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/scripts/hash-password.py from x6nux/laya-ad: direct link, hf CLI and curl.
- Browser
- Download file 363 Bytes
-
https://huggingface.co/x6nux/laya-ad/resolve/main/code/laya-server/scripts/hash-password.py
- Command line
-
hf download hf://x6nux/laya-ad/code/laya-server/scripts/hash-password.py
-
curl -L -o hash-password.py https://huggingface.co/x6nux/laya-ad/resolve/main/code/laya-server/scripts/hash-password.py
363 Bytes
| from getpass import getpass | |
| from argon2 import PasswordHasher | |
| password = getpass("New administrator password: ") | |
| confirmation = getpass("Confirm password: ") | |
| if len(password) < 10: | |
| raise SystemExit("Use at least 10 characters") | |
| if password != confirmation: | |
| raise SystemExit("Passwords did not match") | |
| print(PasswordHasher().hash(password)) | |