Why did we open-source our inference engine? Read the post

opensearch-project/opensearch-neural-sparse-encoding-v1

The model should be selected considering search relevance, model inference and retrieval efficiency(FLOPS). We benchmark models' zero-shot performance on a subset of BEIR benchmark: TrecCovid,NFCorpus,NQ,HotpotQA,FiQA,ArguAna,Touche,DBPedia,SCIDOCS,FEVER,Climate FEVER,SciFact,Quora.

Overview

Architecture
BERT
Parameters
133M
Tasks
Encode
Outputs
Sparse
Dimensions
Sparse: 30,522
Max Sequence Length
512 tokens
License
apache-2.0
Languages
en

Benchmarks

CQADupstackPhysicsRetrieval

scientific retrieval en

Duplicate question retrieval from StackExchange Physics

Corpus: 38,314 Queries: 1,039
Quality
ndcg at 10 0.3652
map at 10 0.3090
mrr at 10 0.3574
Performance L4 b1 c16
Corpus 28.5K tok/s
Corpus p50 69.1ms
Query 2.4K tok/s
Query p50 66.5ms
Reference →

CosQA

technology retrieval en

Code search with natural language queries

Corpus: 6,267 Queries: 500
Quality
ndcg at 10 0.2435
map at 10 0.1868
mrr at 10 0.1985
Performance L4 b1 c16
Corpus 15.2K tok/s
Corpus p50 52.0ms
Query 2.0K tok/s
Query p50 46.6ms
Reference →

FiQA2018

finance retrieval en

Financial opinion mining and question answering

Corpus: 57,599 Queries: 648
Quality
ndcg at 10 0.2632
map at 10 0.2004
mrr at 10 0.3273
Performance L4 b1 c16
Corpus 40.2K tok/s
Corpus p50 59.4ms
Query 3.6K tok/s
Query p50 50.0ms
Reference →

LegalBenchConsumerContractsQA

legal retrieval en

Question answering on consumer contracts

Corpus: 153 Queries: 396
Quality
ndcg at 10 0.7272
map at 10 0.6626
mrr at 10 0.6635
Performance L4 b1 c16
Corpus 89.1K tok/s
Corpus p50 86.7ms
Query 5.1K tok/s
Query p50 50.1ms
Reference →

NFCorpus

medical retrieval en

Biomedical literature search from NutritionFacts.org

Corpus: 3,593 Queries: 323
Quality
ndcg at 10 0.3600
map at 10 0.1403
mrr at 10 0.5690
Performance L4 b1 c16
Corpus 58.7K tok/s
Corpus p50 76.0ms
Query 1.5K tok/s
Query p50 51.0ms
Reference →

SCIDOCS

scientific retrieval en

Citation prediction, document classification, and recommendation for scientific papers

Corpus: 25,656 Queries: 1,000
Quality
ndcg at 10 0.1537
map at 10 0.0877
mrr at 10 0.2688
Performance L4 b1 c16
Corpus 44.8K tok/s
Corpus p50 63.2ms
Query 3.5K tok/s
Query p50 49.5ms
Reference →

SciFact

scientific retrieval en

Scientific claim verification using research literature

Corpus: 5,183 Queries: 300
Quality
ndcg at 10 0.6998
map at 10 0.6522
mrr at 10 0.6684
Performance L4 b1 c16
Corpus 54.1K tok/s
Corpus p50 75.7ms
Query 5.2K tok/s
Query p50 50.0ms
Reference →

StackOverflowQA

technology retrieval en

Programming question answering from Stack Overflow

Corpus: 19,931 Queries: 1,994
Quality
ndcg at 10 0.7703
map at 10 0.7392
mrr at 10 0.7392
Performance L4 b1 c16
Corpus 52.6K tok/s
Corpus p50 68.8ms
Query 65.6K tok/s
Query p50 71.9ms
Reference →

Self-hosted inference for search & document processing

Cut API costs by 50x, boost quality with 85+ SOTA models, and keep your data in your own cloud.

Github 2.0K

Contact us

Tell us about your use case and we'll get back to you shortly.