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/.gitattributes from x6nux/laya-ad: direct link, hf CLI and curl.
- Browser
- Download file 151 Bytes
-
https://huggingface.co/x6nux/laya-ad/resolve/main/code/.gitattributes
- Command line
-
hf download hf://x6nux/laya-ad/code/.gitattributes
-
curl -L -o .gitattributes https://huggingface.co/x6nux/laya-ad/resolve/main/code/.gitattributes
151 Bytes
| # 统一换行符:仓库内一律 LF,避免 Windows CRLF 轮换噪音 | |
| * text=auto eol=lf | |
| *.safetensors binary | |
| *.gz binary | |
| *.zip binary | |
| *.pt binary | |