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Question Answering
Question Answering On Nq Beir
Question Answering On Nq Beir
Metrics
nDCG@10
Results
Performance results of various models on this benchmark
Columns
Model Name
nDCG@10
Paper Title
Blended RAG
0.67
Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers
monoT5-3B
0.633
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
BM25+CE
0.533
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
SGPT-BE-5.8B
0.524
SGPT: GPT Sentence Embeddings for Semantic Search
ColBERT
0.524
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
SGPT-CE-6.1B
0.401
SGPT: GPT Sentence Embeddings for Semantic Search
0 of 6 row(s) selected.
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HyperAI
HyperAI
Home
Console
Docs
News
Papers
Tutorials
Datasets
Wiki
SOTA
LLM Models
GPU Leaderboard
Events
Search
About
Terms of Service
Privacy Policy
English
HyperAI
HyperAI
Toggle Sidebar
Search the site…
⌘
K
Command Palette
Search for a command to run...
Console
Home
SOTA
Question Answering
Question Answering On Nq Beir
Question Answering On Nq Beir
Metrics
nDCG@10
Results
Performance results of various models on this benchmark
Columns
Model Name
nDCG@10
Paper Title
Blended RAG
0.67
Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers
monoT5-3B
0.633
No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrieval
BM25+CE
0.533
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
SGPT-BE-5.8B
0.524
SGPT: GPT Sentence Embeddings for Semantic Search
ColBERT
0.524
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
SGPT-CE-6.1B
0.401
SGPT: GPT Sentence Embeddings for Semantic Search
0 of 6 row(s) selected.
Previous
Next
Question Answering On Nq Beir | SOTA | HyperAI