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Minibase/DeId-Small
DeId-Small is a machine learning model from Minibase. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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.gguf145 MB Β· 100%
From the Hugging Face model README
A compact, privacy-focused text de-identification model for removing personal identifiers while preserving meaning.
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</div>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.
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
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
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
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]."
# 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 jane.smith@company.com 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."]
# 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)
| 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 |
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:
| 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:
# 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]."
# 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]."
# 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]."
# Input: "Employee John Doe earns $85,000 annually."
# Output: "Employee [FIRSTNAME_1] Doe earns [CURRENCYSYMBOL_1][AMOUNT_1] annually."
# Input: "Contact jane.smith@company.com for details."
# Output: "Contact [EMAIL_1] for details."
# 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 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 |
Here are real examples of the model in action, tested across different sectors and text types:
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].
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: rdavis@lawfirm.com, (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].
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: mchen@techcorp.com. 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]).
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: lisa.rodriguez@email.com.
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].
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 david.wilson@gmail.com 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]!
Input:
Meeting notes: Dr. Amanda White (awhite@hospital.org, (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].
| 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 |
| 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 |
# 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/
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}
}
This model is released under the Apache License 2.0.
Built with β€οΈ by the Minibase team
Making AI more accessible for everyone
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