Instructions to use dnouv/privacy-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dnouv/privacy-filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dnouv/privacy-filter")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dnouv/privacy-filter") model = AutoModelForTokenClassification.from_pretrained("dnouv/privacy-filter") - Transformers.js
How to use dnouv/privacy-filter with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'dnouv/privacy-filter'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c16a313ae1cb46f49f41fc0f5720860fb397b3dd97213defd17b763753715e46
- Size of remote file:
- 160 kB
- SHA256:
- 8f7dee8b46d096f052b359375dfba5d983cc4d18c44a783bf548615c472f8dea
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