Upload PII detection model OpenMed-PII-QwenMed-XLarge-600M-v1

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,326 @@
+---
+language:
+  - en
+license: apache-2.0
+base_model: Qwen/Qwen3-Embedding-0.6B
+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-QwenMed-600M-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: 0.9124
+            name: F1 (micro)
+          - type: precision
+            value: 0.9075
+            name: Precision
+          - type: recall
+            value: 0.9173
+            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-QwenMed-600M-v1
+
+**PII Detection Model** | 600M Parameters | Open Source
+
+[![F1 Score](https://img.shields.io/badge/F1-91.24%25-brightgreen)]() [![Precision](https://img.shields.io/badge/Precision-90.75%25-blue)]() [![Recall](https://img.shields.io/badge/Recall-91.73%25-orange)]()
+
+## Model Description
+
+**OpenMed-PII-QwenMed-600M-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** | **0.9124** |
+| Precision | 0.9075 |
+| Recall | 0.9173 |
+| Macro F1 | 0.9268 |
+| Weighted F1 | 0.9121 |
+| Accuracy | 0.9913 |
+
+### 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 |
+|:---|:---:|:---:|:---:|:---:|
+| `blood_type` | 0.992 | 0.992 | 0.992 | 132 |
+| `biometric_identifier` | 0.991 | 0.991 | 0.991 | 224 |
+| `swift_bic` | 0.990 | 0.980 | 1.000 | 97 |
+| `cvv` | 0.989 | 1.000 | 0.978 | 92 |
+| `bank_routing_number` | 0.988 | 0.992 | 0.984 | 128 |
+
+### Challenging Entities
+
+These entity types have lower performance and may benefit from additional post-processing:
+
+| Entity | F1 | Precision | Recall | Support |
+|:---|:---:|:---:|:---:|:---:|
+| `county` | 0.821 | 0.770 | 0.879 | 313 |
+| `company_name` | 0.811 | 0.778 | 0.846 | 857 |
+| `time` | 0.784 | 0.778 | 0.789 | 459 |
+| `date_time` | 0.642 | 0.583 | 0.714 | 227 |
+| `occupation` | 0.567 | 0.609 | 0.530 | 692 |
+
+## 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-QwenMed-600M-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-QwenMed-600M-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-QwenMed-600M-v1: PII Detection Model},
+  author = {OpenMed Science},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/openmed/OpenMed-PII-QwenMed-600M-v1}
+}
+```
+
+## Links
+
+- **Organization**: [OpenMed](https://huggingface.co/OpenMed)

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

all_results.json

@@ -0,0 +1,24 @@
+{
+    "epoch": 3.0,
+    "eval_accuracy": 0.9925593277143512,
+    "eval_f1": 0.9153898869254491,
+    "eval_loss": 0.13752934336662292,
+    "eval_precision": 0.9089826471588975,
+    "eval_recall": 0.9218880946512201,
+    "eval_runtime": 44.5278,
+    "eval_samples_per_second": 112.29,
+    "eval_steps_per_second": 1.774,
+    "test_accuracy": 0.9924453410344638,
+    "test_f1": 0.9140644974746899,
+    "test_loss": 0.13219766318798065,
+    "test_precision": 0.9066935772994649,
+    "test_recall": 0.9215562429483753,
+    "test_runtime": 434.1499,
+    "test_samples_per_second": 103.651,
+    "test_steps_per_second": 1.622,
+    "total_flos": 1.06313819966443e+17,
+    "train_loss": 0.22251781229526407,
+    "train_runtime": 4069.8044,
+    "train_samples_per_second": 36.857,
+    "train_steps_per_second": 1.152
+}

chat_template.jinja

@@ -0,0 +1,85 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,276 @@
+{
+  "architectures": [
+    "Qwen3ForTokenClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "O",
+    "1": "B-account_number",
+    "2": "B-age",
+    "3": "B-api_key",
+    "4": "B-bank_routing_number",
+    "5": "B-biometric_identifier",
+    "6": "B-blood_type",
+    "7": "B-certificate_license_number",
+    "8": "B-city",
+    "9": "B-company_name",
+    "10": "B-coordinate",
+    "11": "B-country",
+    "12": "B-county",
+    "13": "B-credit_debit_card",
+    "14": "B-customer_id",
+    "15": "B-cvv",
+    "16": "B-date",
+    "17": "B-date_of_birth",
+    "18": "B-date_time",
+    "19": "B-device_identifier",
+    "20": "B-education_level",
+    "21": "B-email",
+    "22": "B-employee_id",
+    "23": "B-employment_status",
+    "24": "B-fax_number",
+    "25": "B-first_name",
+    "26": "B-gender",
+    "27": "B-health_plan_beneficiary_number",
+    "28": "B-http_cookie",
+    "29": "B-ipv4",
+    "30": "B-ipv6",
+    "31": "B-language",
+    "32": "B-last_name",
+    "33": "B-license_plate",
+    "34": "B-mac_address",
+    "35": "B-medical_record_number",
+    "36": "B-occupation",
+    "37": "B-password",
+    "38": "B-phone_number",
+    "39": "B-pin",
+    "40": "B-political_view",
+    "41": "B-postcode",
+    "42": "B-race_ethnicity",
+    "43": "B-religious_belief",
+    "44": "B-sexuality",
+    "45": "B-ssn",
+    "46": "B-state",
+    "47": "B-street_address",
+    "48": "B-swift_bic",
+    "49": "B-tax_id",
+    "50": "B-time",
+    "51": "B-unique_id",
+    "52": "B-url",
+    "53": "B-user_name",
+    "54": "B-vehicle_identifier",
+    "55": "I-account_number",
+    "56": "I-api_key",
+    "57": "I-biometric_identifier",
+    "58": "I-blood_type",
+    "59": "I-certificate_license_number",
+    "60": "I-city",
+    "61": "I-company_name",
+    "62": "I-coordinate",
+    "63": "I-country",
+    "64": "I-county",
+    "65": "I-credit_debit_card",
+    "66": "I-customer_id",
+    "67": "I-date",
+    "68": "I-date_of_birth",
+    "69": "I-date_time",
+    "70": "I-device_identifier",
+    "71": "I-education_level",
+    "72": "I-email",
+    "73": "I-employee_id",
+    "74": "I-employment_status",
+    "75": "I-fax_number",
+    "76": "I-first_name",
+    "77": "I-gender",
+    "78": "I-health_plan_beneficiary_number",
+    "79": "I-http_cookie",
+    "80": "I-ipv4",
+    "81": "I-ipv6",
+    "82": "I-language",
+    "83": "I-last_name",
+    "84": "I-license_plate",
+    "85": "I-mac_address",
+    "86": "I-medical_record_number",
+    "87": "I-occupation",
+    "88": "I-password",
+    "89": "I-phone_number",
+    "90": "I-pin",
+    "91": "I-political_view",
+    "92": "I-postcode",
+    "93": "I-race_ethnicity",
+    "94": "I-religious_belief",
+    "95": "I-sexuality",
+    "96": "I-ssn",
+    "97": "I-state",
+    "98": "I-street_address",
+    "99": "I-swift_bic",
+    "100": "I-tax_id",
+    "101": "I-time",
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