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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ansulev/OpenAI-Privacy-Filter") model = AutoModelForTokenClassification.from_pretrained("ansulev/OpenAI-Privacy-Filter", device_map="auto") - 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
Download onnx/model.onnx_data from ansulev/OpenAI-Privacy-Filter: direct link, hf CLI and curl.
- Browser
- Download file 1.82 GB
-
https://huggingface.co/ansulev/OpenAI-Privacy-Filter/resolve/main/onnx/model.onnx_data
- Command line
-
hf download hf://ansulev/OpenAI-Privacy-Filter/onnx/model.onnx_data
-
curl -L -o model.onnx_data https://huggingface.co/ansulev/OpenAI-Privacy-Filter/resolve/main/onnx/model.onnx_data
1.82 GB
- Xet hash:
- 489a77b9690febfa2765aa5d246c4cc5031698eec4a6c3c2d528fe3d242f9815
- Size of remote file:
- 1.82 GB
- SHA256:
- 26bfdd86ff0794f92a0164fb8d4cc4a2ff63f1912866355da530a8757dfe9877
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