Feature Extraction
sentence-transformers
Safetensors
English
modernvbert
sparse-retrieval
splade
visual-document-retrieval
multimodal
information-retrieval
inference-free
sparse-encoder
custom_code
Instructions to use naver/v-splade-quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/v-splade-quality with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("naver/v-splade-quality", trust_remote_code=True) queries = ["Which planet is known as the Red Planet?"] documents = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from naver/v-splade-quality: direct link, hf CLI and curl.
- Browser
- Download file 553 Bytes
-
https://huggingface.co/naver/v-splade-quality/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://naver/v-splade-quality/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/naver/v-splade-quality/resolve/main/preprocessor_config.json
553 Bytes
| { | |
| "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 | |
| } | |
| } |