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
File size: 613 Bytes
b7836ce | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"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": {}
}
}
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