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")# 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 tokenizer_config.json from RedHatAI/privacy-filter: direct link, hf CLI and curl.
- Browser
- Download file 234 Bytes
-
https://huggingface.co/RedHatAI/privacy-filter/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://RedHatAI/privacy-filter/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/RedHatAI/privacy-filter/resolve/main/tokenizer_config.json
234 Bytes
| { | |
| "backend": "tokenizers", | |
| "eos_token": "<|endoftext|>", | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 128000, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |