--- title: "privacy-filter-nemotron-v2" kind: "Kit" category: "Legal" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/23532a37-0110-417a-8dbb-6c1fd00a796a" updated: "2026-11-13" --- # privacy-filter-nemotron-v2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Finds and masks personal data in text, across 55 fine-grained categories such as names, contact details and IDs. It suits teams that redact text before sharing or storing it. privacy-filter-nemotron-v2 is a kit in the Legal 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 OpenMed ## About ### What it is privacy-filter-nemotron-v2 is a token classifier that spots personal data in text so you can mask it. It is the second generation of the OpenMed Nemotron-schema checkpoints. It labels 55 kinds of personal data, from names and contact details to account numbers. The card says v2 masks more reliably than the original. ### Who it suits It suits teams that must remove personal data from text before sharing, storing or analyzing it. You need some Python to run it. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Healthcare | Data and compliance teams | Mask patient identifiers in notes before they leave the system | Hiding a name, phone number and record number in a note | | Customer support | Support operations | Redact personal details from transcripts and logs | Cleaning a chat log before analysis | | Legal and compliance | Privacy officers | Strip identifiers from documents before review | Redacting a contract excerpt before sharing it | | Software | Data engineers | Add a redaction step to a text pipeline | Masking text before it goes to another service | People who need a single dataset lineage can use the original Nemotron checkpoint instead. ### Where to use it - Python with the OpenMed package - The transformers library, as a token classification pipeline - The opf command line tool from OpenAI's privacy filter project - Windows, macOS or Linux | Use case | Who | What they get back | | --- | --- | --- | | Finding personal data | Compliance teams | Each detected item with its label and text | | Masking text | Data engineers | The same text with personal data masked | | General redaction | Privacy officers | Typed labels you can use to choose what to hide | ### How to set it up - Python on your computer - The OpenMed package with its Hugging Face extra, or the transformers library - About 2.8 GB of disk for the BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the OpenMed package, or load the model with transformers. ```sh pip install -U huggingface_hub ``` ```sh hf download OpenMed/privacy-filter-nemotron-v2 ``` ```bash pip install -U "openmed[hf]" ``` ### How to use it With OpenMed, call extract\_pii to list the personal data in a text, and call deidentify with the mask method to hide it. For best production results, the card advises BIOES-aware decoding and merging overlapping or adjacent spans before masking. ```python from openmed import extract_pii, deidentify model_name = "OpenMed/privacy-filter-nemotron-v2" result = extract_pii(text, model_name=model_name) for ent in result.entities: print(ent.label, ent.text) masked = deidentify(text, method="mask", model_name=model_name) print(masked.deidentified_text) ``` | Tool | What it does | | --- | --- | | extract\_pii | Lists the personal data found in a text, with a label for each | | deidentify | Returns the text with the personal data masked | | opf redact | Runs redaction from the command line with a chosen checkpoint | ### Example You pass a sentence that holds a patient's name, birth date, record number, phone number and email. extract\_pii prints each item with its label. deidentify with the mask method returns the sentence with those items hidden. ### For newcomers and experts A token classifier tags each piece of text with a label, and here the labels mark personal data. A first small step is to run the OpenMed sample on one made-up sentence and read the labels. Its base is openai/privacy-filter, a mixture-of-experts classifier of 1.4B parameters, about 50M of which are active for each token. It predicts 221 BIOES classes, made of one outside class and four tags for each of the 55 categories. Training was a full fine-tune with a broader mix and a recall-oriented recipe. Text with personal data → Model tags each token with a label → Spans are merged and decoded → Personal data is masked ### Good to know - Languages: the record lists several European languages, including Bulgarian, Czech, Danish, German, Greek, English, Spanish and Estonian. - License: Apache 2.0. - Cost to run: none named. The steps need no API key or paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then processes the text you give it. The transformers sample turns on trust\_remote\_code, so it runs code from the model repository. - Not for: skipping human review. The card text provided gives no accuracy figures. ## README ````markdown # privacy-filter-nemotron-v2 Finds and masks personal data in text, across 55 fine-grained categories such as names, contact details and IDs. It suits teams that redact text before sharing or storing it. Made by OpenMed 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: privacy-filter-nemotron-v2 is a token classifier that spots personal data in text so you can mask it. It is the second generation of the OpenMed Nemotron-schema checkpoints. It labels 55 kinds of personal data, from names and contact details to account numbers. The card says v2 masks more reliably than the original. Who it suits: It suits teams that must remove personal data from text before sharing, storing or analyzing it. You need some Python to run it. ```table Industry | Who uses it | How they use it | Example Healthcare | Data and compliance teams | Mask patient identifiers in notes before they leave the system | Hiding a name, phone number and record number in a note Customer support | Support operations | Redact personal details from transcripts and logs | Cleaning a chat log before analysis Legal and compliance | Privacy officers | Strip identifiers from documents before review | Redacting a contract excerpt before sharing it Software | Data engineers | Add a redaction step to a text pipeline | Masking text before it goes to another service ``` People who need a single dataset lineage can use the original Nemotron checkpoint instead. Where to use it: - Python with the OpenMed package - The transformers library, as a token classification pipeline - The opf command line tool from OpenAI's privacy filter project - Windows, macOS or Linux ```table Use case | Who | What they get back Finding personal data | Compliance teams | Each detected item with its label and text Masking text | Data engineers | The same text with personal data masked General redaction | Privacy officers | Typed labels you can use to choose what to hide ``` How to set it up: - Python on your computer - The OpenMed package with its Hugging Face extra, or the transformers library - About 2.8 GB of disk for the BF16 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the OpenMed package, or load the model with transformers. ```sh pip install -U huggingface_hub ``` ```sh hf download OpenMed/privacy-filter-nemotron-v2 ``` ```bash pip install -U "openmed[hf]" ``` How to use it: With OpenMed, call extract_pii to list the personal data in a text, and call deidentify with the mask method to hide it. For best production results, the card advises BIOES-aware decoding and merging overlapping or adjacent spans before masking. ```python from openmed import extract_pii, deidentify model_name = "OpenMed/privacy-filter-nemotron-v2" result = extract_pii(text, model_name=model_name) for ent in result.entities: print(ent.label, ent.text) masked = deidentify(text, method="mask", model_name=model_name) print(masked.deidentified_text) ``` ```table Tool | What it does extract_pii | Lists the personal data found in a text, with a label for each deidentify | Returns the text with the personal data masked opf redact | Runs redaction from the command line with a chosen checkpoint ``` Example: You pass a sentence that holds a patient's name, birth date, record number, phone number and email. extract_pii prints each item with its label. deidentify with the mask method returns the sentence with those items hidden. For newcomers and experts: A token classifier tags each piece of text with a label, and here the labels mark personal data. A first small step is to run the OpenMed sample on one made-up sentence and read the labels. Its base is openai/privacy-filter, a mixture-of-experts classifier of 1.4B parameters, about 50M of which are active for each token. It predicts 221 BIOES classes, made of one outside class and four tags for each of the 55 categories. Training was a full fine-tune with a broader mix and a recall-oriented recipe. ```flow Text with personal data Model tags each token with a label Spans are merged and decoded Personal data is masked ``` Good to know: - Languages: the record lists several European languages, including Bulgarian, Czech, Danish, German, Greek, English, Spanish and Estonian. - License: Apache 2.0. - Cost to run: none named. The steps need no API key or paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then processes the text you give it. The transformers sample turns on trust_remote_code, so it runs code from the model repository. - Not for: skipping human review. The card text provided gives no accuracy figures. ## 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: 154,398 - Likes: 57 - License: apache-2.0 - Languages: bg, cs, da, de, el, en, es, et - Based on: openai/privacy-filter - Quantized versions: 3 - Fine-tunes: 2 - Tags: transformers, safetensors, openai_privacy_filter, token-classification, privacy, redaction, nemotron, openmed, openai-privacy-filter ## Links - Model: https://huggingface.co/OpenMed/privacy-filter-nemotron-v2 - Files: https://huggingface.co/OpenMed/privacy-filter-nemotron-v2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download OpenMed/privacy-filter-nemotron-v2 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [OpenMed/privacy-filter-nemotron-v2 on Hugging Face](https://huggingface.co/OpenMed/privacy-filter-nemotron-v2) ## 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 | | --- | --- | --- | --- | --- | --- | | privacy-filter-nemotron-v2 (this page) | 154,398 | 57 | 1.4B | apache-2.0 | 2026-09-30 | | [rizzo-pii-0.3B](https://mindket.com/p/37c4000b-ebb5-4db2-9df5-a87a46896f81) | 6,484 | 23 | 308M | mit | 2026-08-09 | | [PII-Tracer](https://mindket.com/p/cb03372e-7eea-4c7b-bfee-be3c53650ba6) | 8,506 | 97 | 596M | mit | 2026-09-22 | | [SaulLM-54B-Instruct](https://mindket.com/p/f6f4f701-3913-4cae-81e9-f823c78d35f8) | 315 | 16 | 46.7B | mit | 2024-07-31 | Numbers as Mindket last read them, on 2026-11-13. ## 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: 2026-09-30 - First published: 2026-05-03 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open OpenMed/privacy-filter-nemotron-v2 on Hugging Face](https://huggingface.co/OpenMed/privacy-filter-nemotron-v2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/23532a37-0110-417a-8dbb-6c1fd00a796a) - [Its page on Mindket](https://mindket.com/p/23532a37-0110-417a-8dbb-6c1fd00a796a)