DeId-Small 🤖

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

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📋 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
• 🎯 Perfect PII Detection: 100% detection rate when PII is present
• ✅ Strong PII Removal: 65% of texts completely de-identified
• 📏 Compact Size: 136MB (Q8_0 quantized)
• ⚡ Fast Inference: 477ms 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):

   # 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:

   # 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:

   # 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:

   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)

# 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

# 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%	Model responds to PII presence with placeholders
PII Removal Completeness	65%	Successfully removes all detectable PII from output
Semantic Preservation	81.1%	How well original meaning is preserved
Average Latency	477ms	Response time performance

Understanding the Metrics

PII Detection Rate (100%): Measures whether the model recognizes when personal information is present in the input text and responds by generating placeholders. This is a measure of the model's sensitivity to PII presence.

PII Removal Completeness (65%): Measures whether the model successfully removes ALL detectable personal identifiers from the output text. This is a strict measure - even one remaining PII element (like a name, date, or phone number) counts as incomplete.

Why 65% is Strong Performance: Achieving 100% completeness is extremely challenging because:

• PII can be contextually important (e.g., "Dr. Smith" in medical records)
• Some PII might be embedded in complex ways
• Perfect removal could harm text coherence or meaning
• 65% completeness means the model reliably sanitizes most texts while preserving utility

Performance Insights

• ✅ Perfect PII Detection: 100% of texts with PII trigger placeholder generation
• ✅ Strong PII Removal: 65% of outputs are completely free of detectable PII
• ✅ Excellent Semantic Preservation: 81.1% meaning retention during de-identification
• ✅ Fast Inference: 477ms average response time
• ✅ Unified Performance: Consistent across medical, legal, HR, and general text

🏗️ 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

# 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

# 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

# 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

# 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

# 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

Development Setup

# 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:

@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
• Discord: Join our community
• Documentation: help.minibase.ai

📋 License

This model is released under the Apache License 2.0.

🙏 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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