Upload Japanese PII detection model OpenMed-PII-Japanese-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 +1,302 @@
+---
+language:
+  - ar
+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
+  - arabic
+  - pytorch
+  - transformers
+  - openmed
+pipeline_tag: token-classification
+library_name: transformers
+metrics:
+  - f1
+  - precision
+  - recall
+model-index:
+  - name: OpenMed-PII-Arabic-QwenMed-XLarge-600M-v1
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        dataset:
+          name: AI4Privacy + Synthetic Arabic PII
+          type: ai4privacy/pii-masking-200k
+          split: test
+        metrics:
+          - type: f1
+            value: 0.7929
+            name: F1 (micro)
+          - type: precision
+            value: 0.7832
+            name: Precision
+          - type: recall
+            value: 0.8028
+            name: Recall
+widget:
+  - text: "د. أحمد محمد (رقم الهوية: 1234567890) يمكن التواصل معه عبر ahmed.mohammed@hospital.sa أو +966 50 123 4567. العنوان: شارع الملك فهد 25، الرياض 11564."
+    example_title: Clinical Note with PII (Arabic)
+---
+
+# OpenMed-PII-Arabic-QwenMed-XLarge-600M-v1
+
+**Arabic PII Detection Model** | 600M Parameters | Open Source
+
+[![F1 Score](https://img.shields.io/badge/F1-79.29%25-brightgreen)]() [![Precision](https://img.shields.io/badge/Precision-78.32%25-blue)]() [![Recall](https://img.shields.io/badge/Recall-80.28%25-orange)]()
+
+## Model Description
+
+**OpenMed-PII-Arabic-QwenMed-XLarge-600M-v1** is a transformer-based token classification model fine-tuned for **Personally Identifiable Information (PII) detection in Arabic text**. This model identifies and classifies **54 types of sensitive information** including names, addresses, social security numbers, medical record numbers, and more.
+
+### Key Features
+
+- **Arabic-Optimized**: Specifically trained on Arabic text for optimal performance
+- **High Accuracy**: Achieves strong F1 scores across diverse PII categories
+- **Comprehensive Coverage**: Detects 55+ entity types spanning personal, financial, medical, and contact information
+- **Privacy-Focused**: Designed for de-identification and compliance with GDPR and other privacy regulations
+- **Production-Ready**: Optimized for real-world text processing pipelines
+
+## Performance
+
+Evaluated on the Arabic test split (AI4Privacy + synthetic data):
+
+| Metric | Score |
+|:---|:---:|
+| **Micro F1** | **0.7929** |
+| Precision | 0.7832 |
+| Recall | 0.8028 |
+| Macro F1 | 0.4080 |
+| Weighted F1 | 0.8030 |
+| Accuracy | 0.8920 |
+
+### Top 3 Arabic PII Models
+
+| Rank | Model | F1 | Precision | Recall |
+|:---:|:---|:---:|:---:|:---:|
+| 1 | [OpenMed-PII-Arabic-BigMed-Large-560M-v1](https://huggingface.co/OpenMed/OpenMed-PII-Arabic-BigMed-Large-560M-v1) | 0.8410 | 0.8531 | 0.8292 |
+| 2 | [OpenMed-PII-Arabic-NomicMed-Large-395M-v1](https://huggingface.co/OpenMed/OpenMed-PII-Arabic-NomicMed-Large-395M-v1) | 0.8281 | 0.8126 | 0.8441 |
+| **3** | **[OpenMed-PII-Arabic-QwenMed-XLarge-600M-v1](https://huggingface.co/OpenMed/OpenMed-PII-Arabic-QwenMed-XLarge-600M-v1)** | **0.7929** | **0.7832** | **0.8028** |
+
+## Supported Entity Types
+
+This model detects **54 PII entity types** organized into categories:
+
+<details>
+<summary><strong>Identifiers</strong> (22 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `ACCOUNTNAME` | Accountname |
+| `BANKACCOUNT` | Bankaccount |
+| `BIC` | Bic |
+| `BITCOINADDRESS` | Bitcoinaddress |
+| `CREDITCARD` | Creditcard |
+| `CREDITCARDISSUER` | Creditcardissuer |
+| `CVV` | Cvv |
+| `ETHEREUMADDRESS` | Ethereumaddress |
+| `IBAN` | Iban |
+| `IMEI` | Imei |
+| ... | *and 12 more* |
+
+</details>
+
+<details>
+<summary><strong>Personal Info</strong> (11 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `AGE` | Age |
+| `DATEOFBIRTH` | Dateofbirth |
+| `EYECOLOR` | Eyecolor |
+| `FIRSTNAME` | Firstname |
+| `GENDER` | Gender |
+| `HEIGHT` | Height |
+| `LASTNAME` | Lastname |
+| `MIDDLENAME` | Middlename |
+| `OCCUPATION` | Occupation |
+| `PREFIX` | Prefix |
+| ... | *and 1 more* |
+
+</details>
+
+<details>
+<summary><strong>Contact Info</strong> (2 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `EMAIL` | Email |
+| `PHONE` | Phone |
+
+</details>
+
+<details>
+<summary><strong>Location</strong> (9 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `BUILDINGNUMBER` | Buildingnumber |
+| `CITY` | City |
+| `COUNTY` | County |
+| `GPSCOORDINATES` | Gpscoordinates |
+| `ORDINALDIRECTION` | Ordinaldirection |
+| `SECONDARYADDRESS` | Secondaryaddress |
+| `STATE` | State |
+| `STREET` | Street |
+| `ZIPCODE` | Zipcode |
+
+</details>
+
+<details>
+<summary><strong>Organization</strong> (3 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `JOBDEPARTMENT` | Jobdepartment |
+| `JOBTITLE` | Jobtitle |
+| `ORGANIZATION` | Organization |
+
+</details>
+
+<details>
+<summary><strong>Financial</strong> (5 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `AMOUNT` | Amount |
+| `CURRENCY` | Currency |
+| `CURRENCYCODE` | Currencycode |
+| `CURRENCYNAME` | Currencyname |
+| `CURRENCYSYMBOL` | Currencysymbol |
+
+</details>
+
+<details>
+<summary><strong>Temporal</strong> (2 types)</summary>
+
+| Entity | Description |
+|:---|:---|
+| `DATE` | Date |
+| `TIME` | Time |
+
+</details>
+
+## Usage
+
+### Quick Start
+
+```python
+from transformers import pipeline
+
+# Load the PII detection pipeline
+ner = pipeline("ner", model="OpenMed/OpenMed-PII-Arabic-QwenMed-XLarge-600M-v1", aggregation_strategy="simple")
+
+text = """
+المريض خالد العتيبي (تاريخ الميلاد: 15/03/1985، رقم الهوية: 9876543210) تم فحصه اليوم.
+التواصل: khaled.otaibi@email.sa، الهاتف: +966 50 123 4567.
+العنوان: شارع العليا 42، الرياض 11432.
+"""
+
+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-Arabic-QwenMed-XLarge-600M-v1"
+model = AutoModelForTokenClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+texts = [
+    "المريض خالد العتيبي (تاريخ الميلاد: 15/03/1985، رقم الهوية: 9876543210) تم فحصه اليوم.",
+    "التواصل: khaled.otaibi@email.sa، الهاتف: +966 50 123 4567.",
+]
+
+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
+
+This model was trained on a combination of:
+
+- **[AI4Privacy PII Masking 200K](https://huggingface.co/datasets/ai4privacy/pii-masking-200k)**: Multilingual base dataset (200K records across 8 languages)
+- **[NVIDIA Nemotron-PII](https://huggingface.co/datasets/nvidia/Nemotron-PII)**: Seed dataset for synthetic data generation
+- **Synthetic Arabic Data**: ~25K high-quality samples generated with locale-specific formatting (National ID format, +966 phones, Arabic names, SAR/ر.س currency)
+
+- **Format**: BIO-tagged token classification
+- **Labels**: 76 BIO tags (54 entity types)
+
+### Training Configuration
+
+- **Max Sequence Length**: 512 tokens
+- **Framework**: Hugging Face Transformers + Trainer API
+
+## Intended Use & Limitations
+
+### Intended Use
+
+- **De-identification**: Automated redaction of PII in Arabic clinical notes, medical records, and documents
+- **Compliance**: Supporting GDPR, and other 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
+- **Language**: Optimized for Arabic text; may not perform well on other languages
+
+## Citation
+
+```bibtex
+@misc{openmed-pii-2026,
+  title = {OpenMed-PII-Arabic-QwenMed-XLarge-600M-v1: Arabic PII Detection Model},
+  author = {OpenMed Science},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/OpenMed/OpenMed-PII-Arabic-QwenMed-XLarge-600M-v1}
+}
+```
+
+## Links
+
+- **Organization**: [OpenMed](https://huggingface.co/OpenMed)

all_results.json

@@ -0,0 +1,28 @@
+{
+    "epoch": 3.0,
+    "eval_accuracy": 0.888999548715395,
+    "eval_f1": 0.7939432989690722,
+    "eval_loss": 0.5661426782608032,
+    "eval_macro_f1": 0.3903729497661312,
+    "eval_precision": 0.7801202912314024,
+    "eval_recall": 0.8082650049196458,
+    "eval_runtime": 2.8617,
+    "eval_samples_per_second": 535.703,
+    "eval_steps_per_second": 16.773,
+    "eval_weighted_f1": 0.8033758003197404,
+    "test_accuracy": 0.8919662851455586,
+    "test_f1": 0.7929066580686763,
+    "test_loss": 0.5452415943145752,
+    "test_macro_f1": 0.40795926781161085,
+    "test_precision": 0.7832346009299955,
+    "test_recall": 0.8028205797858449,
+    "test_runtime": 2.8812,
+    "test_samples_per_second": 532.077,
+    "test_steps_per_second": 16.66,
+    "test_weighted_f1": 0.803044059461668,
+    "total_flos": 5481934918516736.0,
+    "train_loss": 3.2885495954089694,
+    "train_runtime": 335.4941,
+    "train_samples_per_second": 109.683,
+    "train_steps_per_second": 1.717
+}

chat_template.jinja

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+{%- 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 %}

classification_report.txt

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+Classification Report for Japanese PII Detection
+Model: Qwen/Qwen3-Embedding-0.6B
+============================================================
+
+                  precision    recall  f1-score   support
+
+     ACCOUNTNAME       0.28      0.49      0.35       117
+             AGE       0.65      0.65      0.65       202
+          AMOUNT       0.22      0.27      0.24       385
+     BANKACCOUNT       0.85      0.83      0.84       594
+             BIC       0.00      0.00      0.00         0
+  BITCOINADDRESS       0.00      0.00      0.00        34
+  BUILDINGNUMBER       0.35      0.52      0.41       283
+            CITY       0.25      0.39      0.30       275
+          COUNTY       0.24      0.61      0.34        90
+      CREDITCARD       1.00      0.58      0.73        26
+        CURRENCY       0.00      0.00      0.00        86
+    CURRENCYCODE       0.12      0.50      0.20        12
+  CURRENCYSYMBOL       0.60      0.07      0.12        46
+            DATE       0.49      0.52      0.51       546
+     DATEOFBIRTH       0.61      0.61      0.61       101
+           EMAIL       0.98      0.98      0.98      1913
+        EYECOLOR       0.00      0.00      0.00         1
+       FIRSTNAME       0.99      0.99      0.99      3107
+          GENDER       0.36      0.13      0.19        39
+          HEIGHT       0.27      0.26      0.27        54
+            IBAN       0.72      0.79      0.76       239
+            IMEI       0.00      0.00      0.00        15
+       IPADDRESS       1.00      0.87      0.93        15
+   JOBDEPARTMENT       0.69      0.88      0.77       136
+        JOBTITLE       0.27      0.31      0.29        72
+        LASTNAME       0.99      0.93      0.96      3227
+    MASKEDNUMBER       0.72      0.12      0.20       179
+      MIDDLENAME       0.00      0.00      0.00         2
+      OCCUPATION       0.31      0.04      0.08        90
+ORDINALDIRECTION       0.00      0.00      0.00        41
+    ORGANIZATION       0.32      0.35      0.34       265
+        PASSWORD       0.70      0.88      0.78        26
+           PHONE       0.95      0.97      0.96       306
+          PREFIX       0.93      0.84      0.88      1573
+SECONDARYADDRESS       0.00      0.00      0.00        20
+             SEX       0.00      0.00      0.00         9
+             SSN       0.86      0.91      0.88       317
+           STATE       0.00      0.00      0.00        13
+          STREET       0.12      0.23      0.16       192
+            TIME       0.36      0.54      0.44       228
+             URL       0.92      0.76      0.83        45
+        USERNAME       0.50      0.44      0.47       133
+             VIN       0.00      0.00      0.00         9
+             VRM       0.00      0.00      0.00        16
+         ZIPCODE       0.90      0.91      0.91       237
+
+       micro avg       0.78      0.80      0.79     15316
+       macro avg       0.43      0.43      0.41     15316
+    weighted avg       0.82      0.80      0.80     15316

config.json

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+{
+  "architectures": [
+    "Qwen3ForTokenClassification"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "O",
+    "1": "B-ACCOUNTNAME",
+    "2": "B-AGE",
+    "3": "B-AMOUNT",
+    "4": "B-BANKACCOUNT",
+    "5": "B-BIC",
+    "6": "B-BITCOINADDRESS",
+    "7": "B-BUILDINGNUMBER",
+    "8": "B-CITY",
+    "9": "B-COUNTY",
+    "10": "B-CREDITCARD",
+    "11": "B-CREDITCARDISSUER",
+    "12": "B-CURRENCY",
+    "13": "B-CURRENCYCODE",
+    "14": "B-CURRENCYNAME",
+    "15": "B-CURRENCYSYMBOL",
+    "16": "B-CVV",
+    "17": "B-DATE",
+    "18": "B-DATEOFBIRTH",
+    "19": "B-EMAIL",
+    "20": "B-ETHEREUMADDRESS",
+    "21": "B-EYECOLOR",
+    "22": "B-FIRSTNAME",
+    "23": "B-GENDER",
+    "24": "B-GPSCOORDINATES",
+    "25": "B-HEIGHT",
+    "26": "B-IBAN",
+    "27": "B-IMEI",
+    "28": "B-IPADDRESS",
+    "29": "B-JOBDEPARTMENT",
+    "30": "B-JOBTITLE",
+    "31": "B-LASTNAME",
+    "32": "B-LITECOINADDRESS",
+    "33": "B-MACADDRESS",
+    "34": "B-MASKEDNUMBER",
+    "35": "B-MIDDLENAME",
+    "36": "B-OCCUPATION",
+    "37": "B-ORDINALDIRECTION",
+    "38": "B-ORGANIZATION",
+    "39": "B-PASSWORD",
+    "40": "B-PHONE",
+    "41": "B-PIN",
+    "42": "B-PREFIX",
+    "43": "B-SECONDARYADDRESS",
+    "44": "B-SEX",
+    "45": "B-SSN",
+    "46": "B-STATE",
+    "47": "B-STREET",
+    "48": "B-TIME",
+    "49": "B-URL",
+    "50": "B-USERAGENT",
+    "51": "B-USERNAME",
+    "52": "B-VIN",
+    "53": "B-VRM",
+    "54": "B-ZIPCODE",
+    "55": "I-ACCOUNTNAME",
+    "56": "I-AGE",
+    "57": "I-AMOUNT",
+    "58": "I-CITY",
+    "59": "I-COUNTY",
+    "60": "I-CURRENCY",
+    "61": "I-CURRENCYNAME",
+    "62": "I-DATE",
+    "63": "I-DATEOFBIRTH",
+    "64": "I-EYECOLOR",
+    "65": "I-GENDER",
+    "66": "I-HEIGHT",
+    "67": "I-JOBTITLE",
+    "68": "I-ORGANIZATION",
+    "69": "I-PHONE",
+    "70": "I-SECONDARYADDRESS",
+    "71": "I-SSN",
+    "72": "I-STATE",
+    "73": "I-STREET",
+    "74": "I-TIME",
+    "75": "I-USERAGENT"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "B-ACCOUNTNAME": 1,
+    "B-AGE": 2,
+    "B-AMOUNT": 3,
+    "B-BANKACCOUNT": 4,
+    "B-BIC": 5,
+    "B-BITCOINADDRESS": 6,
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