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-efficient with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/v-splade-efficient with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("naver/v-splade-efficient", 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 v-splade-logo.png from naver/v-splade-efficient: direct link, hf CLI and curl.
- Browser
- Download file 1.69 MB
-
https://huggingface.co/naver/v-splade-efficient/resolve/main/v-splade-logo.png
- Command line
-
hf download hf://naver/v-splade-efficient/v-splade-logo.png
-
curl -L -o v-splade-logo.png https://huggingface.co/naver/v-splade-efficient/resolve/main/v-splade-logo.png
1.69 MB

- Xet hash:
- 005c82c3fde893521bdb566244d15f09dfaf185441b35d03c32c0523076beae2
- Size of remote file:
- 1.69 MB
- SHA256:
- 067fb1d13c567629e151f8f61eb4331cfd5b13717d9a34290ba9a44d85796bf5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.