Feature Extraction
Transformers
Safetensors
English
Korean
multilingual
qwen3_5
vision-language
embedding
multimodal-embedding
mmeb
custom_code
Instructions to use Urock-AI/eddy-vl_embedding_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Urock-AI/eddy-vl_embedding_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Urock-AI/eddy-vl_embedding_v2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Urock-AI/eddy-vl_embedding_v2", trust_remote_code=True) model = AutoModel.from_pretrained("Urock-AI/eddy-vl_embedding_v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Show kept-score bars on a 0 to 100 scale
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README.md
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 760 190" role="img" aria-label="Share of the teacher score kept by this model">
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<title>Kept versus the teacher. Scale is 0 to 100.</title>
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