anonymizer

分类: 开发与编程 | 上传者: NVIDIA-NeMo | 下载: 0 | 版本: v1.0（最新）
当用户想要对文本数据集进行匿名化、编辑 PII、对自由文本数据进行去标识化或重写文本以删除敏感或可推断的识别信息时使用。生成一个可运行的 Python 脚本，该脚本调用 NeMo Anonymizer 管道（检测 → 替换或重写）。
更新日志: Source: GitHub https://github.com/NVIDIA-NeMo/Anonymizer
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当前层级: tree/main/skills/anonymizer/
📁 workflows/
- 📄 interactive.md 5.5 KB
📄 SKILL.md 10.7 KB
SKILL.md

---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
name: anonymizer
description: Use when the user wants to anonymize a text dataset, redact PII, de-identify free-text data, or rewrite text to remove sensitive or inferable identifying information. Produces a runnable Python script that calls the NeMo Anonymizer pipeline (detection → replace or rewrite).
argument-hint: [describe the dataset and how you want it anonymized]
---

# Before You Start

Do not explore the workspace first. The workflow's data-inspection step shows you what you need.

# Goal

Anonymize a text dataset using NeMo Anonymizer in the way the user describes:

$ARGUMENTS

The output is a single runnable Python script that builds an `AnonymizerConfig`, previews results on a few rows, inspects failures and quality metrics, and (on user approval) runs the full pipeline. The script is the durable artifact — the user keeps it for re-runs, version control, and production.

# Workflow

Read `workflows/interactive.md` and follow it. Anonymization is high-stakes — there is no autopilot mode. Even when the user says "you decide" or "be opinionated", you still ask the minimum questions needed to choose `risk_tolerance` and phrase `privacy_goal`, because those are fundamentally the user's calls based on their regulatory and business context.

# Rules

- **Always preview before running the full pipeline.** Preview is cheap; a full run can be expensive and slow.
- **If `result.failed_records` is non-empty after preview, fix that *before* tweaking strategy.** Dropped rows are a model/provider/infra problem (rate limits, auth, etc.), not a config problem. Strategy knobs won't help. See `docs/troubleshooting.md` "Did the run actually complete cleanly?".
- **Ask the user which mode.** Briefly describe both: Replace detects entities and replaces each in place (faster, cheaper, keeps shape); Rewrite transforms the full text to also remove inferable identifiers (more expensive, may restructure). Use the data shape as a hint — free-text with implicit identifiers (clinical notes, biographies, depositions) leans Rewrite; structured records / log lines lean Replace — but the user picks.
- **For cross-record consistency** (same value → same replacement everywhere), use `Hash`, not `Substitute`. `Substitute` is consistent within a row only.
- **In Replace mode, default to `Substitute`** if the user hasn't specified a strategy. It's the most general-purpose choice and matches the bulk of production usage.
- **`Annotate` is for inspection, not production.** Its output keeps the original entity text and is not privacy-safe. Use it during iteration to confirm detection is working, then switch.
- **Always set `AnonymizerInput.data_summary`**, even briefly. It is the single cheapest quality lever and it improves both detection and rewrite.
- **Never claim privacy guarantees.** Anonymizer is best-effort. Outputs may need human review depending on `risk_tolerance`. Tell the user this when you finalize.

# Usage Tips and Common Pitfalls

- **`Detect.entity_labels=None` (the default) is permissive** — the augmenter LLM may invent labels not in `DEFAULT_ENTITY_LABELS`. Setting an explicit list switches to **strict mode** where *only* the listed labels are detected. To add domain labels, *extend* the default, don't replace it: `entity_labels=[*DEFAULT_ENTITY_LABELS, "clinical_facility", ...]` (`DEFAULT_ENTITY_LABELS` is a tuple, so unpack it into a list). Match the snake_case convention of `DEFAULT_ENTITY_LABELS`.
- **GLiNER is zero-shot** — entity labels are natural-language concept names (e.g. `"clinical_facility"`, `"internal_project_codename"`), not codes or enum values. Any concept you can name in English is a label GLiNER can detect.
- **`Rewrite.instructions` is a dead field today** — it exists on the model but the rewrite engine never reads it. Do not use it. Put rewriter guidance in `privacy_goal.protect` / `privacy_goal.preserve` instead.
- **`risk_tolerance` only applies to Rewrite mode**, not Replace.
- **`PrivacyGoal.protect` and `.preserve` must each be 10–1000 chars and at least 3 words.** Be specific (categories, named identifiers, structural facets); avoid generic phrasing like "preserve meaning".
- **Validator pool is the only model role with built-in load-spreading.** Set `entity_validator: [a, b, c]` in `models.yaml` if rate limits drop rows. Other roles (rewriter, evaluator, etc.) are single-alias.

# Reference Docs

The agent should consult these as it goes — *do not* try to enumerate field reference inline:

- [`docs/concepts/choosing-a-strategy.md`](../../docs/concepts/choosing-a-strategy.md) — decision tree for picking mode (Replace vs Rewrite), strategy (Substitute / Redact / Annotate / Hash), risk tolerance, privacy goal phrasing, and detection knobs.
- [`docs/troubleshooting.md`](../../docs/troubleshooting.md) — symptom-first guide for diagnosing dropped rows, leakage, low utility, and pipeline failures. Read **section by section as symptoms appear**, not all at once.
- [`docs/concepts/detection.md`](../../docs/concepts/detection.md) — detection internals (GLiNER threshold semantics, label catalogue, augmenter/validator behavior).
- [`docs/concepts/models.md`](../../docs/concepts/models.md) — model role configuration, validator pools.

# Troubleshooting

Environment-level issues only. Quality and pipeline issues are in `docs/troubleshooting.md`.

- **`anonymizer` not installed:** Tell the user `nemo-anonymizer` is not in this Python environment (requires Python ≥ 3.11). Ask if they want you to install it (`pip install nemo-anonymizer`) or do it themselves. Do not install without permission.
- **Model aliases not configured:** Anonymizer can't run without `models.yaml` and `providers.yaml`. Tell the user to set these up — see `docs/concepts/models.md`. If they don't have a config yet, point them at `src/anonymizer/config/default_model_configs/` for the shipped defaults.
- **LLM calls failing at preview:** Usually an auth issue (missing or invalid API key), a network problem, or a wrong base URL. See `docs/troubleshooting.md` "Validation passed but `preview` errors at LLM call".

# Output Template

Write a Python script to the current directory. Name it after the dataset (e.g. `anonymize_clinical_notes.py`, `anonymize_support_logs.py`). Use this template — fill in the TODO markers, drop unused sections.

```python
"""Anonymize <dataset> using NeMo Anonymizer.

Generated by the anonymizer agent skill.

Usage:
    python <this_script>.py            # preview on 5 rows (fast, cheap)
    python <this_script>.py --full     # run on the full dataset
"""

from __future__ import annotations

import argparse
import sys

from anonymizer import (
    Anonymizer,
    AnonymizerConfig,
    AnonymizerInput,
    DEFAULT_ENTITY_LABELS,
    Detect,
    # Pick what you need:
    # Replace mode:
    Substitute, Redact, Annotate, Hash,
    # Rewrite mode:
    Rewrite, PrivacyGoal,
)


def build_config() -> tuple[AnonymizerInput, AnonymizerConfig]:
    """Single source of truth for what we anonymize and how."""
    data = AnonymizerInput(
        source="TODO: path to .csv / .parquet / .jsonl",
        text_column="TODO: name of the text column",
        data_summary="TODO: one-line description of the data (domain, genre, anything non-obvious)",
    )

    detect = Detect(
        # Add domain labels by *extending* the default, not replacing it.
        # entity_labels=[*DEFAULT_ENTITY_LABELS, "clinical_facility", "diagnosis_code"],
        gliner_threshold=0.3,  # default; lower (0.2) for recall, raise (0.5) for cost savings
    )

    # ---- Pick ONE of the two strategies below ----

    # Replace mode (Substitute | Redact | Annotate | Hash):
    # config = AnonymizerConfig(detect=detect, replace=Substitute(
    #     instructions="TODO: short hint about the domain (e.g. names should remain plausible "
    #                  "for the original cultural context)",
    # ))

    # Rewrite mode (free-text de-identification with inferable-identifier suppression):
    config = AnonymizerConfig(
        detect=detect,
        rewrite=Rewrite(
            privacy_goal=PrivacyGoal(
                protect="TODO: what must not appear in the output, even by inference",
                preserve="TODO: what must be kept so the rewritten text is still useful",
            ),
            risk_tolerance="low",          # minimal | low | moderate | high
            strict_entity_protection=False, # True = force every detected entity into a protective disposition
            max_repair_iterations=3,
        ),
    )
    return data, config


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--full", action="store_true", help="Run on full dataset (default: preview 5 rows)")
    parser.add_argument("--num-records", type=int, default=5, help="Rows to preview (ignored with --full)")
    args = parser.parse_args()

    anonymizer = Anonymizer()
    data, config = build_config()

    if args.full:
        result = anonymizer.run(config=config, data=data)
        out_path = "output.parquet"  # TODO: change path/format (.csv, .jsonl) as needed
        result.dataframe.to_parquet(out_path)
        print(f"Wrote {len(result.dataframe)} rows to {out_path}")
    else:
        result = anonymizer.preview(config=config, data=data, num_records=args.num_records)
        print(f"Previewed {len(result.dataframe)} rows.")

        # Save preview output so you can investigate without re-running.
        # trace_dataframe is a superset of dataframe — it has the user-facing
        # columns plus internal columns (validation decisions, sensitivity
        # dispositions, etc.) that explain why entities were kept, dropped,
        # or rewritten.
        result.trace_dataframe.to_parquet("preview.parquet")
        print("Saved: preview.parquet (load with pd.read_parquet)")

    # Failure-first protocol: dropped rows are infra issues, not strategy issues.
    if result.failed_records:
        print(f"\n⚠️  {len(result.failed_records)} record(s) failed:")
        for fr in result.failed_records[:3]:
            print(f"   - record_id={fr.record_id} step={fr.step} reason={fr.reason}")
        print("\nFix dropped rows before tweaking strategy. See docs/troubleshooting.md.")
        sys.exit(1)

    # Rewrite-mode quality summary (skip for Replace mode).
    if config.rewrite is not None:
        df = result.dataframe
        print(f"\nleakage_mass:   mean={df['leakage_mass'].mean():.3f}  max={df['leakage_mass'].max():.3f}")
        print(f"utility_score:  mean={df['utility_score'].mean():.3f}  min={df['utility_score'].min():.3f}")
        print(f"flagged for review: {int(df['needs_human_review'].sum())} / {len(df)}")


if __name__ == "__main__":
    main()
```
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anonymizer

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