Feature Extraction
sentence-transformers
PyTorch
English
bert
splade
query-expansion
document-expansion
bag-of-words
passage-retrieval
knowledge-distillation
sparse-encoder
sparse
text-embeddings-inference
Instructions to use naver/splade-cocondenser-selfdistil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade-cocondenser-selfdistil with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("naver/splade-cocondenser-selfdistil") 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) - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from naver/splade-cocondenser-selfdistil: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/naver/splade-cocondenser-selfdistil/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://naver/splade-cocondenser-selfdistil/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/naver/splade-cocondenser-selfdistil/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 752dc47be60b1b8dd0a3897e1628a926f7343d63859266bfb66f56db57ca43ac
- Size of remote file:
- 438 MB
- SHA256:
- 0a44b51cf90c504462e69a0b84d68d15d1f4552c0d3f5483efab137495c9b9f9
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