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 SentenceTransformer model = SentenceTransformer("naver/v-splade-efficient", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "types": { | |
| "query_0_VSPLADEStaticEmbedding": "modeling_st_vsplade.VSPLADEStaticEmbedding", | |
| "": "sentence_transformers.base.modules.transformer.Transformer", | |
| "document_1_SpladePooling": "sentence_transformers.sparse_encoder.modules.splade_pooling.SpladePooling" | |
| }, | |
| "structure": { | |
| "query": [ | |
| "query_0_VSPLADEStaticEmbedding" | |
| ], | |
| "document": [ | |
| "", | |
| "document_1_SpladePooling" | |
| ] | |
| }, | |
| "parameters": { | |
| "default_route": "document", | |
| "allow_empty_key": true, | |
| "route_mappings": {} | |
| } | |
| } | |