Text Classification
Transformers
Safetensors
Chinese
spam-detection
advertising-detection
ad-classification
chinese
modernbert
mmbert
multi-task
four-head
Instructions to use x6nux/laya-ad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use x6nux/laya-ad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="x6nux/laya-ad")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("x6nux/laya-ad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download code/laya-server/frontend/index.html from x6nux/laya-ad: direct link, hf CLI and curl.
- Browser
- Download file 374 Bytes
-
https://huggingface.co/x6nux/laya-ad/resolve/main/code/laya-server/frontend/index.html
- Command line
-
hf download hf://x6nux/laya-ad/code/laya-server/frontend/index.html
-
curl -L -o index.html https://huggingface.co/x6nux/laya-ad/resolve/main/code/laya-server/frontend/index.html
374 Bytes
| <html lang="zh-CN"> | |
| <head> | |
| <meta charset="UTF-8" /> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0" /> | |
| <link rel="icon" type="image/png" href="/laya-server-logo-black.png" /> | |
| <title>LAYA SERVER</title> | |
| </head> | |
| <body><div id="root"></div><script type="module" src="/src/main.tsx"></script></body> | |
| </html> | |