Feature Extraction
Transformers
Safetensors
English
modernvbert
sparse-retrieval
splade
visual-document-retrieval
multimodal
information-retrieval
inference-free
Instructions to use naver/v-splade-efficient with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naver/v-splade-efficient with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="naver/v-splade-efficient")# Load model directly from transformers import AutoProcessor, BiModernVBert processor = AutoProcessor.from_pretrained("naver/v-splade-efficient") model = BiModernVBert.from_pretrained("naver/v-splade-efficient") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_convert_rgb": true, | |
| "do_image_splitting": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "Idefics3ImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_image_size": { | |
| "longest_edge": 512 | |
| }, | |
| "processor_class": "Idefics3Processor", | |
| "resample": 1, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 2048 | |
| } | |
| } |