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
Ctrl+K
- Jun09_23-30-40_krylov-ws-rc-hkbfnxxazg1felkugb4qk0oco4ykdebqdongetxaf60-zbbr4
- Jun11_21-07-07_krylov-ws-rc-zhbv1x9wqubvyv3gih-jjzq9iuxjblrvtyrcc76w7bw-4xh5b
- Jun11_23-22-26_krylov-ws-rc-zhbv1x9wqubvyv3gih-jjzq9iuxjblrvtyrcc76w7bw-4xh5b
- Jun11_23-24-41_krylov-ws-rc-zhbv1x9wqubvyv3gih-jjzq9iuxjblrvtyrcc76w7bw-4xh5b
- Jun12_17-05-59_krylov-ws-rc-xm7qizwvte-ts49k-walw4ckotairpqp874niwgpirk-8tgd2
- Jun12_18-00-10_krylov-ws-rc-xm7qizwvte-ts49k-walw4ckotairpqp874niwgpirk-dvz9l