2817 lines
69 KiB
Markdown
2817 lines
69 KiB
Markdown
---
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library_name: sentence-transformers
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pipeline_tag: sentence-similarity
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tags:
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- feature-extraction
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- sentence-similarity
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- mteb
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- transformers
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- transformers.js
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model-index:
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- name: epoch_0_model
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results:
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_counterfactual
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name: MTEB AmazonCounterfactualClassification (en)
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config: en
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split: test
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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metrics:
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- type: accuracy
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value: 76.8507462686567
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- type: ap
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value: 40.592189159090495
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- type: f1
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value: 71.01634655512476
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_polarity
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name: MTEB AmazonPolarityClassification
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config: default
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split: test
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046
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metrics:
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- type: accuracy
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value: 91.51892500000001
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- type: ap
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value: 88.50346762975335
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- type: f1
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value: 91.50342077459624
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_reviews_multi
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name: MTEB AmazonReviewsClassification (en)
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config: en
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split: test
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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metrics:
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- type: accuracy
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value: 47.364
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- type: f1
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value: 46.72708080922794
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- task:
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type: Retrieval
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dataset:
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type: arguana
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name: MTEB ArguAna
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 25.178
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- type: map_at_10
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value: 40.244
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- type: map_at_100
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value: 41.321999999999996
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- type: map_at_1000
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value: 41.331
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- type: map_at_3
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value: 35.016999999999996
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- type: map_at_5
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value: 37.99
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- type: mrr_at_1
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value: 25.605
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- type: mrr_at_10
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value: 40.422000000000004
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- type: mrr_at_100
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value: 41.507
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- type: mrr_at_1000
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value: 41.516
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- type: mrr_at_3
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value: 35.23
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- type: mrr_at_5
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value: 38.15
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- type: ndcg_at_1
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value: 25.178
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- type: ndcg_at_10
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value: 49.258
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- type: ndcg_at_100
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value: 53.776
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- type: ndcg_at_1000
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value: 53.995000000000005
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- type: ndcg_at_3
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value: 38.429
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- type: ndcg_at_5
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value: 43.803
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- type: precision_at_1
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value: 25.178
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- type: precision_at_10
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value: 7.831
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- type: precision_at_100
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value: 0.979
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- type: precision_at_1000
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value: 0.1
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- type: precision_at_3
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value: 16.121
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- type: precision_at_5
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value: 12.29
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- type: recall_at_1
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value: 25.178
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- type: recall_at_10
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value: 78.307
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- type: recall_at_100
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value: 97.866
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- type: recall_at_1000
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value: 99.57300000000001
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- type: recall_at_3
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value: 48.364000000000004
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- type: recall_at_5
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value: 61.451
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- task:
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type: Clustering
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dataset:
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type: mteb/arxiv-clustering-p2p
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name: MTEB ArxivClusteringP2P
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config: default
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split: test
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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metrics:
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- type: v_measure
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value: 45.93034494751465
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- task:
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type: Clustering
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dataset:
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type: mteb/arxiv-clustering-s2s
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name: MTEB ArxivClusteringS2S
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config: default
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split: test
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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metrics:
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- type: v_measure
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value: 36.64579480054327
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- task:
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type: Reranking
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dataset:
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type: mteb/askubuntudupquestions-reranking
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name: MTEB AskUbuntuDupQuestions
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config: default
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split: test
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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metrics:
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- type: map
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value: 60.601310529222054
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- type: mrr
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value: 75.04484896451656
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- task:
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type: STS
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dataset:
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type: mteb/biosses-sts
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name: MTEB BIOSSES
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config: default
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split: test
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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metrics:
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- type: cos_sim_pearson
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value: 88.57797718095814
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- type: cos_sim_spearman
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value: 86.47064499110101
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- type: euclidean_pearson
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value: 87.4559602783142
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- type: euclidean_spearman
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value: 86.47064499110101
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- type: manhattan_pearson
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value: 87.7232764230245
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- type: manhattan_spearman
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value: 86.91222131777742
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- task:
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type: Classification
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dataset:
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type: mteb/banking77
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name: MTEB Banking77Classification
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config: default
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split: test
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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metrics:
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- type: accuracy
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value: 84.5422077922078
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- type: f1
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value: 84.47657456950589
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- task:
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type: Clustering
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dataset:
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type: mteb/biorxiv-clustering-p2p
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name: MTEB BiorxivClusteringP2P
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config: default
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split: test
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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metrics:
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- type: v_measure
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value: 38.48953561974464
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- task:
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type: Clustering
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dataset:
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type: mteb/biorxiv-clustering-s2s
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name: MTEB BiorxivClusteringS2S
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config: default
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split: test
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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metrics:
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- type: v_measure
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value: 32.75995857510105
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackAndroidRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 30.008000000000003
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- type: map_at_10
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value: 39.51
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- type: map_at_100
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value: 40.841
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- type: map_at_1000
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value: 40.973
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- type: map_at_3
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value: 36.248999999999995
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- type: map_at_5
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value: 38.096999999999994
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- type: mrr_at_1
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value: 36.481
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- type: mrr_at_10
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value: 44.818000000000005
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- type: mrr_at_100
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value: 45.64
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- type: mrr_at_1000
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value: 45.687
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- type: mrr_at_3
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value: 42.036
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- type: mrr_at_5
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value: 43.782
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- type: ndcg_at_1
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value: 36.481
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- type: ndcg_at_10
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value: 45.152
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- type: ndcg_at_100
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value: 50.449
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- type: ndcg_at_1000
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value: 52.76499999999999
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- type: ndcg_at_3
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value: 40.161
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- type: ndcg_at_5
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value: 42.577999999999996
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- type: precision_at_1
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value: 36.481
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- type: precision_at_10
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value: 8.369
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- type: precision_at_100
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value: 1.373
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- type: precision_at_1000
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value: 0.186
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- type: precision_at_3
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value: 18.693
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- type: precision_at_5
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value: 13.533999999999999
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- type: recall_at_1
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value: 30.008000000000003
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- type: recall_at_10
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value: 56.108999999999995
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- type: recall_at_100
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value: 78.55499999999999
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- type: recall_at_1000
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value: 93.659
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- type: recall_at_3
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value: 41.754999999999995
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- type: recall_at_5
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value: 48.296
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackEnglishRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 30.262
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- type: map_at_10
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value: 40.139
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- type: map_at_100
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value: 41.394
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- type: map_at_1000
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value: 41.526
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- type: map_at_3
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value: 37.155
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- type: map_at_5
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value: 38.785
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- type: mrr_at_1
|
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value: 38.153
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- type: mrr_at_10
|
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value: 46.369
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- type: mrr_at_100
|
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value: 47.072
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- type: mrr_at_1000
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value: 47.111999999999995
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- type: mrr_at_3
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value: 44.268
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- type: mrr_at_5
|
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value: 45.389
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- type: ndcg_at_1
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value: 38.153
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- type: ndcg_at_10
|
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value: 45.925
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- type: ndcg_at_100
|
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value: 50.394000000000005
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- type: ndcg_at_1000
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value: 52.37500000000001
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- type: ndcg_at_3
|
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value: 41.754000000000005
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- type: ndcg_at_5
|
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value: 43.574
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- type: precision_at_1
|
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value: 38.153
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- type: precision_at_10
|
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value: 8.796
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- type: precision_at_100
|
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value: 1.432
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- type: precision_at_1000
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value: 0.189
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- type: precision_at_3
|
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value: 20.318
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- type: precision_at_5
|
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value: 14.395
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- type: recall_at_1
|
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value: 30.262
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- type: recall_at_10
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value: 55.72200000000001
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- type: recall_at_100
|
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value: 74.97500000000001
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- type: recall_at_1000
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value: 87.342
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- type: recall_at_3
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value: 43.129
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- type: recall_at_5
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value: 48.336
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGamingRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 39.951
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- type: map_at_10
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value: 51.248000000000005
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- type: map_at_100
|
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value: 52.188
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- type: map_at_1000
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value: 52.247
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- type: map_at_3
|
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value: 48.211
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- type: map_at_5
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value: 49.797000000000004
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- type: mrr_at_1
|
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value: 45.329
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- type: mrr_at_10
|
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value: 54.749
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- type: mrr_at_100
|
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value: 55.367999999999995
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- type: mrr_at_1000
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value: 55.400000000000006
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- type: mrr_at_3
|
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value: 52.382
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- type: mrr_at_5
|
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value: 53.649
|
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- type: ndcg_at_1
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value: 45.329
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- type: ndcg_at_10
|
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value: 56.847
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- type: ndcg_at_100
|
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value: 60.738
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- type: ndcg_at_1000
|
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value: 61.976
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- type: ndcg_at_3
|
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value: 51.59
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- type: ndcg_at_5
|
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value: 53.915
|
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- type: precision_at_1
|
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value: 45.329
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- type: precision_at_10
|
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value: 8.959
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- type: precision_at_100
|
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value: 1.187
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- type: precision_at_1000
|
|
value: 0.134
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- type: precision_at_3
|
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value: 22.612
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- type: precision_at_5
|
|
value: 15.273
|
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- type: recall_at_1
|
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value: 39.951
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- type: recall_at_10
|
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value: 70.053
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- type: recall_at_100
|
|
value: 86.996
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- type: recall_at_1000
|
|
value: 95.707
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- type: recall_at_3
|
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value: 56.032000000000004
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- type: recall_at_5
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value: 61.629999999999995
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- task:
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type: Retrieval
|
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGisRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 25.566
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- type: map_at_10
|
|
value: 33.207
|
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- type: map_at_100
|
|
value: 34.166000000000004
|
|
- type: map_at_1000
|
|
value: 34.245
|
|
- type: map_at_3
|
|
value: 30.94
|
|
- type: map_at_5
|
|
value: 32.01
|
|
- type: mrr_at_1
|
|
value: 27.345000000000002
|
|
- type: mrr_at_10
|
|
value: 35.193000000000005
|
|
- type: mrr_at_100
|
|
value: 35.965
|
|
- type: mrr_at_1000
|
|
value: 36.028999999999996
|
|
- type: mrr_at_3
|
|
value: 32.806000000000004
|
|
- type: mrr_at_5
|
|
value: 34.021
|
|
- type: ndcg_at_1
|
|
value: 27.345000000000002
|
|
- type: ndcg_at_10
|
|
value: 37.891999999999996
|
|
- type: ndcg_at_100
|
|
value: 42.664
|
|
- type: ndcg_at_1000
|
|
value: 44.757000000000005
|
|
- type: ndcg_at_3
|
|
value: 33.123000000000005
|
|
- type: ndcg_at_5
|
|
value: 35.035
|
|
- type: precision_at_1
|
|
value: 27.345000000000002
|
|
- type: precision_at_10
|
|
value: 5.763
|
|
- type: precision_at_100
|
|
value: 0.859
|
|
- type: precision_at_1000
|
|
value: 0.108
|
|
- type: precision_at_3
|
|
value: 13.71
|
|
- type: precision_at_5
|
|
value: 9.401
|
|
- type: recall_at_1
|
|
value: 25.566
|
|
- type: recall_at_10
|
|
value: 50.563
|
|
- type: recall_at_100
|
|
value: 72.86399999999999
|
|
- type: recall_at_1000
|
|
value: 88.68599999999999
|
|
- type: recall_at_3
|
|
value: 37.43
|
|
- type: recall_at_5
|
|
value: 41.894999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
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type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackMathematicaRetrieval
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|
config: default
|
|
split: test
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|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 16.663
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|
- type: map_at_10
|
|
value: 23.552
|
|
- type: map_at_100
|
|
value: 24.538
|
|
- type: map_at_1000
|
|
value: 24.661
|
|
- type: map_at_3
|
|
value: 21.085
|
|
- type: map_at_5
|
|
value: 22.391
|
|
- type: mrr_at_1
|
|
value: 20.025000000000002
|
|
- type: mrr_at_10
|
|
value: 27.643
|
|
- type: mrr_at_100
|
|
value: 28.499999999999996
|
|
- type: mrr_at_1000
|
|
value: 28.582
|
|
- type: mrr_at_3
|
|
value: 25.083
|
|
- type: mrr_at_5
|
|
value: 26.544
|
|
- type: ndcg_at_1
|
|
value: 20.025000000000002
|
|
- type: ndcg_at_10
|
|
value: 28.272000000000002
|
|
- type: ndcg_at_100
|
|
value: 33.353
|
|
- type: ndcg_at_1000
|
|
value: 36.454
|
|
- type: ndcg_at_3
|
|
value: 23.579
|
|
- type: ndcg_at_5
|
|
value: 25.685000000000002
|
|
- type: precision_at_1
|
|
value: 20.025000000000002
|
|
- type: precision_at_10
|
|
value: 5.187
|
|
- type: precision_at_100
|
|
value: 0.897
|
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- type: precision_at_1000
|
|
value: 0.13
|
|
- type: precision_at_3
|
|
value: 10.987
|
|
- type: precision_at_5
|
|
value: 8.06
|
|
- type: recall_at_1
|
|
value: 16.663
|
|
- type: recall_at_10
|
|
value: 38.808
|
|
- type: recall_at_100
|
|
value: 61.305
|
|
- type: recall_at_1000
|
|
value: 83.571
|
|
- type: recall_at_3
|
|
value: 25.907999999999998
|
|
- type: recall_at_5
|
|
value: 31.214
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.695999999999998
|
|
- type: map_at_10
|
|
value: 37.018
|
|
- type: map_at_100
|
|
value: 38.263000000000005
|
|
- type: map_at_1000
|
|
value: 38.371
|
|
- type: map_at_3
|
|
value: 34.226
|
|
- type: map_at_5
|
|
value: 35.809999999999995
|
|
- type: mrr_at_1
|
|
value: 32.916000000000004
|
|
- type: mrr_at_10
|
|
value: 42.067
|
|
- type: mrr_at_100
|
|
value: 42.925000000000004
|
|
- type: mrr_at_1000
|
|
value: 42.978
|
|
- type: mrr_at_3
|
|
value: 39.637
|
|
- type: mrr_at_5
|
|
value: 41.134
|
|
- type: ndcg_at_1
|
|
value: 32.916000000000004
|
|
- type: ndcg_at_10
|
|
value: 42.539
|
|
- type: ndcg_at_100
|
|
value: 47.873
|
|
- type: ndcg_at_1000
|
|
value: 50.08200000000001
|
|
- type: ndcg_at_3
|
|
value: 37.852999999999994
|
|
- type: ndcg_at_5
|
|
value: 40.201
|
|
- type: precision_at_1
|
|
value: 32.916000000000004
|
|
- type: precision_at_10
|
|
value: 7.5840000000000005
|
|
- type: precision_at_100
|
|
value: 1.199
|
|
- type: precision_at_1000
|
|
value: 0.155
|
|
- type: precision_at_3
|
|
value: 17.485
|
|
- type: precision_at_5
|
|
value: 12.512
|
|
- type: recall_at_1
|
|
value: 27.695999999999998
|
|
- type: recall_at_10
|
|
value: 53.638
|
|
- type: recall_at_100
|
|
value: 76.116
|
|
- type: recall_at_1000
|
|
value: 91.069
|
|
- type: recall_at_3
|
|
value: 41.13
|
|
- type: recall_at_5
|
|
value: 46.872
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.108
|
|
- type: map_at_10
|
|
value: 33.372
|
|
- type: map_at_100
|
|
value: 34.656
|
|
- type: map_at_1000
|
|
value: 34.768
|
|
- type: map_at_3
|
|
value: 30.830999999999996
|
|
- type: map_at_5
|
|
value: 32.204
|
|
- type: mrr_at_1
|
|
value: 29.110000000000003
|
|
- type: mrr_at_10
|
|
value: 37.979
|
|
- type: mrr_at_100
|
|
value: 38.933
|
|
- type: mrr_at_1000
|
|
value: 38.988
|
|
- type: mrr_at_3
|
|
value: 35.731
|
|
- type: mrr_at_5
|
|
value: 36.963
|
|
- type: ndcg_at_1
|
|
value: 29.110000000000003
|
|
- type: ndcg_at_10
|
|
value: 38.635000000000005
|
|
- type: ndcg_at_100
|
|
value: 44.324999999999996
|
|
- type: ndcg_at_1000
|
|
value: 46.747
|
|
- type: ndcg_at_3
|
|
value: 34.37
|
|
- type: ndcg_at_5
|
|
value: 36.228
|
|
- type: precision_at_1
|
|
value: 29.110000000000003
|
|
- type: precision_at_10
|
|
value: 6.963
|
|
- type: precision_at_100
|
|
value: 1.146
|
|
- type: precision_at_1000
|
|
value: 0.152
|
|
- type: precision_at_3
|
|
value: 16.400000000000002
|
|
- type: precision_at_5
|
|
value: 11.552999999999999
|
|
- type: recall_at_1
|
|
value: 24.108
|
|
- type: recall_at_10
|
|
value: 49.597
|
|
- type: recall_at_100
|
|
value: 73.88900000000001
|
|
- type: recall_at_1000
|
|
value: 90.62400000000001
|
|
- type: recall_at_3
|
|
value: 37.662
|
|
- type: recall_at_5
|
|
value: 42.565
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.00791666666667
|
|
- type: map_at_10
|
|
value: 33.287749999999996
|
|
- type: map_at_100
|
|
value: 34.41141666666667
|
|
- type: map_at_1000
|
|
value: 34.52583333333333
|
|
- type: map_at_3
|
|
value: 30.734416666666668
|
|
- type: map_at_5
|
|
value: 32.137166666666666
|
|
- type: mrr_at_1
|
|
value: 29.305666666666664
|
|
- type: mrr_at_10
|
|
value: 37.22966666666666
|
|
- type: mrr_at_100
|
|
value: 38.066583333333334
|
|
- type: mrr_at_1000
|
|
value: 38.12616666666667
|
|
- type: mrr_at_3
|
|
value: 34.92275
|
|
- type: mrr_at_5
|
|
value: 36.23333333333334
|
|
- type: ndcg_at_1
|
|
value: 29.305666666666664
|
|
- type: ndcg_at_10
|
|
value: 38.25533333333333
|
|
- type: ndcg_at_100
|
|
value: 43.25266666666666
|
|
- type: ndcg_at_1000
|
|
value: 45.63583333333334
|
|
- type: ndcg_at_3
|
|
value: 33.777166666666666
|
|
- type: ndcg_at_5
|
|
value: 35.85
|
|
- type: precision_at_1
|
|
value: 29.305666666666664
|
|
- type: precision_at_10
|
|
value: 6.596416666666667
|
|
- type: precision_at_100
|
|
value: 1.0784166666666668
|
|
- type: precision_at_1000
|
|
value: 0.14666666666666664
|
|
- type: precision_at_3
|
|
value: 15.31075
|
|
- type: precision_at_5
|
|
value: 10.830916666666667
|
|
- type: recall_at_1
|
|
value: 25.00791666666667
|
|
- type: recall_at_10
|
|
value: 49.10933333333333
|
|
- type: recall_at_100
|
|
value: 71.09216666666667
|
|
- type: recall_at_1000
|
|
value: 87.77725000000001
|
|
- type: recall_at_3
|
|
value: 36.660916666666665
|
|
- type: recall_at_5
|
|
value: 41.94149999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.521
|
|
- type: map_at_10
|
|
value: 30.043
|
|
- type: map_at_100
|
|
value: 30.936000000000003
|
|
- type: map_at_1000
|
|
value: 31.022
|
|
- type: map_at_3
|
|
value: 27.926000000000002
|
|
- type: map_at_5
|
|
value: 29.076999999999998
|
|
- type: mrr_at_1
|
|
value: 26.227
|
|
- type: mrr_at_10
|
|
value: 32.822
|
|
- type: mrr_at_100
|
|
value: 33.61
|
|
- type: mrr_at_1000
|
|
value: 33.672000000000004
|
|
- type: mrr_at_3
|
|
value: 30.776999999999997
|
|
- type: mrr_at_5
|
|
value: 31.866
|
|
- type: ndcg_at_1
|
|
value: 26.227
|
|
- type: ndcg_at_10
|
|
value: 34.041
|
|
- type: ndcg_at_100
|
|
value: 38.394
|
|
- type: ndcg_at_1000
|
|
value: 40.732
|
|
- type: ndcg_at_3
|
|
value: 30.037999999999997
|
|
- type: ndcg_at_5
|
|
value: 31.845000000000002
|
|
- type: precision_at_1
|
|
value: 26.227
|
|
- type: precision_at_10
|
|
value: 5.244999999999999
|
|
- type: precision_at_100
|
|
value: 0.808
|
|
- type: precision_at_1000
|
|
value: 0.107
|
|
- type: precision_at_3
|
|
value: 12.679000000000002
|
|
- type: precision_at_5
|
|
value: 8.773
|
|
- type: recall_at_1
|
|
value: 23.521
|
|
- type: recall_at_10
|
|
value: 43.633
|
|
- type: recall_at_100
|
|
value: 63.126000000000005
|
|
- type: recall_at_1000
|
|
value: 80.765
|
|
- type: recall_at_3
|
|
value: 32.614
|
|
- type: recall_at_5
|
|
value: 37.15
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 16.236
|
|
- type: map_at_10
|
|
value: 22.898
|
|
- type: map_at_100
|
|
value: 23.878
|
|
- type: map_at_1000
|
|
value: 24.009
|
|
- type: map_at_3
|
|
value: 20.87
|
|
- type: map_at_5
|
|
value: 22.025
|
|
- type: mrr_at_1
|
|
value: 19.339000000000002
|
|
- type: mrr_at_10
|
|
value: 26.382
|
|
- type: mrr_at_100
|
|
value: 27.245
|
|
- type: mrr_at_1000
|
|
value: 27.33
|
|
- type: mrr_at_3
|
|
value: 24.386
|
|
- type: mrr_at_5
|
|
value: 25.496000000000002
|
|
- type: ndcg_at_1
|
|
value: 19.339000000000002
|
|
- type: ndcg_at_10
|
|
value: 27.139999999999997
|
|
- type: ndcg_at_100
|
|
value: 31.944
|
|
- type: ndcg_at_1000
|
|
value: 35.077999999999996
|
|
- type: ndcg_at_3
|
|
value: 23.424
|
|
- type: ndcg_at_5
|
|
value: 25.188
|
|
- type: precision_at_1
|
|
value: 19.339000000000002
|
|
- type: precision_at_10
|
|
value: 4.8309999999999995
|
|
- type: precision_at_100
|
|
value: 0.845
|
|
- type: precision_at_1000
|
|
value: 0.128
|
|
- type: precision_at_3
|
|
value: 10.874
|
|
- type: precision_at_5
|
|
value: 7.825
|
|
- type: recall_at_1
|
|
value: 16.236
|
|
- type: recall_at_10
|
|
value: 36.513
|
|
- type: recall_at_100
|
|
value: 57.999
|
|
- type: recall_at_1000
|
|
value: 80.512
|
|
- type: recall_at_3
|
|
value: 26.179999999999996
|
|
- type: recall_at_5
|
|
value: 30.712
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.11
|
|
- type: map_at_10
|
|
value: 31.566
|
|
- type: map_at_100
|
|
value: 32.647
|
|
- type: map_at_1000
|
|
value: 32.753
|
|
- type: map_at_3
|
|
value: 29.24
|
|
- type: map_at_5
|
|
value: 30.564999999999998
|
|
- type: mrr_at_1
|
|
value: 28.265
|
|
- type: mrr_at_10
|
|
value: 35.504000000000005
|
|
- type: mrr_at_100
|
|
value: 36.436
|
|
- type: mrr_at_1000
|
|
value: 36.503
|
|
- type: mrr_at_3
|
|
value: 33.349000000000004
|
|
- type: mrr_at_5
|
|
value: 34.622
|
|
- type: ndcg_at_1
|
|
value: 28.265
|
|
- type: ndcg_at_10
|
|
value: 36.192
|
|
- type: ndcg_at_100
|
|
value: 41.388000000000005
|
|
- type: ndcg_at_1000
|
|
value: 43.948
|
|
- type: ndcg_at_3
|
|
value: 31.959
|
|
- type: ndcg_at_5
|
|
value: 33.998
|
|
- type: precision_at_1
|
|
value: 28.265
|
|
- type: precision_at_10
|
|
value: 5.989
|
|
- type: precision_at_100
|
|
value: 0.9650000000000001
|
|
- type: precision_at_1000
|
|
value: 0.13
|
|
- type: precision_at_3
|
|
value: 14.335
|
|
- type: precision_at_5
|
|
value: 10.112
|
|
- type: recall_at_1
|
|
value: 24.11
|
|
- type: recall_at_10
|
|
value: 46.418
|
|
- type: recall_at_100
|
|
value: 69.314
|
|
- type: recall_at_1000
|
|
value: 87.397
|
|
- type: recall_at_3
|
|
value: 34.724
|
|
- type: recall_at_5
|
|
value: 39.925
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.091
|
|
- type: map_at_10
|
|
value: 29.948999999999998
|
|
- type: map_at_100
|
|
value: 31.502000000000002
|
|
- type: map_at_1000
|
|
value: 31.713
|
|
- type: map_at_3
|
|
value: 27.464
|
|
- type: map_at_5
|
|
value: 28.968
|
|
- type: mrr_at_1
|
|
value: 26.482
|
|
- type: mrr_at_10
|
|
value: 34.009
|
|
- type: mrr_at_100
|
|
value: 35.081
|
|
- type: mrr_at_1000
|
|
value: 35.138000000000005
|
|
- type: mrr_at_3
|
|
value: 31.785000000000004
|
|
- type: mrr_at_5
|
|
value: 33.178999999999995
|
|
- type: ndcg_at_1
|
|
value: 26.482
|
|
- type: ndcg_at_10
|
|
value: 35.008
|
|
- type: ndcg_at_100
|
|
value: 41.272999999999996
|
|
- type: ndcg_at_1000
|
|
value: 43.972
|
|
- type: ndcg_at_3
|
|
value: 30.804
|
|
- type: ndcg_at_5
|
|
value: 33.046
|
|
- type: precision_at_1
|
|
value: 26.482
|
|
- type: precision_at_10
|
|
value: 6.462
|
|
- type: precision_at_100
|
|
value: 1.431
|
|
- type: precision_at_1000
|
|
value: 0.22899999999999998
|
|
- type: precision_at_3
|
|
value: 14.360999999999999
|
|
- type: precision_at_5
|
|
value: 10.474
|
|
- type: recall_at_1
|
|
value: 22.091
|
|
- type: recall_at_10
|
|
value: 45.125
|
|
- type: recall_at_100
|
|
value: 72.313
|
|
- type: recall_at_1000
|
|
value: 89.503
|
|
- type: recall_at_3
|
|
value: 33.158
|
|
- type: recall_at_5
|
|
value: 39.086999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 19.883
|
|
- type: map_at_10
|
|
value: 26.951000000000004
|
|
- type: map_at_100
|
|
value: 27.927999999999997
|
|
- type: map_at_1000
|
|
value: 28.022000000000002
|
|
- type: map_at_3
|
|
value: 24.616
|
|
- type: map_at_5
|
|
value: 25.917
|
|
- type: mrr_at_1
|
|
value: 21.996
|
|
- type: mrr_at_10
|
|
value: 29.221000000000004
|
|
- type: mrr_at_100
|
|
value: 30.024
|
|
- type: mrr_at_1000
|
|
value: 30.095
|
|
- type: mrr_at_3
|
|
value: 26.833000000000002
|
|
- type: mrr_at_5
|
|
value: 28.155
|
|
- type: ndcg_at_1
|
|
value: 21.996
|
|
- type: ndcg_at_10
|
|
value: 31.421
|
|
- type: ndcg_at_100
|
|
value: 36.237
|
|
- type: ndcg_at_1000
|
|
value: 38.744
|
|
- type: ndcg_at_3
|
|
value: 26.671
|
|
- type: ndcg_at_5
|
|
value: 28.907
|
|
- type: precision_at_1
|
|
value: 21.996
|
|
- type: precision_at_10
|
|
value: 5.009
|
|
- type: precision_at_100
|
|
value: 0.799
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 11.275
|
|
- type: precision_at_5
|
|
value: 8.059
|
|
- type: recall_at_1
|
|
value: 19.883
|
|
- type: recall_at_10
|
|
value: 43.132999999999996
|
|
- type: recall_at_100
|
|
value: 65.654
|
|
- type: recall_at_1000
|
|
value: 84.492
|
|
- type: recall_at_3
|
|
value: 30.209000000000003
|
|
- type: recall_at_5
|
|
value: 35.616
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.756
|
|
- type: map_at_10
|
|
value: 30.378
|
|
- type: map_at_100
|
|
value: 32.537
|
|
- type: map_at_1000
|
|
value: 32.717
|
|
- type: map_at_3
|
|
value: 25.599
|
|
- type: map_at_5
|
|
value: 28.372999999999998
|
|
- type: mrr_at_1
|
|
value: 41.303
|
|
- type: mrr_at_10
|
|
value: 53.483999999999995
|
|
- type: mrr_at_100
|
|
value: 54.106
|
|
- type: mrr_at_1000
|
|
value: 54.127
|
|
- type: mrr_at_3
|
|
value: 50.315
|
|
- type: mrr_at_5
|
|
value: 52.396
|
|
- type: ndcg_at_1
|
|
value: 41.303
|
|
- type: ndcg_at_10
|
|
value: 40.503
|
|
- type: ndcg_at_100
|
|
value: 47.821000000000005
|
|
- type: ndcg_at_1000
|
|
value: 50.788
|
|
- type: ndcg_at_3
|
|
value: 34.364
|
|
- type: ndcg_at_5
|
|
value: 36.818
|
|
- type: precision_at_1
|
|
value: 41.303
|
|
- type: precision_at_10
|
|
value: 12.463000000000001
|
|
- type: precision_at_100
|
|
value: 2.037
|
|
- type: precision_at_1000
|
|
value: 0.26
|
|
- type: precision_at_3
|
|
value: 25.798
|
|
- type: precision_at_5
|
|
value: 19.896
|
|
- type: recall_at_1
|
|
value: 17.756
|
|
- type: recall_at_10
|
|
value: 46.102
|
|
- type: recall_at_100
|
|
value: 70.819
|
|
- type: recall_at_1000
|
|
value: 87.21799999999999
|
|
- type: recall_at_3
|
|
value: 30.646
|
|
- type: recall_at_5
|
|
value: 38.022
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 9.033
|
|
- type: map_at_10
|
|
value: 20.584
|
|
- type: map_at_100
|
|
value: 29.518
|
|
- type: map_at_1000
|
|
value: 31.186000000000003
|
|
- type: map_at_3
|
|
value: 14.468
|
|
- type: map_at_5
|
|
value: 17.177
|
|
- type: mrr_at_1
|
|
value: 69.75
|
|
- type: mrr_at_10
|
|
value: 77.025
|
|
- type: mrr_at_100
|
|
value: 77.36699999999999
|
|
- type: mrr_at_1000
|
|
value: 77.373
|
|
- type: mrr_at_3
|
|
value: 75.583
|
|
- type: mrr_at_5
|
|
value: 76.396
|
|
- type: ndcg_at_1
|
|
value: 58.5
|
|
- type: ndcg_at_10
|
|
value: 45.033
|
|
- type: ndcg_at_100
|
|
value: 49.071
|
|
- type: ndcg_at_1000
|
|
value: 56.056
|
|
- type: ndcg_at_3
|
|
value: 49.936
|
|
- type: ndcg_at_5
|
|
value: 47.471999999999994
|
|
- type: precision_at_1
|
|
value: 69.75
|
|
- type: precision_at_10
|
|
value: 35.775
|
|
- type: precision_at_100
|
|
value: 11.594999999999999
|
|
- type: precision_at_1000
|
|
value: 2.062
|
|
- type: precision_at_3
|
|
value: 52.5
|
|
- type: precision_at_5
|
|
value: 45.300000000000004
|
|
- type: recall_at_1
|
|
value: 9.033
|
|
- type: recall_at_10
|
|
value: 26.596999999999998
|
|
- type: recall_at_100
|
|
value: 54.607000000000006
|
|
- type: recall_at_1000
|
|
value: 76.961
|
|
- type: recall_at_3
|
|
value: 15.754999999999999
|
|
- type: recall_at_5
|
|
value: 20.033
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 48.345000000000006
|
|
- type: f1
|
|
value: 43.4514918068706
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 71.29100000000001
|
|
- type: map_at_10
|
|
value: 81.059
|
|
- type: map_at_100
|
|
value: 81.341
|
|
- type: map_at_1000
|
|
value: 81.355
|
|
- type: map_at_3
|
|
value: 79.74799999999999
|
|
- type: map_at_5
|
|
value: 80.612
|
|
- type: mrr_at_1
|
|
value: 76.40299999999999
|
|
- type: mrr_at_10
|
|
value: 84.615
|
|
- type: mrr_at_100
|
|
value: 84.745
|
|
- type: mrr_at_1000
|
|
value: 84.748
|
|
- type: mrr_at_3
|
|
value: 83.776
|
|
- type: mrr_at_5
|
|
value: 84.343
|
|
- type: ndcg_at_1
|
|
value: 76.40299999999999
|
|
- type: ndcg_at_10
|
|
value: 84.981
|
|
- type: ndcg_at_100
|
|
value: 86.00999999999999
|
|
- type: ndcg_at_1000
|
|
value: 86.252
|
|
- type: ndcg_at_3
|
|
value: 82.97
|
|
- type: ndcg_at_5
|
|
value: 84.152
|
|
- type: precision_at_1
|
|
value: 76.40299999999999
|
|
- type: precision_at_10
|
|
value: 10.446
|
|
- type: precision_at_100
|
|
value: 1.1199999999999999
|
|
- type: precision_at_1000
|
|
value: 0.116
|
|
- type: precision_at_3
|
|
value: 32.147999999999996
|
|
- type: precision_at_5
|
|
value: 20.135
|
|
- type: recall_at_1
|
|
value: 71.29100000000001
|
|
- type: recall_at_10
|
|
value: 93.232
|
|
- type: recall_at_100
|
|
value: 97.363
|
|
- type: recall_at_1000
|
|
value: 98.905
|
|
- type: recall_at_3
|
|
value: 87.893
|
|
- type: recall_at_5
|
|
value: 90.804
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 18.667
|
|
- type: map_at_10
|
|
value: 30.853
|
|
- type: map_at_100
|
|
value: 32.494
|
|
- type: map_at_1000
|
|
value: 32.677
|
|
- type: map_at_3
|
|
value: 26.91
|
|
- type: map_at_5
|
|
value: 29.099000000000004
|
|
- type: mrr_at_1
|
|
value: 37.191
|
|
- type: mrr_at_10
|
|
value: 46.171
|
|
- type: mrr_at_100
|
|
value: 47.056
|
|
- type: mrr_at_1000
|
|
value: 47.099000000000004
|
|
- type: mrr_at_3
|
|
value: 44.059
|
|
- type: mrr_at_5
|
|
value: 45.147
|
|
- type: ndcg_at_1
|
|
value: 37.191
|
|
- type: ndcg_at_10
|
|
value: 38.437
|
|
- type: ndcg_at_100
|
|
value: 44.62
|
|
- type: ndcg_at_1000
|
|
value: 47.795
|
|
- type: ndcg_at_3
|
|
value: 35.003
|
|
- type: ndcg_at_5
|
|
value: 36.006
|
|
- type: precision_at_1
|
|
value: 37.191
|
|
- type: precision_at_10
|
|
value: 10.586
|
|
- type: precision_at_100
|
|
value: 1.688
|
|
- type: precision_at_1000
|
|
value: 0.22699999999999998
|
|
- type: precision_at_3
|
|
value: 23.302
|
|
- type: precision_at_5
|
|
value: 17.006
|
|
- type: recall_at_1
|
|
value: 18.667
|
|
- type: recall_at_10
|
|
value: 45.367000000000004
|
|
- type: recall_at_100
|
|
value: 68.207
|
|
- type: recall_at_1000
|
|
value: 87.072
|
|
- type: recall_at_3
|
|
value: 32.129000000000005
|
|
- type: recall_at_5
|
|
value: 37.719
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 39.494
|
|
- type: map_at_10
|
|
value: 66.223
|
|
- type: map_at_100
|
|
value: 67.062
|
|
- type: map_at_1000
|
|
value: 67.11500000000001
|
|
- type: map_at_3
|
|
value: 62.867
|
|
- type: map_at_5
|
|
value: 64.994
|
|
- type: mrr_at_1
|
|
value: 78.987
|
|
- type: mrr_at_10
|
|
value: 84.585
|
|
- type: mrr_at_100
|
|
value: 84.773
|
|
- type: mrr_at_1000
|
|
value: 84.77900000000001
|
|
- type: mrr_at_3
|
|
value: 83.592
|
|
- type: mrr_at_5
|
|
value: 84.235
|
|
- type: ndcg_at_1
|
|
value: 78.987
|
|
- type: ndcg_at_10
|
|
value: 73.64
|
|
- type: ndcg_at_100
|
|
value: 76.519
|
|
- type: ndcg_at_1000
|
|
value: 77.51
|
|
- type: ndcg_at_3
|
|
value: 68.893
|
|
- type: ndcg_at_5
|
|
value: 71.585
|
|
- type: precision_at_1
|
|
value: 78.987
|
|
- type: precision_at_10
|
|
value: 15.529000000000002
|
|
- type: precision_at_100
|
|
value: 1.7770000000000001
|
|
- type: precision_at_1000
|
|
value: 0.191
|
|
- type: precision_at_3
|
|
value: 44.808
|
|
- type: precision_at_5
|
|
value: 29.006999999999998
|
|
- type: recall_at_1
|
|
value: 39.494
|
|
- type: recall_at_10
|
|
value: 77.643
|
|
- type: recall_at_100
|
|
value: 88.825
|
|
- type: recall_at_1000
|
|
value: 95.321
|
|
- type: recall_at_3
|
|
value: 67.211
|
|
- type: recall_at_5
|
|
value: 72.519
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 85.55959999999999
|
|
- type: ap
|
|
value: 80.7246500384617
|
|
- type: f1
|
|
value: 85.52336485065454
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.631
|
|
- type: map_at_10
|
|
value: 36.264
|
|
- type: map_at_100
|
|
value: 37.428
|
|
- type: map_at_1000
|
|
value: 37.472
|
|
- type: map_at_3
|
|
value: 32.537
|
|
- type: map_at_5
|
|
value: 34.746
|
|
- type: mrr_at_1
|
|
value: 24.312
|
|
- type: mrr_at_10
|
|
value: 36.858000000000004
|
|
- type: mrr_at_100
|
|
value: 37.966
|
|
- type: mrr_at_1000
|
|
value: 38.004
|
|
- type: mrr_at_3
|
|
value: 33.188
|
|
- type: mrr_at_5
|
|
value: 35.367
|
|
- type: ndcg_at_1
|
|
value: 24.312
|
|
- type: ndcg_at_10
|
|
value: 43.126999999999995
|
|
- type: ndcg_at_100
|
|
value: 48.642
|
|
- type: ndcg_at_1000
|
|
value: 49.741
|
|
- type: ndcg_at_3
|
|
value: 35.589
|
|
- type: ndcg_at_5
|
|
value: 39.515
|
|
- type: precision_at_1
|
|
value: 24.312
|
|
- type: precision_at_10
|
|
value: 6.699
|
|
- type: precision_at_100
|
|
value: 0.9450000000000001
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 15.153
|
|
- type: precision_at_5
|
|
value: 11.065999999999999
|
|
- type: recall_at_1
|
|
value: 23.631
|
|
- type: recall_at_10
|
|
value: 64.145
|
|
- type: recall_at_100
|
|
value: 89.41
|
|
- type: recall_at_1000
|
|
value: 97.83500000000001
|
|
- type: recall_at_3
|
|
value: 43.769000000000005
|
|
- type: recall_at_5
|
|
value: 53.169
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.4108527131783
|
|
- type: f1
|
|
value: 93.1415880261038
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.24806201550388
|
|
- type: f1
|
|
value: 60.531916308197175
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 73.71553463349024
|
|
- type: f1
|
|
value: 71.70753174900791
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.79757901815736
|
|
- type: f1
|
|
value: 77.83719850433258
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 33.74193296622113
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 30.64257594108566
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 30.811018518883625
|
|
- type: mrr
|
|
value: 31.910376577445003
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.409
|
|
- type: map_at_10
|
|
value: 13.093
|
|
- type: map_at_100
|
|
value: 16.256999999999998
|
|
- type: map_at_1000
|
|
value: 17.617
|
|
- type: map_at_3
|
|
value: 9.555
|
|
- type: map_at_5
|
|
value: 11.428
|
|
- type: mrr_at_1
|
|
value: 45.201
|
|
- type: mrr_at_10
|
|
value: 54.179
|
|
- type: mrr_at_100
|
|
value: 54.812000000000005
|
|
- type: mrr_at_1000
|
|
value: 54.840999999999994
|
|
- type: mrr_at_3
|
|
value: 51.909000000000006
|
|
- type: mrr_at_5
|
|
value: 53.519000000000005
|
|
- type: ndcg_at_1
|
|
value: 43.189
|
|
- type: ndcg_at_10
|
|
value: 35.028
|
|
- type: ndcg_at_100
|
|
value: 31.226
|
|
- type: ndcg_at_1000
|
|
value: 39.678000000000004
|
|
- type: ndcg_at_3
|
|
value: 40.596
|
|
- type: ndcg_at_5
|
|
value: 38.75
|
|
- type: precision_at_1
|
|
value: 44.582
|
|
- type: precision_at_10
|
|
value: 25.974999999999998
|
|
- type: precision_at_100
|
|
value: 7.793
|
|
- type: precision_at_1000
|
|
value: 2.036
|
|
- type: precision_at_3
|
|
value: 38.493
|
|
- type: precision_at_5
|
|
value: 33.994
|
|
- type: recall_at_1
|
|
value: 5.409
|
|
- type: recall_at_10
|
|
value: 16.875999999999998
|
|
- type: recall_at_100
|
|
value: 30.316
|
|
- type: recall_at_1000
|
|
value: 60.891
|
|
- type: recall_at_3
|
|
value: 10.688
|
|
- type: recall_at_5
|
|
value: 13.832
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 36.375
|
|
- type: map_at_10
|
|
value: 51.991
|
|
- type: map_at_100
|
|
value: 52.91400000000001
|
|
- type: map_at_1000
|
|
value: 52.93600000000001
|
|
- type: map_at_3
|
|
value: 48.014
|
|
- type: map_at_5
|
|
value: 50.381
|
|
- type: mrr_at_1
|
|
value: 40.759
|
|
- type: mrr_at_10
|
|
value: 54.617000000000004
|
|
- type: mrr_at_100
|
|
value: 55.301
|
|
- type: mrr_at_1000
|
|
value: 55.315000000000005
|
|
- type: mrr_at_3
|
|
value: 51.516
|
|
- type: mrr_at_5
|
|
value: 53.435
|
|
- type: ndcg_at_1
|
|
value: 40.759
|
|
- type: ndcg_at_10
|
|
value: 59.384
|
|
- type: ndcg_at_100
|
|
value: 63.157
|
|
- type: ndcg_at_1000
|
|
value: 63.654999999999994
|
|
- type: ndcg_at_3
|
|
value: 52.114000000000004
|
|
- type: ndcg_at_5
|
|
value: 55.986000000000004
|
|
- type: precision_at_1
|
|
value: 40.759
|
|
- type: precision_at_10
|
|
value: 9.411999999999999
|
|
- type: precision_at_100
|
|
value: 1.153
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 23.329
|
|
- type: precision_at_5
|
|
value: 16.256999999999998
|
|
- type: recall_at_1
|
|
value: 36.375
|
|
- type: recall_at_10
|
|
value: 79.053
|
|
- type: recall_at_100
|
|
value: 95.167
|
|
- type: recall_at_1000
|
|
value: 98.82
|
|
- type: recall_at_3
|
|
value: 60.475
|
|
- type: recall_at_5
|
|
value: 69.327
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 70.256
|
|
- type: map_at_10
|
|
value: 83.8
|
|
- type: map_at_100
|
|
value: 84.425
|
|
- type: map_at_1000
|
|
value: 84.444
|
|
- type: map_at_3
|
|
value: 80.906
|
|
- type: map_at_5
|
|
value: 82.717
|
|
- type: mrr_at_1
|
|
value: 80.97999999999999
|
|
- type: mrr_at_10
|
|
value: 87.161
|
|
- type: mrr_at_100
|
|
value: 87.262
|
|
- type: mrr_at_1000
|
|
value: 87.263
|
|
- type: mrr_at_3
|
|
value: 86.175
|
|
- type: mrr_at_5
|
|
value: 86.848
|
|
- type: ndcg_at_1
|
|
value: 80.97999999999999
|
|
- type: ndcg_at_10
|
|
value: 87.697
|
|
- type: ndcg_at_100
|
|
value: 88.959
|
|
- type: ndcg_at_1000
|
|
value: 89.09899999999999
|
|
- type: ndcg_at_3
|
|
value: 84.83800000000001
|
|
- type: ndcg_at_5
|
|
value: 86.401
|
|
- type: precision_at_1
|
|
value: 80.97999999999999
|
|
- type: precision_at_10
|
|
value: 13.261000000000001
|
|
- type: precision_at_100
|
|
value: 1.5150000000000001
|
|
- type: precision_at_1000
|
|
value: 0.156
|
|
- type: precision_at_3
|
|
value: 37.01
|
|
- type: precision_at_5
|
|
value: 24.298000000000002
|
|
- type: recall_at_1
|
|
value: 70.256
|
|
- type: recall_at_10
|
|
value: 94.935
|
|
- type: recall_at_100
|
|
value: 99.274
|
|
- type: recall_at_1000
|
|
value: 99.928
|
|
- type: recall_at_3
|
|
value: 86.602
|
|
- type: recall_at_5
|
|
value: 91.133
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 56.322692497613104
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 61.895813503775074
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 4.338
|
|
- type: map_at_10
|
|
value: 10.767
|
|
- type: map_at_100
|
|
value: 12.537999999999998
|
|
- type: map_at_1000
|
|
value: 12.803999999999998
|
|
- type: map_at_3
|
|
value: 7.788
|
|
- type: map_at_5
|
|
value: 9.302000000000001
|
|
- type: mrr_at_1
|
|
value: 21.4
|
|
- type: mrr_at_10
|
|
value: 31.637999999999998
|
|
- type: mrr_at_100
|
|
value: 32.688
|
|
- type: mrr_at_1000
|
|
value: 32.756
|
|
- type: mrr_at_3
|
|
value: 28.433000000000003
|
|
- type: mrr_at_5
|
|
value: 30.178
|
|
- type: ndcg_at_1
|
|
value: 21.4
|
|
- type: ndcg_at_10
|
|
value: 18.293
|
|
- type: ndcg_at_100
|
|
value: 25.274
|
|
- type: ndcg_at_1000
|
|
value: 30.284
|
|
- type: ndcg_at_3
|
|
value: 17.391000000000002
|
|
- type: ndcg_at_5
|
|
value: 15.146999999999998
|
|
- type: precision_at_1
|
|
value: 21.4
|
|
- type: precision_at_10
|
|
value: 9.48
|
|
- type: precision_at_100
|
|
value: 1.949
|
|
- type: precision_at_1000
|
|
value: 0.316
|
|
- type: precision_at_3
|
|
value: 16.167
|
|
- type: precision_at_5
|
|
value: 13.22
|
|
- type: recall_at_1
|
|
value: 4.338
|
|
- type: recall_at_10
|
|
value: 19.213
|
|
- type: recall_at_100
|
|
value: 39.562999999999995
|
|
- type: recall_at_1000
|
|
value: 64.08
|
|
- type: recall_at_3
|
|
value: 9.828000000000001
|
|
- type: recall_at_5
|
|
value: 13.383000000000001
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.42568163642142
|
|
- type: cos_sim_spearman
|
|
value: 78.5797159641342
|
|
- type: euclidean_pearson
|
|
value: 80.22151260811604
|
|
- type: euclidean_spearman
|
|
value: 78.5797151953878
|
|
- type: manhattan_pearson
|
|
value: 80.21224215864788
|
|
- type: manhattan_spearman
|
|
value: 78.55641478381344
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.44020710812569
|
|
- type: cos_sim_spearman
|
|
value: 78.91631735081286
|
|
- type: euclidean_pearson
|
|
value: 81.64188964182102
|
|
- type: euclidean_spearman
|
|
value: 78.91633286881678
|
|
- type: manhattan_pearson
|
|
value: 81.69294748512496
|
|
- type: manhattan_spearman
|
|
value: 78.93438558002656
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.27165426412311
|
|
- type: cos_sim_spearman
|
|
value: 85.40429140249618
|
|
- type: euclidean_pearson
|
|
value: 84.7509580724893
|
|
- type: euclidean_spearman
|
|
value: 85.40429140249618
|
|
- type: manhattan_pearson
|
|
value: 84.76488289321308
|
|
- type: manhattan_spearman
|
|
value: 85.4256793698708
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.138851760732
|
|
- type: cos_sim_spearman
|
|
value: 81.64101363896586
|
|
- type: euclidean_pearson
|
|
value: 82.55165038934942
|
|
- type: euclidean_spearman
|
|
value: 81.64105257080502
|
|
- type: manhattan_pearson
|
|
value: 82.52802949883335
|
|
- type: manhattan_spearman
|
|
value: 81.61255430718158
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.0654695484029
|
|
- type: cos_sim_spearman
|
|
value: 87.20408521902229
|
|
- type: euclidean_pearson
|
|
value: 86.8110651362115
|
|
- type: euclidean_spearman
|
|
value: 87.20408521902229
|
|
- type: manhattan_pearson
|
|
value: 86.77984656478691
|
|
- type: manhattan_spearman
|
|
value: 87.1719947099227
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.77823915496512
|
|
- type: cos_sim_spearman
|
|
value: 85.43566325729779
|
|
- type: euclidean_pearson
|
|
value: 84.5396956658821
|
|
- type: euclidean_spearman
|
|
value: 85.43566325729779
|
|
- type: manhattan_pearson
|
|
value: 84.5665398848169
|
|
- type: manhattan_spearman
|
|
value: 85.44375870303232
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (en-en)
|
|
config: en-en
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.20030208471798
|
|
- type: cos_sim_spearman
|
|
value: 87.20485505076539
|
|
- type: euclidean_pearson
|
|
value: 88.10588324368722
|
|
- type: euclidean_spearman
|
|
value: 87.20485505076539
|
|
- type: manhattan_pearson
|
|
value: 87.92324770415183
|
|
- type: manhattan_spearman
|
|
value: 87.0571314561877
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (en)
|
|
config: en
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 63.06093161604453
|
|
- type: cos_sim_spearman
|
|
value: 64.2163140357722
|
|
- type: euclidean_pearson
|
|
value: 65.27589680994006
|
|
- type: euclidean_spearman
|
|
value: 64.2163140357722
|
|
- type: manhattan_pearson
|
|
value: 65.45904383711101
|
|
- type: manhattan_spearman
|
|
value: 64.55404716679305
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.32976164578706
|
|
- type: cos_sim_spearman
|
|
value: 85.54302197678368
|
|
- type: euclidean_pearson
|
|
value: 85.26307149193056
|
|
- type: euclidean_spearman
|
|
value: 85.54302197678368
|
|
- type: manhattan_pearson
|
|
value: 85.26647282029371
|
|
- type: manhattan_spearman
|
|
value: 85.5316135265568
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 81.44675968318754
|
|
- type: mrr
|
|
value: 94.92741826075158
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 56.34400000000001
|
|
- type: map_at_10
|
|
value: 65.927
|
|
- type: map_at_100
|
|
value: 66.431
|
|
- type: map_at_1000
|
|
value: 66.461
|
|
- type: map_at_3
|
|
value: 63.529
|
|
- type: map_at_5
|
|
value: 64.818
|
|
- type: mrr_at_1
|
|
value: 59.333000000000006
|
|
- type: mrr_at_10
|
|
value: 67.54599999999999
|
|
- type: mrr_at_100
|
|
value: 67.892
|
|
- type: mrr_at_1000
|
|
value: 67.917
|
|
- type: mrr_at_3
|
|
value: 65.778
|
|
- type: mrr_at_5
|
|
value: 66.794
|
|
- type: ndcg_at_1
|
|
value: 59.333000000000006
|
|
- type: ndcg_at_10
|
|
value: 70.5
|
|
- type: ndcg_at_100
|
|
value: 72.688
|
|
- type: ndcg_at_1000
|
|
value: 73.483
|
|
- type: ndcg_at_3
|
|
value: 66.338
|
|
- type: ndcg_at_5
|
|
value: 68.265
|
|
- type: precision_at_1
|
|
value: 59.333000000000006
|
|
- type: precision_at_10
|
|
value: 9.3
|
|
- type: precision_at_100
|
|
value: 1.053
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 25.889
|
|
- type: precision_at_5
|
|
value: 16.866999999999997
|
|
- type: recall_at_1
|
|
value: 56.34400000000001
|
|
- type: recall_at_10
|
|
value: 82.789
|
|
- type: recall_at_100
|
|
value: 92.767
|
|
- type: recall_at_1000
|
|
value: 99
|
|
- type: recall_at_3
|
|
value: 71.64399999999999
|
|
- type: recall_at_5
|
|
value: 76.322
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.75742574257426
|
|
- type: cos_sim_ap
|
|
value: 93.52081548447406
|
|
- type: cos_sim_f1
|
|
value: 87.33850129198966
|
|
- type: cos_sim_precision
|
|
value: 90.37433155080214
|
|
- type: cos_sim_recall
|
|
value: 84.5
|
|
- type: dot_accuracy
|
|
value: 99.75742574257426
|
|
- type: dot_ap
|
|
value: 93.52081548447406
|
|
- type: dot_f1
|
|
value: 87.33850129198966
|
|
- type: dot_precision
|
|
value: 90.37433155080214
|
|
- type: dot_recall
|
|
value: 84.5
|
|
- type: euclidean_accuracy
|
|
value: 99.75742574257426
|
|
- type: euclidean_ap
|
|
value: 93.52081548447406
|
|
- type: euclidean_f1
|
|
value: 87.33850129198966
|
|
- type: euclidean_precision
|
|
value: 90.37433155080214
|
|
- type: euclidean_recall
|
|
value: 84.5
|
|
- type: manhattan_accuracy
|
|
value: 99.75841584158415
|
|
- type: manhattan_ap
|
|
value: 93.4975678585854
|
|
- type: manhattan_f1
|
|
value: 87.26708074534162
|
|
- type: manhattan_precision
|
|
value: 90.45064377682404
|
|
- type: manhattan_recall
|
|
value: 84.3
|
|
- type: max_accuracy
|
|
value: 99.75841584158415
|
|
- type: max_ap
|
|
value: 93.52081548447406
|
|
- type: max_f1
|
|
value: 87.33850129198966
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 64.31437036686651
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 33.25569319007206
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 49.90474939720706
|
|
- type: mrr
|
|
value: 50.568115503777264
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 29.866828641244712
|
|
- type: cos_sim_spearman
|
|
value: 30.077555055873866
|
|
- type: dot_pearson
|
|
value: 29.866832988572266
|
|
- type: dot_spearman
|
|
value: 30.077555055873866
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.232
|
|
- type: map_at_10
|
|
value: 2.094
|
|
- type: map_at_100
|
|
value: 11.971
|
|
- type: map_at_1000
|
|
value: 28.158
|
|
- type: map_at_3
|
|
value: 0.688
|
|
- type: map_at_5
|
|
value: 1.114
|
|
- type: mrr_at_1
|
|
value: 88
|
|
- type: mrr_at_10
|
|
value: 93.4
|
|
- type: mrr_at_100
|
|
value: 93.4
|
|
- type: mrr_at_1000
|
|
value: 93.4
|
|
- type: mrr_at_3
|
|
value: 93
|
|
- type: mrr_at_5
|
|
value: 93.4
|
|
- type: ndcg_at_1
|
|
value: 84
|
|
- type: ndcg_at_10
|
|
value: 79.923
|
|
- type: ndcg_at_100
|
|
value: 61.17
|
|
- type: ndcg_at_1000
|
|
value: 53.03
|
|
- type: ndcg_at_3
|
|
value: 84.592
|
|
- type: ndcg_at_5
|
|
value: 82.821
|
|
- type: precision_at_1
|
|
value: 88
|
|
- type: precision_at_10
|
|
value: 85
|
|
- type: precision_at_100
|
|
value: 63.019999999999996
|
|
- type: precision_at_1000
|
|
value: 23.554
|
|
- type: precision_at_3
|
|
value: 89.333
|
|
- type: precision_at_5
|
|
value: 87.2
|
|
- type: recall_at_1
|
|
value: 0.232
|
|
- type: recall_at_10
|
|
value: 2.255
|
|
- type: recall_at_100
|
|
value: 14.823
|
|
- type: recall_at_1000
|
|
value: 49.456
|
|
- type: recall_at_3
|
|
value: 0.718
|
|
- type: recall_at_5
|
|
value: 1.175
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 2.547
|
|
- type: map_at_10
|
|
value: 11.375
|
|
- type: map_at_100
|
|
value: 18.194
|
|
- type: map_at_1000
|
|
value: 19.749
|
|
- type: map_at_3
|
|
value: 5.825
|
|
- type: map_at_5
|
|
value: 8.581
|
|
- type: mrr_at_1
|
|
value: 32.653
|
|
- type: mrr_at_10
|
|
value: 51.32
|
|
- type: mrr_at_100
|
|
value: 51.747
|
|
- type: mrr_at_1000
|
|
value: 51.747
|
|
- type: mrr_at_3
|
|
value: 47.278999999999996
|
|
- type: mrr_at_5
|
|
value: 48.605
|
|
- type: ndcg_at_1
|
|
value: 29.592000000000002
|
|
- type: ndcg_at_10
|
|
value: 28.151
|
|
- type: ndcg_at_100
|
|
value: 39.438
|
|
- type: ndcg_at_1000
|
|
value: 50.769
|
|
- type: ndcg_at_3
|
|
value: 30.758999999999997
|
|
- type: ndcg_at_5
|
|
value: 30.366
|
|
- type: precision_at_1
|
|
value: 32.653
|
|
- type: precision_at_10
|
|
value: 25.714
|
|
- type: precision_at_100
|
|
value: 8.041
|
|
- type: precision_at_1000
|
|
value: 1.555
|
|
- type: precision_at_3
|
|
value: 33.333
|
|
- type: precision_at_5
|
|
value: 31.837
|
|
- type: recall_at_1
|
|
value: 2.547
|
|
- type: recall_at_10
|
|
value: 18.19
|
|
- type: recall_at_100
|
|
value: 49.538
|
|
- type: recall_at_1000
|
|
value: 83.86
|
|
- type: recall_at_3
|
|
value: 7.329
|
|
- type: recall_at_5
|
|
value: 11.532
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.4952
|
|
- type: ap
|
|
value: 14.793362635531409
|
|
- type: f1
|
|
value: 55.204635551516915
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 61.5365025466893
|
|
- type: f1
|
|
value: 61.81742556334845
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 49.05531070301185
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 86.51725576682364
|
|
- type: cos_sim_ap
|
|
value: 75.2292304265163
|
|
- type: cos_sim_f1
|
|
value: 69.54022988505749
|
|
- type: cos_sim_precision
|
|
value: 63.65629110039457
|
|
- type: cos_sim_recall
|
|
value: 76.62269129287598
|
|
- type: dot_accuracy
|
|
value: 86.51725576682364
|
|
- type: dot_ap
|
|
value: 75.22922386081054
|
|
- type: dot_f1
|
|
value: 69.54022988505749
|
|
- type: dot_precision
|
|
value: 63.65629110039457
|
|
- type: dot_recall
|
|
value: 76.62269129287598
|
|
- type: euclidean_accuracy
|
|
value: 86.51725576682364
|
|
- type: euclidean_ap
|
|
value: 75.22925730473472
|
|
- type: euclidean_f1
|
|
value: 69.54022988505749
|
|
- type: euclidean_precision
|
|
value: 63.65629110039457
|
|
- type: euclidean_recall
|
|
value: 76.62269129287598
|
|
- type: manhattan_accuracy
|
|
value: 86.52321630804077
|
|
- type: manhattan_ap
|
|
value: 75.20608115037336
|
|
- type: manhattan_f1
|
|
value: 69.60000000000001
|
|
- type: manhattan_precision
|
|
value: 64.37219730941705
|
|
- type: manhattan_recall
|
|
value: 75.75197889182058
|
|
- type: max_accuracy
|
|
value: 86.52321630804077
|
|
- type: max_ap
|
|
value: 75.22925730473472
|
|
- type: max_f1
|
|
value: 69.60000000000001
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 89.34877944657896
|
|
- type: cos_sim_ap
|
|
value: 86.71257569277373
|
|
- type: cos_sim_f1
|
|
value: 79.10386355986088
|
|
- type: cos_sim_precision
|
|
value: 76.91468470434214
|
|
- type: cos_sim_recall
|
|
value: 81.4213119802895
|
|
- type: dot_accuracy
|
|
value: 89.34877944657896
|
|
- type: dot_ap
|
|
value: 86.71257133133368
|
|
- type: dot_f1
|
|
value: 79.10386355986088
|
|
- type: dot_precision
|
|
value: 76.91468470434214
|
|
- type: dot_recall
|
|
value: 81.4213119802895
|
|
- type: euclidean_accuracy
|
|
value: 89.34877944657896
|
|
- type: euclidean_ap
|
|
value: 86.71257651501476
|
|
- type: euclidean_f1
|
|
value: 79.10386355986088
|
|
- type: euclidean_precision
|
|
value: 76.91468470434214
|
|
- type: euclidean_recall
|
|
value: 81.4213119802895
|
|
- type: manhattan_accuracy
|
|
value: 89.35848177901967
|
|
- type: manhattan_ap
|
|
value: 86.69330615469126
|
|
- type: manhattan_f1
|
|
value: 79.13867741453949
|
|
- type: manhattan_precision
|
|
value: 76.78881807647741
|
|
- type: manhattan_recall
|
|
value: 81.63689559593472
|
|
- type: max_accuracy
|
|
value: 89.35848177901967
|
|
- type: max_ap
|
|
value: 86.71257651501476
|
|
- type: max_f1
|
|
value: 79.13867741453949
|
|
license: apache-2.0
|
|
language:
|
|
- en
|
|
new_version: nomic-ai/nomic-embed-text-v1.5
|
|
---
|
|
|
|
|
|
# nomic-embed-text-v1: A Reproducible Long Context (8192) Text Embedder
|
|
|
|
`nomic-embed-text-v1` is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.
|
|
|
|
# Performance Benchmarks
|
|
|
|
| Name | SeqLen | MTEB | LoCo | Jina Long Context | Open Weights | Open Training Code | Open Data |
|
|
| :-------------------------------:| :----- | :-------- | :------: | :---------------: | :-----------: | :----------------: | :---------- |
|
|
| nomic-embed-text-v1 | 8192 | **62.39** |**85.53** | 54.16 | ✅ | ✅ | ✅ |
|
|
| jina-embeddings-v2-base-en | 8192 | 60.39 | 85.45 | 51.90 | ✅ | ❌ | ❌ |
|
|
| text-embedding-3-small | 8191 | 62.26 | 82.40 | **58.20** | ❌ | ❌ | ❌ |
|
|
| text-embedding-ada-002 | 8191 | 60.99 | 52.7 | 55.25 | ❌ | ❌ | ❌ |
|
|
|
|
|
|
**Exciting Update!**: `nomic-embed-text-v1` is now multimodal! [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1) is aligned to the embedding space of `nomic-embed-text-v1`, meaning any text embedding is multimodal!
|
|
|
|
## Usage
|
|
|
|
**Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed.
|
|
|
|
For example, if you are implementing a RAG application, you embed your documents as `search_document: <text here>` and embed your user queries as `search_query: <text here>`.
|
|
|
|
## Task instruction prefixes
|
|
|
|
### `search_document`
|
|
|
|
#### Purpose: embed texts as documents from a dataset
|
|
|
|
This prefix is used for embedding texts as documents, for example as documents for a RAG index.
|
|
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
|
|
sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
|
|
embeddings = model.encode(sentences)
|
|
print(embeddings)
|
|
```
|
|
|
|
### `search_query`
|
|
|
|
#### Purpose: embed texts as questions to answer
|
|
|
|
This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application.
|
|
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
|
|
sentences = ['search_query: Who is Laurens van Der Maaten?']
|
|
embeddings = model.encode(sentences)
|
|
print(embeddings)
|
|
```
|
|
|
|
### `clustering`
|
|
|
|
#### Purpose: embed texts to group them into clusters
|
|
|
|
This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates.
|
|
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
|
|
sentences = ['clustering: the quick brown fox']
|
|
embeddings = model.encode(sentences)
|
|
print(embeddings)
|
|
```
|
|
|
|
### `classification`
|
|
|
|
#### Purpose: embed texts to classify them
|
|
|
|
This prefix is used for embedding texts into vectors that will be used as features for a classification model
|
|
|
|
```python
|
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from sentence_transformers import SentenceTransformer
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|
|
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model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
|
|
sentences = ['classification: the quick brown fox']
|
|
embeddings = model.encode(sentences)
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|
print(embeddings)
|
|
```
|
|
|
|
### Sentence Transformers
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
|
|
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
|
|
embeddings = model.encode(sentences)
|
|
print(embeddings)
|
|
```
|
|
|
|
### Transformers
|
|
|
|
```python
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from transformers import AutoTokenizer, AutoModel
|
|
|
|
def mean_pooling(model_output, attention_mask):
|
|
token_embeddings = model_output[0]
|
|
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
|
|
|
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
|
|
model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
|
|
model.eval()
|
|
|
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
|
|
|
with torch.no_grad():
|
|
model_output = model(**encoded_input)
|
|
|
|
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
|
|
embeddings = F.normalize(embeddings, p=2, dim=1)
|
|
print(embeddings)
|
|
```
|
|
|
|
The model natively supports scaling of the sequence length past 2048 tokens. To do so,
|
|
|
|
```diff
|
|
- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
|
|
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
|
|
|
|
|
|
- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
|
|
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)
|
|
```
|
|
|
|
### Transformers.js
|
|
|
|
```js
|
|
import { pipeline } from '@xenova/transformers';
|
|
|
|
// Create a feature extraction pipeline
|
|
const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1', {
|
|
quantized: false, // Comment out this line to use the quantized version
|
|
});
|
|
|
|
// Compute sentence embeddings
|
|
const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'];
|
|
const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
|
|
console.log(embeddings);
|
|
```
|
|
|
|
## Nomic API
|
|
|
|
The easiest way to get started with Nomic Embed is through the Nomic Embedding API.
|
|
|
|
Generating embeddings with the `nomic` Python client is as easy as
|
|
|
|
```python
|
|
from nomic import embed
|
|
|
|
output = embed.text(
|
|
texts=['Nomic Embedding API', '#keepAIOpen'],
|
|
model='nomic-embed-text-v1',
|
|
task_type='search_document'
|
|
)
|
|
|
|
print(output)
|
|
```
|
|
|
|
For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text)
|
|
|
|
|
|
## Training
|
|
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
|
|
|
|
[](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
|
|
|
|
We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
|
|
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
|
|
|
|
In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
|
|
|
|
For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
|
|
|
|
Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
|
|
|
|
|
|
# Join the Nomic Community
|
|
|
|
- Nomic: [https://nomic.ai](https://nomic.ai)
|
|
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
|
|
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
|
|
|
|
|
|
# Citation
|
|
|
|
If you find the model, dataset, or training code useful, please cite our work
|
|
|
|
```bibtex
|
|
@misc{nussbaum2024nomic,
|
|
title={Nomic Embed: Training a Reproducible Long Context Text Embedder},
|
|
author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
|
|
year={2024},
|
|
eprint={2402.01613},
|
|
archivePrefix={arXiv},
|
|
primaryClass={cs.CL}
|
|
}
|
|
``` |