Text Classification
Transformers
Safetensors
Chinese
English
content-moderation
safety
encoder
qwen2
bidirectional
chinese
Instructions to use louivis/encoder-guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use louivis/encoder-guard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="louivis/encoder-guard")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("louivis/encoder-guard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download backbone/tokenizer.json from louivis/encoder-guard: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/louivis/encoder-guard/resolve/main/backbone/tokenizer.json
- Command line
-
hf download hf://louivis/encoder-guard/backbone/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/louivis/encoder-guard/resolve/main/backbone/tokenizer.json
11.4 MB
- Xet hash:
- 360944ea94439f68451bf305d8191f404d17220f6dab98faa570fc11b2f57c6a
- Size of remote file:
- 11.4 MB
- SHA256:
- 7d3b8cace3c6283818f885ef7ee8ca7b7ae7431c9b29245b0c33a0cc73113ab8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.