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/backend/tests/conftest.py from x6nux/laya-ad: direct link, hf CLI and curl.
- Browser
- Download file 578 Bytes
-
https://huggingface.co/x6nux/laya-ad/resolve/main/code/laya-server/backend/tests/conftest.py
- Command line
-
hf download hf://x6nux/laya-ad/code/laya-server/backend/tests/conftest.py
-
curl -L -o conftest.py https://huggingface.co/x6nux/laya-ad/resolve/main/code/laya-server/backend/tests/conftest.py
578 Bytes
| import time | |
| from contextlib import ExitStack | |
| import pytest | |
| from fastapi.testclient import TestClient | |
| def client_factory(): | |
| with ExitStack() as stack: | |
| yield lambda *args, **kwargs: stack.enter_context(TestClient(*args, **kwargs)) | |
| def wait_for_usage(client, requests): | |
| deadline = time.monotonic() + 5 | |
| while True: | |
| usage = client.get('/internal/usage').json() | |
| if usage['totals']['requests'] == requests: | |
| return usage | |
| assert time.monotonic() < deadline, usage | |
| time.sleep(0.01) | |