zeke/openai-privacy-filter

Detect and redact personally identifiable information (PII) in text. Wraps OpenAI Privacy Filter via HuggingFace transformers.

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10 runs
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Playground API Examples README Versions

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

cog-openai-privacy-filter

Cog packaging of OpenAI Privacy Filter for deployment on Replicate.

What it does

OpenAI Privacy Filter is a 1.5B parameter token-classification model (50M active via mixture-of-experts) that detects personally identifiable information (PII) in unstructured text. It produces structured spans with labels and confidence scores, plus a redacted version of the input text with placeholders.

The model detects 8 categories of PII:

Label Examples
private_person Names of private individuals
private_email Email addresses
private_phone Phone numbers
private_address Postal addresses
private_date Dates of birth, appointment dates, etc.
private_url URLs that identify individuals
account_number Bank accounts, credit cards, IDs
secret API keys, passwords, tokens

Example

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "input": {
      "text": "My name is Alice Smith and my email is alice@example.com. My API key is sk-abc123."
    }
  }' \
  https://api.replicate.com/v1/models/zeke/openai-privacy-filter/predictions

Output:

{
  "redacted_text": "My name is [PRIVATE_PERSON] and my email is [PRIVATE_EMAIL]. My API key is [SECRET].",
  "spans": [
    { "label": "private_person", "text": "Alice Smith", "score": 0.999, "start": 11, "end": 22 },
    { "label": "private_email", "text": "alice@example.com", "score": 0.999, "start": 39, "end": 56 },
    { "label": "secret", "text": "sk-abc123", "score": 0.999, "start": 73, "end": 82 }
  ],
  "summary": {
    "span_count": 3,
    "by_label": { "private_email": 1, "private_person": 1, "secret": 1 }
  }
}

Inputs

Input Type Default Description
text string (required) Text to scan for PII
aggregation_strategy enum simple How to group token-level predictions: none, simple, first, average, max

Outputs

Field Type Description
redacted_text string Input text with detected PII replaced by [LABEL] placeholders
spans array Detected spans, each with label, text, score, start, end
summary object Aggregate stats: span_count and by_label counts

How the Cog model works

This is a thin wrapper around HuggingFace’s transformers.pipeline for token classification.

  1. setup() loads the openai/privacy-filter checkpoint via transformers.pipeline(task="token-classification"). This downloads the model from HuggingFace Hub on the first cold boot (~2.8 GB) and pins it to GPU if available. Subsequent predictions on a warm container reuse the loaded model.
  2. predict() runs the input text through the pipeline with the chosen aggregation_strategy. The pipeline returns token-level predictions grouped into spans.
  3. Adjacent spans with the same label are merged so multi-token entities (like full names or long emails) come back as a single span.
  4. Span boundaries are trimmed of leading and trailing whitespace, since the tokenizer often includes a leading space in subword tokens.
  5. The redacted text is built by replacing each span’s character range with [LABEL].

The Cog config (cog.yaml) declares Python 3.12, torch 2.6 with CUDA 12.6, transformers, and accelerate. Replicate runs the model on a T4 GPU.

Running locally

git clone https://github.com/zeke/cog-openai-privacy-filter
cd cog-openai-privacy-filter
cog predict -i text="Alice emailed bob@example.com from +1 (415) 555-0124"

To push a new version to Replicate:

cog push r8.im/zeke/openai-privacy-filter

Addendum: why not the official opf package?

OpenAI ships an official inference package, opf, that loads the same weights with a custom Viterbi CRF decoder for more coherent span boundaries. I tried that path first.

It didn’t work out of the box on Replicate. The opf setup consistently failed with:

worker setup failed: setup failed: IndexError: map::at

That error comes from a C++/Rust extension, not Python. The most likely culprit is tiktoken, which opf uses for tokenization. tiktoken’s Rust BPE backend lazily downloads BPE merge files at runtime (e.g. o200k_base.tiktoken from openaipublic.blob.core.windows.net). When that download fails or returns malformed data, the Rust code throws map::at. The Replicate prediction container couldn’t reach that URL cleanly, and even pre-caching the BPE file at build time didn’t resolve it.

I switched to HuggingFace transformers, which:

  • Uses the bundled tokenizer.json file from the HF repo instead of tiktoken
  • Loads weights via standard AutoModelForTokenClassification instead of opf’s custom MXFP4 unpacking and MoE sharding
  • Has well-tested model caching, GPU dispatch, and pipeline aggregation

The trade-off is losing the Viterbi CRF decoder. In practice the model still produces high-confidence, accurate spans (>99% on simple tests), and merging adjacent same-label spans recovers most of the span-coherence benefit.

Model created