Instructions to use RedHatAI/privacy-filter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/privacy-filter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RedHatAI/privacy-filter")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RedHatAI/privacy-filter") model = AutoModelForTokenClassification.from_pretrained("RedHatAI/privacy-filter", device_map="auto") - Transformers.js
How to use RedHatAI/privacy-filter with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('token-classification', 'RedHatAI/privacy-filter'); - Notebooks
- Google Colab
- Kaggle
Download onnx/model_quantized.onnx from RedHatAI/privacy-filter: direct link, hf CLI and curl.
- Browser
- Download file 162 kB
-
https://huggingface.co/RedHatAI/privacy-filter/resolve/main/onnx/model_quantized.onnx
- Command line
-
hf download hf://RedHatAI/privacy-filter/onnx/model_quantized.onnx
-
curl -L -o model_quantized.onnx https://huggingface.co/RedHatAI/privacy-filter/resolve/main/onnx/model_quantized.onnx
162 kB
- Xet hash:
- 89499d94ee0fe69cf43d747315bd473243cd87db71f4ebc7e23c2cf332851abd
- Size of remote file:
- 162 kB
- SHA256:
- a325fb5341567a73c94e91ec5e49060d38d9b16111f518ad34773039a0c9c098
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