NeMo Anonymizer

Detect and replace sensitive entities in text using LLM-powered workflows.

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What can you do with Anonymizer?

• Detect entities using GLiNER-PII and LLM-based augmentation and validation
• Replace with 4 strategies — LLM-generated substitute, redact, annotate, or hash (deterministic, local)
• Preview results before full runs with display_record() visualization

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Quick Start

1. Install

git clone https://github.com/NVIDIA-NeMo/Anonymizer.git
cd Anonymizer
make install

2. Set up model providers

By default, Anonymizer uses models hosted on build.nvidia.com — GLiNER-PII for entity detection and a text LLM for augmentation/validation. You can also bring your own models via custom provider configs.

Use the default build.nvidia.com setup as a convenient way to experiment with Anonymizer and iterate on small samples. For privacy-sensitive or production data, point Anonymizer at a secure endpoint you trust and to which you are comfortable sending data. Request and token rate limits on build.nvidia.com vary by account and model access, and lower-volume development access can be slow for full-dataset runs.

export NVIDIA_API_KEY="your-nvidia-api-key"

3. Anonymize text

CLI

Tip: All examples below use uv run to invoke commands. If you prefer, activate the venv with source .venv/bin/activate and run commands directly.

# Preview on a small sample
uv run anonymizer preview --source data.csv --replace redact

# Full run with output file
uv run anonymizer run --source data.csv --replace redact --output result.csv

# Validate config without running
uv run anonymizer validate --source data.csv --replace hash

Run anonymizer --help or anonymizer <subcommand> --help for all options.

Python API

from anonymizer import Anonymizer, AnonymizerConfig, AnonymizerInput, Redact

# Uses default model providers (build.nvidia.com) via NVIDIA_API_KEY env var
anonymizer = Anonymizer()

config = AnonymizerConfig(replace=Redact())

preview = anonymizer.preview(
    config=config,
    data=AnonymizerInput(source="data.csv", text_column="text"),
    num_records=3,
)

# Visualize with entity highlights and replacement map
preview.display_record()

# Most important columns only
preview.dataframe

# Full pipeline trace, including internal underscore-prefixed columns
preview.trace_dataframe

For custom model endpoints, pass a providers YAML:

anonymizer = Anonymizer(model_providers="path/to/model_providers.yaml")

Language And Regional Coverage

Anonymizer has been tested most extensively on English-language data. Multilingual quality has not yet been evaluated systematically across languages, domains, and models.

Although testing so far has been primarily in English, the supported entity set is not limited to U.S.-specific identifiers. Detection and anonymization can also apply to international formats such as non-U.S. phone numbers, addresses, legal references, and national or regional identification numbers, though coverage will vary by language, region, and model configuration.

If you are working with another language, we encourage you to experiment on a small sample first with preview(), validate detected entities and transformed output carefully, and adjust your model providers and model configs as needed.

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Replacement Strategies

Strategy	Output for "Alice" (first_name)	Configurable
Substitute	Maya	instructions
Redact	[REDACTED_FIRST_NAME]	format_template
Annotate	<Alice, first_name>	format_template
Hash	<HASH_FIRST_NAME_3bc51062973c>	format_template, algorithm, digest_length

from anonymizer import Redact, Annotate, Hash, Substitute

# Constant redaction
AnonymizerConfig(replace=Redact(format_template="****"))

# Deterministic hash with short digest
AnonymizerConfig(replace=Hash(algorithm="sha256", digest_length=8))

# LLM-generated contextual replacements
AnonymizerConfig(replace=Substitute())

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Development

make install-dev          # Install with dev dependencies
make test                 # Run tests
make coverage             # Run with coverage report
make check-all            # Lint + format check
anonymizer --help         # CLI usage
make install-pre-commit   # Install pre-commit hooks

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Requirements

• Python 3.11+
• NeMo Data Designer (installed as dependency)
• NVIDIA API key for default model providers (GLiNER-PII + text LLM), or custom model endpoints

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License

Apache License 2.0 — see LICENSE for details.