Instructions to use openai/privacy-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/privacy-filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="openai/privacy-filter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("openai/privacy-filter") model = AutoModelForTokenClassification.from_pretrained("openai/privacy-filter") - Transformers.js
How to use openai/privacy-filter with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'openai/privacy-filter'); - Inference
- Notebooks
- Google Colab
- Kaggle
Request: Release of corrected PII-Masking-300k labels used in Privacy Filter evaluation
#23
by itsgnani - opened
Hi team @OpenAIDevs ,
Thank you for releasing Privacy Filter and for documenting the label cleaning process in the model card.
Would it be possible to release the corrected dataset (or corrected evaluation split) used for these results?
It would be extremely valuable for reproducibility and benchmarking of PII detection systems.
Thank you for considering this request.