metadata
pipeline_tag: sentence-similarity
tags:
- finetuner
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
datasets:
- jinaai/negation-dataset
language: en
license: apache-2.0
model-index:
- name: jina-embedding-b-en-v1
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 66.73134328358208
- type: ap
value: 28.30575908745204
- type: f1
value: 60.02420130946191
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 67.6068
- type: ap
value: 63.5899352938589
- type: f1
value: 65.64285334357656
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 31.178
- type: f1
value: 29.68460843733487
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.964
- type: map_at_10
value: 40.217999999999996
- type: map_at_100
value: 41.263
- type: map_at_1000
value: 41.277
- type: map_at_3
value: 35.183
- type: map_at_5
value: 38.045
- type: mrr_at_1
value: 25.107000000000003
- type: mrr_at_10
value: 40.272999999999996
- type: mrr_at_100
value: 41.318
- type: mrr_at_1000
value: 41.333
- type: mrr_at_3
value: 35.242000000000004
- type: mrr_at_5
value: 38.101
- type: ndcg_at_1
value: 24.964
- type: ndcg_at_10
value: 49.006
- type: ndcg_at_100
value: 53.446000000000005
- type: ndcg_at_1000
value: 53.813
- type: ndcg_at_3
value: 38.598
- type: ndcg_at_5
value: 43.74
- type: precision_at_1
value: 24.964
- type: precision_at_10
value: 7.724
- type: precision_at_100
value: 0.966
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 16.169
- type: precision_at_5
value: 12.191
- type: recall_at_1
value: 24.964
- type: recall_at_10
value: 77.24
- type: recall_at_100
value: 96.586
- type: recall_at_1000
value: 99.431
- type: recall_at_3
value: 48.506
- type: recall_at_5
value: 60.953
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.25203906042786
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 29.07648348376354
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.4029266143623
- type: mrr
value: 75.45750340764191
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.92280995704714
- type: cos_sim_spearman
value: 83.58082010833608
- type: euclidean_pearson
value: 48.64744162695948
- type: euclidean_spearman
value: 48.817377397301556
- type: manhattan_pearson
value: 48.87684776623195
- type: manhattan_spearman
value: 48.94268145725884
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.05519480519482
- type: f1
value: 83.94978356890618
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 32.2033276486685
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 26.631954164406014
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.625
- type: map_at_10
value: 40.037
- type: map_at_100
value: 41.52
- type: map_at_1000
value: 41.654
- type: map_at_3
value: 36.818
- type: map_at_5
value: 38.426
- type: mrr_at_1
value: 35.336
- type: mrr_at_10
value: 45.395
- type: mrr_at_100
value: 46.221000000000004
- type: mrr_at_1000
value: 46.264
- type: mrr_at_3
value: 42.823
- type: mrr_at_5
value: 44.204
- type: ndcg_at_1
value: 35.336
- type: ndcg_at_10
value: 46.326
- type: ndcg_at_100
value: 51.795
- type: ndcg_at_1000
value: 53.834
- type: ndcg_at_3
value: 41.299
- type: ndcg_at_5
value: 43.247
- type: precision_at_1
value: 35.336
- type: precision_at_10
value: 8.627
- type: precision_at_100
value: 1.428
- type: precision_at_1000
value: 0.197
- type: precision_at_3
value: 19.647000000000002
- type: precision_at_5
value: 13.733999999999998
- type: recall_at_1
value: 29.625
- type: recall_at_10
value: 59.165
- type: recall_at_100
value: 81.675
- type: recall_at_1000
value: 94.17
- type: recall_at_3
value: 44.485
- type: recall_at_5
value: 50.198
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.687
- type: map_at_10
value: 36.062
- type: map_at_100
value: 37.263000000000005
- type: map_at_1000
value: 37.397999999999996
- type: map_at_3
value: 32.967
- type: map_at_5
value: 34.75
- type: mrr_at_1
value: 33.885
- type: mrr_at_10
value: 42.632999999999996
- type: mrr_at_100
value: 43.305
- type: mrr_at_1000
value: 43.354
- type: mrr_at_3
value: 39.958
- type: mrr_at_5
value: 41.63
- type: ndcg_at_1
value: 33.885
- type: ndcg_at_10
value: 42.001
- type: ndcg_at_100
value: 46.436
- type: ndcg_at_1000
value: 48.774
- type: ndcg_at_3
value: 37.183
- type: ndcg_at_5
value: 39.605000000000004
- type: precision_at_1
value: 33.885
- type: precision_at_10
value: 7.962
- type: precision_at_100
value: 1.283
- type: precision_at_1000
value: 0.18
- type: precision_at_3
value: 17.855999999999998
- type: precision_at_5
value: 13.083
- type: recall_at_1
value: 26.687
- type: recall_at_10
value: 52.75
- type: recall_at_100
value: 71.324
- type: recall_at_1000
value: 86.356
- type: recall_at_3
value: 38.83
- type: recall_at_5
value: 45.23
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 34.02
- type: map_at_10
value: 45.751999999999995
- type: map_at_100
value: 46.867
- type: map_at_1000
value: 46.93
- type: map_at_3
value: 42.409
- type: map_at_5
value: 44.464999999999996
- type: mrr_at_1
value: 38.307
- type: mrr_at_10
value: 48.718
- type: mrr_at_100
value: 49.509
- type: mrr_at_1000
value: 49.542
- type: mrr_at_3
value: 46.007999999999996
- type: mrr_at_5
value: 47.766999999999996
- type: ndcg_at_1
value: 38.307
- type: ndcg_at_10
value: 51.666999999999994
- type: ndcg_at_100
value: 56.242000000000004
- type: ndcg_at_1000
value: 57.477999999999994
- type: ndcg_at_3
value: 45.912
- type: ndcg_at_5
value: 49.106
- type: precision_at_1
value: 38.307
- type: precision_at_10
value: 8.476
- type: precision_at_100
value: 1.176
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 20.522000000000002
- type: precision_at_5
value: 14.557999999999998
- type: recall_at_1
value: 34.02
- type: recall_at_10
value: 66.046
- type: recall_at_100
value: 85.817
- type: recall_at_1000
value: 94.453
- type: recall_at_3
value: 51.059
- type: recall_at_5
value: 58.667
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.939
- type: map_at_10
value: 32.627
- type: map_at_100
value: 33.617999999999995
- type: map_at_1000
value: 33.701
- type: map_at_3
value: 30.11
- type: map_at_5
value: 31.380000000000003
- type: mrr_at_1
value: 25.989
- type: mrr_at_10
value: 34.655
- type: mrr_at_100
value: 35.502
- type: mrr_at_1000
value: 35.563
- type: mrr_at_3
value: 32.109
- type: mrr_at_5
value: 33.426
- type: ndcg_at_1
value: 25.989
- type: ndcg_at_10
value: 37.657000000000004
- type: ndcg_at_100
value: 42.467
- type: ndcg_at_1000
value: 44.677
- type: ndcg_at_3
value: 32.543
- type: ndcg_at_5
value: 34.74
- type: precision_at_1
value: 25.989
- type: precision_at_10
value: 5.876
- type: precision_at_100
value: 0.8710000000000001
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 13.861
- type: precision_at_5
value: 9.626999999999999
- type: recall_at_1
value: 23.939
- type: recall_at_10
value: 51.28
- type: recall_at_100
value: 73.428
- type: recall_at_1000
value: 90.309
- type: recall_at_3
value: 37.245
- type: recall_at_5
value: 42.541000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 15.082
- type: map_at_10
value: 22.486
- type: map_at_100
value: 23.687
- type: map_at_1000
value: 23.807000000000002
- type: map_at_3
value: 20.076
- type: map_at_5
value: 21.362000000000002
- type: mrr_at_1
value: 18.532
- type: mrr_at_10
value: 26.605
- type: mrr_at_100
value: 27.628999999999998
- type: mrr_at_1000
value: 27.698
- type: mrr_at_3
value: 23.964
- type: mrr_at_5
value: 25.319000000000003
- type: ndcg_at_1
value: 18.532
- type: ndcg_at_10
value: 27.474999999999998
- type: ndcg_at_100
value: 33.357
- type: ndcg_at_1000
value: 36.361
- type: ndcg_at_3
value: 22.851
- type: ndcg_at_5
value: 24.87
- type: precision_at_1
value: 18.532
- type: precision_at_10
value: 5.210999999999999
- type: precision_at_100
value: 0.9329999999999999
- type: precision_at_1000
value: 0.134
- type: precision_at_3
value: 11.235000000000001
- type: precision_at_5
value: 8.134
- type: recall_at_1
value: 15.082
- type: recall_at_10
value: 38.759
- type: recall_at_100
value: 64.621
- type: recall_at_1000
value: 86.162
- type: recall_at_3
value: 26.055
- type: recall_at_5
value: 31.208999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.759999999999998
- type: map_at_10
value: 33.706
- type: map_at_100
value: 35
- type: map_at_1000
value: 35.134
- type: map_at_3
value: 30.789
- type: map_at_5
value: 32.427
- type: mrr_at_1
value: 29.548000000000002
- type: mrr_at_10
value: 38.521
- type: mrr_at_100
value: 39.432
- type: mrr_at_1000
value: 39.494
- type: mrr_at_3
value: 35.691
- type: mrr_at_5
value: 37.424
- type: ndcg_at_1
value: 29.548000000000002
- type: ndcg_at_10
value: 39.301
- type: ndcg_at_100
value: 44.907000000000004
- type: ndcg_at_1000
value: 47.494
- type: ndcg_at_3
value: 34.08
- type: ndcg_at_5
value: 36.649
- type: precision_at_1
value: 29.548000000000002
- type: precision_at_10
value: 7.084
- type: precision_at_100
value: 1.169
- type: precision_at_1000
value: 0.158
- type: precision_at_3
value: 15.881
- type: precision_at_5
value: 11.53
- type: recall_at_1
value: 24.759999999999998
- type: recall_at_10
value: 51.202000000000005
- type: recall_at_100
value: 74.542
- type: recall_at_1000
value: 91.669
- type: recall_at_3
value: 36.892
- type: recall_at_5
value: 43.333
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.247999999999998
- type: map_at_10
value: 31.878
- type: map_at_100
value: 33.135
- type: map_at_1000
value: 33.263999999999996
- type: map_at_3
value: 29.406
- type: map_at_5
value: 30.602
- type: mrr_at_1
value: 28.767
- type: mrr_at_10
value: 36.929
- type: mrr_at_100
value: 37.844
- type: mrr_at_1000
value: 37.913000000000004
- type: mrr_at_3
value: 34.589
- type: mrr_at_5
value: 35.908
- type: ndcg_at_1
value: 28.767
- type: ndcg_at_10
value: 37.172
- type: ndcg_at_100
value: 42.842
- type: ndcg_at_1000
value: 45.534
- type: ndcg_at_3
value: 32.981
- type: ndcg_at_5
value: 34.628
- type: precision_at_1
value: 28.767
- type: precision_at_10
value: 6.678000000000001
- type: precision_at_100
value: 1.1199999999999999
- type: precision_at_1000
value: 0.155
- type: precision_at_3
value: 15.715000000000002
- type: precision_at_5
value: 10.913
- type: recall_at_1
value: 23.247999999999998
- type: recall_at_10
value: 48.16
- type: recall_at_100
value: 72.753
- type: recall_at_1000
value: 90.8
- type: recall_at_3
value: 35.961999999999996
- type: recall_at_5
value: 40.504
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.825583333333334
- type: map_at_10
value: 32.2845
- type: map_at_100
value: 33.48566666666667
- type: map_at_1000
value: 33.60833333333333
- type: map_at_3
value: 29.604916666666664
- type: map_at_5
value: 31.015333333333334
- type: mrr_at_1
value: 27.850916666666663
- type: mrr_at_10
value: 36.122416666666666
- type: mrr_at_100
value: 37.01275
- type: mrr_at_1000
value: 37.07566666666667
- type: mrr_at_3
value: 33.665749999999996
- type: mrr_at_5
value: 35.00916666666667
- type: ndcg_at_1
value: 27.850916666666663
- type: ndcg_at_10
value: 37.47625
- type: ndcg_at_100
value: 42.74433333333334
- type: ndcg_at_1000
value: 45.21991666666667
- type: ndcg_at_3
value: 32.70916666666667
- type: ndcg_at_5
value: 34.80658333333333
- type: precision_at_1
value: 27.850916666666663
- type: precision_at_10
value: 6.5761666666666665
- type: precision_at_100
value: 1.0879999999999999
- type: precision_at_1000
value: 0.15058333333333332
- type: precision_at_3
value: 14.933833333333336
- type: precision_at_5
value: 10.607249999999999
- type: recall_at_1
value: 23.825583333333334
- type: recall_at_10
value: 49.100500000000004
- type: recall_at_100
value: 72.21133333333334
- type: recall_at_1000
value: 89.34791666666666
- type: recall_at_3
value: 35.90525
- type: recall_at_5
value: 41.24583333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.343
- type: map_at_10
value: 27.313
- type: map_at_100
value: 28.316999999999997
- type: map_at_1000
value: 28.406
- type: map_at_3
value: 25.06
- type: map_at_5
value: 26.409
- type: mrr_at_1
value: 23.313
- type: mrr_at_10
value: 29.467
- type: mrr_at_100
value: 30.348999999999997
- type: mrr_at_1000
value: 30.42
- type: mrr_at_3
value: 27.173000000000002
- type: mrr_at_5
value: 28.461
- type: ndcg_at_1
value: 23.313
- type: ndcg_at_10
value: 31.183
- type: ndcg_at_100
value: 36.252
- type: ndcg_at_1000
value: 38.582
- type: ndcg_at_3
value: 26.838
- type: ndcg_at_5
value: 29.042
- type: precision_at_1
value: 23.313
- type: precision_at_10
value: 4.9079999999999995
- type: precision_at_100
value: 0.808
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 11.299
- type: precision_at_5
value: 8.097999999999999
- type: recall_at_1
value: 21.343
- type: recall_at_10
value: 41.047
- type: recall_at_100
value: 64.372
- type: recall_at_1000
value: 81.499
- type: recall_at_3
value: 29.337000000000003
- type: recall_at_5
value: 34.756
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.595
- type: map_at_10
value: 23.433
- type: map_at_100
value: 24.578
- type: map_at_1000
value: 24.709999999999997
- type: map_at_3
value: 21.268
- type: map_at_5
value: 22.393
- type: mrr_at_1
value: 20.131
- type: mrr_at_10
value: 27.026
- type: mrr_at_100
value: 28.003
- type: mrr_at_1000
value: 28.083999999999996
- type: mrr_at_3
value: 24.966
- type: mrr_at_5
value: 26.064999999999998
- type: ndcg_at_1
value: 20.131
- type: ndcg_at_10
value: 27.846
- type: ndcg_at_100
value: 33.318999999999996
- type: ndcg_at_1000
value: 36.403
- type: ndcg_at_3
value: 23.883
- type: ndcg_at_5
value: 25.595000000000002
- type: precision_at_1
value: 20.131
- type: precision_at_10
value: 5.034000000000001
- type: precision_at_100
value: 0.9079999999999999
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 11.23
- type: precision_at_5
value: 8.032
- type: recall_at_1
value: 16.595
- type: recall_at_10
value: 37.576
- type: recall_at_100
value: 62.044
- type: recall_at_1000
value: 83.97
- type: recall_at_3
value: 26.631
- type: recall_at_5
value: 31.002000000000002
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.85
- type: map_at_10
value: 32.762
- type: map_at_100
value: 33.896
- type: map_at_1000
value: 34.006
- type: map_at_3
value: 29.965000000000003
- type: map_at_5
value: 31.485999999999997
- type: mrr_at_1
value: 28.731
- type: mrr_at_10
value: 36.504999999999995
- type: mrr_at_100
value: 37.364999999999995
- type: mrr_at_1000
value: 37.431
- type: mrr_at_3
value: 34.033
- type: mrr_at_5
value: 35.4
- type: ndcg_at_1
value: 28.731
- type: ndcg_at_10
value: 37.788
- type: ndcg_at_100
value: 43.1
- type: ndcg_at_1000
value: 45.623999999999995
- type: ndcg_at_3
value: 32.717
- type: ndcg_at_5
value: 35.024
- type: precision_at_1
value: 28.731
- type: precision_at_10
value: 6.371
- type: precision_at_100
value: 1.02
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 14.521
- type: precision_at_5
value: 10.41
- type: recall_at_1
value: 24.85
- type: recall_at_10
value: 49.335
- type: recall_at_100
value: 72.792
- type: recall_at_1000
value: 90.525
- type: recall_at_3
value: 35.698
- type: recall_at_5
value: 41.385
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.016000000000002
- type: map_at_10
value: 32.126
- type: map_at_100
value: 33.786
- type: map_at_1000
value: 34.012
- type: map_at_3
value: 29.256
- type: map_at_5
value: 30.552
- type: mrr_at_1
value: 27.272999999999996
- type: mrr_at_10
value: 35.967
- type: mrr_at_100
value: 37.082
- type: mrr_at_1000
value: 37.146
- type: mrr_at_3
value: 33.531
- type: mrr_at_5
value: 34.697
- type: ndcg_at_1
value: 27.272999999999996
- type: ndcg_at_10
value: 37.945
- type: ndcg_at_100
value: 43.928
- type: ndcg_at_1000
value: 46.772999999999996
- type: ndcg_at_3
value: 33.111000000000004
- type: ndcg_at_5
value: 34.794000000000004
- type: precision_at_1
value: 27.272999999999996
- type: precision_at_10
value: 7.53
- type: precision_at_100
value: 1.512
- type: precision_at_1000
value: 0.241
- type: precision_at_3
value: 15.547
- type: precision_at_5
value: 11.146
- type: recall_at_1
value: 23.016000000000002
- type: recall_at_10
value: 49.576
- type: recall_at_100
value: 75.74600000000001
- type: recall_at_1000
value: 94.069
- type: recall_at_3
value: 35.964
- type: recall_at_5
value: 40.455999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.742
- type: map_at_10
value: 29.232000000000003
- type: map_at_100
value: 30.160999999999998
- type: map_at_1000
value: 30.278
- type: map_at_3
value: 27.134999999999998
- type: map_at_5
value: 27.932000000000002
- type: mrr_at_1
value: 24.399
- type: mrr_at_10
value: 31.048
- type: mrr_at_100
value: 31.912000000000003
- type: mrr_at_1000
value: 31.999
- type: mrr_at_3
value: 29.144
- type: mrr_at_5
value: 29.809
- type: ndcg_at_1
value: 24.399
- type: ndcg_at_10
value: 33.354
- type: ndcg_at_100
value: 38.287
- type: ndcg_at_1000
value: 41.105000000000004
- type: ndcg_at_3
value: 29.112
- type: ndcg_at_5
value: 30.379
- type: precision_at_1
value: 24.399
- type: precision_at_10
value: 5.157
- type: precision_at_100
value: 0.828
- type: precision_at_1000
value: 0.11800000000000001
- type: precision_at_3
value: 11.892
- type: precision_at_5
value: 8.022
- type: recall_at_1
value: 22.742
- type: recall_at_10
value: 44.31
- type: recall_at_100
value: 67.422
- type: recall_at_1000
value: 88.193
- type: recall_at_3
value: 32.705
- type: recall_at_5
value: 35.669000000000004
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 9.067
- type: map_at_10
value: 14.821000000000002
- type: map_at_100
value: 16.195
- type: map_at_1000
value: 16.359
- type: map_at_3
value: 12.666
- type: map_at_5
value: 13.675999999999998
- type: mrr_at_1
value: 20.326
- type: mrr_at_10
value: 29.798000000000002
- type: mrr_at_100
value: 30.875000000000004
- type: mrr_at_1000
value: 30.928
- type: mrr_at_3
value: 26.678
- type: mrr_at_5
value: 28.433000000000003
- type: ndcg_at_1
value: 20.326
- type: ndcg_at_10
value: 21.477
- type: ndcg_at_100
value: 27.637
- type: ndcg_at_1000
value: 30.953000000000003
- type: ndcg_at_3
value: 17.456
- type: ndcg_at_5
value: 18.789
- type: precision_at_1
value: 20.326
- type: precision_at_10
value: 6.482
- type: precision_at_100
value: 1.302
- type: precision_at_1000
value: 0.191
- type: precision_at_3
value: 12.53
- type: precision_at_5
value: 9.603
- type: recall_at_1
value: 9.067
- type: recall_at_10
value: 26.246000000000002
- type: recall_at_100
value: 47.837
- type: recall_at_1000
value: 66.637
- type: recall_at_3
value: 16.468
- type: recall_at_5
value: 20.088
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 7.563000000000001
- type: map_at_10
value: 15.22
- type: map_at_100
value: 20.048
- type: map_at_1000
value: 21.17
- type: map_at_3
value: 11.627
- type: map_at_5
value: 13.239
- type: mrr_at_1
value: 56.25
- type: mrr_at_10
value: 64.846
- type: mrr_at_100
value: 65.405
- type: mrr_at_1000
value: 65.41799999999999
- type: mrr_at_3
value: 63.125
- type: mrr_at_5
value: 64.1
- type: ndcg_at_1
value: 45
- type: ndcg_at_10
value: 32.437
- type: ndcg_at_100
value: 35.483
- type: ndcg_at_1000
value: 42.186
- type: ndcg_at_3
value: 37.297000000000004
- type: ndcg_at_5
value: 34.697
- type: precision_at_1
value: 56.25
- type: precision_at_10
value: 25.15
- type: precision_at_100
value: 7.539999999999999
- type: precision_at_1000
value: 1.678
- type: precision_at_3
value: 40.666999999999994
- type: precision_at_5
value: 33.45
- type: recall_at_1
value: 7.563000000000001
- type: recall_at_10
value: 19.969
- type: recall_at_100
value: 40.113
- type: recall_at_1000
value: 61.72299999999999
- type: recall_at_3
value: 12.950999999999999
- type: recall_at_5
value: 15.690999999999999
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.675000000000004
- type: f1
value: 40.779372586075105
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.406
- type: map_at_10
value: 67.69500000000001
- type: map_at_100
value: 68.08
- type: map_at_1000
value: 68.095
- type: map_at_3
value: 65.688
- type: map_at_5
value: 66.93
- type: mrr_at_1
value: 61.941
- type: mrr_at_10
value: 72.513
- type: mrr_at_100
value: 72.83699999999999
- type: mrr_at_1000
value: 72.844
- type: mrr_at_3
value: 70.60499999999999
- type: mrr_at_5
value: 71.807
- type: ndcg_at_1
value: 61.941
- type: ndcg_at_10
value: 73.29
- type: ndcg_at_100
value: 74.96300000000001
- type: ndcg_at_1000
value: 75.28200000000001
- type: ndcg_at_3
value: 69.491
- type: ndcg_at_5
value: 71.573
- type: precision_at_1
value: 61.941
- type: precision_at_10
value: 9.388
- type: precision_at_100
value: 1.0290000000000001
- type: precision_at_1000
value: 0.107
- type: precision_at_3
value: 27.423
- type: precision_at_5
value: 17.627000000000002
- type: recall_at_1
value: 57.406
- type: recall_at_10
value: 85.975
- type: recall_at_100
value: 93.29899999999999
- type: recall_at_1000
value: 95.531
- type: recall_at_3
value: 75.624
- type: recall_at_5
value: 80.78999999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.314999999999998
- type: map_at_10
value: 26.678
- type: map_at_100
value: 28.322000000000003
- type: map_at_1000
value: 28.519
- type: map_at_3
value: 23.105
- type: map_at_5
value: 24.808
- type: mrr_at_1
value: 33.333
- type: mrr_at_10
value: 41.453
- type: mrr_at_100
value: 42.339
- type: mrr_at_1000
value: 42.39
- type: mrr_at_3
value: 38.863
- type: mrr_at_5
value: 40.159
- type: ndcg_at_1
value: 33.333
- type: ndcg_at_10
value: 34.062
- type: ndcg_at_100
value: 40.595
- type: ndcg_at_1000
value: 44.124
- type: ndcg_at_3
value: 30.689
- type: ndcg_at_5
value: 31.255
- type: precision_at_1
value: 33.333
- type: precision_at_10
value: 9.722
- type: precision_at_100
value: 1.6480000000000001
- type: precision_at_1000
value: 0.22699999999999998
- type: precision_at_3
value: 20.936
- type: precision_at_5
value: 15.154
- type: recall_at_1
value: 16.314999999999998
- type: recall_at_10
value: 41.221000000000004
- type: recall_at_100
value: 65.857
- type: recall_at_1000
value: 87.327
- type: recall_at_3
value: 27.435
- type: recall_at_5
value: 32.242
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.978
- type: map_at_10
value: 43.784
- type: map_at_100
value: 44.547
- type: map_at_1000
value: 44.614
- type: map_at_3
value: 41.317
- type: map_at_5
value: 42.812
- type: mrr_at_1
value: 63.956999999999994
- type: mrr_at_10
value: 70.502
- type: mrr_at_100
value: 70.845
- type: mrr_at_1000
value: 70.865
- type: mrr_at_3
value: 69.192
- type: mrr_at_5
value: 69.994
- type: ndcg_at_1
value: 63.956999999999994
- type: ndcg_at_10
value: 52.782
- type: ndcg_at_100
value: 55.78999999999999
- type: ndcg_at_1000
value: 57.289
- type: ndcg_at_3
value: 48.864000000000004
- type: ndcg_at_5
value: 50.964
- type: precision_at_1
value: 63.956999999999994
- type: precision_at_10
value: 10.809000000000001
- type: precision_at_100
value: 1.319
- type: precision_at_1000
value: 0.152
- type: precision_at_3
value: 30.2
- type: precision_at_5
value: 19.787
- type: recall_at_1
value: 31.978
- type: recall_at_10
value: 54.045
- type: recall_at_100
value: 65.928
- type: recall_at_1000
value: 75.976
- type: recall_at_3
value: 45.300000000000004
- type: recall_at_5
value: 49.467
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 63.8708
- type: ap
value: 59.02002684158838
- type: f1
value: 63.650055896985315
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 19.834
- type: map_at_10
value: 31.317
- type: map_at_100
value: 32.576
- type: map_at_1000
value: 32.631
- type: map_at_3
value: 27.728
- type: map_at_5
value: 29.720000000000002
- type: mrr_at_1
value: 20.43
- type: mrr_at_10
value: 31.868999999999996
- type: mrr_at_100
value: 33.074999999999996
- type: mrr_at_1000
value: 33.123999999999995
- type: mrr_at_3
value: 28.333000000000002
- type: mrr_at_5
value: 30.305
- type: ndcg_at_1
value: 20.43
- type: ndcg_at_10
value: 37.769000000000005
- type: ndcg_at_100
value: 43.924
- type: ndcg_at_1000
value: 45.323
- type: ndcg_at_3
value: 30.422
- type: ndcg_at_5
value: 33.98
- type: precision_at_1
value: 20.43
- type: precision_at_10
value: 6.027
- type: precision_at_100
value: 0.9119999999999999
- type: precision_at_1000
value: 0.10300000000000001
- type: precision_at_3
value: 12.985
- type: precision_at_5
value: 9.593
- type: recall_at_1
value: 19.834
- type: recall_at_10
value: 57.647000000000006
- type: recall_at_100
value: 86.276
- type: recall_at_1000
value: 97.065
- type: recall_at_3
value: 37.616
- type: recall_at_5
value: 46.171
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.52530779753762
- type: f1
value: 91.4004687820246
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.82717738258093
- type: f1
value: 56.791387113030346
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.09280430396772
- type: f1
value: 68.92843467363518
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.2542030934768
- type: f1
value: 76.22211319699834
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 29.604407852989457
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 25.011863718751183
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.55552172383111
- type: mrr
value: 32.65475731770242
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.968
- type: map_at_10
value: 10.703999999999999
- type: map_at_100
value: 13.316
- type: map_at_1000
value: 14.674000000000001
- type: map_at_3
value: 7.809000000000001
- type: map_at_5
value: 9.268
- type: mrr_at_1
value: 41.796
- type: mrr_at_10
value: 50.558
- type: mrr_at_100
value: 51.125
- type: mrr_at_1000
value: 51.184
- type: mrr_at_3
value: 48.349
- type: mrr_at_5
value: 49.572
- type: ndcg_at_1
value: 39.783
- type: ndcg_at_10
value: 30.375999999999998
- type: ndcg_at_100
value: 27.648
- type: ndcg_at_1000
value: 36.711
- type: ndcg_at_3
value: 35.053
- type: ndcg_at_5
value: 33.278999999999996
- type: precision_at_1
value: 41.796
- type: precision_at_10
value: 22.663
- type: precision_at_100
value: 7.210999999999999
- type: precision_at_1000
value: 1.984
- type: precision_at_3
value: 33.127
- type: precision_at_5
value: 29.102
- type: recall_at_1
value: 4.968
- type: recall_at_10
value: 14.469999999999999
- type: recall_at_100
value: 28.188000000000002
- type: recall_at_1000
value: 60.769
- type: recall_at_3
value: 8.737
- type: recall_at_5
value: 11.539000000000001
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.958
- type: map_at_10
value: 40.6
- type: map_at_100
value: 41.754000000000005
- type: map_at_1000
value: 41.792
- type: map_at_3
value: 36.521
- type: map_at_5
value: 38.866
- type: mrr_at_1
value: 30.330000000000002
- type: mrr_at_10
value: 43.013
- type: mrr_at_100
value: 43.89
- type: mrr_at_1000
value: 43.917
- type: mrr_at_3
value: 39.489000000000004
- type: mrr_at_5
value: 41.504999999999995
- type: ndcg_at_1
value: 30.330000000000002
- type: ndcg_at_10
value: 47.878
- type: ndcg_at_100
value: 52.761
- type: ndcg_at_1000
value: 53.69500000000001
- type: ndcg_at_3
value: 40.061
- type: ndcg_at_5
value: 43.980000000000004
- type: precision_at_1
value: 30.330000000000002
- type: precision_at_10
value: 8.048
- type: precision_at_100
value: 1.076
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 18.299000000000003
- type: precision_at_5
value: 13.25
- type: recall_at_1
value: 26.958
- type: recall_at_10
value: 67.72399999999999
- type: recall_at_100
value: 89.02600000000001
- type: recall_at_1000
value: 96.029
- type: recall_at_3
value: 47.332
- type: recall_at_5
value: 56.36600000000001
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 69.926
- type: map_at_10
value: 83.797
- type: map_at_100
value: 84.42699999999999
- type: map_at_1000
value: 84.446
- type: map_at_3
value: 80.78
- type: map_at_5
value: 82.669
- type: mrr_at_1
value: 80.44
- type: mrr_at_10
value: 86.79
- type: mrr_at_100
value: 86.90299999999999
- type: mrr_at_1000
value: 86.904
- type: mrr_at_3
value: 85.753
- type: mrr_at_5
value: 86.478
- type: ndcg_at_1
value: 80.44
- type: ndcg_at_10
value: 87.634
- type: ndcg_at_100
value: 88.9
- type: ndcg_at_1000
value: 89.03
- type: ndcg_at_3
value: 84.622
- type: ndcg_at_5
value: 86.29
- type: precision_at_1
value: 80.44
- type: precision_at_10
value: 13.305
- type: precision_at_100
value: 1.524
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 36.957
- type: precision_at_5
value: 24.328
- type: recall_at_1
value: 69.926
- type: recall_at_10
value: 94.99300000000001
- type: recall_at_100
value: 99.345
- type: recall_at_1000
value: 99.97
- type: recall_at_3
value: 86.465
- type: recall_at_5
value: 91.121
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 42.850644235471144
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 52.547875398320734
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.328
- type: map_at_10
value: 10.479
- type: map_at_100
value: 12.25
- type: map_at_1000
value: 12.522
- type: map_at_3
value: 7.548000000000001
- type: map_at_5
value: 9.039
- type: mrr_at_1
value: 21.3
- type: mrr_at_10
value: 30.678
- type: mrr_at_100
value: 31.77
- type: mrr_at_1000
value: 31.831
- type: mrr_at_3
value: 27.500000000000004
- type: mrr_at_5
value: 29.375
- type: ndcg_at_1
value: 21.3
- type: ndcg_at_10
value: 17.626
- type: ndcg_at_100
value: 25.03
- type: ndcg_at_1000
value: 30.055
- type: ndcg_at_3
value: 16.744999999999997
- type: ndcg_at_5
value: 14.729999999999999
- type: precision_at_1
value: 21.3
- type: precision_at_10
value: 9.09
- type: precision_at_100
value: 1.989
- type: precision_at_1000
value: 0.32
- type: precision_at_3
value: 15.467
- type: precision_at_5
value: 12.879999999999999
- type: recall_at_1
value: 4.328
- type: recall_at_10
value: 18.412
- type: recall_at_100
value: 40.363
- type: recall_at_1000
value: 64.997
- type: recall_at_3
value: 9.408
- type: recall_at_5
value: 13.048000000000002
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.1338589503896
- type: cos_sim_spearman
value: 79.1378154534123
- type: euclidean_pearson
value: 73.17857462509251
- type: euclidean_spearman
value: 70.79268955610539
- type: manhattan_pearson
value: 72.8280251705823
- type: manhattan_spearman
value: 70.60323787229834
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 84.21604641858598
- type: cos_sim_spearman
value: 75.06080146054282
- type: euclidean_pearson
value: 69.44429285856924
- type: euclidean_spearman
value: 58.240130690046456
- type: manhattan_pearson
value: 69.07597314234852
- type: manhattan_spearman
value: 58.08224335836159
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 80.2252849321165
- type: cos_sim_spearman
value: 80.85907200101076
- type: euclidean_pearson
value: 70.85619832878055
- type: euclidean_spearman
value: 71.59417341887324
- type: manhattan_pearson
value: 70.55842192345895
- type: manhattan_spearman
value: 71.30332994715893
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 80.50469360654135
- type: cos_sim_spearman
value: 76.12917164308409
- type: euclidean_pearson
value: 70.4070213910491
- type: euclidean_spearman
value: 66.97320451942113
- type: manhattan_pearson
value: 70.24834290119863
- type: manhattan_spearman
value: 66.9047074173091
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 84.70140350059746
- type: cos_sim_spearman
value: 85.55427877110485
- type: euclidean_pearson
value: 63.4780453371435
- type: euclidean_spearman
value: 64.65485395077273
- type: manhattan_pearson
value: 63.64869846572011
- type: manhattan_spearman
value: 64.87219311596813
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 79.4416477676503
- type: cos_sim_spearman
value: 81.2094925260351
- type: euclidean_pearson
value: 68.372257553367
- type: euclidean_spearman
value: 69.47792807911692
- type: manhattan_pearson
value: 68.17773583183664
- type: manhattan_spearman
value: 69.31505452732998
- 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: 88.94688403351994
- type: cos_sim_spearman
value: 88.97626967707933
- type: euclidean_pearson
value: 74.09942728422159
- type: euclidean_spearman
value: 72.91022362666948
- type: manhattan_pearson
value: 74.11262432880199
- type: manhattan_spearman
value: 72.82115894578564
- 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: 67.42605802805606
- type: cos_sim_spearman
value: 66.22330559222408
- type: euclidean_pearson
value: 50.15272876367891
- type: euclidean_spearman
value: 60.695400782452715
- type: manhattan_pearson
value: 50.17076569264417
- type: manhattan_spearman
value: 60.3761281869747
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 82.85939227596093
- type: cos_sim_spearman
value: 82.57071649593358
- type: euclidean_pearson
value: 72.18291316100125
- type: euclidean_spearman
value: 70.70702024402348
- type: manhattan_pearson
value: 72.36789718833687
- type: manhattan_spearman
value: 70.92789721402387
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 79.31107201598611
- type: mrr
value: 93.66321314850727
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 45.428000000000004
- type: map_at_10
value: 54.730000000000004
- type: map_at_100
value: 55.421
- type: map_at_1000
value: 55.47299999999999
- type: map_at_3
value: 52.333
- type: map_at_5
value: 53.72
- type: mrr_at_1
value: 48.333
- type: mrr_at_10
value: 56.601
- type: mrr_at_100
value: 57.106
- type: mrr_at_1000
value: 57.154
- type: mrr_at_3
value: 54.611
- type: mrr_at_5
value: 55.87800000000001
- type: ndcg_at_1
value: 48.333
- type: ndcg_at_10
value: 59.394999999999996
- type: ndcg_at_100
value: 62.549
- type: ndcg_at_1000
value: 63.941
- type: ndcg_at_3
value: 55.096000000000004
- type: ndcg_at_5
value: 57.325
- type: precision_at_1
value: 48.333
- type: precision_at_10
value: 8.1
- type: precision_at_100
value: 0.983
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 21.889
- type: precision_at_5
value: 14.533
- type: recall_at_1
value: 45.428000000000004
- type: recall_at_10
value: 71.806
- type: recall_at_100
value: 86.533
- type: recall_at_1000
value: 97.5
- type: recall_at_3
value: 60.228
- type: recall_at_5
value: 65.90599999999999
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8029702970297
- type: cos_sim_ap
value: 95.48085242816634
- type: cos_sim_f1
value: 89.86653484923382
- type: cos_sim_precision
value: 88.85630498533725
- type: cos_sim_recall
value: 90.9
- type: dot_accuracy
value: 99.21881188118812
- type: dot_ap
value: 55.14126603018576
- type: dot_f1
value: 55.22458628841608
- type: dot_precision
value: 52.37668161434977
- type: dot_recall
value: 58.4
- type: euclidean_accuracy
value: 99.64356435643565
- type: euclidean_ap
value: 84.52487064474103
- type: euclidean_f1
value: 80.53908355795149
- type: euclidean_precision
value: 87.36842105263159
- type: euclidean_recall
value: 74.7
- type: manhattan_accuracy
value: 99.63861386138613
- type: manhattan_ap
value: 84.1994288662172
- type: manhattan_f1
value: 80.38482095136291
- type: manhattan_precision
value: 86.33754305396096
- type: manhattan_recall
value: 75.2
- type: max_accuracy
value: 99.8029702970297
- type: max_ap
value: 95.48085242816634
- type: max_f1
value: 89.86653484923382
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 48.06508273111389
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 31.36169910951664
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 50.110601218420356
- type: mrr
value: 50.90277777777777
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.63669555287747
- type: cos_sim_spearman
value: 30.708042454053853
- type: dot_pearson
value: 20.309025749838924
- type: dot_spearman
value: 21.511758746817165
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.201
- type: map_at_10
value: 1.405
- type: map_at_100
value: 7.359999999999999
- type: map_at_1000
value: 17.858
- type: map_at_3
value: 0.494
- type: map_at_5
value: 0.757
- type: mrr_at_1
value: 74
- type: mrr_at_10
value: 84.89999999999999
- type: mrr_at_100
value: 84.89999999999999
- type: mrr_at_1000
value: 84.89999999999999
- type: mrr_at_3
value: 84
- type: mrr_at_5
value: 84.89999999999999
- type: ndcg_at_1
value: 68
- type: ndcg_at_10
value: 60.571
- type: ndcg_at_100
value: 46.016
- type: ndcg_at_1000
value: 41.277
- type: ndcg_at_3
value: 63.989
- type: ndcg_at_5
value: 61.41
- type: precision_at_1
value: 74
- type: precision_at_10
value: 65.2
- type: precision_at_100
value: 47.04
- type: precision_at_1000
value: 18.416
- type: precision_at_3
value: 68
- type: precision_at_5
value: 66.4
- type: recall_at_1
value: 0.201
- type: recall_at_10
value: 1.763
- type: recall_at_100
value: 11.008999999999999
- type: recall_at_1000
value: 38.509
- type: recall_at_3
value: 0.551
- type: recall_at_5
value: 0.881
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.4040000000000001
- type: map_at_10
value: 7.847999999999999
- type: map_at_100
value: 12.908
- type: map_at_1000
value: 14.37
- type: map_at_3
value: 3.6450000000000005
- type: map_at_5
value: 4.93
- type: mrr_at_1
value: 18.367
- type: mrr_at_10
value: 32.576
- type: mrr_at_100
value: 34.163
- type: mrr_at_1000
value: 34.18
- type: mrr_at_3
value: 28.571
- type: mrr_at_5
value: 30.918
- type: ndcg_at_1
value: 15.306000000000001
- type: ndcg_at_10
value: 18.59
- type: ndcg_at_100
value: 30.394
- type: ndcg_at_1000
value: 42.198
- type: ndcg_at_3
value: 18.099
- type: ndcg_at_5
value: 16.955000000000002
- type: precision_at_1
value: 16.326999999999998
- type: precision_at_10
value: 17.959
- type: precision_at_100
value: 6.755
- type: precision_at_1000
value: 1.4529999999999998
- type: precision_at_3
value: 20.408
- type: precision_at_5
value: 18.367
- type: recall_at_1
value: 1.4040000000000001
- type: recall_at_10
value: 14.048
- type: recall_at_100
value: 42.150999999999996
- type: recall_at_1000
value: 77.85600000000001
- type: recall_at_3
value: 4.819
- type: recall_at_5
value: 7.13
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 66.1456
- type: ap
value: 11.631023858569064
- type: f1
value: 50.128196455722254
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 56.850594227504246
- type: f1
value: 56.82313689360827
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 38.060423744064764
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.43702688204088
- type: cos_sim_ap
value: 68.30176948820142
- type: cos_sim_f1
value: 64.25430330443524
- type: cos_sim_precision
value: 61.33365315423362
- type: cos_sim_recall
value: 67.46701846965699
- type: dot_accuracy
value: 77.76718126005842
- type: dot_ap
value: 37.510516716176305
- type: dot_f1
value: 43.53859496964441
- type: dot_precision
value: 32.428940568475454
- type: dot_recall
value: 66.2269129287599
- type: euclidean_accuracy
value: 82.10049472492102
- type: euclidean_ap
value: 61.64354520687271
- type: euclidean_f1
value: 59.804144841721694
- type: euclidean_precision
value: 52.604166666666664
- type: euclidean_recall
value: 69.28759894459104
- type: manhattan_accuracy
value: 82.22566609048101
- type: manhattan_ap
value: 61.753431124879974
- type: manhattan_f1
value: 59.77735297424941
- type: manhattan_precision
value: 52.0870076425632
- type: manhattan_recall
value: 70.13192612137203
- type: max_accuracy
value: 84.43702688204088
- type: max_ap
value: 68.30176948820142
- type: max_f1
value: 64.25430330443524
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.81515116233942
- type: cos_sim_ap
value: 85.33305785100573
- type: cos_sim_f1
value: 78.11202938475667
- type: cos_sim_precision
value: 74.68567816253424
- type: cos_sim_recall
value: 81.86787804126887
- type: dot_accuracy
value: 82.50475414289595
- type: dot_ap
value: 69.87015340174045
- type: dot_f1
value: 65.94174480373633
- type: dot_precision
value: 61.40362525728703
- type: dot_recall
value: 71.20418848167539
- type: euclidean_accuracy
value: 83.05778709201692
- type: euclidean_ap
value: 70.54206653977498
- type: euclidean_f1
value: 62.98969847356943
- type: euclidean_precision
value: 61.55033063923585
- type: euclidean_recall
value: 64.49799815214044
- type: manhattan_accuracy
value: 83.0034540303489
- type: manhattan_ap
value: 70.53997987198404
- type: manhattan_f1
value: 62.95875898600075
- type: manhattan_precision
value: 61.89555125725339
- type: manhattan_recall
value: 64.05913150600554
- type: max_accuracy
value: 88.81515116233942
- type: max_ap
value: 85.33305785100573
- type: max_f1
value: 78.11202938475667
The text embedding set trained by Jina AI, Finetuner team.
Intented Usage & Model Info
jina-embedding-b-en-v1 is a language model that has been trained using Jina AI's Linnaeus-Clean dataset.
This dataset consists of 380 million pairs of sentences, which include both query-document pairs.
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
The Linnaeus-Full dataset, from which the Linnaeus-Clean dataset is derived, originally contained 1.6 billion sentence pairs.
The model has a range of use cases, including information retrieval, semantic textual similarity, text reranking, and more.
With a standard size of 110 million parameters, the model enables fast inference while delivering better performance than our small model. It is recommended to use a single GPU for inference. Additionally, we provide the following options:
jina-embedding-t-en-v1: 14 million parameters.jina-embedding-s-en-v1: 35 million parametersjina-embedding-b-en-v1: 110 million parameters (you are here).jina-embedding-l-en-v1: 330 million parameters.jina-embedding-1b-en-v1: 1.2 billion parameters, 10 times bert-base (soon).jina-embedding-6b-en-v1: 6 billion parameters, 30 times bert-base (soon).
Data & Parameters
Please checkout our technical blog.
Metrics
We compared the model against all-minilm-l6-v2/all-mpnet-base-v2 from sbert and text-embeddings-ada-002 from OpenAI:
| Name | param | dimension |
|---|---|---|
| all-minilm-l6-v2 | 23m | 384 |
| all-mpnet-base-v2 | 110m | 768 |
| ada-embedding-002 | Unknown/OpenAI API | 1536 |
| jina-embedding-t-en-v1 | 14m | 312 |
| jina-embedding-s-en-v1 | 35m | 512 |
| jina-embedding-b-en-v1 | 110m | 768 |
| jina-embedding-l-en-v1 | 330m | 1024 |
| Name | STS12 | STS13 | STS14 | STS15 | STS16 | STS17 | TRECOVID | Quora | SciFact |
|---|---|---|---|---|---|---|---|---|---|
| all-minilm-l6-v2 | 0.724 | 0.806 | 0.756 | 0.854 | 0.79 | 0.876 | 0.473 | 0.876 | 0.645 |
| all-mpnet-base-v2 | 0.726 | 0.835 | 0.78 | 0.857 | 0.8 | 0.906 | 0.513 | 0.875 | 0.656 |
| ada-embedding-002 | 0.698 | 0.833 | 0.761 | 0.861 | 0.86 | 0.903 | 0.685 | 0.876 | 0.726 |
| jina-embedding-t-en-v1 | 0.717 | 0.773 | 0.731 | 0.829 | 0.777 | 0.860 | 0.482 | 0.840 | 0.522 |
| jina-embedding-s-en-v1 | 0.743 | 0.786 | 0.738 | 0.837 | 0.80 | 0.875 | 0.523 | 0.857 | 0.524 |
| jina-embedding-b-en-v1 | 0.751 | 0.809 | 0.761 | 0.856 | 0.812 | 0.890 | 0.606 | 0.876 | 0.594 |
| jina-embedding-l-en-v1 | 0.745 | 0.832 | 0.781 | 0.869 | 0.837 | 0.902 | 0.573 | 0.881 | 0.598 |
Usage
Usage with Jina AI Finetuner:
!pip install finetuner
import finetuner
model = finetuner.build_model('jinaai/jina-embedding-b-en-v1')
embeddings = finetuner.encode(
model=model,
data=['how is the weather today', 'What is the current weather like today?']
)
print(finetuner.cos_sim(embeddings[0], embeddings[1]))
Use with sentence-transformers:
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['how is the weather today', 'What is the current weather like today?']
model = SentenceTransformer('jinaai/jina-embedding-b-en-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
Fine-tuning
Please consider Finetuner.
Plans
- The development of
jina-embedding-s-en-v2is currently underway with two main objectives: improving performance and increasing the maximum sequence length. - We are currently working on a bilingual embedding model that combines English and X language. The upcoming model will be called
jina-embedding-s/b/l-de-v1.
Contact
Join our Discord community and chat with other community members about ideas.
Citation
If you find Jina Embeddings useful in your research, please cite the following paper:
@misc{günther2023jina,
title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models},
author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
year={2023},
eprint={2307.11224},
archivePrefix={arXiv},
primaryClass={cs.CL}
}