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
File size: 409 Bytes
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"do_convert_rgb": null,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
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],
"image_processor_type": "SiglipImageProcessor",
"image_std": [
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],
"processor_class": "VisionTextDualEncoderProcessor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 224,
"width": 224
}
}
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