2726 lines
66 KiB
Markdown
2726 lines
66 KiB
Markdown
---
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tags:
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- mteb
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- Sentence Transformers
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- sentence-similarity
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- sentence-transformers
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model-index:
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- name: e5-small
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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: 76.22388059701493
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- type: ap
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value: 40.27466219523129
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- type: f1
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value: 70.60533006025108
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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: 87.525775
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- type: ap
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value: 83.51063993897611
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- type: f1
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value: 87.49342736805572
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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: 42.611999999999995
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- type: f1
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value: 42.05088045932892
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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: 23.826
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- type: map_at_10
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value: 38.269
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- type: map_at_100
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value: 39.322
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- type: map_at_1000
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value: 39.344
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- type: map_at_3
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value: 33.428000000000004
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- type: map_at_5
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value: 36.063
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- type: mrr_at_1
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value: 24.253
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- type: mrr_at_10
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value: 38.425
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- type: mrr_at_100
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value: 39.478
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- type: mrr_at_1000
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value: 39.5
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- type: mrr_at_3
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value: 33.606
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- type: mrr_at_5
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value: 36.195
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- type: ndcg_at_1
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value: 23.826
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- type: ndcg_at_10
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value: 46.693
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- type: ndcg_at_100
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value: 51.469
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- type: ndcg_at_1000
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value: 52.002
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- type: ndcg_at_3
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value: 36.603
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- type: ndcg_at_5
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value: 41.365
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- type: precision_at_1
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value: 23.826
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- type: precision_at_10
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value: 7.383000000000001
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- type: precision_at_100
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value: 0.9530000000000001
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- type: precision_at_1000
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value: 0.099
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- type: precision_at_3
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value: 15.268
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- type: precision_at_5
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value: 11.479000000000001
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- type: recall_at_1
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value: 23.826
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- type: recall_at_10
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value: 73.82600000000001
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- type: recall_at_100
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value: 95.306
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- type: recall_at_1000
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value: 99.431
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- type: recall_at_3
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value: 45.804
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- type: recall_at_5
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value: 57.397
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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: 44.13995374767436
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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: 37.13950072624313
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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: 59.35843292105327
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- type: mrr
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value: 73.72312359846987
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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: 84.55140418324174
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- type: cos_sim_spearman
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value: 84.21637675860022
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- type: euclidean_pearson
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value: 81.26069614610006
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- type: euclidean_spearman
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value: 83.25069210421785
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- type: manhattan_pearson
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value: 80.17441422581014
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- type: manhattan_spearman
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value: 81.87596198487877
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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: 81.87337662337661
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- type: f1
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value: 81.76647866926402
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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: 35.80600542614507
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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: 31.86321613256603
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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: 32.054
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- type: map_at_10
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value: 40.699999999999996
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- type: map_at_100
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value: 41.818
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- type: map_at_1000
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value: 41.959999999999994
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- type: map_at_3
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value: 37.742
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- type: map_at_5
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value: 39.427
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- type: mrr_at_1
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value: 38.769999999999996
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- type: mrr_at_10
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value: 46.150000000000006
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- type: mrr_at_100
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value: 46.865
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- type: mrr_at_1000
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value: 46.925
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- type: mrr_at_3
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value: 43.705
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- type: mrr_at_5
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value: 45.214999999999996
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- type: ndcg_at_1
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value: 38.769999999999996
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- type: ndcg_at_10
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value: 45.778
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- type: ndcg_at_100
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value: 50.38
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- type: ndcg_at_1000
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value: 52.922999999999995
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- type: ndcg_at_3
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value: 41.597
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- type: ndcg_at_5
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value: 43.631
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- type: precision_at_1
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value: 38.769999999999996
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- type: precision_at_10
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value: 8.269
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- type: precision_at_100
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value: 1.278
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- type: precision_at_1000
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value: 0.178
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- type: precision_at_3
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value: 19.266
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- type: precision_at_5
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value: 13.705
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- type: recall_at_1
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value: 32.054
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- type: recall_at_10
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value: 54.947
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- type: recall_at_100
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value: 74.79599999999999
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- type: recall_at_1000
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value: 91.40899999999999
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- type: recall_at_3
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value: 42.431000000000004
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- type: recall_at_5
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value: 48.519
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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: 29.035
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- type: map_at_10
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value: 38.007000000000005
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- type: map_at_100
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value: 39.125
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- type: map_at_1000
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value: 39.251999999999995
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- type: map_at_3
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value: 35.77
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- type: map_at_5
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value: 37.057
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- type: mrr_at_1
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value: 36.497
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- type: mrr_at_10
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value: 44.077
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- type: mrr_at_100
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value: 44.743
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- type: mrr_at_1000
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value: 44.79
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- type: mrr_at_3
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value: 42.123
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- type: mrr_at_5
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value: 43.308
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- type: ndcg_at_1
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value: 36.497
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- type: ndcg_at_10
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value: 42.986000000000004
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- type: ndcg_at_100
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value: 47.323
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- type: ndcg_at_1000
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value: 49.624
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- type: ndcg_at_3
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value: 39.805
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- type: ndcg_at_5
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value: 41.286
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- type: precision_at_1
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value: 36.497
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- type: precision_at_10
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value: 7.8340000000000005
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- type: precision_at_100
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value: 1.269
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- type: precision_at_1000
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value: 0.178
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- type: precision_at_3
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value: 19.023
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- type: precision_at_5
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value: 13.248
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- type: recall_at_1
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value: 29.035
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- type: recall_at_10
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value: 51.06
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- type: recall_at_100
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value: 69.64099999999999
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- type: recall_at_1000
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value: 84.49
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- type: recall_at_3
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value: 41.333999999999996
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- type: recall_at_5
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value: 45.663
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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: 37.239
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- type: map_at_10
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value: 47.873
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- type: map_at_100
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value: 48.842999999999996
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- type: map_at_1000
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value: 48.913000000000004
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- type: map_at_3
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value: 45.050000000000004
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- type: map_at_5
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value: 46.498
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- type: mrr_at_1
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value: 42.508
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- type: mrr_at_10
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value: 51.44
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- type: mrr_at_100
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value: 52.087
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- type: mrr_at_1000
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value: 52.129999999999995
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- type: mrr_at_3
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value: 49.164
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- type: mrr_at_5
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value: 50.343
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- type: ndcg_at_1
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value: 42.508
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- type: ndcg_at_10
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value: 53.31399999999999
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- type: ndcg_at_100
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value: 57.245000000000005
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- type: ndcg_at_1000
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value: 58.794000000000004
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- type: ndcg_at_3
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value: 48.295
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- type: ndcg_at_5
|
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value: 50.415
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- type: precision_at_1
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value: 42.508
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- type: precision_at_10
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value: 8.458
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- type: precision_at_100
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value: 1.133
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- type: precision_at_1000
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value: 0.132
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- type: precision_at_3
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value: 21.191
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- type: precision_at_5
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value: 14.307
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- type: recall_at_1
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value: 37.239
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- type: recall_at_10
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value: 65.99000000000001
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- type: recall_at_100
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value: 82.99499999999999
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- type: recall_at_1000
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value: 94.128
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- type: recall_at_3
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value: 52.382
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- type: recall_at_5
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value: 57.648999999999994
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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: 23.039
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- type: map_at_10
|
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value: 29.694
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- type: map_at_100
|
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value: 30.587999999999997
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- type: map_at_1000
|
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value: 30.692999999999998
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- type: map_at_3
|
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value: 27.708
|
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- type: map_at_5
|
|
value: 28.774
|
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- type: mrr_at_1
|
|
value: 24.633
|
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- type: mrr_at_10
|
|
value: 31.478
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- type: mrr_at_100
|
|
value: 32.299
|
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- type: mrr_at_1000
|
|
value: 32.381
|
|
- type: mrr_at_3
|
|
value: 29.435
|
|
- type: mrr_at_5
|
|
value: 30.446
|
|
- type: ndcg_at_1
|
|
value: 24.633
|
|
- type: ndcg_at_10
|
|
value: 33.697
|
|
- type: ndcg_at_100
|
|
value: 38.080000000000005
|
|
- type: ndcg_at_1000
|
|
value: 40.812
|
|
- type: ndcg_at_3
|
|
value: 29.654000000000003
|
|
- type: ndcg_at_5
|
|
value: 31.474000000000004
|
|
- type: precision_at_1
|
|
value: 24.633
|
|
- type: precision_at_10
|
|
value: 5.0729999999999995
|
|
- type: precision_at_100
|
|
value: 0.753
|
|
- type: precision_at_1000
|
|
value: 0.10300000000000001
|
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- type: precision_at_3
|
|
value: 12.279
|
|
- type: precision_at_5
|
|
value: 8.452
|
|
- type: recall_at_1
|
|
value: 23.039
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|
- type: recall_at_10
|
|
value: 44.275999999999996
|
|
- type: recall_at_100
|
|
value: 64.4
|
|
- type: recall_at_1000
|
|
value: 85.135
|
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- type: recall_at_3
|
|
value: 33.394
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- type: recall_at_5
|
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value: 37.687
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- task:
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type: Retrieval
|
|
dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackMathematicaRetrieval
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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: 13.594999999999999
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- type: map_at_10
|
|
value: 19.933999999999997
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|
- type: map_at_100
|
|
value: 20.966
|
|
- type: map_at_1000
|
|
value: 21.087
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- type: map_at_3
|
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value: 17.749000000000002
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- type: map_at_5
|
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value: 19.156000000000002
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- type: mrr_at_1
|
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value: 17.662
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- type: mrr_at_10
|
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value: 24.407
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- type: mrr_at_100
|
|
value: 25.385
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- type: mrr_at_1000
|
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value: 25.465
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- type: mrr_at_3
|
|
value: 22.056
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- type: mrr_at_5
|
|
value: 23.630000000000003
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- type: ndcg_at_1
|
|
value: 17.662
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- type: ndcg_at_10
|
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value: 24.391
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- type: ndcg_at_100
|
|
value: 29.681
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- type: ndcg_at_1000
|
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value: 32.923
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- type: ndcg_at_3
|
|
value: 20.271
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- type: ndcg_at_5
|
|
value: 22.621
|
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- type: precision_at_1
|
|
value: 17.662
|
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- type: precision_at_10
|
|
value: 4.44
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- type: precision_at_100
|
|
value: 0.8200000000000001
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- type: precision_at_1000
|
|
value: 0.125
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- type: precision_at_3
|
|
value: 9.577
|
|
- type: precision_at_5
|
|
value: 7.313
|
|
- type: recall_at_1
|
|
value: 13.594999999999999
|
|
- type: recall_at_10
|
|
value: 33.976
|
|
- type: recall_at_100
|
|
value: 57.43000000000001
|
|
- type: recall_at_1000
|
|
value: 80.958
|
|
- type: recall_at_3
|
|
value: 22.897000000000002
|
|
- type: recall_at_5
|
|
value: 28.714000000000002
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.683
|
|
- type: map_at_10
|
|
value: 35.068
|
|
- type: map_at_100
|
|
value: 36.311
|
|
- type: map_at_1000
|
|
value: 36.436
|
|
- type: map_at_3
|
|
value: 32.371
|
|
- type: map_at_5
|
|
value: 33.761
|
|
- type: mrr_at_1
|
|
value: 32.435
|
|
- type: mrr_at_10
|
|
value: 40.721000000000004
|
|
- type: mrr_at_100
|
|
value: 41.535
|
|
- type: mrr_at_1000
|
|
value: 41.593
|
|
- type: mrr_at_3
|
|
value: 38.401999999999994
|
|
- type: mrr_at_5
|
|
value: 39.567
|
|
- type: ndcg_at_1
|
|
value: 32.435
|
|
- type: ndcg_at_10
|
|
value: 40.538000000000004
|
|
- type: ndcg_at_100
|
|
value: 45.963
|
|
- type: ndcg_at_1000
|
|
value: 48.400999999999996
|
|
- type: ndcg_at_3
|
|
value: 36.048
|
|
- type: ndcg_at_5
|
|
value: 37.899
|
|
- type: precision_at_1
|
|
value: 32.435
|
|
- type: precision_at_10
|
|
value: 7.1129999999999995
|
|
- type: precision_at_100
|
|
value: 1.162
|
|
- type: precision_at_1000
|
|
value: 0.156
|
|
- type: precision_at_3
|
|
value: 16.683
|
|
- type: precision_at_5
|
|
value: 11.684
|
|
- type: recall_at_1
|
|
value: 26.683
|
|
- type: recall_at_10
|
|
value: 51.517
|
|
- type: recall_at_100
|
|
value: 74.553
|
|
- type: recall_at_1000
|
|
value: 90.649
|
|
- type: recall_at_3
|
|
value: 38.495000000000005
|
|
- type: recall_at_5
|
|
value: 43.495
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.186
|
|
- type: map_at_10
|
|
value: 31.972
|
|
- type: map_at_100
|
|
value: 33.117000000000004
|
|
- type: map_at_1000
|
|
value: 33.243
|
|
- type: map_at_3
|
|
value: 29.423
|
|
- type: map_at_5
|
|
value: 30.847
|
|
- type: mrr_at_1
|
|
value: 29.794999999999998
|
|
- type: mrr_at_10
|
|
value: 36.767
|
|
- type: mrr_at_100
|
|
value: 37.645
|
|
- type: mrr_at_1000
|
|
value: 37.716
|
|
- type: mrr_at_3
|
|
value: 34.513
|
|
- type: mrr_at_5
|
|
value: 35.791000000000004
|
|
- type: ndcg_at_1
|
|
value: 29.794999999999998
|
|
- type: ndcg_at_10
|
|
value: 36.786
|
|
- type: ndcg_at_100
|
|
value: 41.94
|
|
- type: ndcg_at_1000
|
|
value: 44.830999999999996
|
|
- type: ndcg_at_3
|
|
value: 32.504
|
|
- type: ndcg_at_5
|
|
value: 34.404
|
|
- type: precision_at_1
|
|
value: 29.794999999999998
|
|
- type: precision_at_10
|
|
value: 6.518
|
|
- type: precision_at_100
|
|
value: 1.0659999999999998
|
|
- type: precision_at_1000
|
|
value: 0.149
|
|
- type: precision_at_3
|
|
value: 15.296999999999999
|
|
- type: precision_at_5
|
|
value: 10.731
|
|
- type: recall_at_1
|
|
value: 24.186
|
|
- type: recall_at_10
|
|
value: 46.617
|
|
- type: recall_at_100
|
|
value: 68.75
|
|
- type: recall_at_1000
|
|
value: 88.864
|
|
- type: recall_at_3
|
|
value: 34.199
|
|
- type: recall_at_5
|
|
value: 39.462
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.22083333333333
|
|
- type: map_at_10
|
|
value: 31.606666666666662
|
|
- type: map_at_100
|
|
value: 32.6195
|
|
- type: map_at_1000
|
|
value: 32.739999999999995
|
|
- type: map_at_3
|
|
value: 29.37825
|
|
- type: map_at_5
|
|
value: 30.596083333333336
|
|
- type: mrr_at_1
|
|
value: 28.607916666666668
|
|
- type: mrr_at_10
|
|
value: 35.54591666666666
|
|
- type: mrr_at_100
|
|
value: 36.33683333333333
|
|
- type: mrr_at_1000
|
|
value: 36.40624999999999
|
|
- type: mrr_at_3
|
|
value: 33.526250000000005
|
|
- type: mrr_at_5
|
|
value: 34.6605
|
|
- type: ndcg_at_1
|
|
value: 28.607916666666668
|
|
- type: ndcg_at_10
|
|
value: 36.07966666666667
|
|
- type: ndcg_at_100
|
|
value: 40.73308333333333
|
|
- type: ndcg_at_1000
|
|
value: 43.40666666666666
|
|
- type: ndcg_at_3
|
|
value: 32.23525
|
|
- type: ndcg_at_5
|
|
value: 33.97083333333333
|
|
- type: precision_at_1
|
|
value: 28.607916666666668
|
|
- type: precision_at_10
|
|
value: 6.120333333333335
|
|
- type: precision_at_100
|
|
value: 0.9921666666666668
|
|
- type: precision_at_1000
|
|
value: 0.14091666666666666
|
|
- type: precision_at_3
|
|
value: 14.54975
|
|
- type: precision_at_5
|
|
value: 10.153166666666667
|
|
- type: recall_at_1
|
|
value: 24.22083333333333
|
|
- type: recall_at_10
|
|
value: 45.49183333333334
|
|
- type: recall_at_100
|
|
value: 66.28133333333332
|
|
- type: recall_at_1000
|
|
value: 85.16541666666667
|
|
- type: recall_at_3
|
|
value: 34.6485
|
|
- type: recall_at_5
|
|
value: 39.229749999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 21.842
|
|
- type: map_at_10
|
|
value: 27.573999999999998
|
|
- type: map_at_100
|
|
value: 28.410999999999998
|
|
- type: map_at_1000
|
|
value: 28.502
|
|
- type: map_at_3
|
|
value: 25.921
|
|
- type: map_at_5
|
|
value: 26.888
|
|
- type: mrr_at_1
|
|
value: 24.08
|
|
- type: mrr_at_10
|
|
value: 29.915999999999997
|
|
- type: mrr_at_100
|
|
value: 30.669
|
|
- type: mrr_at_1000
|
|
value: 30.746000000000002
|
|
- type: mrr_at_3
|
|
value: 28.349000000000004
|
|
- type: mrr_at_5
|
|
value: 29.246
|
|
- type: ndcg_at_1
|
|
value: 24.08
|
|
- type: ndcg_at_10
|
|
value: 30.898999999999997
|
|
- type: ndcg_at_100
|
|
value: 35.272999999999996
|
|
- type: ndcg_at_1000
|
|
value: 37.679
|
|
- type: ndcg_at_3
|
|
value: 27.881
|
|
- type: ndcg_at_5
|
|
value: 29.432000000000002
|
|
- type: precision_at_1
|
|
value: 24.08
|
|
- type: precision_at_10
|
|
value: 4.678
|
|
- type: precision_at_100
|
|
value: 0.744
|
|
- type: precision_at_1000
|
|
value: 0.10300000000000001
|
|
- type: precision_at_3
|
|
value: 11.860999999999999
|
|
- type: precision_at_5
|
|
value: 8.16
|
|
- type: recall_at_1
|
|
value: 21.842
|
|
- type: recall_at_10
|
|
value: 38.66
|
|
- type: recall_at_100
|
|
value: 59.169000000000004
|
|
- type: recall_at_1000
|
|
value: 76.887
|
|
- type: recall_at_3
|
|
value: 30.532999999999998
|
|
- type: recall_at_5
|
|
value: 34.354
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.145
|
|
- type: map_at_10
|
|
value: 22.729
|
|
- type: map_at_100
|
|
value: 23.574
|
|
- type: map_at_1000
|
|
value: 23.695
|
|
- type: map_at_3
|
|
value: 21.044
|
|
- type: map_at_5
|
|
value: 21.981
|
|
- type: mrr_at_1
|
|
value: 20.888
|
|
- type: mrr_at_10
|
|
value: 26.529000000000003
|
|
- type: mrr_at_100
|
|
value: 27.308
|
|
- type: mrr_at_1000
|
|
value: 27.389000000000003
|
|
- type: mrr_at_3
|
|
value: 24.868000000000002
|
|
- type: mrr_at_5
|
|
value: 25.825
|
|
- type: ndcg_at_1
|
|
value: 20.888
|
|
- type: ndcg_at_10
|
|
value: 26.457000000000004
|
|
- type: ndcg_at_100
|
|
value: 30.764000000000003
|
|
- type: ndcg_at_1000
|
|
value: 33.825
|
|
- type: ndcg_at_3
|
|
value: 23.483999999999998
|
|
- type: ndcg_at_5
|
|
value: 24.836
|
|
- type: precision_at_1
|
|
value: 20.888
|
|
- type: precision_at_10
|
|
value: 4.58
|
|
- type: precision_at_100
|
|
value: 0.784
|
|
- type: precision_at_1000
|
|
value: 0.121
|
|
- type: precision_at_3
|
|
value: 10.874
|
|
- type: precision_at_5
|
|
value: 7.639
|
|
- type: recall_at_1
|
|
value: 17.145
|
|
- type: recall_at_10
|
|
value: 33.938
|
|
- type: recall_at_100
|
|
value: 53.672
|
|
- type: recall_at_1000
|
|
value: 76.023
|
|
- type: recall_at_3
|
|
value: 25.363000000000003
|
|
- type: recall_at_5
|
|
value: 29.023
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.275
|
|
- type: map_at_10
|
|
value: 30.438
|
|
- type: map_at_100
|
|
value: 31.489
|
|
- type: map_at_1000
|
|
value: 31.601000000000003
|
|
- type: map_at_3
|
|
value: 28.647
|
|
- type: map_at_5
|
|
value: 29.660999999999998
|
|
- type: mrr_at_1
|
|
value: 28.077999999999996
|
|
- type: mrr_at_10
|
|
value: 34.098
|
|
- type: mrr_at_100
|
|
value: 35.025
|
|
- type: mrr_at_1000
|
|
value: 35.109
|
|
- type: mrr_at_3
|
|
value: 32.4
|
|
- type: mrr_at_5
|
|
value: 33.379999999999995
|
|
- type: ndcg_at_1
|
|
value: 28.077999999999996
|
|
- type: ndcg_at_10
|
|
value: 34.271
|
|
- type: ndcg_at_100
|
|
value: 39.352
|
|
- type: ndcg_at_1000
|
|
value: 42.199
|
|
- type: ndcg_at_3
|
|
value: 30.978
|
|
- type: ndcg_at_5
|
|
value: 32.498
|
|
- type: precision_at_1
|
|
value: 28.077999999999996
|
|
- type: precision_at_10
|
|
value: 5.345
|
|
- type: precision_at_100
|
|
value: 0.897
|
|
- type: precision_at_1000
|
|
value: 0.125
|
|
- type: precision_at_3
|
|
value: 13.526
|
|
- type: precision_at_5
|
|
value: 9.16
|
|
- type: recall_at_1
|
|
value: 24.275
|
|
- type: recall_at_10
|
|
value: 42.362
|
|
- type: recall_at_100
|
|
value: 64.461
|
|
- type: recall_at_1000
|
|
value: 84.981
|
|
- type: recall_at_3
|
|
value: 33.249
|
|
- type: recall_at_5
|
|
value: 37.214999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.358
|
|
- type: map_at_10
|
|
value: 30.062
|
|
- type: map_at_100
|
|
value: 31.189
|
|
- type: map_at_1000
|
|
value: 31.386999999999997
|
|
- type: map_at_3
|
|
value: 27.672
|
|
- type: map_at_5
|
|
value: 28.76
|
|
- type: mrr_at_1
|
|
value: 26.877000000000002
|
|
- type: mrr_at_10
|
|
value: 33.948
|
|
- type: mrr_at_100
|
|
value: 34.746
|
|
- type: mrr_at_1000
|
|
value: 34.816
|
|
- type: mrr_at_3
|
|
value: 31.884
|
|
- type: mrr_at_5
|
|
value: 33.001000000000005
|
|
- type: ndcg_at_1
|
|
value: 26.877000000000002
|
|
- type: ndcg_at_10
|
|
value: 34.977000000000004
|
|
- type: ndcg_at_100
|
|
value: 39.753
|
|
- type: ndcg_at_1000
|
|
value: 42.866
|
|
- type: ndcg_at_3
|
|
value: 30.956
|
|
- type: ndcg_at_5
|
|
value: 32.381
|
|
- type: precision_at_1
|
|
value: 26.877000000000002
|
|
- type: precision_at_10
|
|
value: 6.7
|
|
- type: precision_at_100
|
|
value: 1.287
|
|
- type: precision_at_1000
|
|
value: 0.215
|
|
- type: precision_at_3
|
|
value: 14.360999999999999
|
|
- type: precision_at_5
|
|
value: 10.119
|
|
- type: recall_at_1
|
|
value: 22.358
|
|
- type: recall_at_10
|
|
value: 44.183
|
|
- type: recall_at_100
|
|
value: 67.14
|
|
- type: recall_at_1000
|
|
value: 87.53999999999999
|
|
- type: recall_at_3
|
|
value: 32.79
|
|
- type: recall_at_5
|
|
value: 36.829
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 19.198999999999998
|
|
- type: map_at_10
|
|
value: 25.229000000000003
|
|
- type: map_at_100
|
|
value: 26.003
|
|
- type: map_at_1000
|
|
value: 26.111
|
|
- type: map_at_3
|
|
value: 23.442
|
|
- type: map_at_5
|
|
value: 24.343
|
|
- type: mrr_at_1
|
|
value: 21.072
|
|
- type: mrr_at_10
|
|
value: 27.02
|
|
- type: mrr_at_100
|
|
value: 27.735
|
|
- type: mrr_at_1000
|
|
value: 27.815
|
|
- type: mrr_at_3
|
|
value: 25.416
|
|
- type: mrr_at_5
|
|
value: 26.173999999999996
|
|
- type: ndcg_at_1
|
|
value: 21.072
|
|
- type: ndcg_at_10
|
|
value: 28.862
|
|
- type: ndcg_at_100
|
|
value: 33.043
|
|
- type: ndcg_at_1000
|
|
value: 36.003
|
|
- type: ndcg_at_3
|
|
value: 25.35
|
|
- type: ndcg_at_5
|
|
value: 26.773000000000003
|
|
- type: precision_at_1
|
|
value: 21.072
|
|
- type: precision_at_10
|
|
value: 4.436
|
|
- type: precision_at_100
|
|
value: 0.713
|
|
- type: precision_at_1000
|
|
value: 0.106
|
|
- type: precision_at_3
|
|
value: 10.659
|
|
- type: precision_at_5
|
|
value: 7.32
|
|
- type: recall_at_1
|
|
value: 19.198999999999998
|
|
- type: recall_at_10
|
|
value: 38.376
|
|
- type: recall_at_100
|
|
value: 58.36900000000001
|
|
- type: recall_at_1000
|
|
value: 80.92099999999999
|
|
- type: recall_at_3
|
|
value: 28.715000000000003
|
|
- type: recall_at_5
|
|
value: 32.147
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.9319999999999995
|
|
- type: map_at_10
|
|
value: 10.483
|
|
- type: map_at_100
|
|
value: 11.97
|
|
- type: map_at_1000
|
|
value: 12.171999999999999
|
|
- type: map_at_3
|
|
value: 8.477
|
|
- type: map_at_5
|
|
value: 9.495000000000001
|
|
- type: mrr_at_1
|
|
value: 13.094
|
|
- type: mrr_at_10
|
|
value: 21.282
|
|
- type: mrr_at_100
|
|
value: 22.556
|
|
- type: mrr_at_1000
|
|
value: 22.628999999999998
|
|
- type: mrr_at_3
|
|
value: 18.218999999999998
|
|
- type: mrr_at_5
|
|
value: 19.900000000000002
|
|
- type: ndcg_at_1
|
|
value: 13.094
|
|
- type: ndcg_at_10
|
|
value: 15.811
|
|
- type: ndcg_at_100
|
|
value: 23.035
|
|
- type: ndcg_at_1000
|
|
value: 27.089999999999996
|
|
- type: ndcg_at_3
|
|
value: 11.905000000000001
|
|
- type: ndcg_at_5
|
|
value: 13.377
|
|
- type: precision_at_1
|
|
value: 13.094
|
|
- type: precision_at_10
|
|
value: 5.225
|
|
- type: precision_at_100
|
|
value: 1.2970000000000002
|
|
- type: precision_at_1000
|
|
value: 0.203
|
|
- type: precision_at_3
|
|
value: 8.86
|
|
- type: precision_at_5
|
|
value: 7.309
|
|
- type: recall_at_1
|
|
value: 5.9319999999999995
|
|
- type: recall_at_10
|
|
value: 20.305
|
|
- type: recall_at_100
|
|
value: 46.314
|
|
- type: recall_at_1000
|
|
value: 69.612
|
|
- type: recall_at_3
|
|
value: 11.21
|
|
- type: recall_at_5
|
|
value: 14.773
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 8.674
|
|
- type: map_at_10
|
|
value: 17.822
|
|
- type: map_at_100
|
|
value: 24.794
|
|
- type: map_at_1000
|
|
value: 26.214
|
|
- type: map_at_3
|
|
value: 12.690999999999999
|
|
- type: map_at_5
|
|
value: 15.033
|
|
- type: mrr_at_1
|
|
value: 61.75000000000001
|
|
- type: mrr_at_10
|
|
value: 71.58
|
|
- type: mrr_at_100
|
|
value: 71.923
|
|
- type: mrr_at_1000
|
|
value: 71.932
|
|
- type: mrr_at_3
|
|
value: 70.125
|
|
- type: mrr_at_5
|
|
value: 71.038
|
|
- type: ndcg_at_1
|
|
value: 51
|
|
- type: ndcg_at_10
|
|
value: 38.637
|
|
- type: ndcg_at_100
|
|
value: 42.398
|
|
- type: ndcg_at_1000
|
|
value: 48.962
|
|
- type: ndcg_at_3
|
|
value: 43.29
|
|
- type: ndcg_at_5
|
|
value: 40.763
|
|
- type: precision_at_1
|
|
value: 61.75000000000001
|
|
- type: precision_at_10
|
|
value: 30.125
|
|
- type: precision_at_100
|
|
value: 9.53
|
|
- type: precision_at_1000
|
|
value: 1.9619999999999997
|
|
- type: precision_at_3
|
|
value: 45.583
|
|
- type: precision_at_5
|
|
value: 38.95
|
|
- type: recall_at_1
|
|
value: 8.674
|
|
- type: recall_at_10
|
|
value: 23.122
|
|
- type: recall_at_100
|
|
value: 47.46
|
|
- type: recall_at_1000
|
|
value: 67.662
|
|
- type: recall_at_3
|
|
value: 13.946
|
|
- type: recall_at_5
|
|
value: 17.768
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 46.86000000000001
|
|
- type: f1
|
|
value: 41.343580452760776
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 36.609
|
|
- type: map_at_10
|
|
value: 47.552
|
|
- type: map_at_100
|
|
value: 48.283
|
|
- type: map_at_1000
|
|
value: 48.321
|
|
- type: map_at_3
|
|
value: 44.869
|
|
- type: map_at_5
|
|
value: 46.509
|
|
- type: mrr_at_1
|
|
value: 39.214
|
|
- type: mrr_at_10
|
|
value: 50.434999999999995
|
|
- type: mrr_at_100
|
|
value: 51.122
|
|
- type: mrr_at_1000
|
|
value: 51.151
|
|
- type: mrr_at_3
|
|
value: 47.735
|
|
- type: mrr_at_5
|
|
value: 49.394
|
|
- type: ndcg_at_1
|
|
value: 39.214
|
|
- type: ndcg_at_10
|
|
value: 53.52400000000001
|
|
- type: ndcg_at_100
|
|
value: 56.997
|
|
- type: ndcg_at_1000
|
|
value: 57.975
|
|
- type: ndcg_at_3
|
|
value: 48.173
|
|
- type: ndcg_at_5
|
|
value: 51.05800000000001
|
|
- type: precision_at_1
|
|
value: 39.214
|
|
- type: precision_at_10
|
|
value: 7.573
|
|
- type: precision_at_100
|
|
value: 0.9440000000000001
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 19.782
|
|
- type: precision_at_5
|
|
value: 13.453000000000001
|
|
- type: recall_at_1
|
|
value: 36.609
|
|
- type: recall_at_10
|
|
value: 69.247
|
|
- type: recall_at_100
|
|
value: 84.99600000000001
|
|
- type: recall_at_1000
|
|
value: 92.40899999999999
|
|
- type: recall_at_3
|
|
value: 54.856
|
|
- type: recall_at_5
|
|
value: 61.797000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 16.466
|
|
- type: map_at_10
|
|
value: 27.060000000000002
|
|
- type: map_at_100
|
|
value: 28.511999999999997
|
|
- type: map_at_1000
|
|
value: 28.693
|
|
- type: map_at_3
|
|
value: 22.777
|
|
- type: map_at_5
|
|
value: 25.086000000000002
|
|
- type: mrr_at_1
|
|
value: 32.716
|
|
- type: mrr_at_10
|
|
value: 41.593999999999994
|
|
- type: mrr_at_100
|
|
value: 42.370000000000005
|
|
- type: mrr_at_1000
|
|
value: 42.419000000000004
|
|
- type: mrr_at_3
|
|
value: 38.143
|
|
- type: mrr_at_5
|
|
value: 40.288000000000004
|
|
- type: ndcg_at_1
|
|
value: 32.716
|
|
- type: ndcg_at_10
|
|
value: 34.795
|
|
- type: ndcg_at_100
|
|
value: 40.58
|
|
- type: ndcg_at_1000
|
|
value: 43.993
|
|
- type: ndcg_at_3
|
|
value: 29.573
|
|
- type: ndcg_at_5
|
|
value: 31.583
|
|
- type: precision_at_1
|
|
value: 32.716
|
|
- type: precision_at_10
|
|
value: 9.937999999999999
|
|
- type: precision_at_100
|
|
value: 1.585
|
|
- type: precision_at_1000
|
|
value: 0.22
|
|
- type: precision_at_3
|
|
value: 19.496
|
|
- type: precision_at_5
|
|
value: 15.247
|
|
- type: recall_at_1
|
|
value: 16.466
|
|
- type: recall_at_10
|
|
value: 42.886
|
|
- type: recall_at_100
|
|
value: 64.724
|
|
- type: recall_at_1000
|
|
value: 85.347
|
|
- type: recall_at_3
|
|
value: 26.765
|
|
- type: recall_at_5
|
|
value: 33.603
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 33.025
|
|
- type: map_at_10
|
|
value: 47.343
|
|
- type: map_at_100
|
|
value: 48.207
|
|
- type: map_at_1000
|
|
value: 48.281
|
|
- type: map_at_3
|
|
value: 44.519
|
|
- type: map_at_5
|
|
value: 46.217000000000006
|
|
- type: mrr_at_1
|
|
value: 66.05
|
|
- type: mrr_at_10
|
|
value: 72.94699999999999
|
|
- type: mrr_at_100
|
|
value: 73.289
|
|
- type: mrr_at_1000
|
|
value: 73.30499999999999
|
|
- type: mrr_at_3
|
|
value: 71.686
|
|
- type: mrr_at_5
|
|
value: 72.491
|
|
- type: ndcg_at_1
|
|
value: 66.05
|
|
- type: ndcg_at_10
|
|
value: 56.338
|
|
- type: ndcg_at_100
|
|
value: 59.599999999999994
|
|
- type: ndcg_at_1000
|
|
value: 61.138000000000005
|
|
- type: ndcg_at_3
|
|
value: 52.034000000000006
|
|
- type: ndcg_at_5
|
|
value: 54.352000000000004
|
|
- type: precision_at_1
|
|
value: 66.05
|
|
- type: precision_at_10
|
|
value: 11.693000000000001
|
|
- type: precision_at_100
|
|
value: 1.425
|
|
- type: precision_at_1000
|
|
value: 0.163
|
|
- type: precision_at_3
|
|
value: 32.613
|
|
- type: precision_at_5
|
|
value: 21.401999999999997
|
|
- type: recall_at_1
|
|
value: 33.025
|
|
- type: recall_at_10
|
|
value: 58.467
|
|
- type: recall_at_100
|
|
value: 71.242
|
|
- type: recall_at_1000
|
|
value: 81.452
|
|
- type: recall_at_3
|
|
value: 48.92
|
|
- type: recall_at_5
|
|
value: 53.504
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 75.5492
|
|
- type: ap
|
|
value: 69.42911637216271
|
|
- type: f1
|
|
value: 75.39113704261024
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.173
|
|
- type: map_at_10
|
|
value: 35.453
|
|
- type: map_at_100
|
|
value: 36.573
|
|
- type: map_at_1000
|
|
value: 36.620999999999995
|
|
- type: map_at_3
|
|
value: 31.655
|
|
- type: map_at_5
|
|
value: 33.823
|
|
- type: mrr_at_1
|
|
value: 23.868000000000002
|
|
- type: mrr_at_10
|
|
value: 36.085
|
|
- type: mrr_at_100
|
|
value: 37.15
|
|
- type: mrr_at_1000
|
|
value: 37.193
|
|
- type: mrr_at_3
|
|
value: 32.376
|
|
- type: mrr_at_5
|
|
value: 34.501
|
|
- type: ndcg_at_1
|
|
value: 23.854
|
|
- type: ndcg_at_10
|
|
value: 42.33
|
|
- type: ndcg_at_100
|
|
value: 47.705999999999996
|
|
- type: ndcg_at_1000
|
|
value: 48.91
|
|
- type: ndcg_at_3
|
|
value: 34.604
|
|
- type: ndcg_at_5
|
|
value: 38.473
|
|
- type: precision_at_1
|
|
value: 23.854
|
|
- type: precision_at_10
|
|
value: 6.639
|
|
- type: precision_at_100
|
|
value: 0.932
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 14.685
|
|
- type: precision_at_5
|
|
value: 10.782
|
|
- type: recall_at_1
|
|
value: 23.173
|
|
- type: recall_at_10
|
|
value: 63.441
|
|
- type: recall_at_100
|
|
value: 88.25
|
|
- type: recall_at_1000
|
|
value: 97.438
|
|
- type: recall_at_3
|
|
value: 42.434
|
|
- type: recall_at_5
|
|
value: 51.745
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.05426356589147
|
|
- type: f1
|
|
value: 91.88068588063942
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 73.23985408116735
|
|
- type: f1
|
|
value: 55.858906745287506
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 72.21923335574984
|
|
- type: f1
|
|
value: 70.0174116204253
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 75.77673167451245
|
|
- type: f1
|
|
value: 75.44811354778666
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 31.340414710728737
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 28.196676760061578
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 29.564149683482206
|
|
- type: mrr
|
|
value: 30.28995474250486
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.93
|
|
- type: map_at_10
|
|
value: 12.828000000000001
|
|
- type: map_at_100
|
|
value: 15.501000000000001
|
|
- type: map_at_1000
|
|
value: 16.791
|
|
- type: map_at_3
|
|
value: 9.727
|
|
- type: map_at_5
|
|
value: 11.318999999999999
|
|
- type: mrr_at_1
|
|
value: 47.678
|
|
- type: mrr_at_10
|
|
value: 55.893
|
|
- type: mrr_at_100
|
|
value: 56.491
|
|
- type: mrr_at_1000
|
|
value: 56.53
|
|
- type: mrr_at_3
|
|
value: 54.386
|
|
- type: mrr_at_5
|
|
value: 55.516
|
|
- type: ndcg_at_1
|
|
value: 45.975
|
|
- type: ndcg_at_10
|
|
value: 33.928999999999995
|
|
- type: ndcg_at_100
|
|
value: 30.164
|
|
- type: ndcg_at_1000
|
|
value: 38.756
|
|
- type: ndcg_at_3
|
|
value: 41.077000000000005
|
|
- type: ndcg_at_5
|
|
value: 38.415
|
|
- type: precision_at_1
|
|
value: 47.678
|
|
- type: precision_at_10
|
|
value: 24.365000000000002
|
|
- type: precision_at_100
|
|
value: 7.344
|
|
- type: precision_at_1000
|
|
value: 1.994
|
|
- type: precision_at_3
|
|
value: 38.184000000000005
|
|
- type: precision_at_5
|
|
value: 33.003
|
|
- type: recall_at_1
|
|
value: 5.93
|
|
- type: recall_at_10
|
|
value: 16.239
|
|
- type: recall_at_100
|
|
value: 28.782999999999998
|
|
- type: recall_at_1000
|
|
value: 60.11
|
|
- type: recall_at_3
|
|
value: 10.700999999999999
|
|
- type: recall_at_5
|
|
value: 13.584
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 36.163000000000004
|
|
- type: map_at_10
|
|
value: 51.520999999999994
|
|
- type: map_at_100
|
|
value: 52.449
|
|
- type: map_at_1000
|
|
value: 52.473000000000006
|
|
- type: map_at_3
|
|
value: 47.666
|
|
- type: map_at_5
|
|
value: 50.043000000000006
|
|
- type: mrr_at_1
|
|
value: 40.266999999999996
|
|
- type: mrr_at_10
|
|
value: 54.074
|
|
- type: mrr_at_100
|
|
value: 54.722
|
|
- type: mrr_at_1000
|
|
value: 54.739000000000004
|
|
- type: mrr_at_3
|
|
value: 51.043000000000006
|
|
- type: mrr_at_5
|
|
value: 52.956
|
|
- type: ndcg_at_1
|
|
value: 40.238
|
|
- type: ndcg_at_10
|
|
value: 58.73199999999999
|
|
- type: ndcg_at_100
|
|
value: 62.470000000000006
|
|
- type: ndcg_at_1000
|
|
value: 63.083999999999996
|
|
- type: ndcg_at_3
|
|
value: 51.672
|
|
- type: ndcg_at_5
|
|
value: 55.564
|
|
- type: precision_at_1
|
|
value: 40.238
|
|
- type: precision_at_10
|
|
value: 9.279
|
|
- type: precision_at_100
|
|
value: 1.139
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 23.078000000000003
|
|
- type: precision_at_5
|
|
value: 16.176
|
|
- type: recall_at_1
|
|
value: 36.163000000000004
|
|
- type: recall_at_10
|
|
value: 77.88199999999999
|
|
- type: recall_at_100
|
|
value: 93.83399999999999
|
|
- type: recall_at_1000
|
|
value: 98.465
|
|
- type: recall_at_3
|
|
value: 59.857000000000006
|
|
- type: recall_at_5
|
|
value: 68.73599999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 70.344
|
|
- type: map_at_10
|
|
value: 83.907
|
|
- type: map_at_100
|
|
value: 84.536
|
|
- type: map_at_1000
|
|
value: 84.557
|
|
- type: map_at_3
|
|
value: 80.984
|
|
- type: map_at_5
|
|
value: 82.844
|
|
- type: mrr_at_1
|
|
value: 81.02000000000001
|
|
- type: mrr_at_10
|
|
value: 87.158
|
|
- type: mrr_at_100
|
|
value: 87.268
|
|
- type: mrr_at_1000
|
|
value: 87.26899999999999
|
|
- type: mrr_at_3
|
|
value: 86.17
|
|
- type: mrr_at_5
|
|
value: 86.87
|
|
- type: ndcg_at_1
|
|
value: 81.02000000000001
|
|
- type: ndcg_at_10
|
|
value: 87.70700000000001
|
|
- type: ndcg_at_100
|
|
value: 89.004
|
|
- type: ndcg_at_1000
|
|
value: 89.139
|
|
- type: ndcg_at_3
|
|
value: 84.841
|
|
- type: ndcg_at_5
|
|
value: 86.455
|
|
- type: precision_at_1
|
|
value: 81.02000000000001
|
|
- type: precision_at_10
|
|
value: 13.248999999999999
|
|
- type: precision_at_100
|
|
value: 1.516
|
|
- type: precision_at_1000
|
|
value: 0.156
|
|
- type: precision_at_3
|
|
value: 36.963
|
|
- type: precision_at_5
|
|
value: 24.33
|
|
- type: recall_at_1
|
|
value: 70.344
|
|
- type: recall_at_10
|
|
value: 94.75099999999999
|
|
- type: recall_at_100
|
|
value: 99.30499999999999
|
|
- type: recall_at_1000
|
|
value: 99.928
|
|
- type: recall_at_3
|
|
value: 86.506
|
|
- type: recall_at_5
|
|
value: 91.083
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 42.873718018378305
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 56.39477366450528
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 3.868
|
|
- type: map_at_10
|
|
value: 9.611
|
|
- type: map_at_100
|
|
value: 11.087
|
|
- type: map_at_1000
|
|
value: 11.332
|
|
- type: map_at_3
|
|
value: 6.813
|
|
- type: map_at_5
|
|
value: 8.233
|
|
- type: mrr_at_1
|
|
value: 19
|
|
- type: mrr_at_10
|
|
value: 28.457
|
|
- type: mrr_at_100
|
|
value: 29.613
|
|
- type: mrr_at_1000
|
|
value: 29.695
|
|
- type: mrr_at_3
|
|
value: 25.55
|
|
- type: mrr_at_5
|
|
value: 27.29
|
|
- type: ndcg_at_1
|
|
value: 19
|
|
- type: ndcg_at_10
|
|
value: 16.419
|
|
- type: ndcg_at_100
|
|
value: 22.817999999999998
|
|
- type: ndcg_at_1000
|
|
value: 27.72
|
|
- type: ndcg_at_3
|
|
value: 15.379000000000001
|
|
- type: ndcg_at_5
|
|
value: 13.645
|
|
- type: precision_at_1
|
|
value: 19
|
|
- type: precision_at_10
|
|
value: 8.540000000000001
|
|
- type: precision_at_100
|
|
value: 1.7819999999999998
|
|
- type: precision_at_1000
|
|
value: 0.297
|
|
- type: precision_at_3
|
|
value: 14.267
|
|
- type: precision_at_5
|
|
value: 12.04
|
|
- type: recall_at_1
|
|
value: 3.868
|
|
- type: recall_at_10
|
|
value: 17.288
|
|
- type: recall_at_100
|
|
value: 36.144999999999996
|
|
- type: recall_at_1000
|
|
value: 60.199999999999996
|
|
- type: recall_at_3
|
|
value: 8.688
|
|
- type: recall_at_5
|
|
value: 12.198
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.96614722598582
|
|
- type: cos_sim_spearman
|
|
value: 78.9003023008781
|
|
- type: euclidean_pearson
|
|
value: 81.01829384436505
|
|
- type: euclidean_spearman
|
|
value: 78.93248416788914
|
|
- type: manhattan_pearson
|
|
value: 81.1665428926402
|
|
- type: manhattan_spearman
|
|
value: 78.93264116287453
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.54613363895993
|
|
- type: cos_sim_spearman
|
|
value: 75.1883451602451
|
|
- type: euclidean_pearson
|
|
value: 79.70320886899894
|
|
- type: euclidean_spearman
|
|
value: 74.5917140136796
|
|
- type: manhattan_pearson
|
|
value: 79.82157067185999
|
|
- type: manhattan_spearman
|
|
value: 74.74185720594735
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 81.30430156721782
|
|
- type: cos_sim_spearman
|
|
value: 81.79962989974364
|
|
- type: euclidean_pearson
|
|
value: 80.89058823224924
|
|
- type: euclidean_spearman
|
|
value: 81.35929372984597
|
|
- type: manhattan_pearson
|
|
value: 81.12204370487478
|
|
- type: manhattan_spearman
|
|
value: 81.6248963282232
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 81.13064504403134
|
|
- type: cos_sim_spearman
|
|
value: 78.48371403924872
|
|
- type: euclidean_pearson
|
|
value: 80.16794919665591
|
|
- type: euclidean_spearman
|
|
value: 78.29216082221699
|
|
- type: manhattan_pearson
|
|
value: 80.22308565207301
|
|
- type: manhattan_spearman
|
|
value: 78.37829229948022
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.52918899541099
|
|
- type: cos_sim_spearman
|
|
value: 87.49276894673142
|
|
- type: euclidean_pearson
|
|
value: 86.77440570164254
|
|
- type: euclidean_spearman
|
|
value: 87.5753295736756
|
|
- type: manhattan_pearson
|
|
value: 86.86098573892133
|
|
- type: manhattan_spearman
|
|
value: 87.65848591821947
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.86805307244882
|
|
- type: cos_sim_spearman
|
|
value: 84.58066253757511
|
|
- type: euclidean_pearson
|
|
value: 84.38377000876991
|
|
- type: euclidean_spearman
|
|
value: 85.1837278784528
|
|
- type: manhattan_pearson
|
|
value: 84.41903291363842
|
|
- type: manhattan_spearman
|
|
value: 85.19023736251052
|
|
- 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: 86.77218560282436
|
|
- type: cos_sim_spearman
|
|
value: 87.94243515296604
|
|
- type: euclidean_pearson
|
|
value: 88.22800939214864
|
|
- type: euclidean_spearman
|
|
value: 87.91106839439841
|
|
- type: manhattan_pearson
|
|
value: 88.17063269848741
|
|
- type: manhattan_spearman
|
|
value: 87.72751904126062
|
|
- 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: 60.40731554300387
|
|
- type: cos_sim_spearman
|
|
value: 63.76300532966479
|
|
- type: euclidean_pearson
|
|
value: 62.94727878229085
|
|
- type: euclidean_spearman
|
|
value: 63.678039531461216
|
|
- type: manhattan_pearson
|
|
value: 63.00661039863549
|
|
- type: manhattan_spearman
|
|
value: 63.6282591984376
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.92731569745344
|
|
- type: cos_sim_spearman
|
|
value: 86.36336704300167
|
|
- type: euclidean_pearson
|
|
value: 86.09122224841195
|
|
- type: euclidean_spearman
|
|
value: 86.2116149319238
|
|
- type: manhattan_pearson
|
|
value: 86.07879456717032
|
|
- type: manhattan_spearman
|
|
value: 86.2022069635119
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 79.75976311752326
|
|
- type: mrr
|
|
value: 94.15782837351466
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 51.193999999999996
|
|
- type: map_at_10
|
|
value: 61.224999999999994
|
|
- type: map_at_100
|
|
value: 62.031000000000006
|
|
- type: map_at_1000
|
|
value: 62.066
|
|
- type: map_at_3
|
|
value: 59.269000000000005
|
|
- type: map_at_5
|
|
value: 60.159
|
|
- type: mrr_at_1
|
|
value: 53.667
|
|
- type: mrr_at_10
|
|
value: 62.74999999999999
|
|
- type: mrr_at_100
|
|
value: 63.39399999999999
|
|
- type: mrr_at_1000
|
|
value: 63.425
|
|
- type: mrr_at_3
|
|
value: 61.389
|
|
- type: mrr_at_5
|
|
value: 61.989000000000004
|
|
- type: ndcg_at_1
|
|
value: 53.667
|
|
- type: ndcg_at_10
|
|
value: 65.596
|
|
- type: ndcg_at_100
|
|
value: 68.906
|
|
- type: ndcg_at_1000
|
|
value: 69.78999999999999
|
|
- type: ndcg_at_3
|
|
value: 62.261
|
|
- type: ndcg_at_5
|
|
value: 63.453
|
|
- type: precision_at_1
|
|
value: 53.667
|
|
- type: precision_at_10
|
|
value: 8.667
|
|
- type: precision_at_100
|
|
value: 1.04
|
|
- type: precision_at_1000
|
|
value: 0.11100000000000002
|
|
- type: precision_at_3
|
|
value: 24.556
|
|
- type: precision_at_5
|
|
value: 15.6
|
|
- type: recall_at_1
|
|
value: 51.193999999999996
|
|
- type: recall_at_10
|
|
value: 77.156
|
|
- type: recall_at_100
|
|
value: 91.43299999999999
|
|
- type: recall_at_1000
|
|
value: 98.333
|
|
- type: recall_at_3
|
|
value: 67.994
|
|
- type: recall_at_5
|
|
value: 71.14399999999999
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.81485148514851
|
|
- type: cos_sim_ap
|
|
value: 95.28896513388551
|
|
- type: cos_sim_f1
|
|
value: 90.43478260869566
|
|
- type: cos_sim_precision
|
|
value: 92.56544502617801
|
|
- type: cos_sim_recall
|
|
value: 88.4
|
|
- type: dot_accuracy
|
|
value: 99.30594059405941
|
|
- type: dot_ap
|
|
value: 61.6432597455472
|
|
- type: dot_f1
|
|
value: 59.46481665014866
|
|
- type: dot_precision
|
|
value: 58.93909626719057
|
|
- type: dot_recall
|
|
value: 60
|
|
- type: euclidean_accuracy
|
|
value: 99.81980198019802
|
|
- type: euclidean_ap
|
|
value: 95.21411049527
|
|
- type: euclidean_f1
|
|
value: 91.06090373280944
|
|
- type: euclidean_precision
|
|
value: 89.47876447876449
|
|
- type: euclidean_recall
|
|
value: 92.7
|
|
- type: manhattan_accuracy
|
|
value: 99.81782178217821
|
|
- type: manhattan_ap
|
|
value: 95.32449994414968
|
|
- type: manhattan_f1
|
|
value: 90.86395233366436
|
|
- type: manhattan_precision
|
|
value: 90.23668639053254
|
|
- type: manhattan_recall
|
|
value: 91.5
|
|
- type: max_accuracy
|
|
value: 99.81980198019802
|
|
- type: max_ap
|
|
value: 95.32449994414968
|
|
- type: max_f1
|
|
value: 91.06090373280944
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 59.08045614613064
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 30.297802606804748
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 49.12801740706292
|
|
- type: mrr
|
|
value: 50.05592956879722
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 31.523347880124497
|
|
- type: cos_sim_spearman
|
|
value: 31.388214436391014
|
|
- type: dot_pearson
|
|
value: 24.55403435439901
|
|
- type: dot_spearman
|
|
value: 23.50153210841191
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.243
|
|
- type: map_at_10
|
|
value: 1.886
|
|
- type: map_at_100
|
|
value: 10.040000000000001
|
|
- type: map_at_1000
|
|
value: 23.768
|
|
- type: map_at_3
|
|
value: 0.674
|
|
- type: map_at_5
|
|
value: 1.079
|
|
- type: mrr_at_1
|
|
value: 88
|
|
- type: mrr_at_10
|
|
value: 93.667
|
|
- type: mrr_at_100
|
|
value: 93.667
|
|
- type: mrr_at_1000
|
|
value: 93.667
|
|
- type: mrr_at_3
|
|
value: 93.667
|
|
- type: mrr_at_5
|
|
value: 93.667
|
|
- type: ndcg_at_1
|
|
value: 83
|
|
- type: ndcg_at_10
|
|
value: 76.777
|
|
- type: ndcg_at_100
|
|
value: 55.153
|
|
- type: ndcg_at_1000
|
|
value: 47.912
|
|
- type: ndcg_at_3
|
|
value: 81.358
|
|
- type: ndcg_at_5
|
|
value: 80.74799999999999
|
|
- type: precision_at_1
|
|
value: 88
|
|
- type: precision_at_10
|
|
value: 80.80000000000001
|
|
- type: precision_at_100
|
|
value: 56.02
|
|
- type: precision_at_1000
|
|
value: 21.51
|
|
- type: precision_at_3
|
|
value: 86
|
|
- type: precision_at_5
|
|
value: 86
|
|
- type: recall_at_1
|
|
value: 0.243
|
|
- type: recall_at_10
|
|
value: 2.0869999999999997
|
|
- type: recall_at_100
|
|
value: 13.014000000000001
|
|
- type: recall_at_1000
|
|
value: 44.433
|
|
- type: recall_at_3
|
|
value: 0.6910000000000001
|
|
- type: recall_at_5
|
|
value: 1.1440000000000001
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 3.066
|
|
- type: map_at_10
|
|
value: 10.615
|
|
- type: map_at_100
|
|
value: 16.463
|
|
- type: map_at_1000
|
|
value: 17.815
|
|
- type: map_at_3
|
|
value: 5.7860000000000005
|
|
- type: map_at_5
|
|
value: 7.353999999999999
|
|
- type: mrr_at_1
|
|
value: 38.775999999999996
|
|
- type: mrr_at_10
|
|
value: 53.846000000000004
|
|
- type: mrr_at_100
|
|
value: 54.37
|
|
- type: mrr_at_1000
|
|
value: 54.37
|
|
- type: mrr_at_3
|
|
value: 48.980000000000004
|
|
- type: mrr_at_5
|
|
value: 51.735
|
|
- type: ndcg_at_1
|
|
value: 34.694
|
|
- type: ndcg_at_10
|
|
value: 26.811
|
|
- type: ndcg_at_100
|
|
value: 37.342999999999996
|
|
- type: ndcg_at_1000
|
|
value: 47.964
|
|
- type: ndcg_at_3
|
|
value: 30.906
|
|
- type: ndcg_at_5
|
|
value: 27.77
|
|
- type: precision_at_1
|
|
value: 38.775999999999996
|
|
- type: precision_at_10
|
|
value: 23.878
|
|
- type: precision_at_100
|
|
value: 7.632999999999999
|
|
- type: precision_at_1000
|
|
value: 1.469
|
|
- type: precision_at_3
|
|
value: 31.973000000000003
|
|
- type: precision_at_5
|
|
value: 26.939
|
|
- type: recall_at_1
|
|
value: 3.066
|
|
- type: recall_at_10
|
|
value: 17.112
|
|
- type: recall_at_100
|
|
value: 47.723
|
|
- type: recall_at_1000
|
|
value: 79.50500000000001
|
|
- type: recall_at_3
|
|
value: 6.825
|
|
- type: recall_at_5
|
|
value: 9.584
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 72.76460000000002
|
|
- type: ap
|
|
value: 14.944240012137053
|
|
- type: f1
|
|
value: 55.89805777266571
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 63.30503678551217
|
|
- type: f1
|
|
value: 63.57492701921179
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 37.51066495006874
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 86.07021517553794
|
|
- type: cos_sim_ap
|
|
value: 74.15520712370555
|
|
- type: cos_sim_f1
|
|
value: 68.64321608040201
|
|
- type: cos_sim_precision
|
|
value: 65.51558752997602
|
|
- type: cos_sim_recall
|
|
value: 72.0844327176781
|
|
- type: dot_accuracy
|
|
value: 80.23484532395541
|
|
- type: dot_ap
|
|
value: 54.298763810214176
|
|
- type: dot_f1
|
|
value: 53.22254659779924
|
|
- type: dot_precision
|
|
value: 46.32525410476936
|
|
- type: dot_recall
|
|
value: 62.532981530343015
|
|
- type: euclidean_accuracy
|
|
value: 86.04637301066937
|
|
- type: euclidean_ap
|
|
value: 73.85333854233123
|
|
- type: euclidean_f1
|
|
value: 68.77723660599845
|
|
- type: euclidean_precision
|
|
value: 66.87437686939182
|
|
- type: euclidean_recall
|
|
value: 70.79155672823218
|
|
- type: manhattan_accuracy
|
|
value: 85.98676759849795
|
|
- type: manhattan_ap
|
|
value: 73.56016090035973
|
|
- type: manhattan_f1
|
|
value: 68.48878539036647
|
|
- type: manhattan_precision
|
|
value: 63.9505607690547
|
|
- type: manhattan_recall
|
|
value: 73.7203166226913
|
|
- type: max_accuracy
|
|
value: 86.07021517553794
|
|
- type: max_ap
|
|
value: 74.15520712370555
|
|
- type: max_f1
|
|
value: 68.77723660599845
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 88.92769821865176
|
|
- type: cos_sim_ap
|
|
value: 85.78879502899773
|
|
- type: cos_sim_f1
|
|
value: 78.14414083990464
|
|
- type: cos_sim_precision
|
|
value: 74.61651607480563
|
|
- type: cos_sim_recall
|
|
value: 82.0218663381583
|
|
- type: dot_accuracy
|
|
value: 84.95750378390964
|
|
- type: dot_ap
|
|
value: 75.80219641857563
|
|
- type: dot_f1
|
|
value: 70.13966179585681
|
|
- type: dot_precision
|
|
value: 65.71140262361251
|
|
- type: dot_recall
|
|
value: 75.20788420080073
|
|
- type: euclidean_accuracy
|
|
value: 88.93546008460433
|
|
- type: euclidean_ap
|
|
value: 85.72056428301667
|
|
- type: euclidean_f1
|
|
value: 78.14387902598124
|
|
- type: euclidean_precision
|
|
value: 75.3376688344172
|
|
- type: euclidean_recall
|
|
value: 81.16723129042192
|
|
- type: manhattan_accuracy
|
|
value: 88.96262661543835
|
|
- type: manhattan_ap
|
|
value: 85.76605136314335
|
|
- type: manhattan_f1
|
|
value: 78.26696165191743
|
|
- type: manhattan_precision
|
|
value: 75.0990659496179
|
|
- type: manhattan_recall
|
|
value: 81.71388974437943
|
|
- type: max_accuracy
|
|
value: 88.96262661543835
|
|
- type: max_ap
|
|
value: 85.78879502899773
|
|
- type: max_f1
|
|
value: 78.26696165191743
|
|
language:
|
|
- en
|
|
license: mit
|
|
---
|
|
|
|
# E5-small
|
|
|
|
**News (May 2023): please switch to [e5-small-v2](https://huggingface.co/intfloat/e5-small-v2), which has better performance and same method of usage.**
|
|
|
|
[Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://arxiv.org/pdf/2212.03533.pdf).
|
|
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022
|
|
|
|
This model has 12 layers and the embedding size is 384.
|
|
|
|
## Usage
|
|
|
|
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
|
|
|
|
```python
|
|
import torch.nn.functional as F
|
|
|
|
from torch import Tensor
|
|
from transformers import AutoTokenizer, AutoModel
|
|
|
|
|
|
def average_pool(last_hidden_states: Tensor,
|
|
attention_mask: Tensor) -> Tensor:
|
|
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
|
|
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
|
|
|
|
|
|
# Each input text should start with "query: " or "passage: ".
|
|
# For tasks other than retrieval, you can simply use the "query: " prefix.
|
|
input_texts = ['query: how much protein should a female eat',
|
|
'query: summit define',
|
|
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
|
|
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."]
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small')
|
|
model = AutoModel.from_pretrained('intfloat/e5-small')
|
|
|
|
# Tokenize the input texts
|
|
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
|
|
|
|
outputs = model(**batch_dict)
|
|
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
|
|
|
|
# normalize embeddings
|
|
embeddings = F.normalize(embeddings, p=2, dim=1)
|
|
scores = (embeddings[:2] @ embeddings[2:].T) * 100
|
|
print(scores.tolist())
|
|
```
|
|
|
|
## Training Details
|
|
|
|
Please refer to our paper at [https://arxiv.org/pdf/2212.03533.pdf](https://arxiv.org/pdf/2212.03533.pdf).
|
|
|
|
## Benchmark Evaluation
|
|
|
|
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
|
|
on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
|
|
|
|
## Support for Sentence Transformers
|
|
|
|
Below is an example for usage with sentence_transformers.
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
model = SentenceTransformer('intfloat/e5-small')
|
|
input_texts = [
|
|
'query: how much protein should a female eat',
|
|
'query: summit define',
|
|
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
|
|
"passage: Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
|
|
]
|
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embeddings = model.encode(input_texts, normalize_embeddings=True)
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```
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Package requirements
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`pip install sentence_transformers~=2.2.2`
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Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
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## FAQ
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**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
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Yes, this is how the model is trained, otherwise you will see a performance degradation.
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Here are some rules of thumb:
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- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
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- Use "query: " prefix for symmetric tasks such as semantic similarity, paraphrase retrieval.
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- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
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**2. Why are my reproduced results slightly different from reported in the model card?**
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Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
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**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
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This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
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For text embedding tasks like text retrieval or semantic similarity,
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what matters is the relative order of the scores instead of the absolute values,
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so this should not be an issue.
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## Citation
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If you find our paper or models helpful, please consider cite as follows:
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```
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@article{wang2022text,
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title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
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author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
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journal={arXiv preprint arXiv:2212.03533},
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year={2022}
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}
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```
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## Limitations
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This model only works for English texts. Long texts will be truncated to at most 512 tokens.
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