Text Classification
Transformers
Safetensors
English
nli
cross-encoder
qwen3.5
reranker
image-text-to-text
Instructions to use AlexWortega/openjev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlexWortega/openjev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AlexWortega/openjev")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AlexWortega/openjev", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download code/serving/service_notify.py from AlexWortega/openjev: direct link, hf CLI and curl.
- Browser
- Download file 390 Bytes
-
https://huggingface.co/AlexWortega/openjev/resolve/main/code/serving/service_notify.py
- Command line
-
hf download hf://AlexWortega/openjev/code/serving/service_notify.py
-
curl -L -o service_notify.py https://huggingface.co/AlexWortega/openjev/resolve/main/code/serving/service_notify.py
390 Bytes
| """Small dependency-free systemd notification helper.""" | |
| import os | |
| import socket | |
| def notify(message): | |
| address = os.environ.get("NOTIFY_SOCKET") | |
| if not address: | |
| return | |
| if address.startswith("@"): | |
| address = "\0" + address[1:] | |
| with socket.socket(socket.AF_UNIX, socket.SOCK_DGRAM) as sock: | |
| sock.connect(address) | |
| sock.sendall(message.encode()) | |