Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
earth-observation
remote-sensing
satellite-imagery
decision-model
multimodal
vision
calibration
lora
custom_code
Instructions to use mx297/EO-Jev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mx297/EO-Jev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="mx297/EO-Jev", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("mx297/EO-Jev", trust_remote_code=True) model = AutoModel.from_pretrained("mx297/EO-Jev", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vocab.json from mx297/EO-Jev: direct link, hf CLI and curl.
- Browser
- Download file 6.72 MB
-
https://huggingface.co/mx297/EO-Jev/resolve/main/vocab.json
- Command line
-
hf download hf://mx297/EO-Jev/vocab.json
-
curl -L -o vocab.json https://huggingface.co/mx297/EO-Jev/resolve/main/vocab.json
6.72 MB
File too large to display, you can check the raw version instead.