Feature Extraction
sentence-transformers
PyTorch
English
distilbert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
sparse-encoder
sparse
Instructions to use naver/splade_v2_max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade_v2_max with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("naver/splade_v2_max") 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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