---
model-index:
- name: jasper_en_vision_language_v1
results:
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 95.7271
- type: f1
value: 89.25450000000001
- type: f1_weighted
value: 95.8563
- type: ap
value: 67.1563
- type: ap_weighted
value: 67.1563
- type: main_score
value: 95.7271
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 93.7761
- type: f1
value: 90.7582
- type: f1_weighted
value: 93.974
- type: ap
value: 74.88759999999999
- type: ap_weighted
value: 74.88759999999999
- type: main_score
value: 93.7761
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 97.5809
- type: f1
value: 97.5808
- type: f1_weighted
value: 97.5808
- type: ap
value: 96.3911
- type: ap_weighted
value: 96.3911
- type: main_score
value: 97.5809
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 62.918
- type: f1
value: 60.696099999999994
- type: f1_weighted
value: 60.696099999999994
- type: main_score
value: 62.918
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: ndcg_at_1
value: 41.323
- type: ndcg_at_3
value: 56.737
- type: ndcg_at_5
value: 61.254
- type: ndcg_at_10
value: 65.204
- type: ndcg_at_20
value: 66.75
- type: ndcg_at_100
value: 67.254
- type: ndcg_at_1000
value: 67.254
- type: map_at_1
value: 41.323
- type: map_at_3
value: 52.881
- type: map_at_5
value: 55.373000000000005
- type: map_at_10
value: 57.013999999999996
- type: map_at_20
value: 57.457
- type: map_at_100
value: 57.538
- type: map_at_1000
value: 57.538
- type: recall_at_1
value: 41.323
- type: recall_at_3
value: 67.923
- type: recall_at_5
value: 78.947
- type: recall_at_10
value: 91.11
- type: recall_at_20
value: 97.084
- type: recall_at_100
value: 99.644
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 41.323
- type: precision_at_3
value: 22.641
- type: precision_at_5
value: 15.789
- type: precision_at_10
value: 9.110999999999999
- type: precision_at_20
value: 4.854
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 41.6785
- type: mrr_at_3
value: 52.963499999999996
- type: mrr_at_5
value: 55.456399999999995
- type: mrr_at_10
value: 57.104299999999995
- type: mrr_at_20
value: 57.5402
- type: mrr_at_100
value: 57.6213
- type: mrr_at_1000
value: 57.6213
- type: nauc_ndcg_at_1_max
value: -12.633600000000001
- type: nauc_ndcg_at_1_std
value: -19.747700000000002
- type: nauc_ndcg_at_1_diff1
value: 25.820300000000003
- type: nauc_ndcg_at_3_max
value: -8.480799999999999
- type: nauc_ndcg_at_3_std
value: -18.928900000000002
- type: nauc_ndcg_at_3_diff1
value: 19.409499999999998
- type: nauc_ndcg_at_5_max
value: -7.9172
- type: nauc_ndcg_at_5_std
value: -19.2306
- type: nauc_ndcg_at_5_diff1
value: 18.5809
- type: nauc_ndcg_at_10_max
value: -9.7812
- type: nauc_ndcg_at_10_std
value: -19.9964
- type: nauc_ndcg_at_10_diff1
value: 18.0753
- type: nauc_ndcg_at_20_max
value: -8.6546
- type: nauc_ndcg_at_20_std
value: -19.3205
- type: nauc_ndcg_at_20_diff1
value: 20.1741
- type: nauc_ndcg_at_100_max
value: -9.376900000000001
- type: nauc_ndcg_at_100_std
value: -19.5093
- type: nauc_ndcg_at_100_diff1
value: 20.4997
- type: nauc_ndcg_at_1000_max
value: -9.376900000000001
- type: nauc_ndcg_at_1000_std
value: -19.5093
- type: nauc_ndcg_at_1000_diff1
value: 20.4997
- type: nauc_map_at_1_max
value: -12.633600000000001
- type: nauc_map_at_1_std
value: -19.747700000000002
- type: nauc_map_at_1_diff1
value: 25.820300000000003
- type: nauc_map_at_3_max
value: -9.5533
- type: nauc_map_at_3_std
value: -19.3235
- type: nauc_map_at_3_diff1
value: 20.9083
- type: nauc_map_at_5_max
value: -9.3126
- type: nauc_map_at_5_std
value: -19.4593
- type: nauc_map_at_5_diff1
value: 20.5718
- type: nauc_map_at_10_max
value: -10.0766
- type: nauc_map_at_10_std
value: -19.8322
- type: nauc_map_at_10_diff1
value: 20.484099999999998
- type: nauc_map_at_20_max
value: -9.7917
- type: nauc_map_at_20_std
value: -19.6759
- type: nauc_map_at_20_diff1
value: 21.0095
- type: nauc_map_at_100_max
value: -9.8837
- type: nauc_map_at_100_std
value: -19.741400000000002
- type: nauc_map_at_100_diff1
value: 21.067700000000002
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value: -9.8837
- type: nauc_map_at_1000_std
value: -19.741400000000002
- type: nauc_map_at_1000_diff1
value: 21.067700000000002
- type: nauc_recall_at_1_max
value: -12.633600000000001
- type: nauc_recall_at_1_std
value: -19.747700000000002
- type: nauc_recall_at_1_diff1
value: 25.820300000000003
- type: nauc_recall_at_3_max
value: -4.7886999999999995
- type: nauc_recall_at_3_std
value: -17.4764
- type: nauc_recall_at_3_diff1
value: 14.3308
- type: nauc_recall_at_5_max
value: -1.4803
- type: nauc_recall_at_5_std
value: -18.2137
- type: nauc_recall_at_5_diff1
value: 9.4604
- type: nauc_recall_at_10_max
value: -8.8102
- type: nauc_recall_at_10_std
value: -21.8279
- type: nauc_recall_at_10_diff1
value: -5.5388
- type: nauc_recall_at_20_max
value: 25.202400000000004
- type: nauc_recall_at_20_std
value: -4.8613
- type: nauc_recall_at_20_diff1
value: 9.3917
- type: nauc_recall_at_100_max
value: 37.0551
- type: nauc_recall_at_100_std
value: 84.5472
- type: nauc_recall_at_100_diff1
value: 19.5116
- type: nauc_recall_at_1000_max
value: 37.0551
- type: nauc_recall_at_1000_std
value: 84.5472
- type: nauc_recall_at_1000_diff1
value: 19.5116
- type: nauc_precision_at_1_max
value: -12.633600000000001
- type: nauc_precision_at_1_std
value: -19.747700000000002
- type: nauc_precision_at_1_diff1
value: 25.820300000000003
- type: nauc_precision_at_3_max
value: -4.7886999999999995
- type: nauc_precision_at_3_std
value: -17.4764
- type: nauc_precision_at_3_diff1
value: 14.3308
- type: nauc_precision_at_5_max
value: -1.4803
- type: nauc_precision_at_5_std
value: -18.2137
- type: nauc_precision_at_5_diff1
value: 9.4604
- type: nauc_precision_at_10_max
value: -8.8102
- type: nauc_precision_at_10_std
value: -21.8279
- type: nauc_precision_at_10_diff1
value: -5.5388
- type: nauc_precision_at_20_max
value: 25.202400000000004
- type: nauc_precision_at_20_std
value: -4.8613
- type: nauc_precision_at_20_diff1
value: 9.3917
- type: nauc_precision_at_100_max
value: 37.0551
- type: nauc_precision_at_100_std
value: 84.5472
- type: nauc_precision_at_100_diff1
value: 19.5116
- type: nauc_precision_at_1000_max
value: 37.0551
- type: nauc_precision_at_1000_std
value: 84.5472
- type: nauc_precision_at_1000_diff1
value: 19.5116
- type: nauc_mrr_at_1_max
value: -11.9728
- type: nauc_mrr_at_1_std
value: -19.4014
- type: nauc_mrr_at_1_diff1
value: 24.8653
- type: nauc_mrr_at_3_max
value: -9.6607
- type: nauc_mrr_at_3_std
value: -19.1819
- type: nauc_mrr_at_3_diff1
value: 20.0205
- type: nauc_mrr_at_5_max
value: -9.4261
- type: nauc_mrr_at_5_std
value: -19.3098
- type: nauc_mrr_at_5_diff1
value: 19.6347
- type: nauc_mrr_at_10_max
value: -10.1698
- type: nauc_mrr_at_10_std
value: -19.683
- type: nauc_mrr_at_10_diff1
value: 19.4823
- type: nauc_mrr_at_20_max
value: -9.913
- type: nauc_mrr_at_20_std
value: -19.517300000000002
- type: nauc_mrr_at_20_diff1
value: 20.0066
- type: nauc_mrr_at_100_max
value: -10.0053
- type: nauc_mrr_at_100_std
value: -19.5824
- type: nauc_mrr_at_100_diff1
value: 20.061899999999998
- type: nauc_mrr_at_1000_max
value: -10.0053
- type: nauc_mrr_at_1000_std
value: -19.5824
- type: nauc_mrr_at_1000_diff1
value: 20.061899999999998
- type: main_score
value: 65.204
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 55.710300000000004
- type: v_measure_std
value: 14.551900000000002
- type: main_score
value: 55.710300000000004
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 51.004599999999996
- type: v_measure_std
value: 14.868899999999998
- type: main_score
value: 51.004599999999996
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 67.38119999999999
- type: mrr
value: 79.0692
- type: nAUC_map_max
value: 21.9757
- type: nAUC_map_std
value: 14.3545
- type: nAUC_map_diff1
value: 11.82
- type: nAUC_mrr_max
value: 40.3449
- type: nAUC_mrr_std
value: 18.2678
- type: nAUC_mrr_diff1
value: 21.1596
- type: main_score
value: 67.38119999999999
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: pearson
value: 87.4332
- type: spearman
value: 84.65979999999999
- type: cosine_pearson
value: 87.4332
- type: cosine_spearman
value: 84.65979999999999
- type: manhattan_pearson
value: 86.37270000000001
- type: manhattan_spearman
value: 85.20309999999999
- type: euclidean_pearson
value: 86.1672
- type: euclidean_spearman
value: 84.7453
- type: main_score
value: 84.65979999999999
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 87.224
- type: f1
value: 86.7235
- type: f1_weighted
value: 86.7235
- type: main_score
value: 87.224
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 52.04990000000001
- type: v_measure_std
value: 0.7255
- type: main_score
value: 52.04990000000001
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 47.5811
- type: v_measure_std
value: 0.8049000000000001
- type: main_score
value: 47.5811
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval (default)
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
metrics:
- type: ndcg_at_1
value: 46.781
- type: ndcg_at_3
value: 52.111
- type: ndcg_at_5
value: 54.371
- type: ndcg_at_10
value: 57.247
- type: ndcg_at_20
value: 59.122
- type: ndcg_at_100
value: 62.071
- type: ndcg_at_1000
value: 63.32
- type: map_at_1
value: 37.2
- type: map_at_3
value: 46.005
- type: map_at_5
value: 48.32
- type: map_at_10
value: 50.181
- type: map_at_20
value: 51.062
- type: map_at_100
value: 51.735
- type: map_at_1000
value: 51.842
- type: recall_at_1
value: 37.2
- type: recall_at_3
value: 53.94
- type: recall_at_5
value: 60.88
- type: recall_at_10
value: 69.692
- type: recall_at_20
value: 76.711
- type: recall_at_100
value: 90.263
- type: recall_at_1000
value: 97.749
- type: precision_at_1
value: 46.781
- type: precision_at_3
value: 25.894000000000002
- type: precision_at_5
value: 18.541
- type: precision_at_10
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- type: precision_at_20
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- type: precision_at_100
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- type: precision_at_1000
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- type: mrr_at_1
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- type: mrr_at_3
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- type: mrr_at_5
value: 56.0992
- type: mrr_at_10
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- type: mrr_at_20
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- type: mrr_at_100
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- type: mrr_at_1000
value: 57.6415
- type: nauc_ndcg_at_1_max
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revision: 5003b3064772da1887988e05400cf3806fe491f2
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revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
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task:
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name: MTEB CQADupstackRetrieval (default)
revision: CQADupstackRetrieval_is_a_combined_dataset
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type: CQADupstackRetrieval_is_a_combined_dataset
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type: mteb/cqadupstack-stats
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task:
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config: default
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revision: 46989137a86843e03a6195de44b09deda022eec7
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type: mteb/cqadupstack-tex
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value: 37.2582
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value: 90.8258
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value: -28.2975
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value: 37.2582
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value: 90.8258
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value: -28.2975
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value: 90.8258
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value: -28.2975
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value: 37.2582
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value: 90.8258
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task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 99.1701
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value: 99.07639999999999
- type: f1_weighted
value: 99.1712
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value: 99.1701
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 91.48429999999999
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value: 72.55080000000001
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value: 91.72449999999999
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value: 91.48429999999999
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 85.306
- type: f1
value: 83.1076
- type: f1_weighted
value: 84.4685
- type: main_score
value: 85.306
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 91.17349999999999
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value: 90.15950000000001
- type: f1_weighted
value: 90.802
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value: 91.17349999999999
task:
type: Classification
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config: default
name: MTEB MedrxivClusteringP2P (default)
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
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value: 46.7494
- type: v_measure_std
value: 1.2241
- type: main_score
value: 46.7494
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S (default)
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
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value: 44.9016
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value: 1.3978000000000002
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value: 44.9016
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
metrics:
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value: -21.0204
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value: -5.1638
- type: nAUC_map_diff1
value: 10.4531
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value: -15.85
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value: -2.7011
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value: 9.8352
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value: 32.628099999999996
task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus (default)
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
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task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ (default)
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
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value: 63.33
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value: 82.575
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value: 71.487
task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval (default)
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
split: test
type: mteb/quora
metrics:
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value: 83.3
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value: 87.50099999999999
- type: ndcg_at_5
value: 88.998
- type: ndcg_at_10
value: 90.038
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value: 91.049
- type: ndcg_at_1000
value: 91.105
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value: 72.424
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- type: map_at_5
value: 85.616
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- type: map_at_20
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- type: map_at_100
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- type: map_at_1000
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- type: recall_at_1
value: 72.424
- type: recall_at_3
value: 89.155
- type: recall_at_5
value: 93.425
- type: recall_at_10
value: 96.514
- type: recall_at_20
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- type: recall_at_100
value: 99.785
- type: recall_at_1000
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- type: precision_at_1
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value: 38.393
- type: precision_at_5
value: 25.228
- type: precision_at_10
value: 13.647
- type: precision_at_20
value: 7.225
- type: precision_at_100
value: 1.543
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- type: mrr_at_1
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value: 88.3
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- type: mrr_at_20
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- type: mrr_at_1000
value: 89.212
- type: nauc_ndcg_at_1_max
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value: 79.3747
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value: 33.2171
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value: -50.6815
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value: 77.3029
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value: 33.578
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value: -51.302400000000006
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value: 77.73219999999999
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value: 34.1893
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value: -51.231
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value: 77.91510000000001
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value: 34.7652
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value: 77.8851
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value: 35.1212
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value: -47.1897
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value: 77.8819
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value: 35.2096
- type: nauc_ndcg_at_1000_std
value: -47.1269
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value: 77.874
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value: 25.3507
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value: -42.6645
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value: 82.1013
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value: 30.685000000000002
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value: -52.8243
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value: 78.8106
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value: 32.2752
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value: -52.546800000000005
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value: 78.4853
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value: 33.239000000000004
- type: nauc_map_at_10_std
value: -51.5082
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value: 78.1853
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value: 33.644200000000005
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value: -50.1154
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value: 78.0265
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value: 33.7945
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value: -49.1504
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value: 77.9653
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value: 33.813500000000005
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value: -49.119800000000005
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value: 77.9596
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value: 25.3507
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value: -42.6645
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value: 82.1013
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value: 26.4941
- type: nauc_recall_at_3_std
value: -62.044399999999996
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value: 75.3519
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value: 26.5483
- type: nauc_recall_at_5_std
value: -68.6455
- type: nauc_recall_at_5_diff1
value: 74.2904
- type: nauc_recall_at_10_max
value: 28.0472
- type: nauc_recall_at_10_std
value: -80.47109999999999
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value: 74.5668
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value: 29.296699999999998
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value: -77.2526
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value: 73.5218
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value: 29.6001
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value: -36.9144
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value: 71.9031
- type: nauc_recall_at_1000_max
value: 65.9499
- type: nauc_recall_at_1000_std
value: 65.2771
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value: 68.762
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value: 79.3747
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value: 5.2775
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value: 5.7708
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value: -24.618100000000002
- type: nauc_precision_at_5_max
value: 0.9323
- type: nauc_precision_at_5_std
value: 17.5438
- type: nauc_precision_at_5_diff1
value: -36.0304
- type: nauc_precision_at_10_max
value: -2.321
- type: nauc_precision_at_10_std
value: 26.4542
- type: nauc_precision_at_10_diff1
value: -42.2061
- type: nauc_precision_at_20_max
value: -4.122
- type: nauc_precision_at_20_std
value: 33.5662
- type: nauc_precision_at_20_diff1
value: -44.6324
- type: nauc_precision_at_100_max
value: -5.6459
- type: nauc_precision_at_100_std
value: 40.1704
- type: nauc_precision_at_100_diff1
value: -45.8818
- type: nauc_precision_at_1000_max
value: -5.5985
- type: nauc_precision_at_1000_std
value: 40.402100000000004
- type: nauc_precision_at_1000_diff1
value: -46.054
- type: nauc_mrr_at_1_max
value: 36.066500000000005
- type: nauc_mrr_at_1_std
value: -42.5286
- type: nauc_mrr_at_1_diff1
value: 79.3747
- type: nauc_mrr_at_3_max
value: 36.3166
- type: nauc_mrr_at_3_std
value: -45.0789
- type: nauc_mrr_at_3_diff1
value: 78.3276
- type: nauc_mrr_at_5_max
value: 36.365700000000004
- type: nauc_mrr_at_5_std
value: -45.1793
- type: nauc_mrr_at_5_diff1
value: 78.5424
- type: nauc_mrr_at_10_max
value: 36.3128
- type: nauc_mrr_at_10_std
value: -45.0655
- type: nauc_mrr_at_10_diff1
value: 78.5756
- type: nauc_mrr_at_20_max
value: 36.327
- type: nauc_mrr_at_20_std
value: -44.853500000000004
- type: nauc_mrr_at_20_diff1
value: 78.57220000000001
- type: nauc_mrr_at_100_max
value: 36.3322
- type: nauc_mrr_at_100_std
value: -44.8025
- type: nauc_mrr_at_100_diff1
value: 78.57730000000001
- type: nauc_mrr_at_1000_max
value: 36.3314
- type: nauc_mrr_at_1000_std
value: -44.804100000000005
- type: nauc_mrr_at_1000_diff1
value: 78.5778
- type: main_score
value: 90.038
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering (default)
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 73.0223
- type: v_measure_std
value: 2.5644
- type: main_score
value: 73.0223
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P (default)
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 74.7154
- type: v_measure_std
value: 8.2851
- type: main_score
value: 74.7154
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS (default)
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
metrics:
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value: 29.099999999999998
- type: ndcg_at_3
value: 22.891000000000002
- type: ndcg_at_5
value: 20.055
- type: ndcg_at_10
value: 24.637999999999998
- type: ndcg_at_20
value: 28.188999999999997
- type: ndcg_at_100
value: 34.589
- type: ndcg_at_1000
value: 39.93
- type: map_at_1
value: 5.903
- type: map_at_3
value: 10.375
- type: map_at_5
value: 12.503
- type: map_at_10
value: 14.997
- type: map_at_20
value: 16.383
- type: map_at_100
value: 17.769
- type: map_at_1000
value: 18.115000000000002
- type: recall_at_1
value: 5.903
- type: recall_at_3
value: 12.943
- type: recall_at_5
value: 17.818
- type: recall_at_10
value: 26.162999999999997
- type: recall_at_20
value: 34.46
- type: recall_at_100
value: 55.24
- type: recall_at_1000
value: 81.11500000000001
- type: precision_at_1
value: 29.099999999999998
- type: precision_at_3
value: 21.3
- type: precision_at_5
value: 17.599999999999998
- type: precision_at_10
value: 12.920000000000002
- type: precision_at_20
value: 8.5
- type: precision_at_100
value: 2.721
- type: precision_at_1000
value: 0.4
- type: mrr_at_1
value: 29.099999999999998
- type: mrr_at_3
value: 36.8667
- type: mrr_at_5
value: 38.9617
- type: mrr_at_10
value: 40.598600000000005
- type: mrr_at_20
value: 41.3132
- type: mrr_at_100
value: 41.7298
- type: mrr_at_1000
value: 41.7584
- type: nauc_ndcg_at_1_max
value: 18.4908
- type: nauc_ndcg_at_1_std
value: 8.3766
- type: nauc_ndcg_at_1_diff1
value: 22.0483
- type: nauc_ndcg_at_3_max
value: 19.1712
- type: nauc_ndcg_at_3_std
value: 10.407399999999999
- type: nauc_ndcg_at_3_diff1
value: 16.301299999999998
- type: nauc_ndcg_at_5_max
value: 20.069
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value: 11.8195
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value: 16.4224
- type: nauc_ndcg_at_10_max
value: 20.2641
- type: nauc_ndcg_at_10_std
value: 14.7043
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value: 15.1067
- type: nauc_ndcg_at_20_max
value: 21.5015
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value: 14.1651
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value: 12.2911
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value: 22.764499999999998
- type: nauc_ndcg_at_1000_diff1
value: 12.540000000000001
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- type: nauc_map_at_1_std
value: 8.1625
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value: 21.8362
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value: 16.2302
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- type: nauc_precision_at_1000_max
value: 22.1
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value: 37.227700000000006
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value: -2.3548
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value: 18.4908
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value: 8.3766
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value: 22.0483
- type: nauc_mrr_at_3_max
value: 18.3602
- type: nauc_mrr_at_3_std
value: 9.579600000000001
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value: 18.7544
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value: 18.5224
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value: 9.747300000000001
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value: 19.0625
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value: 19.067
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value: 10.7103
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value: 19.444
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value: 19.118399999999998
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value: 10.6384
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value: 19.5223
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value: 19.200300000000002
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value: 10.6891
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value: 19.4009
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value: 19.1676
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value: 10.649799999999999
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value: 19.4127
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value: 24.637999999999998
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: pearson
value: 86.8981
- type: spearman
value: 82.234
- type: cosine_pearson
value: 86.8981
- type: cosine_spearman
value: 82.234
- type: manhattan_pearson
value: 84.1165
- type: manhattan_spearman
value: 82.063
- type: euclidean_pearson
value: 84.0688
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value: 82.2329
- type: main_score
value: 82.234
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: pearson
value: 87.8269
- type: spearman
value: 80.2578
- type: cosine_pearson
value: 87.8269
- type: cosine_spearman
value: 80.2578
- type: manhattan_pearson
value: 85.09020000000001
- type: manhattan_spearman
value: 80.5777
- type: euclidean_pearson
value: 84.71990000000001
- type: euclidean_spearman
value: 80.2547
- type: main_score
value: 80.2578
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: pearson
value: 89.45769999999999
- type: spearman
value: 89.69200000000001
- type: cosine_pearson
value: 89.45769999999999
- type: cosine_spearman
value: 89.69200000000001
- type: manhattan_pearson
value: 89.1685
- type: manhattan_spearman
value: 89.8889
- type: euclidean_pearson
value: 89.07969999999999
- type: euclidean_spearman
value: 89.7003
- type: main_score
value: 89.69200000000001
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: pearson
value: 87.01989999999999
- type: spearman
value: 85.3155
- type: cosine_pearson
value: 87.01989999999999
- type: cosine_spearman
value: 85.3155
- type: manhattan_pearson
value: 86.24159999999999
- type: manhattan_spearman
value: 85.544
- type: euclidean_pearson
value: 86.1032
- type: euclidean_spearman
value: 85.31490000000001
- type: main_score
value: 85.3155
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: pearson
value: 89.593
- type: spearman
value: 90.1574
- type: cosine_pearson
value: 89.593
- type: cosine_spearman
value: 90.1574
- type: manhattan_pearson
value: 89.93910000000001
- type: manhattan_spearman
value: 90.4357
- type: euclidean_pearson
value: 89.76650000000001
- type: euclidean_spearman
value: 90.1554
- type: main_score
value: 90.1574
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: pearson
value: 85.03540000000001
- type: spearman
value: 86.4191
- type: cosine_pearson
value: 85.03540000000001
- type: cosine_spearman
value: 86.4191
- type: manhattan_pearson
value: 85.8992
- type: manhattan_spearman
value: 86.7274
- type: euclidean_pearson
value: 85.638
- type: euclidean_spearman
value: 86.42
- type: main_score
value: 86.4191
task:
type: STS
- dataset:
config: en-ar
name: MTEB STS17 (en-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 54.1098
- type: spearman
value: 52.721
- type: cosine_pearson
value: 54.1098
- type: cosine_spearman
value: 52.721
- type: manhattan_pearson
value: 54.890899999999995
- type: manhattan_spearman
value: 52.727900000000005
- type: euclidean_pearson
value: 54.65410000000001
- type: euclidean_spearman
value: 52.8582
- type: main_score
value: 52.721
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 84.3262
- type: spearman
value: 84.1421
- type: cosine_pearson
value: 84.3262
- type: cosine_spearman
value: 84.1421
- type: manhattan_pearson
value: 83.8999
- type: manhattan_spearman
value: 83.9987
- type: euclidean_pearson
value: 84.0174
- type: euclidean_spearman
value: 84.1711
- type: main_score
value: 84.1421
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 91.38969999999999
- type: spearman
value: 91.00789999999999
- type: cosine_pearson
value: 91.38969999999999
- type: cosine_spearman
value: 91.00789999999999
- type: manhattan_pearson
value: 90.8917
- type: manhattan_spearman
value: 90.7834
- type: euclidean_pearson
value: 91.0361
- type: euclidean_spearman
value: 91.011
- type: main_score
value: 91.00789999999999
task:
type: STS
- dataset:
config: nl-en
name: MTEB STS17 (nl-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 84.636
- type: spearman
value: 84.1977
- type: cosine_pearson
value: 84.636
- type: cosine_spearman
value: 84.1977
- type: manhattan_pearson
value: 84.6895
- type: manhattan_spearman
value: 84.1849
- type: euclidean_pearson
value: 84.753
- type: euclidean_spearman
value: 84.2251
- type: main_score
value: 84.1977
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 85.8595
- type: spearman
value: 86.9965
- type: cosine_pearson
value: 85.8595
- type: cosine_spearman
value: 86.9965
- type: manhattan_pearson
value: 86.92450000000001
- type: manhattan_spearman
value: 87.39489999999999
- type: euclidean_pearson
value: 86.5575
- type: euclidean_spearman
value: 86.997
- type: main_score
value: 86.9965
task:
type: STS
- dataset:
config: it-en
name: MTEB STS17 (it-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 85.959
- type: spearman
value: 86.3103
- type: cosine_pearson
value: 85.959
- type: cosine_spearman
value: 86.3103
- type: manhattan_pearson
value: 85.7486
- type: manhattan_spearman
value: 86.0977
- type: euclidean_pearson
value: 85.8361
- type: euclidean_spearman
value: 86.2741
- type: main_score
value: 86.3103
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 86.2629
- type: spearman
value: 85.8372
- type: cosine_pearson
value: 86.2629
- type: cosine_spearman
value: 85.8372
- type: manhattan_pearson
value: 86.2491
- type: manhattan_spearman
value: 85.4973
- type: euclidean_pearson
value: 86.5074
- type: euclidean_spearman
value: 85.83370000000001
- type: main_score
value: 85.8372
task:
type: STS
- dataset:
config: en-tr
name: MTEB STS17 (en-tr)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 57.5675
- type: spearman
value: 55.564899999999994
- type: cosine_pearson
value: 57.5675
- type: cosine_spearman
value: 55.564899999999994
- type: manhattan_pearson
value: 57.4686
- type: manhattan_spearman
value: 55.3816
- type: euclidean_pearson
value: 57.635099999999994
- type: euclidean_spearman
value: 55.501900000000006
- type: main_score
value: 55.564899999999994
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 76.1237
- type: spearman
value: 76.4
- type: cosine_pearson
value: 76.1237
- type: cosine_spearman
value: 76.4
- type: manhattan_pearson
value: 78.6794
- type: manhattan_spearman
value: 77.2282
- type: euclidean_pearson
value: 77.1688
- type: euclidean_spearman
value: 76.4
- type: main_score
value: 76.4
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 73.89099999999999
- type: spearman
value: 77.8292
- type: cosine_pearson
value: 73.89099999999999
- type: cosine_spearman
value: 77.8292
- type: manhattan_pearson
value: 77.53229999999999
- type: manhattan_spearman
value: 78.3926
- type: euclidean_pearson
value: 76.6734
- type: euclidean_spearman
value: 77.8721
- type: main_score
value: 77.8292
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 68.31099999999999
- type: spearman
value: 68.0764
- type: cosine_pearson
value: 68.31099999999999
- type: cosine_spearman
value: 68.0764
- type: manhattan_pearson
value: 69.7423
- type: manhattan_spearman
value: 68.3212
- type: euclidean_pearson
value: 69.5701
- type: euclidean_spearman
value: 68.2299
- type: main_score
value: 68.0764
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 65.0636
- type: spearman
value: 59.4669
- type: cosine_pearson
value: 65.0636
- type: cosine_spearman
value: 59.4669
- type: manhattan_pearson
value: 69.723
- type: manhattan_spearman
value: 60.3823
- type: euclidean_pearson
value: 68.6454
- type: euclidean_spearman
value: 59.384899999999995
- type: main_score
value: 59.4669
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 73.7603
- type: spearman
value: 73.0799
- type: cosine_pearson
value: 73.7603
- type: cosine_spearman
value: 73.0799
- type: manhattan_pearson
value: 76.2351
- type: manhattan_spearman
value: 73.60719999999999
- type: euclidean_pearson
value: 75.61529999999999
- type: euclidean_spearman
value: 72.9809
- type: main_score
value: 73.0799
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: pearson
value: 88.1242
- type: spearman
value: 88.8456
- type: cosine_pearson
value: 88.1242
- type: cosine_spearman
value: 88.8456
- type: manhattan_pearson
value: 88.6122
- type: manhattan_spearman
value: 89.0198
- type: euclidean_pearson
value: 88.5314
- type: euclidean_spearman
value: 88.8432
- type: main_score
value: 88.8456
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 89.06920000000001
- type: mrr
value: 96.977
- type: nAUC_map_max
value: 49.1778
- type: nAUC_map_std
value: 62.091499999999996
- type: nAUC_map_diff1
value: -13.373899999999999
- type: nAUC_mrr_max
value: 86.7325
- type: nAUC_mrr_std
value: 74.958
- type: nAUC_mrr_diff1
value: 25.6955
- type: main_score
value: 89.06920000000001
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: ndcg_at_1
value: 67.333
- type: ndcg_at_3
value: 75.142
- type: ndcg_at_5
value: 78.873
- type: ndcg_at_10
value: 80.372
- type: ndcg_at_20
value: 80.914
- type: ndcg_at_100
value: 81.563
- type: ndcg_at_1000
value: 81.601
- type: map_at_1
value: 63.99399999999999
- type: map_at_3
value: 72.181
- type: map_at_5
value: 74.665
- type: map_at_10
value: 75.51299999999999
- type: map_at_20
value: 75.693
- type: map_at_100
value: 75.78
- type: map_at_1000
value: 75.78099999999999
- type: recall_at_1
value: 63.99399999999999
- type: recall_at_3
value: 80.63900000000001
- type: recall_at_5
value: 90.017
- type: recall_at_10
value: 94.167
- type: recall_at_20
value: 96.167
- type: recall_at_100
value: 99.667
- type: recall_at_1000
value: 100.0
- type: precision_at_1
value: 67.333
- type: precision_at_3
value: 29.444
- type: precision_at_5
value: 20.067
- type: precision_at_10
value: 10.667
- type: precision_at_20
value: 5.45
- type: precision_at_100
value: 1.127
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 67.3333
- type: mrr_at_3
value: 73.9444
- type: mrr_at_5
value: 76.0444
- type: mrr_at_10
value: 76.4517
- type: mrr_at_20
value: 76.5739
- type: mrr_at_100
value: 76.6556
- type: mrr_at_1000
value: 76.6563
- type: nauc_ndcg_at_1_max
value: 55.373099999999994
- type: nauc_ndcg_at_1_std
value: -11.362
- type: nauc_ndcg_at_1_diff1
value: 81.5232
- type: nauc_ndcg_at_3_max
value: 58.0376
- type: nauc_ndcg_at_3_std
value: -14.4854
- type: nauc_ndcg_at_3_diff1
value: 77.848
- type: nauc_ndcg_at_5_max
value: 55.789699999999996
- type: nauc_ndcg_at_5_std
value: -15.0412
- type: nauc_ndcg_at_5_diff1
value: 77.7873
- type: nauc_ndcg_at_10_max
value: 58.6243
- type: nauc_ndcg_at_10_std
value: -13.867799999999999
- type: nauc_ndcg_at_10_diff1
value: 78.071
- type: nauc_ndcg_at_20_max
value: 58.9032
- type: nauc_ndcg_at_20_std
value: -12.7998
- type: nauc_ndcg_at_20_diff1
value: 78.12010000000001
- type: nauc_ndcg_at_100_max
value: 58.0772
- type: nauc_ndcg_at_100_std
value: -12.4604
- type: nauc_ndcg_at_100_diff1
value: 78.7002
- type: nauc_ndcg_at_1000_max
value: 58.0835
- type: nauc_ndcg_at_1000_std
value: -12.662799999999999
- type: nauc_ndcg_at_1000_diff1
value: 78.7157
- type: nauc_map_at_1_max
value: 45.2711
- type: nauc_map_at_1_std
value: -21.685
- type: nauc_map_at_1_diff1
value: 80.8536
- type: nauc_map_at_3_max
value: 53.970600000000005
- type: nauc_map_at_3_std
value: -18.0178
- type: nauc_map_at_3_diff1
value: 78.95309999999999
- type: nauc_map_at_5_max
value: 54.7643
- type: nauc_map_at_5_std
value: -15.943999999999999
- type: nauc_map_at_5_diff1
value: 78.9665
- type: nauc_map_at_10_max
value: 56.737899999999996
- type: nauc_map_at_10_std
value: -14.355599999999999
- type: nauc_map_at_10_diff1
value: 79.095
- type: nauc_map_at_20_max
value: 56.804
- type: nauc_map_at_20_std
value: -14.033599999999998
- type: nauc_map_at_20_diff1
value: 79.0729
- type: nauc_map_at_100_max
value: 56.7168
- type: nauc_map_at_100_std
value: -14.000000000000002
- type: nauc_map_at_100_diff1
value: 79.1456
- type: nauc_map_at_1000_max
value: 56.7168
- type: nauc_map_at_1000_std
value: -14.003099999999998
- type: nauc_map_at_1000_diff1
value: 79.1458
- type: nauc_recall_at_1_max
value: 45.2711
- type: nauc_recall_at_1_std
value: -21.685
- type: nauc_recall_at_1_diff1
value: 80.8536
- type: nauc_recall_at_3_max
value: 55.911100000000005
- type: nauc_recall_at_3_std
value: -19.1603
- type: nauc_recall_at_3_diff1
value: 72.51689999999999
- type: nauc_recall_at_5_max
value: 50.6317
- type: nauc_recall_at_5_std
value: -21.4004
- type: nauc_recall_at_5_diff1
value: 68.3824
- type: nauc_recall_at_10_max
value: 67.3176
- type: nauc_recall_at_10_std
value: -23.6601
- type: nauc_recall_at_10_diff1
value: 67.20150000000001
- type: nauc_recall_at_20_max
value: 76.566
- type: nauc_recall_at_20_std
value: -12.4873
- type: nauc_recall_at_20_diff1
value: 63.6849
- type: nauc_recall_at_100_max
value: 55.415499999999994
- type: nauc_recall_at_100_std
value: 72.2222
- type: nauc_recall_at_100_diff1
value: 72.2222
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_precision_at_1_max
value: 55.373099999999994
- type: nauc_precision_at_1_std
value: -11.362
- type: nauc_precision_at_1_diff1
value: 81.5232
- type: nauc_precision_at_3_max
value: 59.23969999999999
- type: nauc_precision_at_3_std
value: 17.3868
- type: nauc_precision_at_3_diff1
value: 37.0438
- type: nauc_precision_at_5_max
value: 51.358599999999996
- type: nauc_precision_at_5_std
value: 39.9722
- type: nauc_precision_at_5_diff1
value: 9.2927
- type: nauc_precision_at_10_max
value: 44.6166
- type: nauc_precision_at_10_std
value: 46.4231
- type: nauc_precision_at_10_diff1
value: -7.435700000000001
- type: nauc_precision_at_20_max
value: 41.1852
- type: nauc_precision_at_20_std
value: 51.89359999999999
- type: nauc_precision_at_20_diff1
value: -14.6318
- type: nauc_precision_at_100_max
value: 33.6188
- type: nauc_precision_at_100_std
value: 60.621700000000004
- type: nauc_precision_at_100_diff1
value: -26.376500000000004
- type: nauc_precision_at_1000_max
value: 33.2976
- type: nauc_precision_at_1000_std
value: 60.4508
- type: nauc_precision_at_1000_diff1
value: -27.829500000000003
- type: nauc_mrr_at_1_max
value: 55.373099999999994
- type: nauc_mrr_at_1_std
value: -11.362
- type: nauc_mrr_at_1_diff1
value: 81.5232
- type: nauc_mrr_at_3_max
value: 58.7918
- type: nauc_mrr_at_3_std
value: -11.580300000000001
- type: nauc_mrr_at_3_diff1
value: 78.79310000000001
- type: nauc_mrr_at_5_max
value: 58.13270000000001
- type: nauc_mrr_at_5_std
value: -11.2346
- type: nauc_mrr_at_5_diff1
value: 78.8896
- type: nauc_mrr_at_10_max
value: 58.4249
- type: nauc_mrr_at_10_std
value: -11.842600000000001
- type: nauc_mrr_at_10_diff1
value: 79.0629
- type: nauc_mrr_at_20_max
value: 58.4409
- type: nauc_mrr_at_20_std
value: -11.7012
- type: nauc_mrr_at_20_diff1
value: 79.1097
- type: nauc_mrr_at_100_max
value: 58.3685
- type: nauc_mrr_at_100_std
value: -11.6333
- type: nauc_mrr_at_100_diff1
value: 79.1871
- type: nauc_mrr_at_1000_max
value: 58.3686
- type: nauc_mrr_at_1000_std
value: -11.6365
- type: nauc_mrr_at_1000_diff1
value: 79.1874
- type: main_score
value: 80.372
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: similarity_accuracy
value: 99.8317
- type: similarity_accuracy_threshold
value: 87.7015
- type: similarity_f1
value: 91.2821
- type: similarity_f1_threshold
value: 87.68599999999999
- type: similarity_precision
value: 93.68419999999999
- type: similarity_recall
value: 89.0
- type: similarity_ap
value: 96.249
- type: cosine_accuracy
value: 99.8317
- type: cosine_accuracy_threshold
value: 87.7015
- type: cosine_f1
value: 91.2821
- type: cosine_f1_threshold
value: 87.68599999999999
- type: cosine_precision
value: 93.68419999999999
- type: cosine_recall
value: 89.0
- type: cosine_ap
value: 96.249
- type: manhattan_accuracy
value: 99.83370000000001
- type: manhattan_accuracy_threshold
value: 4133.4675
- type: manhattan_f1
value: 91.52369999999999
- type: manhattan_f1_threshold
value: 4244.392400000001
- type: manhattan_precision
value: 92.3625
- type: manhattan_recall
value: 90.7
- type: manhattan_ap
value: 96.4021
- type: euclidean_accuracy
value: 99.83070000000001
- type: euclidean_accuracy_threshold
value: 49.684200000000004
- type: euclidean_f1
value: 91.24419999999999
- type: euclidean_f1_threshold
value: 49.7175
- type: euclidean_precision
value: 93.4942
- type: euclidean_recall
value: 89.1
- type: euclidean_ap
value: 96.24589999999999
- type: dot_accuracy
value: 99.8277
- type: dot_accuracy_threshold
value: 87.3743
- type: dot_f1
value: 91.1495
- type: dot_f1_threshold
value: 87.3743
- type: dot_precision
value: 92.7536
- type: dot_recall
value: 89.60000000000001
- type: dot_ap
value: 96.23509999999999
- type: max_accuracy
value: 99.83370000000001
- type: max_f1
value: 91.52369999999999
- type: max_precision
value: 93.68419999999999
- type: max_recall
value: 90.7
- type: max_ap
value: 96.4021
- type: main_score
value: 96.4021
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 80.19420000000001
- type: v_measure_std
value: 2.8956
- type: main_score
value: 80.19420000000001
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P (default)
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 49.416900000000005
- type: v_measure_std
value: 1.5938
- type: main_score
value: 49.416900000000005
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 54.579
- type: mrr
value: 55.598800000000004
- type: nAUC_map_max
value: 9.1665
- type: nAUC_map_std
value: 7.8292
- type: nAUC_map_diff1
value: 38.831199999999995
- type: nAUC_mrr_max
value: 9.7435
- type: nAUC_mrr_std
value: 8.030800000000001
- type: nAUC_mrr_diff1
value: 39.2785
- type: main_score
value: 54.579
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: pearson
value: 31.6006
- type: spearman
value: 31.4212
- type: cosine_spearman
value: 31.4212
- type: cosine_pearson
value: 31.6006
- type: dot_spearman
value: 31.5459
- type: dot_pearson
value: 31.6412
- type: main_score
value: 31.4212
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID (default)
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
split: test
type: mteb/trec-covid
metrics:
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- type: ndcg_at_5
value: 88.596
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value: 86.732
- type: ndcg_at_20
value: 84.08099999999999
- type: ndcg_at_100
value: 70.204
- type: ndcg_at_1000
value: 63.29
- type: map_at_1
value: 0.243
- type: map_at_3
value: 0.703
- type: map_at_5
value: 1.142
- type: map_at_10
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- type: map_at_20
value: 4.176
- type: map_at_100
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- type: map_at_1000
value: 37.759
- type: recall_at_1
value: 0.243
- type: recall_at_3
value: 0.726
- type: recall_at_5
value: 1.192
- type: recall_at_10
value: 2.359
- type: recall_at_20
value: 4.539
- type: recall_at_100
value: 17.802
- type: recall_at_1000
value: 59.906000000000006
- type: precision_at_1
value: 94.0
- type: precision_at_3
value: 93.333
- type: precision_at_5
value: 91.60000000000001
- type: precision_at_10
value: 90.0
- type: precision_at_20
value: 87.6
- type: precision_at_100
value: 72.42
- type: precision_at_1000
value: 27.842
- type: mrr_at_1
value: 94.0
- type: mrr_at_3
value: 97.0
- type: mrr_at_5
value: 97.0
- type: mrr_at_10
value: 97.0
- type: mrr_at_20
value: 97.0
- type: mrr_at_100
value: 97.0
- type: mrr_at_1000
value: 97.0
- type: nauc_ndcg_at_1_max
value: 53.2855
- type: nauc_ndcg_at_1_std
value: 57.4638
- type: nauc_ndcg_at_1_diff1
value: 4.4059
- type: nauc_ndcg_at_3_max
value: 37.2953
- type: nauc_ndcg_at_3_std
value: 46.1686
- type: nauc_ndcg_at_3_diff1
value: 17.6265
- type: nauc_ndcg_at_5_max
value: 30.1947
- type: nauc_ndcg_at_5_std
value: 53.0996
- type: nauc_ndcg_at_5_diff1
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- type: nauc_ndcg_at_10_diff1
value: 13.2686
- type: nauc_ndcg_at_20_max
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- type: nauc_ndcg_at_20_std
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value: -12.2539
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- type: nauc_map_at_1_max
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- type: nauc_map_at_1_diff1
value: 13.9958
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- type: nauc_map_at_3_std
value: 30.8674
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value: 25.9597
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- type: nauc_map_at_5_std
value: 34.3742
- type: nauc_map_at_5_diff1
value: 24.567
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value: 38.6623
- type: nauc_map_at_10_diff1
value: 23.482
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- type: nauc_map_at_20_std
value: 41.4966
- type: nauc_map_at_20_diff1
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- type: nauc_map_at_100_max
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- type: nauc_map_at_100_std
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- type: nauc_map_at_1000_max
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- type: nauc_map_at_1000_std
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- type: nauc_map_at_1000_diff1
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- type: nauc_recall_at_1_max
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- type: nauc_recall_at_1_std
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- type: nauc_recall_at_1_diff1
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- type: nauc_recall_at_3_max
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- type: nauc_recall_at_3_std
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- type: nauc_recall_at_3_diff1
value: 25.6479
- type: nauc_recall_at_5_max
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- type: nauc_recall_at_5_std
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- type: nauc_recall_at_5_diff1
value: 22.797700000000003
- type: nauc_recall_at_10_max
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- type: nauc_recall_at_10_std
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- type: nauc_recall_at_20_std
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- type: nauc_recall_at_100_std
value: 48.9423
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- type: nauc_recall_at_1000_max
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- type: nauc_recall_at_1000_std
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- type: nauc_recall_at_1000_diff1
value: -2.8935
- type: nauc_precision_at_1_max
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- type: nauc_precision_at_1_std
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- type: nauc_precision_at_1_diff1
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- type: nauc_precision_at_3_max
value: 49.335699999999996
- type: nauc_precision_at_3_std
value: 45.994
- type: nauc_precision_at_3_diff1
value: 22.622400000000003
- type: nauc_precision_at_5_max
value: 38.3816
- type: nauc_precision_at_5_std
value: 55.8298
- type: nauc_precision_at_5_diff1
value: 8.4025
- type: nauc_precision_at_10_max
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- type: nauc_precision_at_10_std
value: 66.1322
- type: nauc_precision_at_10_diff1
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- type: nauc_precision_at_20_max
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- type: nauc_precision_at_20_std
value: 61.7217
- type: nauc_precision_at_20_diff1
value: -3.4937000000000005
- type: nauc_precision_at_100_max
value: 40.4606
- type: nauc_precision_at_100_std
value: 63.3467
- type: nauc_precision_at_100_diff1
value: -16.487199999999998
- type: nauc_precision_at_1000_max
value: 39.4253
- type: nauc_precision_at_1000_std
value: 18.8794
- type: nauc_precision_at_1000_diff1
value: -18.4566
- type: nauc_mrr_at_1_max
value: 42.343599999999995
- type: nauc_mrr_at_1_std
value: 69.3433
- type: nauc_mrr_at_1_diff1
value: -20.028000000000002
- type: nauc_mrr_at_3_max
value: 42.343599999999995
- type: nauc_mrr_at_3_std
value: 69.3433
- type: nauc_mrr_at_3_diff1
value: -20.028000000000002
- type: nauc_mrr_at_5_max
value: 42.343599999999995
- type: nauc_mrr_at_5_std
value: 69.3433
- type: nauc_mrr_at_5_diff1
value: -20.028000000000002
- type: nauc_mrr_at_10_max
value: 42.343599999999995
- type: nauc_mrr_at_10_std
value: 69.3433
- type: nauc_mrr_at_10_diff1
value: -20.028000000000002
- type: nauc_mrr_at_20_max
value: 42.343599999999995
- type: nauc_mrr_at_20_std
value: 69.3433
- type: nauc_mrr_at_20_diff1
value: -20.028000000000002
- type: nauc_mrr_at_100_max
value: 42.343599999999995
- type: nauc_mrr_at_100_std
value: 69.3433
- type: nauc_mrr_at_100_diff1
value: -20.028000000000002
- type: nauc_mrr_at_1000_max
value: 42.343599999999995
- type: nauc_mrr_at_1000_std
value: 69.3433
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value: -20.028000000000002
- type: main_score
value: 86.732
task:
type: Retrieval
- dataset:
config: default
name: MTEB Touche2020 (default)
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
- type: ndcg_at_1
value: 37.755
- type: ndcg_at_3
value: 37.185
- type: ndcg_at_5
value: 32.564
- type: ndcg_at_10
value: 31.395
- type: ndcg_at_20
value: 30.494
- type: ndcg_at_100
value: 42.617
- type: ndcg_at_1000
value: 53.324000000000005
- type: map_at_1
value: 2.7640000000000002
- type: map_at_3
value: 6.970999999999999
- type: map_at_5
value: 8.518
- type: map_at_10
value: 11.919
- type: map_at_20
value: 14.389
- type: map_at_100
value: 18.799
- type: map_at_1000
value: 20.366
- type: recall_at_1
value: 2.7640000000000002
- type: recall_at_3
value: 8.323
- type: recall_at_5
value: 11.259
- type: recall_at_10
value: 19.088
- type: recall_at_20
value: 26.488
- type: recall_at_100
value: 53.437
- type: recall_at_1000
value: 86.385
- type: precision_at_1
value: 38.775999999999996
- type: precision_at_3
value: 38.095
- type: precision_at_5
value: 31.429000000000002
- type: precision_at_10
value: 27.346999999999998
- type: precision_at_20
value: 19.184
- type: precision_at_100
value: 8.713999999999999
- type: precision_at_1000
value: 1.6119999999999999
- type: mrr_at_1
value: 38.7755
- type: mrr_at_3
value: 52.7211
- type: mrr_at_5
value: 55.9864
- type: mrr_at_10
value: 57.2554
- type: mrr_at_20
value: 57.382999999999996
- type: mrr_at_100
value: 57.5539
- type: mrr_at_1000
value: 57.5539
- type: nauc_ndcg_at_1_max
value: -20.4965
- type: nauc_ndcg_at_1_std
value: -0.3794
- type: nauc_ndcg_at_1_diff1
value: 24.9951
- type: nauc_ndcg_at_3_max
value: -26.1758
- type: nauc_ndcg_at_3_std
value: 12.2535
- type: nauc_ndcg_at_3_diff1
value: 15.765199999999998
- type: nauc_ndcg_at_5_max
value: -27.4523
- type: nauc_ndcg_at_5_std
value: 5.7325
- type: nauc_ndcg_at_5_diff1
value: 12.3423
- type: nauc_ndcg_at_10_max
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- type: nauc_ndcg_at_10_std
value: 3.526
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- type: nauc_ndcg_at_20_max
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value: 6.1578
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- type: nauc_ndcg_at_1000_std
value: 35.9222
- type: nauc_ndcg_at_1000_diff1
value: 5.9171000000000005
- type: nauc_map_at_1_max
value: -22.354
- type: nauc_map_at_1_std
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- type: nauc_map_at_1_diff1
value: 27.7288
- type: nauc_map_at_3_max
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- type: nauc_map_at_5_max
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value: -10.6509
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value: 6.9317
- type: nauc_map_at_10_max
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- type: nauc_map_at_10_std
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- type: nauc_map_at_20_std
value: -2.595
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- type: nauc_map_at_1000_diff1
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- type: nauc_recall_at_1_max
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value: 0.0948
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value: 28.084300000000002
- type: nauc_mrr_at_3_max
value: -26.1233
- type: nauc_mrr_at_3_std
value: 12.6302
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value: 17.8467
- type: nauc_mrr_at_5_max
value: -23.641499999999997
- type: nauc_mrr_at_5_std
value: 8.3047
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value: 20.5063
- type: nauc_mrr_at_10_max
value: -23.5564
- type: nauc_mrr_at_10_std
value: 8.5333
- type: nauc_mrr_at_10_diff1
value: 20.5287
- type: nauc_mrr_at_20_max
value: -23.154700000000002
- type: nauc_mrr_at_20_std
value: 9.079600000000001
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value: 21.3888
- type: nauc_mrr_at_100_max
value: -23.3008
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value: 8.7424
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value: 21.2832
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value: -23.3008
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value: 8.7424
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value: 21.2832
- type: main_score
value: 31.395
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 91.3135
- type: f1
value: 78.12480000000001
- type: f1_weighted
value: 92.4208
- type: ap
value: 41.943599999999996
- type: ap_weighted
value: 41.943599999999996
- type: main_score
value: 91.3135
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 77.24109999999999
- type: f1
value: 77.4964
- type: f1_weighted
value: 77.0248
- type: main_score
value: 77.24109999999999
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 63.132200000000005
- type: v_measure_std
value: 0.7163
- type: main_score
value: 63.132200000000005
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: similarity_accuracy
value: 88.061
- type: similarity_accuracy_threshold
value: 85.9546
- type: similarity_f1
value: 73.36240000000001
- type: similarity_f1_threshold
value: 84.0127
- type: similarity_precision
value: 71.47149999999999
- type: similarity_recall
value: 75.3562
- type: similarity_ap
value: 80.2787
- type: cosine_accuracy
value: 88.061
- type: cosine_accuracy_threshold
value: 85.9546
- type: cosine_f1
value: 73.36240000000001
- type: cosine_f1_threshold
value: 84.0127
- type: cosine_precision
value: 71.47149999999999
- type: cosine_recall
value: 75.3562
- type: cosine_ap
value: 80.2787
- type: manhattan_accuracy
value: 87.8941
- type: manhattan_accuracy_threshold
value: 4374.3263
- type: manhattan_f1
value: 72.929
- type: manhattan_f1_threshold
value: 4637.7289
- type: manhattan_precision
value: 71.3636
- type: manhattan_recall
value: 74.5646
- type: manhattan_ap
value: 79.98140000000001
- type: euclidean_accuracy
value: 88.0789
- type: euclidean_accuracy_threshold
value: 52.9742
- type: euclidean_f1
value: 73.3591
- type: euclidean_f1_threshold
value: 56.4815
- type: euclidean_precision
value: 71.608
- type: euclidean_recall
value: 75.19789999999999
- type: euclidean_ap
value: 80.2656
- type: dot_accuracy
value: 88.0789
- type: dot_accuracy_threshold
value: 85.8034
- type: dot_f1
value: 73.40379999999999
- type: dot_f1_threshold
value: 84.3213
- type: dot_precision
value: 72.6544
- type: dot_recall
value: 74.16890000000001
- type: dot_ap
value: 80.32010000000001
- type: max_accuracy
value: 88.0789
- type: max_f1
value: 73.40379999999999
- type: max_precision
value: 72.6544
- type: max_recall
value: 75.3562
- type: max_ap
value: 80.32010000000001
- type: main_score
value: 80.32010000000001
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: similarity_accuracy
value: 89.93090000000001
- type: similarity_accuracy_threshold
value: 82.1349
- type: similarity_f1
value: 80.1377
- type: similarity_f1_threshold
value: 80.5047
- type: similarity_precision
value: 77.1539
- type: similarity_recall
value: 83.3616
- type: similarity_ap
value: 87.6917
- type: cosine_accuracy
value: 89.93090000000001
- type: cosine_accuracy_threshold
value: 82.1349
- type: cosine_f1
value: 80.1377
- type: cosine_f1_threshold
value: 80.5047
- type: cosine_precision
value: 77.1539
- type: cosine_recall
value: 83.3616
- type: cosine_ap
value: 87.6917
- type: manhattan_accuracy
value: 89.86880000000001
- type: manhattan_accuracy_threshold
value: 4882.7347
- type: manhattan_f1
value: 80.2323
- type: manhattan_f1_threshold
value: 5185.1944
- type: manhattan_precision
value: 76.74889999999999
- type: manhattan_recall
value: 84.0468
- type: manhattan_ap
value: 87.70750000000001
- type: euclidean_accuracy
value: 89.94640000000001
- type: euclidean_accuracy_threshold
value: 59.9149
- type: euclidean_f1
value: 80.1527
- type: euclidean_f1_threshold
value: 62.3611
- type: euclidean_precision
value: 77.2744
- type: euclidean_recall
value: 83.2538
- type: euclidean_ap
value: 87.6922
- type: dot_accuracy
value: 89.9038
- type: dot_accuracy_threshold
value: 82.53049999999999
- type: dot_f1
value: 80.0969
- type: dot_f1_threshold
value: 80.7285
- type: dot_precision
value: 77.5853
- type: dot_recall
value: 82.77640000000001
- type: dot_ap
value: 87.668
- type: max_accuracy
value: 89.94640000000001
- type: max_f1
value: 80.2323
- type: max_precision
value: 77.5853
- type: max_recall
value: 84.0468
- type: max_ap
value: 87.70750000000001
- type: main_score
value: 87.70750000000001
task:
type: PairClassification
datasets:
- BAAI/Infinity-MM
- HuggingFaceFW/fineweb-edu
language:
- en
base_model:
- dunzhang/stella_en_1.5B_v5
- google/siglip-so400m-patch14-384
tags:
- mteb
- sentence-transformers
---
## Introduction
Based on dunzhang/stella_en_1.5B_v5 and google/siglip-so400m-patch14-384.
It can encode both text and image.
The training code, data and report will be released soon.
The core training code will be integrated into the rag-retrieval library(https://github.com/NLPJCL/RAG-Retrieval) in the near future. (Welcome to star)
This work was accomplished during my free time; please grant time a little time.
Here's a short introduction to the training method:
The core idea of jasper and stella is distillation: **Let student model learn teacher model's vectors.**
The training process of jasper have 4 stage:
Stage1&2: Distill from teacher vectors. In jasper model the teacher model is nvidia/NV-Embed-v2 and dunzhang/stella_en_1.5B_v5 (Stage1 and Stage2 will freeze different parameters.)
Stage3: MRL training, I made some modifications to MRL to enable training on unsupervised text
Stage4: Alignment between *jasper token embeddings from image's detailed caption* and *vision embeddings from google/siglip-so400m-patch14-384*.
I use a AdaptiveAvgPool2d to do an adjustment on vision tokens' number and dimensions, this method does not need additional parameters.
**The meaning of distillation is to achieve better results with smaller models or as a way of pre-training, not to hit the top of the leaderboards.**
Actually, I've got first place on MTEB (Chinese and English), I will not release the two models, as I said before, it's meaningless.
## Usage
```python
import torch
from sentence_transformers import SentenceTransformer
DOC1 = """
Blue light is scattered in all directions by the tiny molecules of air in Earth's atmosphere.
Blue is scattered more than other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time.
Closer to the horizon, the sky fades to a lighter blue or white.
"""
DOC2 = """
When choosing colors, you can consider the following factors:
Color theory: Understand how colors work together and how they can evoke different reactions.
Color psychology: Consider how colors affect emotions, behaviors, and responses.
Brand identity: Colors can convey meaning and information about a brand.
Mood: Consider the mood you want to create. For example, brighter colors can feel cheerful, while cooler colors can be calming.
Space: Consider the size of the space and the amount of natural light it receives. Dark colors can make a room feel smaller, while light colors can make it feel larger.
Color wheel: Use the color wheel to identify primary, secondary, and tertiary colors.
Color combinations: Decide how to best complement your preferred color with others.
Color palette: Limit your color palette to a main color and one or two additional colors.
60-30-10 rule: Use a primary color 60% of the time, a secondary color 30% of the time, and an accent color 10% of the time
"""
if __name__ == "__main__":
# load model
use_gpu = False
model_name = "infgrad/jasper_en_vision_language_v1"
model = SentenceTransformer(
model_name,
trust_remote_code=True,
device="cpu" if not use_gpu else "cuda",
model_kwargs={
"torch_dtype": torch.bfloat16 if use_gpu else torch.float32,
"attn_implementation": "sdpa"
},
# vector_dim must be 12288, 1024, 512, 256
## 1024 is recommended
# set is_text_encoder 'True', if you do not encode image
config_kwargs={"is_text_encoder": False, "vector_dim": 1024},
)
# We can reduce the max_seq_length from the default of 2048 for faster encoding
model.max_seq_length = 1024
# data
q_list = [
"Why the sky is blue?",
"how to choose suitable color",
]
doc_list = [
DOC1,
[{"type": "image_path", "content": "./assets/img1.png"}, {"type": "text", "content": "Hope this image helps!"}],
DOC2,
[{"type": "image_path", "content": "./assets/img2.png"}],
]
q_vecs = model.encode(q_list, prompt_name="s2p_query")
doc_vecs = model.encode(doc_list)
# calculate similarity
similarities = model.similarity(q_vecs, doc_vecs)
print(similarities)
# the output is:
# tensor([[0.7775, 0.7594, 0.2429, 0.2187],
# [0.3226, 0.3054, 0.7421, 0.5484]])
```
## Evaluation on MTEB
script: ./scripts/evaluate_en_mteb/run_evaluate_mteb.py
## License
**This model should not be used for any commercial purpose!**