metadata
tags:
- mteb
model-index:
- name: mmarco-sentence-flare-it
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.28358208955223
- type: ap
value: 28.583712225399804
- type: f1
value: 59.773975520814645
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (de)
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 49.28265524625267
- type: ap
value: 70.12705711793366
- type: f1
value: 46.9152621753021
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en-ext)
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 61.13943028485757
- type: ap
value: 15.393299134540122
- type: f1
value: 50.441499676740754
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (ja)
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 44.85010706638115
- type: ap
value: 11.24959111812915
- type: f1
value: 38.4896899038441
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 53.786350000000006
- type: ap
value: 52.711619488611895
- type: f1
value: 52.08639681443221
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 22.954
- type: f1
value: 20.895324325359304
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (de)
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 22.016
- type: f1
value: 20.141551433471214
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (es)
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 23.842000000000002
- type: f1
value: 22.360764368564414
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (fr)
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 24.534000000000002
- type: f1
value: 23.348432665500937
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (ja)
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 20.183999999999997
- type: f1
value: 17.025753479408394
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 20.226000000000003
- type: f1
value: 17.949454130689396
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.64
- type: map_at_10
value: 1.109
- type: map_at_100
value: 1.214
- type: map_at_1000
value: 1.273
- type: map_at_3
value: 0.936
- type: map_at_5
value: 1.032
- type: mrr_at_1
value: 0.64
- type: mrr_at_10
value: 1.109
- type: mrr_at_100
value: 1.214
- type: mrr_at_1000
value: 1.273
- type: mrr_at_3
value: 0.936
- type: mrr_at_5
value: 1.032
- type: ndcg_at_1
value: 0.64
- type: ndcg_at_10
value: 1.401
- type: ndcg_at_100
value: 2.106
- type: ndcg_at_1000
value: 4.484
- type: ndcg_at_3
value: 1.042
- type: ndcg_at_5
value: 1.217
- type: precision_at_1
value: 0.64
- type: precision_at_10
value: 0.23500000000000001
- type: precision_at_100
value: 0.061
- type: precision_at_1000
value: 0.027
- type: precision_at_3
value: 0.44999999999999996
- type: precision_at_5
value: 0.356
- type: recall_at_1
value: 0.64
- type: recall_at_10
value: 2.347
- type: recall_at_100
value: 6.117
- type: recall_at_1000
value: 26.671
- type: recall_at_3
value: 1.351
- type: recall_at_5
value: 1.778
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 10.337297492580117
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 8.41067718068448
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 39.45372039138711
- type: mrr
value: 49.48005979861936
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 2.1060056434908527
- type: cos_sim_spearman
value: -6.396531291473412
- type: euclidean_pearson
value: -1.0319749731423296
- type: euclidean_spearman
value: -5.283855335987313
- type: manhattan_pearson
value: -5.66609061890471
- type: manhattan_spearman
value: -7.173055009189482
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 17.435064935064933
- type: f1
value: 15.631665237965379
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 4.01285243931824
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 3.0046123718115685
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.001
- type: map_at_10
value: 0.023
- type: map_at_100
value: 0.034999999999999996
- type: map_at_1000
value: 0.042
- type: map_at_3
value: 0.001
- type: map_at_5
value: 0.001
- type: mrr_at_1
value: 0.14300000000000002
- type: mrr_at_10
value: 0.173
- type: mrr_at_100
value: 0.203
- type: mrr_at_1000
value: 0.216
- type: mrr_at_3
value: 0.14300000000000002
- type: mrr_at_5
value: 0.14300000000000002
- type: ndcg_at_1
value: 0.14300000000000002
- type: ndcg_at_10
value: 0.11
- type: ndcg_at_100
value: 0.174
- type: ndcg_at_1000
value: 0.526
- type: ndcg_at_3
value: 0.067
- type: ndcg_at_5
value: 0.049
- type: precision_at_1
value: 0.14300000000000002
- type: precision_at_10
value: 0.056999999999999995
- type: precision_at_100
value: 0.02
- type: precision_at_1000
value: 0.01
- type: precision_at_3
value: 0.048
- type: precision_at_5
value: 0.029
- type: recall_at_1
value: 0.001
- type: recall_at_10
value: 0.216
- type: recall_at_100
value: 0.629
- type: recall_at_1000
value: 3.1940000000000004
- type: recall_at_3
value: 0.001
- type: recall_at_5
value: 0.001
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0.04
- type: map_at_100
value: 0.058
- type: map_at_1000
value: 0.065
- type: map_at_3
value: 0.033
- type: map_at_5
value: 0.04
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.066
- type: mrr_at_100
value: 0.099
- type: mrr_at_1000
value: 0.11
- type: mrr_at_3
value: 0.053
- type: mrr_at_5
value: 0.066
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.062
- type: ndcg_at_100
value: 0.182
- type: ndcg_at_1000
value: 0.494
- type: ndcg_at_3
value: 0.055
- type: ndcg_at_5
value: 0.066
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.019
- type: precision_at_100
value: 0.012
- type: precision_at_1000
value: 0.006
- type: precision_at_3
value: 0.042
- type: precision_at_5
value: 0.038
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0.1
- type: recall_at_100
value: 0.626
- type: recall_at_1000
value: 3.012
- type: recall_at_3
value: 0.068
- type: recall_at_5
value: 0.1
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0.01
- type: map_at_100
value: 0.019
- type: map_at_1000
value: 0.026
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.01
- type: mrr_at_100
value: 0.019
- type: mrr_at_1000
value: 0.027
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.022000000000000002
- type: ndcg_at_100
value: 0.09
- type: ndcg_at_1000
value: 0.35500000000000004
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.006
- type: precision_at_100
value: 0.004
- type: precision_at_1000
value: 0.003
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0.063
- type: recall_at_100
value: 0.439
- type: recall_at_1000
value: 2.576
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22599999999999998
- type: map_at_10
value: 0.22599999999999998
- type: map_at_100
value: 0.23500000000000001
- type: map_at_1000
value: 0.241
- type: map_at_3
value: 0.22599999999999998
- type: map_at_5
value: 0.22599999999999998
- type: mrr_at_1
value: 0.22599999999999998
- type: mrr_at_10
value: 0.22599999999999998
- type: mrr_at_100
value: 0.23800000000000002
- type: mrr_at_1000
value: 0.244
- type: mrr_at_3
value: 0.22599999999999998
- type: mrr_at_5
value: 0.22599999999999998
- type: ndcg_at_1
value: 0.22599999999999998
- type: ndcg_at_10
value: 0.22599999999999998
- type: ndcg_at_100
value: 0.317
- type: ndcg_at_1000
value: 0.584
- type: ndcg_at_3
value: 0.22599999999999998
- type: ndcg_at_5
value: 0.22599999999999998
- type: precision_at_1
value: 0.22599999999999998
- type: precision_at_10
value: 0.023
- type: precision_at_100
value: 0.009000000000000001
- type: precision_at_1000
value: 0.004
- type: precision_at_3
value: 0.075
- type: precision_at_5
value: 0.045
- type: recall_at_1
value: 0.22599999999999998
- type: recall_at_10
value: 0.22599999999999998
- type: recall_at_100
value: 0.732
- type: recall_at_1000
value: 2.951
- type: recall_at_3
value: 0.22599999999999998
- type: recall_at_5
value: 0.22599999999999998
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.062
- type: map_at_10
value: 0.062
- type: map_at_100
value: 0.08800000000000001
- type: map_at_1000
value: 0.1
- type: map_at_3
value: 0.062
- type: map_at_5
value: 0.062
- type: mrr_at_1
value: 0.124
- type: mrr_at_10
value: 0.124
- type: mrr_at_100
value: 0.173
- type: mrr_at_1000
value: 0.191
- type: mrr_at_3
value: 0.124
- type: mrr_at_5
value: 0.124
- type: ndcg_at_1
value: 0.124
- type: ndcg_at_10
value: 0.076
- type: ndcg_at_100
value: 0.27
- type: ndcg_at_1000
value: 0.7849999999999999
- type: ndcg_at_3
value: 0.076
- type: ndcg_at_5
value: 0.076
- type: precision_at_1
value: 0.124
- type: precision_at_10
value: 0.012
- type: precision_at_100
value: 0.02
- type: precision_at_1000
value: 0.009000000000000001
- type: precision_at_3
value: 0.041
- type: precision_at_5
value: 0.025
- type: recall_at_1
value: 0.062
- type: recall_at_10
value: 0.062
- type: recall_at_100
value: 0.9119999999999999
- type: recall_at_1000
value: 4.809
- type: recall_at_3
value: 0.062
- type: recall_at_5
value: 0.062
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0.043
- type: map_at_100
value: 0.061
- type: map_at_1000
value: 0.06999999999999999
- type: map_at_3
value: 0
- type: map_at_5
value: 0.043
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.043
- type: mrr_at_100
value: 0.06899999999999999
- type: mrr_at_1000
value: 0.079
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0.043
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.079
- type: ndcg_at_100
value: 0.22599999999999998
- type: ndcg_at_1000
value: 0.5579999999999999
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0.079
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.019
- type: precision_at_100
value: 0.013
- type: precision_at_1000
value: 0.005
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0.038
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0.192
- type: recall_at_100
value: 0.918
- type: recall_at_1000
value: 3.5909999999999997
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0.192
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0.04
- type: map_at_100
value: 0.044000000000000004
- type: map_at_1000
value: 0.052
- type: map_at_3
value: 0.038
- type: map_at_5
value: 0.038
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.054
- type: mrr_at_100
value: 0.07200000000000001
- type: mrr_at_1000
value: 0.084
- type: mrr_at_3
value: 0.038
- type: mrr_at_5
value: 0.038
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.067
- type: ndcg_at_100
value: 0.10300000000000001
- type: ndcg_at_1000
value: 0.488
- type: ndcg_at_3
value: 0.056999999999999995
- type: ndcg_at_5
value: 0.056999999999999995
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.023
- type: precision_at_100
value: 0.006999999999999999
- type: precision_at_1000
value: 0.006
- type: precision_at_3
value: 0.038
- type: precision_at_5
value: 0.023
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0.128
- type: recall_at_100
value: 0.248
- type: recall_at_1000
value: 3.36
- type: recall_at_3
value: 0.11399999999999999
- type: recall_at_5
value: 0.11399999999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0
- type: map_at_100
value: 0.013999999999999999
- type: map_at_1000
value: 0.023
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0.03
- type: mrr_at_1000
value: 0.045
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0
- type: ndcg_at_100
value: 0.066
- type: ndcg_at_1000
value: 0.445
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0
- type: precision_at_100
value: 0.005
- type: precision_at_1000
value: 0.005
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0
- type: recall_at_100
value: 0.24
- type: recall_at_1000
value: 3.1559999999999997
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0
- type: map_at_100
value: 0.006999999999999999
- type: map_at_1000
value: 0.012
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0.011000000000000001
- type: mrr_at_1000
value: 0.018000000000000002
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0
- type: ndcg_at_100
value: 0.055
- type: ndcg_at_1000
value: 0.254
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0
- type: precision_at_100
value: 0.004
- type: precision_at_1000
value: 0.003
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0
- type: recall_at_100
value: 0.27599999999999997
- type: recall_at_1000
value: 1.828
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0.023
- type: map_at_100
value: 0.031
- type: map_at_1000
value: 0.038
- type: map_at_3
value: 0
- type: map_at_5
value: 0.023
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.023
- type: mrr_at_100
value: 0.039
- type: mrr_at_1000
value: 0.048
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0.023
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.04
- type: ndcg_at_100
value: 0.133
- type: ndcg_at_1000
value: 0.395
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0.04
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.009000000000000001
- type: precision_at_100
value: 0.009000000000000001
- type: precision_at_1000
value: 0.004
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0.019
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0.093
- type: recall_at_100
value: 0.598
- type: recall_at_1000
value: 2.59
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0.093
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0
- type: map_at_100
value: 0.015
- type: map_at_1000
value: 0.03
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0.062
- type: mrr_at_1000
value: 0.083
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0
- type: ndcg_at_100
value: 0.17700000000000002
- type: ndcg_at_1000
value: 0.9299999999999999
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0
- type: precision_at_100
value: 0.027999999999999997
- type: precision_at_1000
value: 0.023
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0
- type: recall_at_100
value: 0.894
- type: recall_at_1000
value: 6.639
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0
- type: map_at_100
value: 0
- type: map_at_1000
value: 0.006999999999999999
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0.009000000000000001
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0
- type: ndcg_at_100
value: 0
- type: ndcg_at_1000
value: 0.35200000000000004
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0
- type: precision_at_100
value: 0
- type: precision_at_1000
value: 0.004
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0
- type: recall_at_100
value: 0
- type: recall_at_1000
value: 2.864
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.033
- type: map_at_10
value: 0.034
- type: map_at_100
value: 0.037
- type: map_at_1000
value: 0.039
- type: map_at_3
value: 0.033
- type: map_at_5
value: 0.033
- type: mrr_at_1
value: 0.065
- type: mrr_at_10
value: 0.07200000000000001
- type: mrr_at_100
value: 0.08
- type: mrr_at_1000
value: 0.086
- type: mrr_at_3
value: 0.065
- type: mrr_at_5
value: 0.065
- type: ndcg_at_1
value: 0.065
- type: ndcg_at_10
value: 0.047
- type: ndcg_at_100
value: 0.079
- type: ndcg_at_1000
value: 0.19
- type: ndcg_at_3
value: 0.04
- type: ndcg_at_5
value: 0.04
- type: precision_at_1
value: 0.065
- type: precision_at_10
value: 0.013
- type: precision_at_100
value: 0.005
- type: precision_at_1000
value: 0.002
- type: precision_at_3
value: 0.022000000000000002
- type: precision_at_5
value: 0.013
- type: recall_at_1
value: 0.033
- type: recall_at_10
value: 0.049
- type: recall_at_100
value: 0.186
- type: recall_at_1000
value: 0.9199999999999999
- type: recall_at_3
value: 0.033
- type: recall_at_5
value: 0.033
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0
- type: map_at_100
value: 0.008
- type: map_at_1000
value: 0.008
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0.066
- type: mrr_at_1000
value: 0.077
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0
- type: ndcg_at_100
value: 0.08
- type: ndcg_at_1000
value: 0.131
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0
- type: precision_at_100
value: 0.018000000000000002
- type: precision_at_1000
value: 0.006999999999999999
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0
- type: recall_at_100
value: 0.133
- type: recall_at_1000
value: 0.293
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 19.57
- type: f1
value: 16.51103261738041
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.068
- type: map_at_10
value: 0.104
- type: map_at_100
value: 0.11
- type: map_at_1000
value: 0.11299999999999999
- type: map_at_3
value: 0.095
- type: map_at_5
value: 0.098
- type: mrr_at_1
value: 0.075
- type: mrr_at_10
value: 0.11299999999999999
- type: mrr_at_100
value: 0.11900000000000001
- type: mrr_at_1000
value: 0.123
- type: mrr_at_3
value: 0.10300000000000001
- type: mrr_at_5
value: 0.106
- type: ndcg_at_1
value: 0.075
- type: ndcg_at_10
value: 0.128
- type: ndcg_at_100
value: 0.167
- type: ndcg_at_1000
value: 0.291
- type: ndcg_at_3
value: 0.105
- type: ndcg_at_5
value: 0.11100000000000002
- type: precision_at_1
value: 0.075
- type: precision_at_10
value: 0.021
- type: precision_at_100
value: 0.004
- type: precision_at_1000
value: 0.002
- type: precision_at_3
value: 0.045
- type: precision_at_5
value: 0.03
- type: recall_at_1
value: 0.068
- type: recall_at_10
value: 0.19499999999999998
- type: recall_at_100
value: 0.40299999999999997
- type: recall_at_1000
value: 1.448
- type: recall_at_3
value: 0.128
- type: recall_at_5
value: 0.14300000000000002
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0
- type: map_at_100
value: 0.005
- type: map_at_1000
value: 0.01
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0.011000000000000001
- type: mrr_at_1000
value: 0.026
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0
- type: ndcg_at_100
value: 0.06999999999999999
- type: ndcg_at_1000
value: 0.38899999999999996
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0
- type: precision_at_100
value: 0.008
- type: precision_at_1000
value: 0.006999999999999999
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0
- type: recall_at_100
value: 0.383
- type: recall_at_1000
value: 2.435
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.013999999999999999
- type: map_at_10
value: 0.018000000000000002
- type: map_at_100
value: 0.019
- type: map_at_1000
value: 0.02
- type: map_at_3
value: 0.016
- type: map_at_5
value: 0.016
- type: mrr_at_1
value: 0.027
- type: mrr_at_10
value: 0.034999999999999996
- type: mrr_at_100
value: 0.038
- type: mrr_at_1000
value: 0.039
- type: mrr_at_3
value: 0.032
- type: mrr_at_5
value: 0.032
- type: ndcg_at_1
value: 0.027
- type: ndcg_at_10
value: 0.026
- type: ndcg_at_100
value: 0.038
- type: ndcg_at_1000
value: 0.064
- type: ndcg_at_3
value: 0.021
- type: ndcg_at_5
value: 0.021
- type: precision_at_1
value: 0.027
- type: precision_at_10
value: 0.006999999999999999
- type: precision_at_100
value: 0.002
- type: precision_at_1000
value: 0.001
- type: precision_at_3
value: 0.013999999999999999
- type: precision_at_5
value: 0.008
- type: recall_at_1
value: 0.013999999999999999
- type: recall_at_10
value: 0.034
- type: recall_at_100
value: 0.08800000000000001
- type: recall_at_1000
value: 0.27
- type: recall_at_3
value: 0.02
- type: recall_at_5
value: 0.02
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 55.4624
- type: ap
value: 53.20545827965495
- type: f1
value: 54.40019244805333
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 0.086
- type: map_at_10
value: 0.109
- type: map_at_100
value: 0.11499999999999999
- type: map_at_1000
value: 0.11800000000000001
- type: map_at_3
value: 0.091
- type: map_at_5
value: 0.101
- type: mrr_at_1
value: 0.086
- type: mrr_at_10
value: 0.109
- type: mrr_at_100
value: 0.11499999999999999
- type: mrr_at_1000
value: 0.11800000000000001
- type: mrr_at_3
value: 0.091
- type: mrr_at_5
value: 0.101
- type: ndcg_at_1
value: 0.086
- type: ndcg_at_10
value: 0.133
- type: ndcg_at_100
value: 0.168
- type: ndcg_at_1000
value: 0.259
- type: ndcg_at_3
value: 0.093
- type: ndcg_at_5
value: 0.11100000000000002
- type: precision_at_1
value: 0.086
- type: precision_at_10
value: 0.021
- type: precision_at_100
value: 0.004
- type: precision_at_1000
value: 0.001
- type: precision_at_3
value: 0.033
- type: precision_at_5
value: 0.029
- type: recall_at_1
value: 0.086
- type: recall_at_10
value: 0.215
- type: recall_at_100
value: 0.387
- type: recall_at_1000
value: 1.1560000000000001
- type: recall_at_3
value: 0.1
- type: recall_at_5
value: 0.14300000000000002
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 34.42544459644323
- type: f1
value: 33.610930846065315
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (de)
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 27.511975204282894
- type: f1
value: 25.84277270994464
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (es)
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 29.51967978652435
- type: f1
value: 27.67290779782277
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (fr)
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 26.003758221108676
- type: f1
value: 23.831315642315282
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (hi)
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 20.132664037289356
- type: f1
value: 16.737043939830457
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (th)
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 14.701627486437612
- type: f1
value: 10.849797498613762
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 13.948928408572733
- type: f1
value: 8.562615846233708
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (de)
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 11.814595660749507
- type: f1
value: 5.353787568647624
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (es)
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 12.468312208138759
- type: f1
value: 7.566990405355253
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (fr)
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 9.320388349514563
- type: f1
value: 5.916218245687591
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (hi)
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 3.2377196127644314
- type: f1
value: 1.2053714075016808
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (th)
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 4.4159132007233275
- type: f1
value: 1.1992998118559788
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (it)
config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 22.299932750504368
- type: f1
value: 20.147804322480262
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (it)
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 27.40753194351042
- type: f1
value: 25.187141587127705
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 11.797082944399047
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 11.059126362649126
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 24.452301182586798
- type: mrr
value: 24.374807287562085
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.03
- type: map_at_10
value: 0.075
- type: map_at_100
value: 0.208
- type: map_at_1000
value: 0.529
- type: map_at_3
value: 0.051000000000000004
- type: map_at_5
value: 0.055999999999999994
- type: mrr_at_1
value: 1.238
- type: mrr_at_10
value: 2.939
- type: mrr_at_100
value: 3.927
- type: mrr_at_1000
value: 4.117
- type: mrr_at_3
value: 1.806
- type: mrr_at_5
value: 2.286
- type: ndcg_at_1
value: 1.084
- type: ndcg_at_10
value: 1.133
- type: ndcg_at_100
value: 2.1399999999999997
- type: ndcg_at_1000
value: 9.362
- type: ndcg_at_3
value: 0.9299999999999999
- type: ndcg_at_5
value: 0.958
- type: precision_at_1
value: 1.238
- type: precision_at_10
value: 1.269
- type: precision_at_100
value: 1.155
- type: precision_at_1000
value: 1.0250000000000001
- type: precision_at_3
value: 1.032
- type: precision_at_5
value: 1.053
- type: recall_at_1
value: 0.03
- type: recall_at_10
value: 0.22200000000000003
- type: recall_at_100
value: 3.779
- type: recall_at_1000
value: 29.471000000000004
- type: recall_at_3
value: 0.087
- type: recall_at_5
value: 0.11199999999999999
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0.012
- type: map_at_100
value: 0.025
- type: map_at_1000
value: 0.027
- type: map_at_3
value: 0
- type: map_at_5
value: 0.006999999999999999
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.012
- type: mrr_at_100
value: 0.026
- type: mrr_at_1000
value: 0.029
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0.006999999999999999
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.023
- type: ndcg_at_100
value: 0.092
- type: ndcg_at_1000
value: 0.16999999999999998
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0.012
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.006
- type: precision_at_100
value: 0.004
- type: precision_at_1000
value: 0.001
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0.006
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0.058
- type: recall_at_100
value: 0.377
- type: recall_at_1000
value: 1.009
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0.029
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.943
- type: map_at_10
value: 10.557
- type: map_at_100
value: 10.777000000000001
- type: map_at_1000
value: 10.812
- type: map_at_3
value: 10.137
- type: map_at_5
value: 10.351
- type: mrr_at_1
value: 10.51
- type: mrr_at_10
value: 12.229
- type: mrr_at_100
value: 12.468
- type: mrr_at_1000
value: 12.504999999999999
- type: mrr_at_3
value: 11.777
- type: mrr_at_5
value: 12.014
- type: ndcg_at_1
value: 10.5
- type: ndcg_at_10
value: 11.715
- type: ndcg_at_100
value: 12.925
- type: ndcg_at_1000
value: 14.163
- type: ndcg_at_3
value: 10.968
- type: ndcg_at_5
value: 11.264000000000001
- type: precision_at_1
value: 10.5
- type: precision_at_10
value: 1.696
- type: precision_at_100
value: 0.248
- type: precision_at_1000
value: 0.039
- type: precision_at_3
value: 4.623
- type: precision_at_5
value: 3.012
- type: recall_at_1
value: 8.943
- type: recall_at_10
value: 13.746
- type: recall_at_100
value: 19.521
- type: recall_at_1000
value: 29.255
- type: recall_at_3
value: 11.448
- type: recall_at_5
value: 12.332
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 4.845410629021448
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 11.661900277329933
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.02
- type: map_at_10
value: 0.036000000000000004
- type: map_at_100
value: 0.056999999999999995
- type: map_at_1000
value: 0.07200000000000001
- type: map_at_3
value: 0.03
- type: map_at_5
value: 0.03
- type: mrr_at_1
value: 0.1
- type: mrr_at_10
value: 0.181
- type: mrr_at_100
value: 0.27899999999999997
- type: mrr_at_1000
value: 0.335
- type: mrr_at_3
value: 0.15
- type: mrr_at_5
value: 0.15
- type: ndcg_at_1
value: 0.1
- type: ndcg_at_10
value: 0.079
- type: ndcg_at_100
value: 0.28200000000000003
- type: ndcg_at_1000
value: 1.228
- type: ndcg_at_3
value: 0.077
- type: ndcg_at_5
value: 0.055
- type: precision_at_1
value: 0.1
- type: precision_at_10
value: 0.04
- type: precision_at_100
value: 0.034
- type: precision_at_1000
value: 0.027999999999999997
- type: precision_at_3
value: 0.067
- type: precision_at_5
value: 0.04
- type: recall_at_1
value: 0.02
- type: recall_at_10
value: 0.08
- type: recall_at_100
value: 0.703
- type: recall_at_1000
value: 5.632000000000001
- type: recall_at_3
value: 0.04
- type: recall_at_5
value: 0.04
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 22.985682461739827
- type: cos_sim_spearman
value: 36.63211990852576
- type: euclidean_pearson
value: 30.883409587497358
- type: euclidean_spearman
value: 36.94600975857584
- type: manhattan_pearson
value: 36.736693988156894
- type: manhattan_spearman
value: 38.98446799028811
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 2.5604178523517063
- type: cos_sim_spearman
value: 13.628378324133767
- type: euclidean_pearson
value: 7.9904894312005865
- type: euclidean_spearman
value: 15.090689818973416
- type: manhattan_pearson
value: 14.011092205465575
- type: manhattan_spearman
value: 18.04386210573924
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 18.59414271348264
- type: cos_sim_spearman
value: 23.758346452530105
- type: euclidean_pearson
value: 22.985667268384162
- type: euclidean_spearman
value: 25.143580728437183
- type: manhattan_pearson
value: 28.109316236003
- type: manhattan_spearman
value: 29.403691387442727
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 8.292349216262673
- type: cos_sim_spearman
value: 15.648383623810028
- type: euclidean_pearson
value: 12.136605941196938
- type: euclidean_spearman
value: 16.37547051924145
- type: manhattan_pearson
value: 21.049918496319524
- type: manhattan_spearman
value: 22.168125518695295
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 21.0574858763695
- type: cos_sim_spearman
value: 28.24306393347735
- type: euclidean_pearson
value: 25.67620587891895
- type: euclidean_spearman
value: 28.802005577995292
- type: manhattan_pearson
value: 33.333168689238846
- type: manhattan_spearman
value: 33.7249701052437
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 23.345013082866608
- type: cos_sim_spearman
value: 32.08654087568418
- type: euclidean_pearson
value: 29.1302480053082
- type: euclidean_spearman
value: 32.723960824054224
- type: manhattan_pearson
value: 34.73363269084969
- type: manhattan_spearman
value: 35.946509333697016
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ko-ko)
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: -1.0510693376748088
- type: cos_sim_spearman
value: 3.7330446273344897
- type: euclidean_pearson
value: -0.2108306777168949
- type: euclidean_spearman
value: 3.627369552634812
- type: manhattan_pearson
value: 1.5031538964733262
- type: manhattan_spearman
value: 5.004910973166412
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (ar-ar)
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 11.578393453214481
- type: cos_sim_spearman
value: 21.790827126422034
- type: euclidean_pearson
value: 19.06071141618503
- type: euclidean_spearman
value: 22.161779839314196
- type: manhattan_pearson
value: 17.725623325242474
- type: manhattan_spearman
value: 20.43157514666076
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-ar)
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 17.29239442081278
- type: cos_sim_spearman
value: 16.292056207420202
- type: euclidean_pearson
value: 16.503491974377727
- type: euclidean_spearman
value: 15.541440440884651
- type: manhattan_pearson
value: 21.158901085317268
- type: manhattan_spearman
value: 21.781541830999963
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-de)
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 22.589778414759955
- type: cos_sim_spearman
value: 18.997838545450612
- type: euclidean_pearson
value: 21.90016323186628
- type: euclidean_spearman
value: 18.905160536986692
- type: manhattan_pearson
value: 16.913499882046576
- type: manhattan_spearman
value: 15.669617887287327
- 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: 33.05366168385912
- type: cos_sim_spearman
value: 37.781952608504135
- type: euclidean_pearson
value: 37.085941074268966
- type: euclidean_spearman
value: 37.8364215997913
- type: manhattan_pearson
value: 42.316206028770736
- type: manhattan_spearman
value: 41.74208275697782
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-tr)
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 23.991765076225654
- type: cos_sim_spearman
value: 20.588105042260104
- type: euclidean_pearson
value: 19.712724760717997
- type: euclidean_spearman
value: 19.253030106327383
- type: manhattan_pearson
value: 16.84198288544301
- type: manhattan_spearman
value: 17.61549197324614
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-en)
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 24.968980346008365
- type: cos_sim_spearman
value: 27.252856647926286
- type: euclidean_pearson
value: 24.58496162769602
- type: euclidean_spearman
value: 26.034323771297824
- type: manhattan_pearson
value: 22.40058722998031
- type: manhattan_spearman
value: 24.459230575688714
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (es-es)
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 32.94464820482554
- type: cos_sim_spearman
value: 40.462017863354085
- type: euclidean_pearson
value: 38.88676423326504
- type: euclidean_spearman
value: 40.47190508444464
- type: manhattan_pearson
value: 39.692234741895874
- type: manhattan_spearman
value: 39.478725017400166
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (fr-en)
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 23.342163445031748
- type: cos_sim_spearman
value: 27.27097487628386
- type: euclidean_pearson
value: 24.76789948947651
- type: euclidean_spearman
value: 25.81926286149811
- type: manhattan_pearson
value: 25.475723026689685
- type: manhattan_spearman
value: 25.813420033921787
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (it-en)
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 7.717130622537516
- type: cos_sim_spearman
value: 9.437435044381033
- type: euclidean_pearson
value: 7.9423592614250555
- type: euclidean_spearman
value: 8.010785684725303
- type: manhattan_pearson
value: 8.217253457576026
- type: manhattan_spearman
value: 8.247881477427692
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (nl-en)
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 16.16499446427259
- type: cos_sim_spearman
value: 17.21754435880688
- type: euclidean_pearson
value: 16.325415307049443
- type: euclidean_spearman
value: 16.293339731059863
- type: manhattan_pearson
value: 19.410680542804005
- type: manhattan_spearman
value: 18.95736158862253
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 30.67175493186678
- type: cos_sim_spearman
value: 37.92638638971281
- type: euclidean_pearson
value: 37.47072224334179
- type: euclidean_spearman
value: 39.23036609148336
- type: manhattan_pearson
value: 42.92657347688227
- type: manhattan_spearman
value: 43.93955531904934
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 15.51842992576514
- type: cos_sim_spearman
value: 22.738964472371965
- type: euclidean_pearson
value: 20.68981398028433
- type: euclidean_spearman
value: 23.35518754871016
- type: manhattan_pearson
value: 26.488467867333142
- type: manhattan_spearman
value: 27.318174781418055
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 30.728870040728083
- type: mrr
value: 45.1852849401869
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0.104
- type: map_at_100
value: 0.265
- type: map_at_1000
value: 0.334
- type: map_at_3
value: 0
- type: map_at_5
value: 0.067
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0.104
- type: mrr_at_100
value: 0.27
- type: mrr_at_1000
value: 0.345
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0.067
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0.22899999999999998
- type: ndcg_at_100
value: 1.044
- type: ndcg_at_1000
value: 3.911
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0.129
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0.067
- type: precision_at_100
value: 0.05
- type: precision_at_1000
value: 0.032
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0.067
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0.6669999999999999
- type: recall_at_100
value: 4.583
- type: recall_at_1000
value: 28.910999999999998
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0.333
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.01089108910891
- type: cos_sim_ap
value: 2.649295518982714
- type: cos_sim_f1
value: 6.26322471434617
- type: cos_sim_precision
value: 3.972088030059045
- type: cos_sim_recall
value: 14.799999999999999
- type: dot_accuracy
value: 99.0089108910891
- type: dot_ap
value: 1.713700413108619
- type: dot_f1
value: 5.073705862187179
- type: dot_precision
value: 3.061646669424907
- type: dot_recall
value: 14.799999999999999
- type: euclidean_accuracy
value: 99.01089108910891
- type: euclidean_ap
value: 2.744099763470491
- type: euclidean_f1
value: 6.291706387035273
- type: euclidean_precision
value: 4.611085235211924
- type: euclidean_recall
value: 9.9
- type: manhattan_accuracy
value: 99.01089108910891
- type: manhattan_ap
value: 3.2781717730991327
- type: manhattan_f1
value: 7.68245838668374
- type: manhattan_precision
value: 10.676156583629894
- type: manhattan_recall
value: 6
- type: max_accuracy
value: 99.01089108910891
- type: max_ap
value: 3.2781717730991327
- type: max_f1
value: 7.68245838668374
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 8.602221384568187
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 25.619483506865205
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 20.02237719216371
- type: mrr
value: 18.132453739071387
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.004
- type: map_at_10
value: 0.009000000000000001
- type: map_at_100
value: 0.011000000000000001
- type: map_at_1000
value: 0.016
- type: map_at_3
value: 0.004
- type: map_at_5
value: 0.006999999999999999
- type: mrr_at_1
value: 2
- type: mrr_at_10
value: 4.233
- type: mrr_at_100
value: 4.936
- type: mrr_at_1000
value: 5.103
- type: mrr_at_3
value: 2
- type: mrr_at_5
value: 3.9
- type: ndcg_at_1
value: 1
- type: ndcg_at_10
value: 1.1039999999999999
- type: ndcg_at_100
value: 0.486
- type: ndcg_at_1000
value: 0.666
- type: ndcg_at_3
value: 0.469
- type: ndcg_at_5
value: 1.347
- type: precision_at_1
value: 2
- type: precision_at_10
value: 1.4000000000000001
- type: precision_at_100
value: 0.52
- type: precision_at_1000
value: 0.37
- type: precision_at_3
value: 0.6669999999999999
- type: precision_at_5
value: 2
- type: recall_at_1
value: 0.004
- type: recall_at_10
value: 0.024
- type: recall_at_100
value: 0.09
- type: recall_at_1000
value: 0.807
- type: recall_at_3
value: 0.004
- type: recall_at_5
value: 0.016
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0
- type: map_at_10
value: 0
- type: map_at_100
value: 0
- type: map_at_1000
value: 0
- type: map_at_3
value: 0
- type: map_at_5
value: 0
- type: mrr_at_1
value: 0
- type: mrr_at_10
value: 0
- type: mrr_at_100
value: 0
- type: mrr_at_1000
value: 0
- type: mrr_at_3
value: 0
- type: mrr_at_5
value: 0
- type: ndcg_at_1
value: 0
- type: ndcg_at_10
value: 0
- type: ndcg_at_100
value: 0
- type: ndcg_at_1000
value: 0
- type: ndcg_at_3
value: 0
- type: ndcg_at_5
value: 0
- type: precision_at_1
value: 0
- type: precision_at_10
value: 0
- type: precision_at_100
value: 0
- type: precision_at_1000
value: 0
- type: precision_at_3
value: 0
- type: precision_at_5
value: 0
- type: recall_at_1
value: 0
- type: recall_at_10
value: 0
- type: recall_at_100
value: 0
- type: recall_at_1000
value: 0
- type: recall_at_3
value: 0
- type: recall_at_5
value: 0
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 55.065799999999996
- type: ap
value: 8.3123142340845
- type: f1
value: 41.78797425886187
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 41.177136389360506
- type: f1
value: 40.882588170909244
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 10.046104242285523
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 77.63604935328128
- type: cos_sim_ap
value: 32.380569186976906
- type: cos_sim_f1
value: 38.29160530191458
- type: cos_sim_precision
value: 27.110086708374492
- type: cos_sim_recall
value: 65.17150395778364
- type: dot_accuracy
value: 77.40954878702986
- type: dot_ap
value: 28.34039741384004
- type: dot_f1
value: 37.45059908412271
- type: dot_precision
value: 24.565879351493706
- type: dot_recall
value: 78.7598944591029
- type: euclidean_accuracy
value: 77.63604935328128
- type: euclidean_ap
value: 32.40705726976434
- type: euclidean_f1
value: 38.365584519430676
- type: euclidean_precision
value: 27.524093620927033
- type: euclidean_recall
value: 63.298153034300796
- type: manhattan_accuracy
value: 77.70757584788699
- type: manhattan_ap
value: 33.03410839977045
- type: manhattan_f1
value: 39.04353514063523
- type: manhattan_precision
value: 26.943524927274552
- type: manhattan_recall
value: 70.87071240105541
- type: max_accuracy
value: 77.70757584788699
- type: max_ap
value: 33.03410839977045
- type: max_f1
value: 39.04353514063523
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 75.80626382582373
- type: cos_sim_ap
value: 41.94768516713251
- type: cos_sim_f1
value: 44.69374385849984
- type: cos_sim_precision
value: 34.620263870094725
- type: cos_sim_recall
value: 63.035109331690784
- type: dot_accuracy
value: 74.79528078550084
- type: dot_ap
value: 33.69361208467778
- type: dot_f1
value: 44.620064092118845
- type: dot_precision
value: 34.467567340773684
- type: dot_recall
value: 63.250692947336006
- type: euclidean_accuracy
value: 75.98866767570924
- type: euclidean_ap
value: 42.65497342948604
- type: euclidean_f1
value: 44.794497753619176
- type: euclidean_precision
value: 35.006501950585175
- type: euclidean_recall
value: 62.180474283954425
- type: manhattan_accuracy
value: 76.37870143982613
- type: manhattan_ap
value: 46.65401496383161
- type: manhattan_f1
value: 48.14085011643678
- type: manhattan_precision
value: 36.0535091417839
- type: manhattan_recall
value: 72.42069602710194
- type: max_accuracy
value: 76.37870143982613
- type: max_ap
value: 46.65401496383161
- type: max_f1
value: 48.14085011643678
pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: apache-2.0 datasets: - unicamp-dl/mmarco language: - it library_name: sentence-transformers
mmarco-sentence-flare-it
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer, util
query = "Quante persone vivono a Londra?"
docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]
#Load the model
model = SentenceTransformer('nickprock/mmarco-sentence-flare-it')
#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)
#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
Usage (HuggingFace Transformers)
Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output.last_hidden_state
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)
#Encode text
def encode(texts):
# Tokenize sentences
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
# Perform pooling
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
return embeddings
# Sentences we want sentence embeddings for
query = "Quante persone vivono a Londra?"
docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("nickprock/mmarco-sentence-flare-it")
model = AutoModel.from_pretrained("nickprock/mmarco-sentence-flare-it")
#Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)
#Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
print("Query:", query)
for doc, score in doc_score_pairs:
print(score, doc)
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Training
The model was trained with the parameters:
DataLoader:
torch.utils.data.dataloader.DataLoader of length 7500 with parameters:
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
Loss:
sentence_transformers.losses.TripletLoss.TripletLoss with parameters:
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
Parameters of the fit()-Method:
{
"epochs": 10,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1500,
"warmup_steps": 7500,
"weight_decay": 0.01
}
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
Citing & Authors
More information about the base model here