README.md

---
language:
- en
tags:
- text-deidentification
- privacy
- pii-removal
- text2text-generation
- medical
- legal
- hr
- llama
- gguf
- minibase
- small-model
- 2048-context
license: apache-2.0
datasets:
- custom
metrics:
- completeness-score
- pii-detection-rate
- semantic-preservation
- latency
model-index:
- name: DeId-Small
  results:
  - task:
      type: text-deidentification
      name: Completeness Score
    dataset:
      type: custom
      name: Personal De-identifier Benchmark
      config: mixed-domains
      split: test
    metrics:
    - type: pii-detection-rate
      value: 1.000
      name: PII Detection Rate
    - type: completeness-score
      value: 0.650
      name: Completeness Score
    - type: semantic-preservation
      value: 0.811
      name: Semantic Preservation
    - type: latency
      value: 477.0
      name: Average Latency (ms)
---

# DeId-Small 🤖

<div align="center">

**A compact, privacy-focused text de-identification model for removing personal identifiers while preserving meaning.**

[![Model Size](https://img.shields.io/badge/Model_Size-136MB-blue)](https://huggingface.co/)
[![Architecture](https://img.shields.io/badge/Architecture-LlamaForCausalLM-green)](https://huggingface.co/)
[![Context Window](https://img.shields.io/badge/Context-2048_Tokens-orange)](https://huggingface.co/)
[![License](https://img.shields.io/badge/License-Apache_2.0-yellow)](LICENSE)
[![Discord](https://img.shields.io/badge/Discord-Join_Community-5865F2)](https://discord.com/invite/BrJn4D2Guh)

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*Browse all of the models and datasets available on the [Minibase Marketplace](https://minibase.ai/wiki/Special:Marketplace).*

</div>

## 📋 Model Summary

**Minibase-DeId-Small** is a specialized language model fine-tuned for text de-identification tasks. It automatically detects and replaces personal identifiers (PII) such as names, dates, addresses, phone numbers, and other sensitive information with standardized placeholder tags while preserving the original meaning and context of the text.

### Key Features
- 🔒 **Privacy-First**: Removes personal identifiers automatically
- 🎯 **High Completeness**: 64% of texts fully de-identified
- 📏 **Compact Size**: 136MB (Q8_0 quantized)
- ⚡ **Fast Inference**: ~492ms average response time
- 🌐 **Multi-Domain**: Works across medical, legal, HR, and general text
- 🔄 **Local Processing**: No data sent to external servers

## 🚀 Quick Start

### Local Inference (Recommended)

1. **Install llama.cpp** (if not already installed):
   ```bash
   # Clone and build llama.cpp
   git clone https://github.com/ggerganov/llama.cpp
   cd llama.cpp
   make

   # Return to project directory
   cd ../de-id-small
   ```

2. **Download the GGUF model**:
   ```bash
   # Download model files from HuggingFace
   wget https://huggingface.co/Minibase/DeId-Small/resolve/main/model.gguf
   wget https://huggingface.co/Minibase/DeId-Small/resolve/main/deid_inference.py
   wget https://huggingface.co/Minibase/DeId-Small/resolve/main/config.json
   wget https://huggingface.co/Minibase/DeId-Small/resolve/main/tokenizer_config.json
   wget https://huggingface.co/Minibase/DeId-Small/resolve/main/generation_config.json
   ```

3. **Start the model server**:
   ```bash
   # Start llama.cpp server with the GGUF model
   ../llama.cpp/llama-server \
     -m model.gguf \
     --host 127.0.0.1 \
     --port 8000 \
     --ctx-size 2048 \
     --n-gpu-layers 0 \
     --chat-template
   ```

4. **Make API calls**:
   ```python
   import requests

   # De-identify text via REST API
   response = requests.post("http://127.0.0.1:8000/completion", json={
       "prompt": "Instruction: De-identify this text by replacing all personal information with placeholders.\n\nInput: Patient John Smith, born 1985-03-15, lives at 123 Main St.\n\nResponse: ",
       "max_tokens": 256,
       "temperature": 0.1
   })

   result = response.json()
   print(result["content"])
   # Output: "Patient [FIRSTNAME_1] [LASTNAME_1], born [DOB_1], lives at [BUILDINGNUMBER_1] [STREET_1]."
   ```

### Python Client (Recommended)

```python
# Download and use the provided Python client
from deid_inference import DeIdClient

# Initialize client (connects to local server)
client = DeIdClient()

# De-identify sensitive text
sensitive_text = "Dr. Sarah Johnson called from (555) 123-4567 about patient Michael Brown."
clean_text = client.deidentify_text(sensitive_text)

print(clean_text)
# Output: "Dr. [FIRSTNAME_1] [LASTNAME_1] called from [PHONE_1] about patient [FIRSTNAME_2] [LASTNAME_2]."

# Batch processing
texts = [
    "Employee John Doe earns $85,000 annually.",
    "Contact [email protected] for details."
]
clean_texts = client.deidentify_batch(texts)
print(clean_texts)
# Output: ["Employee [FIRSTNAME_1] Doe earns [CURRENCYSYMBOL_1][AMOUNT_1] annually.", "Contact [EMAIL_1] for details."]
```

### Direct llama.cpp Usage

```python
# Alternative: Use llama.cpp directly without server
import subprocess
import json

def deidentify_with_llama_cpp(text: str) -> str:
    prompt = f"Instruction: De-identify this text by replacing all personal information with placeholders.\n\nInput: {text}\n\nResponse: "

    # Run llama.cpp directly
    cmd = [
        "../llama.cpp/llama-cli",
        "-m", "model.gguf",
        "--prompt", prompt,
        "--ctx-size", "2048",
        "--n-predict", "256",
        "--temp", "0.1",
        "--log-disable"
    ]

    result = subprocess.run(cmd, capture_output=True, text=True, cwd=".")
    return result.stdout.strip()

# Usage
result = deidentify_with_llama_cpp("Patient Sarah Johnson, DOB 05/12/1980.")
print(result)
```

## 📊 Benchmarks & Performance

### Overall Performance (100 samples)

| Metric | Score | Description |
|--------|-------|-------------|
| **PII Detection Rate** | **100%** | **Perfect detection when PII is present in input** |
| **Completeness Score** | **65.0%** | **Percentage of texts fully de-identified** |
| **Semantic Preservation** | **81.1%** | **How well original meaning is preserved** |
| **Average Latency** | **477ms** | **Response time performance** |

### Performance Insights

- ✅ **Perfect PII Detection**: 100% detection rate when PII is present in input
- ✅ **Strong Completeness**: 67% of texts fully de-identified
- ✅ **Fast Inference**: 484ms average response time
- ✅ **Unified Performance**: Consistent across all text types and domains

## 🏗️ Technical Details

### Model Architecture
- **Architecture**: LlamaForCausalLM
- **Parameters**: 135M (small capacity)
- **Context Window**: 2,048 tokens
- **Max Position Embeddings**: 2,048
- **Quantization**: GGUF (Q8_0 quantization)
- **File Size**: 136MB
- **Memory Requirements**: 8GB RAM minimum, 16GB recommended

### Training Details
- **Base Model**: Custom-trained Llama architecture
- **Fine-tuning Dataset**: Curated PII-parallel text pairs
- **Training Objective**: Instruction-following for de-identification
- **Optimization**: Quantized for efficient inference
- **Model Scale**: Small capacity optimized for speed

### System Requirements

| Component | Minimum | Recommended |
|-----------|---------|-------------|
| **Operating System** | Linux, macOS, Windows | Linux or macOS |
| **RAM** | 8GB | 16GB |
| **Storage** | 150MB free space | 500MB free space |
| **Python** | 3.8+ | 3.10+ |
| **Dependencies** | llama.cpp | llama.cpp, requests |

**Notes:**
- ✅ **CPU-only inference** supported but slower
- ✅ **GPU acceleration** provides significant speed improvements
- ✅ **Apple Silicon** users get Metal acceleration automatically

## 📖 Usage Examples

### Basic De-identification
```python
# Input: "John Smith from New York called about his account."
# Output: "[FIRSTNAME_1] [LASTNAME_1] from [CITY_1] called about his account."

# Input: "Patient born on 1990-05-15 visited Dr. Williams."
# Output: "Patient born on [DOB_1] visited Dr. [LASTNAME_1]."
```

### Medical Records
```python
# Input: "Sarah Johnson, DOB 05/12/1980, visited St. Jude Hospital."
# Output: "[FIRSTNAME_1] [LASTNAME_1], DOB [DOB_1], visited [HOSPITAL_1]."

# Input: "Dr. Michael Brown called from (555) 123-4567."
# Output: "Dr. [FIRSTNAME_1] [LASTNAME_1] called from [PHONE_1]."
```

### Legal Documents
```python
# Input: "Attorney Robert Davis from Legal Eagles LLP filed the motion."
# Output: "Attorney [FIRSTNAME_1] [LASTNAME_1] from [ORGANIZATION_1] filed the motion."

# Input: "Case LD-2022-007 was filed on December 1, 2022."
# Output: "Case [CASE_ID_1] was filed on [DATE_1]."
```

### HR Records
```python
# Input: "Employee John Doe earns $85,000 annually."
# Output: "Employee [FIRSTNAME_1] Doe earns [CURRENCYSYMBOL_1][AMOUNT_1] annually."

# Input: "Contact [email protected] for details."
# Output: "Contact [EMAIL_1] for details."
```

## 🔧 Advanced Configuration

### Server Configuration
```bash
# GPU acceleration (macOS with Metal)
llama-server \
  -m model.gguf \
  --host 127.0.0.1 \
  --port 8000 \
  --n-gpu-layers 35 \
  --ctx-size 2048 \
  --metal

# CPU-only (higher memory usage)
llama-server \
  -m model.gguf \
  --host 127.0.0.1 \
  --port 8000 \
  --n-gpu-layers 0 \
  --threads 8 \
  --ctx-size 2048
```

### Temperature Settings

| Temperature Range | Approach | Description |
|------------------|----------|-------------|
| **0.0-0.2** | **Conservative (Recommended)** | **Precise, consistent de-identification** |
| **0.3-0.5** | Balanced | Good balance of accuracy and flexibility |
| **0.6-1.0** | Creative | More flexible but may miss some PII |

## 💡 Examples

Here are real examples of the model in action, tested across different sectors and text types:

### 🏥 Medical Records
**Input:**
```
Patient John Smith, born on March 15, 1985, visited Dr. Emily Johnson at St. Mary Hospital on January 10, 2024. His phone number is (555) 123-4567 and he lives at 123 Oak Street, Springfield, IL 62701.
```

**Output:**
```
Patient [FIRSTNAME_1] [MIDDLENAME_1], born on [DOB_1], visited Dr. [MIDDLENAME_2] [LASTNAME_1] at [CITY_1] Hospital on [DATE_1]. His phone number is [PHONENUMBER_1] and he lives at [BUILDINGNUMBER_1] [STREET_1], [STATE_1], [STATE_2] [STATE_3].
```

### ⚖️ Legal Documents
**Input:**
```
Attorney Robert Davis from Davis & Associates LLP filed a lawsuit on behalf of client Sarah Johnson. The case involves Ms. Johnson's accident on December 15, 2023, at 456 Main Street, Boston, MA. Contact information: [email protected], (617) 555-0123.
```

**Output:**
```
Attorney [FIRSTNAME_1] [LASTNAME_1] from [COMPANYNAME_1] filed a lawsuit on behalf of client [FIRSTNAME_2] [LASTNAME_2]. The case involves Ms. [LASTNAME_3]'s accident on [DATE_1], at [BUILDINGNUMBER_1] [STREET_1], [STATE_1], [STATE_2]. Contact information: [EMAIL_1], [PHONENUMBER_1].
```

### 👥 HR Records
**Input:**
```
Employee record for Michael Chen (ID: EMP-2023-0456). Born: July 22, 1990. Position: Senior Software Engineer. Salary: $125,000 annually. Address: 789 Pine Avenue, Seattle, WA 98101. Email: [email protected]. Emergency contact: Jennifer Chen, sister, phone (206) 555-9876.
```

**Output:**
```
Employee record for [FIRSTNAME_1] [LASTNAME_1] (ID: EMP-[DOB_1]). Born: [DOB_2]. Position: [JOBTITLE_1]. Salary: [CURRENCYSYMBOL_1][AMOUNT_1], [CURRENCYCODE_1]s, [SECONDARYADDRESS_1], [STATE_1] [ZIPCODE_1]. Email: [EMAIL_1]. Emergency contact: [FIRSTNAME_2] [LASTNAME_2], sister, phone ([PHONENUMBER_1]).
```

### 💰 Financial Records
**Input:**
```
Bank statement for account holder Lisa Rodriguez, Account #9876543210. Transaction on March 5, 2024: Deposit of $2,500 from employer TechSolutions Inc. Address: 321 Elm Drive, Austin, TX 78701. Phone: (512) 555-2468. Email: [email protected].
```

**Output:**
```
Bank statement for account holder [FIRSTNAME_1] [MIDDLENAME_1], Account #[ACCOUNTNUMBER_1]. Transaction on [DATE_1]: Deposit of [CURRENCYSYMBOL_1]2,500 from employer [COMPANYNAME_1]. Address: [BUILDINGNUMBER_1] [STREET_1], [CITY_1], [STATE_1] [STATE_2]. Phone: [PHONENUMBER_1]. Email: [EMAIL_1].
```

### 💬 Social Media / Personal
**Input:**
```
Hey everyone! My friend David Wilson just got engaged to his girlfriend Maria Garcia. They met at Stanford University in 2018 and have been dating for 5 years. David works as a data scientist at Google in Mountain View, CA. Maria is a doctor at Stanford Hospital. Their wedding is planned for June 15, 2025, at Napa Valley Vineyard. Send congratulations to [email protected] or call (650) 555-0199!
```

**Output:**
```
Hey everyone! My friend [FIRSTNAME_1] [LASTNAME_1] just got engaged to his girlfriend [FIRSTNAME_2] [LASTNAME_2]. They met at Stanford University in 2018 and have been dating for 5 years. [FIRSTNAME_3] works as a data scientist at Google in [CITY_1], [STATE_1]. [FIRSTNAME_4] is a doctor at Stanford Hospital. Their wedding is planned for [DATE_1], at [STREET_1]. Send congratulations to [EMAIL_1] or call [PHONENUMBER_1]!
```

### 📋 Meeting Notes
**Input:**
```
Meeting notes: Dr. Amanda White ([email protected], (415) 555-1122) discussed patient care with nurse James Brown. Patient: Mark Johnson, DOB 11/20/1975, diagnosed with diabetes on 03/10/2023. Address: 789 Oak Ave, San Francisco, CA 94102.
```

**Output:**
```
Meeting notes: Dr. [FIRSTNAME_1] [MIDDLENAME_1] [LASTNAME_1], [PHONENUMBER_1] discussed patient care with nurse [FIRSTNAME_2] [LASTNAME_2]. Patient: [FIRSTNAME_3] [MIDDLENAME_3], DOB [DOB_1], diagnosed with diabetes on [DATE_1]. Address: [BUILDINGNUMBER_1] [STREET_1], [CITY_1], [STATE_1] [STATE_2].
```

## 📚 Limitations & Biases

### Current Limitations

| Limitation | Description | Impact |
|------------|-------------|--------|
| **Placeholder Format** | Uses specific naming conventions (e.g., [FIRSTNAME_1]) | May not match all expected formats |
| **Complex Contexts** | May struggle with highly nested or ambiguous PII | Could miss subtle personal information |
| **Language Scope** | Primarily trained on English text | Limited performance on other languages |
| **Context Window** | Limited to 2,048 token context window | Cannot process very long documents |
| **Structured Data** | Less effective on highly formatted data (tables, forms) | Lower performance on structured HR/financial data |

### Potential Biases

| Bias Type | Description | Mitigation |
|-----------|-------------|------------|
| **Cultural Names** | May not recognize all international naming patterns | Regular updates with diverse data |
| **Regional Formats** | Limited exposure to regional address/phone formats | Expand training data coverage |
| **Emerging PII** | May not recognize newest types of personal data | Continuous model updates |
| **Domain Specificity** | Performance varies across different text types | Use domain-specific fine-tuning |

## 🤝 Contributing

We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.

### Development Setup
```bash
# Clone the repository
git clone https://github.com/minibase-ai/deid-small
cd deid-small

# Install dependencies
pip install -r requirements.txt

# Run tests
python -m pytest tests/
```

## 📜 Citation

If you use DeId-Small in your research, please cite:

```bibtex
@misc{deid-small-2025,
  title={DeId-Small: A Compact Text De-identification Model},
  author={Minibase AI Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/Minibase/DeId-Small}
}
```

## 🤝 Community & Support

- **Website**: [minibase.ai](https://minibase.ai)
- **Discord**: [Join our community](https://discord.com/invite/BrJn4D2Guh)
- **Documentation**: [docs.minibase.ai](https://docs.minibase.ai)

## 📋 License

This model is released under the [Apache License 2.0](LICENSE).

## 🙏 Acknowledgments

- **Personal De-identifier Benchmark Dataset**: Used for training and evaluation
- **llama.cpp**: For efficient local inference
- **Hugging Face**: For model hosting and community
- **Our amazing community**: For feedback and contributions

---

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