
+
+<details>
+<summary><strong>Network Info</strong> (3 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `device_identifier` | Device Identifier |
+| `ipv4` | Ipv4 |
+| `ipv6` | Ipv6 |
+
+</details>
+
+<details>
+<summary><strong>Temporal</strong> (3 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `date` | Date |
+| `date_time` | Date Time |
+| `time` | Time |
+
+</details>
+
+<details>
+<summary><strong>Organization</strong> (1 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `company_name` | Company Name |
+
+</details>
+
+## Usage
+
+### Quick Start
+
+```python
+from transformers import pipeline
+
+# Load the PII detection pipeline
+ner = pipeline("ner", model="openmed/OpenMed-PII-ClinicalLongformer-149M-v1", aggregation_strategy="simple")
+
+text = """
+Patient John Smith (DOB: 03/15/1985, SSN: 123-45-6789) was seen today.
+Contact: john.smith@email.com, Phone: (555) 123-4567.
+Address: 456 Oak Street, Boston, MA 02108.
+"""
+
+entities = ner(text)
+for entity in entities:
+    print(f"{entity['entity_group']}: {entity['word']} (score: {entity['score']:.3f})")
+```
+
+### De-identification Example
+
+```python
+def redact_pii(text, entities, placeholder='[REDACTED]'):
+    """Replace detected PII with placeholders."""
+    # Sort entities by start position (descending) to preserve offsets
+    sorted_entities = sorted(entities, key=lambda x: x['start'], reverse=True)
+    redacted = text
+    for ent in sorted_entities:
+        redacted = redacted[:ent['start']] + f"[{ent['entity_group']}]" + redacted[ent['end']:]
+    return redacted
+
+# Apply de-identification
+redacted_text = redact_pii(text, entities)
+print(redacted_text)
+```
+
+### Batch Processing
+
+```python
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+import torch
+
+model_name = "openmed/OpenMed-PII-ClinicalLongformer-149M-v1"
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+texts = [
+    "Contact Dr. Jane Doe at jane.doe@hospital.org",
+    "Patient SSN: 987-65-4321, MRN: 12345678",
+]
+
+inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.argmax(outputs.logits, dim=-1)
+```
+
+## Training Details
+
+### Dataset
+
+- **Source**: [NVIDIA Nemotron-PII](https://huggingface.co/datasets/nvidia/Nemotron-PII)
+- **Format**: BIO-tagged token classification
+- **Labels**: 106 total (53 entity types × 2 BIO tags + O)
+- **Splits**: 50K train / 5K validation / 45K test
+
+### Training Configuration
+
+- **Max Sequence Length**: 384 tokens
+- **Label Strategy**: First token only (`label_all_tokens=False`)
+- **Framework**: Hugging Face Transformers + Trainer API
+
+## Intended Use & Limitations
+
+### Intended Use
+
+- **De-identification**: Automated redaction of PII in clinical notes, medical records, and documents
+- **Compliance**: Supporting HIPAA, GDPR, and privacy regulation compliance
+- **Data Preprocessing**: Preparing datasets for research by removing sensitive information
+- **Audit Support**: Identifying PII in document collections
+
+### Limitations
+
+⚠️ **Important**: This model is intended as an **assistive tool**, not a replacement for human review.
+
+- **False Negatives**: Some PII may not be detected; always verify critical applications
+- **Context Sensitivity**: Performance may vary with domain-specific terminology
+- **Challenging Categories**: `occupation`, `time`, and `sexuality` have lower F1 scores
+- **Language**: Primarily trained on English text
+
+## Citation
+
+```bibtex
+@misc{openmed-pii-2026,
+  title = {OpenMed-PII-ClinicalLongformer-149M-v1: PII Detection Model},
+  author = {OpenMed Science},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/openmed/OpenMed-PII-ClinicalLongformer-149M-v1}
+}
+```
+
+## Links
+
+- **Organization**: [OpenMed](https://huggingface.co/OpenMed)

all_results.json

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+{
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+    "eval_f1": 0.954933898181287,
+    "eval_loss": 0.02355710230767727,
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+}

config.json

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+}

eval_results.json

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+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 4096,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}

train_results.json

@@ -0,0 +1,8 @@
+{
+    "epoch": 2.0,
+    "total_flos": 1.5335158946125824e+16,
+    "train_loss": 0.1346272580018119,
+    "train_runtime": 1333.6636,
+    "train_samples_per_second": 75.581,
+    "train_steps_per_second": 2.362
+}