--- title: "OpenAI Privacy Filter" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c0534a87-9fc8-4898-aa8b-34e38f34f9d2" updated: "2026-11-13" --- # OpenAI Privacy Filter > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds and masks personal information such as names and emails in text in a single pass, and runs on your own machine. For teams that need to clean large amounts of text on-premises. OpenAI Privacy Filter is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by openai ## About ### What it is OpenAI Privacy Filter is a model that finds personally identifiable information in text and marks it so it can be masked. It reads a whole input at once and labels each token, then decodes clean spans from those labels. It is built for data sanitization jobs where a team wants a model it can run on its own hardware, with settings it can tune. ### Who it suits It suits developers and data teams who must strip names, emails and similar details from text before they store, share or train on it. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a masking step to a text pipeline | Hide names in logs before sharing | | Data teams | Data engineers | Clean large text sets before training | Remove emails from a corpus | | Support | Operations teams | Redact personal details in tickets | Mask a name in a customer message | | Research | ML engineers | Fine-tune the filter on their own data | Adapt it to a new text style | If your text never holds personal details, or you need a filter for images or audio, you can skip it. ### Where to use it The model works in Python with the transformers library and in JavaScript with transformers.js, where the card shows it running with WebGPU. - Python with transformers on Windows, macOS or Linux - A web browser or Node app through transformers.js - On-premises servers, since you run it yourself - A laptop, given its small size | Use case | Who | What they get back | | --- | --- | --- | | PII detection | Developers | Labeled spans such as private\_person and private\_email | | Text masking | Data teams | Text with personal details marked for removal | | On-premises cleaning | Security teams | Sanitized data that never leaves your servers | ### How to set it up You need Python with the transformers library and PyTorch, or a JavaScript project with transformers.js. The weights come to about 2.8 GB in BF16. - Python with pip, plus transformers and PyTorch - Or Node with the Hugging Face transformers.js package - About 2.8 GB of disk space for BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files, or let the library fetch them by model id. 3. Load the model with the pipeline call. ```sh pip install -U huggingface_hub hf download openai/privacy-filter ``` ### How to use it You pass text to a token-classification pipeline and read the labeled spans it returns. ```python from transformers import pipeline classifier = pipeline( task="token-classification", model="openai/privacy-filter", ) classifier("My name is Alice Smith") ``` | Tool | What it does | | --- | --- | | pipeline | Runs token classification in a single call | | AutoModelForTokenClassification | Gives direct access to the logits and labels | | AutoTokenizer | Turns your text into model inputs | ### Example You send the sentence "My name is Alice Smith" to the classifier. It labels the name as a private person, so you can replace that span with a mask. ### For newcomers and experts If you are new, run the short Python snippet above on one sentence with a name in it and read the labels that come back. That shows what the filter detects. If you know the field, it is an autoregressive checkpoint converted into a bidirectional banded-attention token classifier, with a sparse mixture-of-experts feed-forward stack. Inference uses constrained Viterbi decoding to produce BIOES span labels. Text is tokenized → One forward pass labels every token → Viterbi decoding builds spans → Spans are masked or removed ### Good to know The card lists a 128,000-token context window, so long text needs no chunking. It also describes preset operating points that trade precision against recall. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text you pass in and the model files you download, with no web service named ## README ````markdown # OpenAI Privacy Filter Finds and masks personal information such as names and emails in text in a single pass, and runs on your own machine. For teams that need to clean large amounts of text on-premises. Made by openai on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: OpenAI Privacy Filter is a model that finds personally identifiable information in text and marks it so it can be masked. It reads a whole input at once and labels each token, then decodes clean spans from those labels. It is built for data sanitization jobs where a team wants a model it can run on its own hardware, with settings it can tune. Who it suits: It suits developers and data teams who must strip names, emails and similar details from text before they store, share or train on it. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a masking step to a text pipeline | Hide names in logs before sharing Data teams | Data engineers | Clean large text sets before training | Remove emails from a corpus Support | Operations teams | Redact personal details in tickets | Mask a name in a customer message Research | ML engineers | Fine-tune the filter on their own data | Adapt it to a new text style ``` If your text never holds personal details, or you need a filter for images or audio, you can skip it. Where to use it: The model works in Python with the transformers library and in JavaScript with transformers.js, where the card shows it running with WebGPU. - Python with transformers on Windows, macOS or Linux - A web browser or Node app through transformers.js - On-premises servers, since you run it yourself - A laptop, given its small size ```table Use case | Who | What they get back PII detection | Developers | Labeled spans such as private_person and private_email Text masking | Data teams | Text with personal details marked for removal On-premises cleaning | Security teams | Sanitized data that never leaves your servers ``` How to set it up: You need Python with the transformers library and PyTorch, or a JavaScript project with transformers.js. The weights come to about 2.8 GB in BF16. - Python with pip, plus transformers and PyTorch - Or Node with the Hugging Face transformers.js package - About 2.8 GB of disk space for BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files, or let the library fetch them by model id. 3. Load the model with the pipeline call. ```sh pip install -U huggingface_hub hf download openai/privacy-filter ``` How to use it: You pass text to a token-classification pipeline and read the labeled spans it returns. ```python from transformers import pipeline classifier = pipeline( task="token-classification", model="openai/privacy-filter", ) classifier("My name is Alice Smith") ``` ```table Tool | What it does pipeline | Runs token classification in a single call AutoModelForTokenClassification | Gives direct access to the logits and labels AutoTokenizer | Turns your text into model inputs ``` Example: You send the sentence "My name is Alice Smith" to the classifier. It labels the name as a private person, so you can replace that span with a mask. For newcomers and experts: If you are new, run the short Python snippet above on one sentence with a name in it and read the labels that come back. That shows what the filter detects. If you know the field, it is an autoregressive checkpoint converted into a bidirectional banded-attention token classifier, with a sparse mixture-of-experts feed-forward stack. Inference uses constrained Viterbi decoding to produce BIOES span labels. ```flow Text is tokenized One forward pass labels every token Viterbi decoding builds spans Spans are masked or removed ``` Good to know: The card lists a 128,000-token context window, so long text needs no chunking. It also describes preset operating points that trade precision against recall. - License: Apache 2.0 - Cost to run: none named - Data it reaches: the text you pass in and the model files you download, with no web service named ## On Hugging Face, 13 November 2026 - Task: token classification - Library: transformers - Parameters: 1.4B - Weights: about 2.8 GB in BF16; about 700 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 102,911 - Likes: 1,831 - License: apache-2.0 - Quantized versions: 10 - Fine-tunes: 54 - Tags: transformers, onnx, safetensors, openai_privacy_filter, token-classification, transformers.js ## Links - Model: https://huggingface.co/openai/privacy-filter - Files: https://huggingface.co/openai/privacy-filter/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download openai/privacy-filter ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [openai/privacy-filter on Hugging Face](https://huggingface.co/openai/privacy-filter) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | OpenAI Privacy Filter (this page) | 102,911 | 1,831 | 1.4B | apache-2.0 | 2026-04-22 | | [bert-base-NER](https://mindket.com/p/20b1dab3-7f70-4c06-b584-f3c7be686fe2) | 1,038,928 | 750 | 108M | mit | 2024-10-08 | | [UltraBERT-150M-clairic-extraction](https://mindket.com/p/bb3bb903-2b2c-41c2-9d3b-4243580acdbc) | 138 | 5 | 151M | apache-2.0 | 2026-10-21 | | [nomic-embed-text-v1.5](https://mindket.com/p/55b00ba5-f156-4e3c-93ee-bf7cc7611725) | 11,809,681 | 994 | 137M | apache-2.0 | 2026-04-07 | | [SmolLM2-135M-Instruct](https://mindket.com/p/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af) | 841,106 | 474 | 135M | apache-2.0 | 2025-09-22 | | [Distil-Whisper distil-large-v3](https://mindket.com/p/d6a2c001-9ad9-472b-9889-ae7a3bee3b31) | 754,155 | 386 | 756M | mit | 2026-04-21 | Numbers as Mindket last read them, on 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · tanelpoder · 2026-04-23](https://news.ycombinator.com/item?id=47870901): OpenAI Privacy Filter - [Bluesky · Oakland Privacy · 2026-09-15](https://bsky.app/profile/oaklandprivacy.bsky.social/post/3mvjr45fhtx25): OpenAI says chats pass through a privacy filter designed to remove personal information before human review, but acknowledges the system can miss sensitive details. Reviewers may also see a "user memories summary" containing context about a user's past activity. 2/3 - [Hacker News · unusual\_typo · 2026-06-18](https://news.ycombinator.com/item?id=48579589): Show HN: Local personal data redaction for any AI tools: I built the desktop app that detects and redacts personal data (or PII) locally without sending any text to server. It supports rule-based filtering and AI model-based redaction (eg openai privacy filter). It's open source and free. Please check out the repo and https://pii-gui.vercel.app/ - [Bluesky · @yomimonoid.bsky.social · 2026-09-15](https://bsky.app/profile/yomimonoid.bsky.social/post/3mvk2fswaih2k): OpenAI Contractors Read User ChatGPT Chats Under Project Lily Project Lily: several hundred contractors read real ChatGPT conversations to grade responses. OpenAI's Privacy Filter strips names and numbers, but the company says it can miss identifying details that depend on context. - [Hacker News · meetpateltech · 2026-04-22](https://news.ycombinator.com/item?id=47864601): OpenAI Privacy Filter - [Bluesky · AI Daily Post · 2026-09-14](https://bsky.app/profile/aidailypost.com/post/3mvitkprcpt2g): Did you know OpenAI contract workers can peek at your ChatGPT chats? You can now turn off the default privacy filter. Find out what this means for your convo data and AI training. \#OpenAI \#ChatGPT \#AIprivacy 🔗 aidailypost.com/news/openai-... - [Hacker News · kamban · 2026-04-26](https://news.ycombinator.com/item?id=47906239): OpenAI shipped privacy-filter, a 1.5B PII tagger you can run locally - [Bluesky · ai-nerd.bsky.social · 2026-09-14](https://bsky.app/profile/ai-nerd.bsky.social/post/3mvidukcgvb2e): the data sharing toggle is on by default and opting out only covers new chats. OpenAI even says the privacy filter can miss uncommon identifiers - [Hacker News · handfuloflight · 2026-04-22](https://news.ycombinator.com/item?id=47865614): OpenAI Privacy Filter - [Bluesky · KeiTy · 2026-09-07](https://bsky.app/profile/keity717.bsky.social/post/3muw7xlpcxnkr): 【アンセンサードLLM最前線 \#114】OpenAI Privacy Filter——GPU/CPUで個人データをオフラインマスクするオープンソースモデル https://note.com/keity717/n/n50b31dbb9383 - [Hacker News · neon\_share1 · 2026-05-14](https://news.ycombinator.com/item?id=48137495): GLiNER2-PII: 0.3B open-source PII model outperforms OpenAI's Privacy Filter - [Bluesky · arxiv cs.CR · 2026-08-20](https://bsky.app/profile/arxiv-cs-cr.bsky.social/post/3mtjrrchu5l2k): Charles de Bourcy, Sahra Ghalebikesabi, Avi Schwarzschild, Alex Gorbachev, Mihai Maruseac, Annie Chu, Vol Kyrylov, Tong Mu, Ally Bennett, Andy Nguyen, Casey Meehan, Jessica Gan Lee, Shane Bauer, Harold Nguyen, ... Model Card for OpenAI Privacy Filter https://arxiv.org/abs/2608.18274 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-04-22 - First published: 2026-04-17 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open openai/privacy-filter on Hugging Face](https://huggingface.co/openai/privacy-filter) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c0534a87-9fc8-4898-aa8b-34e38f34f9d2) - [Its page on Mindket](https://mindket.com/p/c0534a87-9fc8-4898-aa8b-34e38f34f9d2)