Feature Extraction
sentence-transformers
Safetensors
Japanese
modernbert
sparse-encoder
splade
sparse
information-retrieval
japanese
learned-sparse-retrieval
text-embeddings-inference
Instructions to use mahiyama/splade-ja-310m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mahiyama/splade-ja-310m with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("mahiyama/splade-ja-310m") 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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