Feature Extraction
sentence-transformers
PyTorch
English
distilbert
splade
sparse-encoder
sparse
text-embeddings-inference
Instructions to use naver/splade-v3-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use naver/splade-v3-distilbert with sentence-transformers:
from sentence_transformers import SparseEncoder model = SparseEncoder("naver/splade-v3-distilbert") 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
Download pytorch_model.bin from naver/splade-v3-distilbert: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/naver/splade-v3-distilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://naver/splade-v3-distilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/naver/splade-v3-distilbert/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- c54c9df2297ec4b06f28786d413295413b4d5454dca5c0255476f0862e640566
- Size of remote file:
- 268 MB
- SHA256:
- ec4c6e9c997b7c61297759ad529ef7ed4f056491aa9fb536c21e26ff7f11ca77
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