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 1_SpladePooling/config.json from naver/splade-v3-distilbert: direct link, hf CLI and curl.
- Browser
- Download file 106 Bytes
-
https://huggingface.co/naver/splade-v3-distilbert/resolve/main/1_SpladePooling/config.json
- Command line
-
hf download hf://naver/splade-v3-distilbert/1_SpladePooling/config.json
-
curl -L -o config.json https://huggingface.co/naver/splade-v3-distilbert/resolve/main/1_SpladePooling/config.json
106 Bytes
| { | |
| "pooling_strategy": "max", | |
| "activation_function": "relu", | |
| "word_embedding_dimension": null | |
| } |