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:
- 1eb7357fbd12a8de35fe8a15fd6712059f5302c7a8c6313f1824811fe5d6bc81
- Size of remote file:
- 1.9 GB
- SHA256:
- 4c12392ea557a904af3ced6f7c736d1a68b265e65e4a1b310fe0cec5c9e7424c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.