Instructions to use Veritone/siglip2-so400m-patch14-384-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veritone/siglip2-so400m-patch14-384-text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="Veritone/siglip2-so400m-patch14-384-text") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("Veritone/siglip2-so400m-patch14-384-text") model = AutoModelForZeroShotImageClassification.from_pretrained("Veritone/siglip2-so400m-patch14-384-text") - Notebooks
- Google Colab
- Kaggle
| { | |
| "word_embedding_dimension": 1152, | |
| "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 | |
| } |