Feature Extraction
sentence-transformers
PyTorch
ONNX
English
bert
splade++
document-expansion
sparse representation
bag-of-words
passage-retrieval
knowledge-distillation
document encoder
sparse-encoder
sparse
splade
text-embeddings-inference
Instructions to use prithivida/Splade_PP_en_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use prithivida/Splade_PP_en_v1 with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("prithivida/Splade_PP_en_v1") 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
Ctrl+K