Feature Extraction
sentence-transformers
PyTorch
Safetensors
English
bert
splade
sparse-encoder
sparse
asymmetric
inference-free
text-embeddings-inference
Instructions to use naver/splade-v3-doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade-v3-doc with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("naver/splade-v3-doc") 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
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