Instructions to use ansulev/OpenAI-Privacy-Filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ansulev/OpenAI-Privacy-Filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ansulev/OpenAI-Privacy-Filter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ansulev/OpenAI-Privacy-Filter") model = AutoModelForTokenClassification.from_pretrained("ansulev/OpenAI-Privacy-Filter") - Transformers.js
How to use ansulev/OpenAI-Privacy-Filter with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'ansulev/OpenAI-Privacy-Filter'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89499d94ee0fe69cf43d747315bd473243cd87db71f4ebc7e23c2cf332851abd
- Size of remote file:
- 162 kB
- SHA256:
- a325fb5341567a73c94e91ec5e49060d38d9b16111f518ad34773039a0c9c098
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.