Feature Extraction
Transformers
TensorBoard
Safetensors
vision-text-dual-encoder
Generated from Trainer
Instructions to use Sarmst/siglip2-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sarmst/siglip2-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Sarmst/siglip2-bert")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Sarmst/siglip2-bert") model = AutoModel.from_pretrained("Sarmst/siglip2-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3f9fd0dd045e0a2be701e700ddb9082625208066b285ac9a2f9d6b38879e294
- Size of remote file:
- 5.78 kB
- SHA256:
- c43001f53bf4ebe8cee8c7da4b2461eae3b3c64b2fb82cf502af7b29972f846b
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