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
siglip2-bert
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9750
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.7.0+cu126
- Datasets 3.1.0
- Tokenizers 0.21.1
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