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README.md

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 ---
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  - name: entities
-    list:
-    - name: content
-      dtype: string
-    - name: label
-      dtype: string
-    - name: start_index
-      dtype: int64
-  splits:
-  - name: train
-    num_bytes: 68336505
-    num_examples: 97453
-  - name: test
-    num_bytes: 12642443
-    num_examples: 17999
-  download_size: 35175208
-  dataset_size: 80978948
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+license: mit
+task_categories:
+- token-classification
+language:
+- he
+tags:
+- privacy
+- token-classification
+- security
+- text-masking
+- hebrew
+- pii-detection
+- ner
+pretty_name: GolemGuard
+size_categories:
+- 100K<n<1M
 ---
+
+# GolemGuard: Hebrew Privacy Information Detection Corpus
+
+this is a snapshot of https://huggingface.co/datasets/CordwainerSmith/GolemGuard
+GolemGuard is a comprehensive Hebrew language dataset specifically designed for training and evaluating models for Personal Identifiable Information (PII) detection and masking. The dataset contains ~600MB of synthetic text data representing various document types and communication formats commonly found in Israeli professional and administrative contexts.
+
+## Source Data
+### Initial Data Collection and Normalization
+
+The dataset combines synthetic data generated from multiple authoritative sources:
+
+1. Names:
+   - Israeli government open data portal (data.gov.il) for first names
+   - Hebrew Wikipedia category for family names
+   - Faker library
+   - Additional curated sources
+
+2. Addresses:
+   - Israeli government datasets for cities
+   - Israeli government street names datasets
+   
+3. Synthetic Identifiers:
+   - ID numbers: Generated following Israeli ID number format and checksum rules
+   - Bank accounts: Generated following Israeli bank account number formats
+   - Postal codes: Based on valid Israeli postal code ranges
+   - Credit cards: Generated following valid card number algorithms
+   - Email addresses: Constructed using collected names and top Israeli email providers
+
+### Entity Types:
+
+| PII Entity Type | Description | Count |
+|----------------|-------------|-------|
+| FIRST_NAME | First names | 131,192 |
+| LAST_NAME | Last names | 115,020 |
+| ID_NUM | Israeli ID numbers | 77,902 |
+| PHONE_NUM | Phone numbers (both mobile and landlines) | 142,795 |
+| DATE | Dates | 77,042 |
+| STREET | Street addresses | 105,381 |
+| CITY | City names | 105,874 |
+| EMAIL | Email addresses | 90,080 |
+| POSTAL_CODE | Israeli postal codes (Mikud) | 93,748 |
+| BANK_ACCOUNT_NUM | Bank account numbers | 31,046 |
+| CC_NUM | Credit card numbers | 1,751 |
+| CC_PROVIDER | Credit card providers | 1,679 |
+
+### Template Diversity
+
+The dataset includes 2,607 unique document templates. Here are the top document types by instance count:
+
+| Template Type | Instance Count |
+|--------------|----------------|
+| meeting_summary | 21,983 |
+| appointment_confirmation | 4,420 |
+| job_application_confirmation | 3,611 |
+| job_application | 3,306 |
+| medical_appointment_confirmation | 2,359 |
+| loan_application_received | 1,988 |
+| job_application_received | 1,832 |
+| bank_account_update | 1,609 |
+| appointment_reminder | 1,567 |
+| medical_appointment_reminder | 1,335 |
+
+### Dataset Size
+- Total Size: ~600MB
+- Number of Examples: 115,453
+- Format: JSONL
+
+### Data Splits
+
+| Split | Number of Instances |
+|-------|-------------------|
+| Training | 97,453 |
+| Test | 18,000 |
+| **Total** | **115,453** |
+
+
+## Languages
+
+- Hebrew (he)
+- Locale: Israel (IL)
+
+## Supported Tasks
+
+- Token Classification
+- Named Entity Recognition (NER)
+- PII Detection
+- Text Masking
+- Privacy-Preserving Text Processing
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance in the dataset contains:
+
+```json
+{
+  "id": "String",                    // Unique identifier
+  "source_text": "String",          // Original text with PII
+  "masked_text": "String",          // Text with PII entities masked
+  "locale": "String",              // Always "IL"
+  "language": "String",            // Always "he"
+  "split": "String",              // "train" or "test"
+  "privacy_mask": [               // List of PII entities with positions
+    {
+      "label": "String",         // Entity type
+      "start": "Integer",       // Start position
+      "end": "Integer",        // End position
+      "value": "String",      // Original value
+      "label_index": "Integer" // Index for multiple entities of same type
+    }
+  ],
+  "span_labels": "List",        // Entity spans in [start, end, label] format
+  "tokens": "List",            // Tokenized text
+  "token_classes": "List",    // Token-level BIO tags
+  "input_ids": "List",       // Model input token IDs
+  "attention_mask": "List",  // Attention mask for padding
+  "offset_mapping": "List", // Character offsets for tokens
+  "template_type": "String" // Document type/template
+}
+```
+### Sample
+```json
+{"id": "entry_000256", "source_text": "משתתף: דולב שם טוב\nתאריך לידה: 28.08.97\nכתובת: סחרוב דוד 158, קריית ים, 2676389\nאימייל: sagitbendor760@live.com\nטלפון: +972-58-2892208\n\nסיכום הפגישה: בפגישה זו נדונה חשיבות שיפור התקשורת הפנימית בין הצוותים. הוחלט לבצע סדנאות להכשרה נוספת בשבוע הבא.", "masked_text": "משתתף: [FIRST_NAME_1] [LAST_NAME_1]\nתאריך לידה: [DATE_1]\nכתובת: [STREET_1], [CITY_1], [POSTAL_CODE_1]\nאימייל: [EMAIL_1]\nטלפון: [PHONE_NUM_1]\n\nסיכום הפגישה: בפגישה זו נדונה חשיבות שיפור התקשורת הפנימית בין הצוותים. הוחלט לבצע סדנאות להכשרה נוספת בשבוע הבא.", "locale": "IL", "language": "he", "split": "train", "privacy_mask": [{"label": "FIRST_NAME", "start": 7, "end": 11, "value": "דולב", "label_index": 1}, {"label": "LAST_NAME", "start": 12, "end": 18, "value": "שם טוב", "label_index": 1}, {"label": "DATE", "start": 31, "end": 39, "value": "28.08.97", "label_index": 1}, {"label": "STREET", "start": 47, "end": 60, "value": "סחרוב דוד 158", "label_index": 1}, {"label": "CITY", "start": 62, "end": 70, "value": "קריית ים", "label_index": 1}, {"label": "POSTAL_CODE", "start": 72, "end": 79, "value": "2676389", "label_index": 1}, {"label": "EMAIL", "start": 88, "end": 111, "value": "sagitbendor760@live.com", "label_index": 1}, {"label": "PHONE_NUM", "start": 119, "end": 134, "value": "+972-58-2892208", "label_index": 1}], "span_labels": [[7, 11, "FIRST_NAME"], [12, 18, "LAST_NAME"], [31, 39, "DATE"], [47, 60, "STREET"], [62, 70, "CITY"], [72, 79, "POSTAL_CODE"], [88, 111, "EMAIL"], [119, 134, "PHONE_NUM"]], "tokens": ["<s>", "▁מ", "שתתף", ":", "▁דו", "לב", "▁שם", "▁טוב", "▁", "תאריך", "▁", "לידה", ":", "▁28.", "08.", "97", "▁כתובת", ":", "▁", "סחר", "וב", "▁דוד", "▁158", ",", "▁קרי", "ית", "▁", "ים", ",", "▁26", "76", "389", "▁אימייל", ":", "▁sa", "git", "ben", "dor", "760", "@", "live", ".", "com", "▁טלפון", ":", "▁+", "97", "2-", "58", "-28", "92", "208", "▁", "סיכום", "▁הפ", "גישה", ":", "▁בפ", "גישה", "▁זו", "▁", "נד", "ונה", "▁", "חשיבות", "▁", "שיפור", "▁התקשורת", "▁הפנימי", "ת", "▁בין", "▁הצוות", "ים", ".", "▁ה", "וח", "לט", "▁לבצע", "▁", "סדנאות", "▁ל", "הכשרה", "▁נוספת", "▁בשבוע", "▁הבא", ".", "</s>"], "token_classes": ["O", "O", "O", "O", "B-FIRST_NAME", "I-FIRST_NAME", "B-LAST_NAME", "I-LAST_NAME", "O", "O", "O", "O", "O", "B-DATE", "I-DATE", "I-DATE", "O", "O", "B-STREET", "I-STREET", "I-STREET", "I-STREET", "I-STREET", "O", "B-CITY", "I-CITY", "I-CITY", "I-CITY", "O", "B-POSTAL_CODE", "I-POSTAL_CODE", "I-POSTAL_CODE", "O", "O", "B-EMAIL", "I-EMAIL", "I-EMAIL", "I-EMAIL", "I-EMAIL", "I-EMAIL", "I-EMAIL", "I-EMAIL", "I-EMAIL", "O", "O", "B-PHONE_NUM", "I-PHONE_NUM", "I-PHONE_NUM", "I-PHONE_NUM", "I-PHONE_NUM", "I-PHONE_NUM", "I-PHONE_NUM", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"], "input_ids": [0, 874, 177834, 12, 18133, 22849, 16804, 19767, 6, 174081, 6, 119583, 12, 12511, 17331, 14773, 86185, 12, 6, 214797, 18340, 67512, 78373, 4, 46050, 2754, 6, 448, 4, 1381, 11835, 119861, 194921, 12, 57, 15769, 776, 1846, 110216, 981, 24056, 5, 277, 167153, 12, 997, 14773, 18504, 10057, 48590, 12231, 154782, 6, 186708, 18338, 58366, 12, 65412, 58366, 8248, 6, 16747, 15703, 6, 148055, 6, 154997, 185983, 214547, 609, 7501, 192819, 448, 5, 364, 15662, 21295, 107543, 6, 199930, 657, 226909, 122485, 132299, 54641, 5, 2], "attention_mask": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], "offset_mapping": [[0, 0], [0, 1], [1, 5], [5, 6], [7, 9], [9, 11], [12, 14], [15, 18], [19, 20], [19, 24], [25, 26], [25, 29], [29, 30], [31, 34], [34, 37], [37, 39], [40, 45], [45, 46], [47, 48], [47, 50], [50, 52], [53, 56], [57, 60], [60, 61], [62, 65], [65, 67], [68, 69], [68, 70], [70, 71], [72, 74], [74, 76], [76, 79], [80, 86], [86, 87], [88, 90], [90, 93], [93, 96], [96, 99], [99, 102], [102, 103], [103, 107], [107, 108], [108, 111], [112, 117], [117, 118], [119, 120], [120, 122], [122, 124], [124, 126], [126, 129], [129, 131], [131, 134], [136, 137], [136, 141], [142, 144], [144, 148], [148, 149], [150, 152], [152, 156], [157, 159], [160, 161], [160, 162], [162, 165], [166, 167], [166, 172], [173, 174], [173, 178], [179, 186], [187, 193], [193, 194], [195, 198], [199, 204], [204, 206], [206, 207], [208, 209], [209, 211], [211, 213], [214, 218], [219, 220], [219, 225], [226, 227], [227, 232], [233, 238], [239, 244], [245, 248], [248, 249], [0, 0]], "template_type": "meeting_summary"}
+```
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The dataset aims to improve privacy protection in Hebrew text processing by enabling better PII detection and masking. This has important applications in:
+
+| Application Area | Description |
+|-----------------|-------------|
+| Regulatory Compliance | Support for GDPR and PPLA requirements |
+| Document Processing | Privacy-preserving text analysis and storage |
+| Information Security | Automated PII detection and protection |
+| Data Loss Prevention | Real-time PII identification and masking |
+
+### Discussion of Biases
+
+1. Geographic Bias
+   - Dataset focuses on Israeli context and formats   
+
+2. Name Distribution
+   - While effort was made to include diverse names, distribution may not perfectly match population demographics
+
+
+## Additional Information
+
+### Dataset Curators
+
+Dataset was generated by [Liran Baba](https://huggingface.co/CordwainerSmith).
+
+
+### Model Training
+
+Dataset was generated for training the [GolemPII-xlm-roberta-v1][model-link] model by Liran Baba (CordwainerSmith), model, demonstrating its effectiveness for PII detection and masking tasks in Hebrew text.
+
+[model-link]: https://huggingface.co/CordwainerSmith/GolemPII-xlm-roberta-v1
+
+### Recommended Uses
+
+| Use Case | Description |
+|----------|-------------|
+| Privacy Protection | Identifying and masking PII in Hebrew documents |
+| Compliance Checking | Automated PII detection for regulatory compliance |
+| Data Sanitization | Cleaning sensitive information from text data |
+| Information Security | Supporting data loss prevention systems |
+
+### Quick Start
+```python
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("GolemGuard")
+
+# Example usage
+sample = dataset['train'][0]
+print(f"Original text: {sample['source_text']}")
+print(f"Masked text: {sample['masked_text']}")
+```
+### Versioning
+
+This is the initial release of the dataset. Future versions may include:
+- Additional template types
+- Expanded entity coverage
+- Enhanced demographic representation
+- Additional language variants
+
+## License
+
+The GolemGuard dataset is released under MIT License with the following additional terms:
+
+```
+MIT License
+
+Copyright (c) 2024 Liran Baba
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this dataset and associated documentation files (the "Dataset"), to deal
+in the Dataset without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Dataset, and to permit persons to whom the Dataset is
+furnished to do so, subject to the following conditions:
+
+1. The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Dataset.
+
+2. Any academic or professional work that uses this Dataset must include an 
+appropriate citation as specified below.
+
+THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE
+DATASET.
+```
+
+### How to Cite
+
+If you use this dataset in your research, project, or application, please include the following citation:
+
+For informal usage (e.g., blog posts, documentation):
+```
+GolemGuard Dataset by Liran Baba (https://huggingface.co/datasets/CordwainerSmith/GolemGuard)
+```
+
+For academic or professional publications:
+```
+Baba, L. (2024). GolemGuard: A Professional Hebrew PII Detection Dataset. 
+Retrieved from https://huggingface.co/datasets/CordwainerSmith/GolemGuard
+
+Related model: GolemPII-xlm-roberta-v1 (https://huggingface.co/CordwainerSmith/GolemPII-xlm-roberta-v1)
+```
+
+### Usage Examples
+
+When referencing in your code:
+```python
+"""
+This code uses the GolemGuard dataset by Liran Baba
+(https://huggingface.co/datasets/CordwainerSmith/GolemGuard)
+"""
+
+from datasets import load_dataset
+dataset = load_dataset("GolemGuard")
+```
+
+When referencing in your model card:
+```yaml
+dataset_info:
+  - name: GolemGuard
+    author: Liran Baba
+    url: https://huggingface.co/datasets/CordwainerSmith/GolemGuard
+    year: 2024
+```

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