Instructions to use Veritone/siglip2-base-patch16-224-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veritone/siglip2-base-patch16-224-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Veritone/siglip2-base-patch16-224-text") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Veritone/siglip2-base-patch16-224-text") model = AutoModelForZeroShotImageClassification.from_pretrained("Veritone/siglip2-base-patch16-224-text", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from Veritone/siglip2-base-patch16-224-text: direct link, hf CLI and curl.
- Browser
- Download file 296 Bytes
-
https://huggingface.co/Veritone/siglip2-base-patch16-224-text/resolve/main/1_Pooling/config.json
- Command line
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hf download hf://Veritone/siglip2-base-patch16-224-text/1_Pooling/config.json
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curl -L -o config.json https://huggingface.co/Veritone/siglip2-base-patch16-224-text/resolve/main/1_Pooling/config.json
296 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": true, | |
| "include_prompt": true | |
| } |