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_data from RedHatAI/privacy-filter: direct link, hf CLI and curl.
- Browser
- Download file 1.62 GB
-
https://huggingface.co/RedHatAI/privacy-filter/resolve/main/onnx/model_quantized.onnx_data
- Command line
-
hf download hf://RedHatAI/privacy-filter/onnx/model_quantized.onnx_data
-
curl -L -o model_quantized.onnx_data https://huggingface.co/RedHatAI/privacy-filter/resolve/main/onnx/model_quantized.onnx_data
1.62 GB
- Xet hash:
- 62512595181f4e3989d37b9082a29807d718350d5e9ddd89277895d4430d822e
- Size of remote file:
- 1.62 GB
- SHA256:
- 50f4c8c7f3c27fbc1fe16d4f74f6f7c3b74ba8f18a262e8b6911854c64c33a6d
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