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
File size: 363 Bytes
0cd599e | 1 2 3 4 5 6 7 8 9 10 11 12 13 | 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))
|