Pretrained Models
Several Sparse Encoder models have been publicly released on the Hugging Face Hub:
Community models: All Sparse Encoder models on Hugging Face.
Models integrate seamlessly with this simple interface:
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("naver/splade-v3")
# Run inference
queries = ["what causes aging fast"]
documents = [
"UV-A light, specifically, is what mainly causes tanning, skin aging, and cataracts, UV-B causes sunburn, skin aging and skin cancer, and UV-C is the strongest, and therefore most effective at killing microorganisms. Again â\x80\x93 single words and multiple bullets.",
"Answers from Ronald Petersen, M.D. Yes, Alzheimer's disease usually worsens slowly. But its speed of progression varies, depending on a person's genetic makeup, environmental factors, age at diagnosis and other medical conditions. Still, anyone diagnosed with Alzheimer's whose symptoms seem to be progressing quickly â\x80\x94 or who experiences a sudden decline â\x80\x94 should see his or her doctor.",
"Bell's palsy and Extreme tiredness and Extreme fatigue (2 causes) Bell's palsy and Extreme tiredness and Hepatitis (2 causes) Bell's palsy and Extreme tiredness and Liver pain (2 causes) Bell's palsy and Extreme tiredness and Lymph node swelling in children (2 causes)",
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[11.3768, 10.8296, 4.3457]])
Core SPLADE Models
MS MARCO Passage Retrieval serves as the gold standard dataset, featuring authentic user queries from Bing search engine paired with expertly annotated relevant text passages. Models trained on this benchmark demonstrate exceptional effectiveness as embedding models for production search systems. Performance scores reflect evaluation on this dataset, it’s a good indication but shouldn’t be the only parameters to take into account.
BEIR (Benchmarking IR) provides a heterogeneous benchmark for evaluation of information retrieval models across in our case 13 diverse datasets. The avg nDCG@10 scores represent the average performance across all 13 datasets.
Note that all the numbers of below are extracted information from different papers. These models represent the backbone of sparse neural retrieval:
Model Name | MS MARCO MRR@10 | BEIR-13 avg nDCG@10 | Parameters |
---|---|---|---|
opensearch-project/opensearch-neural-sparse-encoding-v2-distill | NA | 52.8 | 67M |
opensearch-project/opensearch-neural-sparse-encoding-v1 | NA | 52.4 | 133M |
naver/splade-v3 | 40.2 | 51.7 | 109M |
ibm-granite/granite-embedding-30m-sparse | NA | 50.8 | 30M |
naver/splade-cocondenser-selfdistil | 37.6 | 50.7 | 109M |
naver/splade_v2_distil | 36.8 | 50.6 | 67M |
naver/splade-cocondenser-ensembledistil | 38.0 | 50.5 | 109M |
naver/splade-v3-distilbert | 38.7 | 50.0 | 67M |
prithivida/Splade_PP_en_v2 | 37.8 | 49.4 | 109M |
naver/splade-v3-lexical | 40.0 | 49.1 | 109M |
prithivida/Splade_PP_en_v1 | 37.2 | 48.7 | 109M |
naver/splade_v2_max | 34.0 | 46.4 | 67M |
Inference-Free SPLADE Models
Inference-free Splade uses for the documents part a traditional Splade architecture and for the query part is an SparseStaticEmbedding
module, which just returns a pre-computed score for every token in the query. So for these models we lose the query expansion, but query inference becomes near instant, which is very valuable for speed optimization.
Model Name | BEIR-13 avg nDCG@10 | Parameters |
---|---|---|
opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte | 54.6 | 137M |
opensearch-project/opensearch-neural-sparse-encoding-doc-v3-distill | 51.7 | 67M |
opensearch-project/opensearch-neural-sparse-encoding-doc-v2-distill | 50.4 | 67M |
opensearch-project/opensearch-neural-sparse-encoding-doc-v2-mini | 49.7 | 23M |
opensearch-project/opensearch-neural-sparse-encoding-doc-v1 | 49.0 | 133M |
naver/splade-v3-doc | 47.0 | 109M |
Model Collections
These are collections of models that are available on the Hugging Face Hub: