metadata
base_model: avsolatorio/GIST-all-MiniLM-L6-v2
inference: true
language:
- en
library_name: sentence-transformers
license: mit
model-index:
- name: GIST-all-MiniLM-L6-v2
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 72.8955223880597
- type: ap
value: 35.447605103320775
- type: f1
value: 66.82951715365854
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 87.19474999999998
- type: ap
value: 83.09577890808514
- type: f1
value: 87.13833121762009
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 42.556000000000004
- type: f1
value: 42.236256693772276
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: None
split: test
type: arguana
metrics:
- type: map_at_1
value: 26.884999999999998
- type: map_at_10
value: 42.364000000000004
- type: map_at_100
value: 43.382
- type: map_at_1000
value: 43.391000000000005
- type: map_at_3
value: 37.162
- type: map_at_5
value: 40.139
- type: mrr_at_1
value: 26.884999999999998
- type: mrr_at_10
value: 42.193999999999996
- type: mrr_at_100
value: 43.211
- type: mrr_at_1000
value: 43.221
- type: mrr_at_3
value: 36.949
- type: mrr_at_5
value: 40.004
- type: ndcg_at_1
value: 26.884999999999998
- type: ndcg_at_10
value: 51.254999999999995
- type: ndcg_at_100
value: 55.481
- type: ndcg_at_1000
value: 55.68300000000001
- type: ndcg_at_3
value: 40.565
- type: ndcg_at_5
value: 45.882
- type: precision_at_1
value: 26.884999999999998
- type: precision_at_10
value: 7.9799999999999995
- type: precision_at_100
value: 0.98
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 16.808999999999997
- type: precision_at_5
value: 12.645999999999999
- type: recall_at_1
value: 26.884999999999998
- type: recall_at_10
value: 79.801
- type: recall_at_100
value: 98.009
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 50.427
- type: recall_at_5
value: 63.229
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 45.31044837358167
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 35.44751738734691
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 62.96517580629869
- type: mrr
value: 76.30051004704744
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cos_sim_pearson
value: 83.97262600499639
- type: cos_sim_spearman
value: 81.25787561220484
- type: euclidean_pearson
value: 64.96260261677082
- type: euclidean_spearman
value: 64.17616109254686
- type: manhattan_pearson
value: 65.05620628102835
- type: manhattan_spearman
value: 64.71171546419122
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 84.2435064935065
- type: f1
value: 84.2334859253828
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 38.38358435972693
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 31.093619653843124
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 35.016999999999996
- type: map_at_10
value: 47.019
- type: map_at_100
value: 48.634
- type: map_at_1000
value: 48.757
- type: map_at_3
value: 43.372
- type: map_at_5
value: 45.314
- type: mrr_at_1
value: 43.491
- type: mrr_at_10
value: 53.284
- type: mrr_at_100
value: 54.038
- type: mrr_at_1000
value: 54.071000000000005
- type: mrr_at_3
value: 51.001
- type: mrr_at_5
value: 52.282
- type: ndcg_at_1
value: 43.491
- type: ndcg_at_10
value: 53.498999999999995
- type: ndcg_at_100
value: 58.733999999999995
- type: ndcg_at_1000
value: 60.307
- type: ndcg_at_3
value: 48.841
- type: ndcg_at_5
value: 50.76199999999999
- type: precision_at_1
value: 43.491
- type: precision_at_10
value: 10.315000000000001
- type: precision_at_100
value: 1.6209999999999998
- type: precision_at_1000
value: 0.20500000000000002
- type: precision_at_3
value: 23.462
- type: precision_at_5
value: 16.652
- type: recall_at_1
value: 35.016999999999996
- type: recall_at_10
value: 64.92
- type: recall_at_100
value: 86.605
- type: recall_at_1000
value: 96.174
- type: recall_at_3
value: 50.99
- type: recall_at_5
value: 56.93
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackEnglishRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 29.866
- type: map_at_10
value: 40.438
- type: map_at_100
value: 41.77
- type: map_at_1000
value: 41.913
- type: map_at_3
value: 37.634
- type: map_at_5
value: 39.226
- type: mrr_at_1
value: 37.834
- type: mrr_at_10
value: 46.765
- type: mrr_at_100
value: 47.410000000000004
- type: mrr_at_1000
value: 47.461
- type: mrr_at_3
value: 44.735
- type: mrr_at_5
value: 46.028000000000006
- type: ndcg_at_1
value: 37.834
- type: ndcg_at_10
value: 46.303
- type: ndcg_at_100
value: 50.879
- type: ndcg_at_1000
value: 53.112
- type: ndcg_at_3
value: 42.601
- type: ndcg_at_5
value: 44.384
- type: precision_at_1
value: 37.834
- type: precision_at_10
value: 8.898
- type: precision_at_100
value: 1.4409999999999998
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 20.977
- type: precision_at_5
value: 14.841
- type: recall_at_1
value: 29.866
- type: recall_at_10
value: 56.06100000000001
- type: recall_at_100
value: 75.809
- type: recall_at_1000
value: 89.875
- type: recall_at_3
value: 44.707
- type: recall_at_5
value: 49.846000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGamingRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 38.985
- type: map_at_10
value: 51.165000000000006
- type: map_at_100
value: 52.17
- type: map_at_1000
value: 52.229000000000006
- type: map_at_3
value: 48.089999999999996
- type: map_at_5
value: 49.762
- type: mrr_at_1
value: 44.577
- type: mrr_at_10
value: 54.493
- type: mrr_at_100
value: 55.137
- type: mrr_at_1000
value: 55.167
- type: mrr_at_3
value: 52.079
- type: mrr_at_5
value: 53.518
- type: ndcg_at_1
value: 44.577
- type: ndcg_at_10
value: 56.825
- type: ndcg_at_100
value: 60.842
- type: ndcg_at_1000
value: 62.015
- type: ndcg_at_3
value: 51.699
- type: ndcg_at_5
value: 54.11
- type: precision_at_1
value: 44.577
- type: precision_at_10
value: 9.11
- type: precision_at_100
value: 1.206
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 23.156
- type: precision_at_5
value: 15.737000000000002
- type: recall_at_1
value: 38.985
- type: recall_at_10
value: 70.164
- type: recall_at_100
value: 87.708
- type: recall_at_1000
value: 95.979
- type: recall_at_3
value: 56.285
- type: recall_at_5
value: 62.303
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGisRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 28.137
- type: map_at_10
value: 36.729
- type: map_at_100
value: 37.851
- type: map_at_1000
value: 37.932
- type: map_at_3
value: 34.074
- type: map_at_5
value: 35.398
- type: mrr_at_1
value: 30.621
- type: mrr_at_10
value: 39.007
- type: mrr_at_100
value: 39.961
- type: mrr_at_1000
value: 40.02
- type: mrr_at_3
value: 36.591
- type: mrr_at_5
value: 37.806
- type: ndcg_at_1
value: 30.621
- type: ndcg_at_10
value: 41.772
- type: ndcg_at_100
value: 47.181
- type: ndcg_at_1000
value: 49.053999999999995
- type: ndcg_at_3
value: 36.577
- type: ndcg_at_5
value: 38.777
- type: precision_at_1
value: 30.621
- type: precision_at_10
value: 6.372999999999999
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 15.367
- type: precision_at_5
value: 10.531
- type: recall_at_1
value: 28.137
- type: recall_at_10
value: 55.162
- type: recall_at_100
value: 79.931
- type: recall_at_1000
value: 93.67
- type: recall_at_3
value: 41.057
- type: recall_at_5
value: 46.327
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackMathematicaRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 16.798
- type: map_at_10
value: 25.267
- type: map_at_100
value: 26.579000000000004
- type: map_at_1000
value: 26.697
- type: map_at_3
value: 22.456
- type: map_at_5
value: 23.912
- type: mrr_at_1
value: 20.771
- type: mrr_at_10
value: 29.843999999999998
- type: mrr_at_100
value: 30.849
- type: mrr_at_1000
value: 30.916
- type: mrr_at_3
value: 27.156000000000002
- type: mrr_at_5
value: 28.518
- type: ndcg_at_1
value: 20.771
- type: ndcg_at_10
value: 30.792
- type: ndcg_at_100
value: 36.945
- type: ndcg_at_1000
value: 39.619
- type: ndcg_at_3
value: 25.52
- type: ndcg_at_5
value: 27.776
- type: precision_at_1
value: 20.771
- type: precision_at_10
value: 5.734
- type: precision_at_100
value: 1.031
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 12.148
- type: precision_at_5
value: 9.055
- type: recall_at_1
value: 16.798
- type: recall_at_10
value: 43.332
- type: recall_at_100
value: 70.016
- type: recall_at_1000
value: 88.90400000000001
- type: recall_at_3
value: 28.842000000000002
- type: recall_at_5
value: 34.37
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackPhysicsRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 31.180000000000003
- type: map_at_10
value: 41.78
- type: map_at_100
value: 43.102000000000004
- type: map_at_1000
value: 43.222
- type: map_at_3
value: 38.505
- type: map_at_5
value: 40.443
- type: mrr_at_1
value: 37.824999999999996
- type: mrr_at_10
value: 47.481
- type: mrr_at_100
value: 48.268
- type: mrr_at_1000
value: 48.313
- type: mrr_at_3
value: 44.946999999999996
- type: mrr_at_5
value: 46.492
- type: ndcg_at_1
value: 37.824999999999996
- type: ndcg_at_10
value: 47.827
- type: ndcg_at_100
value: 53.407000000000004
- type: ndcg_at_1000
value: 55.321
- type: ndcg_at_3
value: 42.815
- type: ndcg_at_5
value: 45.363
- type: precision_at_1
value: 37.824999999999996
- type: precision_at_10
value: 8.652999999999999
- type: precision_at_100
value: 1.354
- type: precision_at_1000
value: 0.172
- type: precision_at_3
value: 20.372
- type: precision_at_5
value: 14.591000000000001
- type: recall_at_1
value: 31.180000000000003
- type: recall_at_10
value: 59.894000000000005
- type: recall_at_100
value: 83.722
- type: recall_at_1000
value: 95.705
- type: recall_at_3
value: 45.824
- type: recall_at_5
value: 52.349999999999994
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackProgrammersRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 24.66
- type: map_at_10
value: 34.141
- type: map_at_100
value: 35.478
- type: map_at_1000
value: 35.594
- type: map_at_3
value: 30.446
- type: map_at_5
value: 32.583
- type: mrr_at_1
value: 29.909000000000002
- type: mrr_at_10
value: 38.949
- type: mrr_at_100
value: 39.803
- type: mrr_at_1000
value: 39.867999999999995
- type: mrr_at_3
value: 35.921
- type: mrr_at_5
value: 37.753
- type: ndcg_at_1
value: 29.909000000000002
- type: ndcg_at_10
value: 40.012
- type: ndcg_at_100
value: 45.707
- type: ndcg_at_1000
value: 48.15
- type: ndcg_at_3
value: 34.015
- type: ndcg_at_5
value: 37.002
- type: precision_at_1
value: 29.909000000000002
- type: precision_at_10
value: 7.693999999999999
- type: precision_at_100
value: 1.2229999999999999
- type: precision_at_1000
value: 0.16
- type: precision_at_3
value: 16.323999999999998
- type: precision_at_5
value: 12.306000000000001
- type: recall_at_1
value: 24.66
- type: recall_at_10
value: 52.478
- type: recall_at_100
value: 77.051
- type: recall_at_1000
value: 93.872
- type: recall_at_3
value: 36.382999999999996
- type: recall_at_5
value: 43.903999999999996
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 26.768416666666667
- type: map_at_10
value: 36.2485
- type: map_at_100
value: 37.520833333333336
- type: map_at_1000
value: 37.64033333333334
- type: map_at_3
value: 33.25791666666667
- type: map_at_5
value: 34.877250000000004
- type: mrr_at_1
value: 31.65408333333334
- type: mrr_at_10
value: 40.43866666666667
- type: mrr_at_100
value: 41.301249999999996
- type: mrr_at_1000
value: 41.357499999999995
- type: mrr_at_3
value: 37.938916666666664
- type: mrr_at_5
value: 39.35183333333334
- type: ndcg_at_1
value: 31.65408333333334
- type: ndcg_at_10
value: 41.76983333333334
- type: ndcg_at_100
value: 47.138
- type: ndcg_at_1000
value: 49.33816666666667
- type: ndcg_at_3
value: 36.76683333333333
- type: ndcg_at_5
value: 39.04441666666666
- type: precision_at_1
value: 31.65408333333334
- type: precision_at_10
value: 7.396249999999998
- type: precision_at_100
value: 1.1974166666666666
- type: precision_at_1000
value: 0.15791666666666668
- type: precision_at_3
value: 16.955583333333333
- type: precision_at_5
value: 12.09925
- type: recall_at_1
value: 26.768416666666667
- type: recall_at_10
value: 53.82366666666667
- type: recall_at_100
value: 77.39600000000002
- type: recall_at_1000
value: 92.46300000000001
- type: recall_at_3
value: 39.90166666666667
- type: recall_at_5
value: 45.754000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackStatsRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 24.369
- type: map_at_10
value: 32.025
- type: map_at_100
value: 33.08
- type: map_at_1000
value: 33.169
- type: map_at_3
value: 29.589
- type: map_at_5
value: 30.894
- type: mrr_at_1
value: 27.301
- type: mrr_at_10
value: 34.64
- type: mrr_at_100
value: 35.556
- type: mrr_at_1000
value: 35.616
- type: mrr_at_3
value: 32.515
- type: mrr_at_5
value: 33.666000000000004
- type: ndcg_at_1
value: 27.301
- type: ndcg_at_10
value: 36.386
- type: ndcg_at_100
value: 41.598
- type: ndcg_at_1000
value: 43.864999999999995
- type: ndcg_at_3
value: 32.07
- type: ndcg_at_5
value: 34.028999999999996
- type: precision_at_1
value: 27.301
- type: precision_at_10
value: 5.782
- type: precision_at_100
value: 0.923
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 13.804
- type: precision_at_5
value: 9.693
- type: recall_at_1
value: 24.369
- type: recall_at_10
value: 47.026
- type: recall_at_100
value: 70.76400000000001
- type: recall_at_1000
value: 87.705
- type: recall_at_3
value: 35.366
- type: recall_at_5
value: 40.077
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackTexRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 17.878
- type: map_at_10
value: 25.582
- type: map_at_100
value: 26.848
- type: map_at_1000
value: 26.985
- type: map_at_3
value: 22.997
- type: map_at_5
value: 24.487000000000002
- type: mrr_at_1
value: 22.023
- type: mrr_at_10
value: 29.615000000000002
- type: mrr_at_100
value: 30.656
- type: mrr_at_1000
value: 30.737
- type: mrr_at_3
value: 27.322999999999997
- type: mrr_at_5
value: 28.665000000000003
- type: ndcg_at_1
value: 22.023
- type: ndcg_at_10
value: 30.476999999999997
- type: ndcg_at_100
value: 36.258
- type: ndcg_at_1000
value: 39.287
- type: ndcg_at_3
value: 25.995
- type: ndcg_at_5
value: 28.174
- type: precision_at_1
value: 22.023
- type: precision_at_10
value: 5.657
- type: precision_at_100
value: 1.01
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 12.491
- type: precision_at_5
value: 9.112
- type: recall_at_1
value: 17.878
- type: recall_at_10
value: 41.155
- type: recall_at_100
value: 66.62599999999999
- type: recall_at_1000
value: 88.08200000000001
- type: recall_at_3
value: 28.505000000000003
- type: recall_at_5
value: 34.284
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackUnixRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 26.369999999999997
- type: map_at_10
value: 36.115
- type: map_at_100
value: 37.346000000000004
- type: map_at_1000
value: 37.449
- type: map_at_3
value: 32.976
- type: map_at_5
value: 34.782000000000004
- type: mrr_at_1
value: 30.784
- type: mrr_at_10
value: 40.014
- type: mrr_at_100
value: 40.913
- type: mrr_at_1000
value: 40.967999999999996
- type: mrr_at_3
value: 37.205
- type: mrr_at_5
value: 38.995999999999995
- type: ndcg_at_1
value: 30.784
- type: ndcg_at_10
value: 41.797000000000004
- type: ndcg_at_100
value: 47.355000000000004
- type: ndcg_at_1000
value: 49.535000000000004
- type: ndcg_at_3
value: 36.29
- type: ndcg_at_5
value: 39.051
- type: precision_at_1
value: 30.784
- type: precision_at_10
value: 7.164
- type: precision_at_100
value: 1.122
- type: precision_at_1000
value: 0.14200000000000002
- type: precision_at_3
value: 16.636
- type: precision_at_5
value: 11.996
- type: recall_at_1
value: 26.369999999999997
- type: recall_at_10
value: 55.010000000000005
- type: recall_at_100
value: 79.105
- type: recall_at_1000
value: 94.053
- type: recall_at_3
value: 40.139
- type: recall_at_5
value: 47.089
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWebmastersRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 26.421
- type: map_at_10
value: 35.253
- type: map_at_100
value: 36.97
- type: map_at_1000
value: 37.195
- type: map_at_3
value: 32.068000000000005
- type: map_at_5
value: 33.763
- type: mrr_at_1
value: 31.423000000000002
- type: mrr_at_10
value: 39.995999999999995
- type: mrr_at_100
value: 40.977999999999994
- type: mrr_at_1000
value: 41.024
- type: mrr_at_3
value: 36.989
- type: mrr_at_5
value: 38.629999999999995
- type: ndcg_at_1
value: 31.423000000000002
- type: ndcg_at_10
value: 41.382000000000005
- type: ndcg_at_100
value: 47.532000000000004
- type: ndcg_at_1000
value: 49.829
- type: ndcg_at_3
value: 35.809000000000005
- type: ndcg_at_5
value: 38.308
- type: precision_at_1
value: 31.423000000000002
- type: precision_at_10
value: 7.885000000000001
- type: precision_at_100
value: 1.609
- type: precision_at_1000
value: 0.246
- type: precision_at_3
value: 16.469
- type: precision_at_5
value: 12.174
- type: recall_at_1
value: 26.421
- type: recall_at_10
value: 53.618
- type: recall_at_100
value: 80.456
- type: recall_at_1000
value: 94.505
- type: recall_at_3
value: 37.894
- type: recall_at_5
value: 44.352999999999994
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWordpressRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 21.54
- type: map_at_10
value: 29.468
- type: map_at_100
value: 30.422
- type: map_at_1000
value: 30.542
- type: map_at_3
value: 26.888
- type: map_at_5
value: 27.962999999999997
- type: mrr_at_1
value: 23.29
- type: mrr_at_10
value: 31.176
- type: mrr_at_100
value: 32.046
- type: mrr_at_1000
value: 32.129000000000005
- type: mrr_at_3
value: 28.804999999999996
- type: mrr_at_5
value: 29.868
- type: ndcg_at_1
value: 23.29
- type: ndcg_at_10
value: 34.166000000000004
- type: ndcg_at_100
value: 39.217999999999996
- type: ndcg_at_1000
value: 41.964
- type: ndcg_at_3
value: 28.970000000000002
- type: ndcg_at_5
value: 30.797
- type: precision_at_1
value: 23.29
- type: precision_at_10
value: 5.489999999999999
- type: precision_at_100
value: 0.874
- type: precision_at_1000
value: 0.122
- type: precision_at_3
value: 12.261
- type: precision_at_5
value: 8.503
- type: recall_at_1
value: 21.54
- type: recall_at_10
value: 47.064
- type: recall_at_100
value: 70.959
- type: recall_at_1000
value: 91.032
- type: recall_at_3
value: 32.828
- type: recall_at_5
value: 37.214999999999996
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: None
split: test
type: climate-fever
metrics:
- type: map_at_1
value: 10.102
- type: map_at_10
value: 17.469
- type: map_at_100
value: 19.244
- type: map_at_1000
value: 19.435
- type: map_at_3
value: 14.257
- type: map_at_5
value: 16.028000000000002
- type: mrr_at_1
value: 22.866
- type: mrr_at_10
value: 33.535
- type: mrr_at_100
value: 34.583999999999996
- type: mrr_at_1000
value: 34.622
- type: mrr_at_3
value: 29.946
- type: mrr_at_5
value: 32.157000000000004
- type: ndcg_at_1
value: 22.866
- type: ndcg_at_10
value: 25.16
- type: ndcg_at_100
value: 32.347
- type: ndcg_at_1000
value: 35.821
- type: ndcg_at_3
value: 19.816
- type: ndcg_at_5
value: 22.026
- type: precision_at_1
value: 22.866
- type: precision_at_10
value: 8.072
- type: precision_at_100
value: 1.5709999999999997
- type: precision_at_1000
value: 0.22200000000000003
- type: precision_at_3
value: 14.701
- type: precision_at_5
value: 11.960999999999999
- type: recall_at_1
value: 10.102
- type: recall_at_10
value: 31.086000000000002
- type: recall_at_100
value: 55.896
- type: recall_at_1000
value: 75.375
- type: recall_at_3
value: 18.343999999999998
- type: recall_at_5
value: 24.102
task:
type: Retrieval
- dataset:
config: default
name: MTEB DBPedia
revision: None
split: test
type: dbpedia-entity
metrics:
- type: map_at_1
value: 7.961
- type: map_at_10
value: 16.058
- type: map_at_100
value: 21.878
- type: map_at_1000
value: 23.156
- type: map_at_3
value: 12.206999999999999
- type: map_at_5
value: 13.747000000000002
- type: mrr_at_1
value: 60.5
- type: mrr_at_10
value: 68.488
- type: mrr_at_100
value: 69.02199999999999
- type: mrr_at_1000
value: 69.03200000000001
- type: mrr_at_3
value: 66.792
- type: mrr_at_5
value: 67.62899999999999
- type: ndcg_at_1
value: 49.125
- type: ndcg_at_10
value: 34.827999999999996
- type: ndcg_at_100
value: 38.723
- type: ndcg_at_1000
value: 45.988
- type: ndcg_at_3
value: 40.302
- type: ndcg_at_5
value: 36.781000000000006
- type: precision_at_1
value: 60.5
- type: precision_at_10
value: 26.825
- type: precision_at_100
value: 8.445
- type: precision_at_1000
value: 1.7000000000000002
- type: precision_at_3
value: 43.25
- type: precision_at_5
value: 34.5
- type: recall_at_1
value: 7.961
- type: recall_at_10
value: 20.843
- type: recall_at_100
value: 43.839
- type: recall_at_1000
value: 67.33
- type: recall_at_3
value: 13.516
- type: recall_at_5
value: 15.956000000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 52.06000000000001
- type: f1
value: 47.21494728335567
task:
type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: None
split: test
type: fever
metrics:
- type: map_at_1
value: 56.798
- type: map_at_10
value: 67.644
- type: map_at_100
value: 68.01700000000001
- type: map_at_1000
value: 68.038
- type: map_at_3
value: 65.539
- type: map_at_5
value: 66.912
- type: mrr_at_1
value: 61.221000000000004
- type: mrr_at_10
value: 71.97099999999999
- type: mrr_at_100
value: 72.262
- type: mrr_at_1000
value: 72.27
- type: mrr_at_3
value: 70.052
- type: mrr_at_5
value: 71.324
- type: ndcg_at_1
value: 61.221000000000004
- type: ndcg_at_10
value: 73.173
- type: ndcg_at_100
value: 74.779
- type: ndcg_at_1000
value: 75.229
- type: ndcg_at_3
value: 69.291
- type: ndcg_at_5
value: 71.552
- type: precision_at_1
value: 61.221000000000004
- type: precision_at_10
value: 9.449
- type: precision_at_100
value: 1.0370000000000001
- type: precision_at_1000
value: 0.109
- type: precision_at_3
value: 27.467999999999996
- type: precision_at_5
value: 17.744
- type: recall_at_1
value: 56.798
- type: recall_at_10
value: 85.991
- type: recall_at_100
value: 92.973
- type: recall_at_1000
value: 96.089
- type: recall_at_3
value: 75.576
- type: recall_at_5
value: 81.12
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA2018
revision: None
split: test
type: fiqa
metrics:
- type: map_at_1
value: 18.323
- type: map_at_10
value: 30.279
- type: map_at_100
value: 32.153999999999996
- type: map_at_1000
value: 32.339
- type: map_at_3
value: 26.336
- type: map_at_5
value: 28.311999999999998
- type: mrr_at_1
value: 35.339999999999996
- type: mrr_at_10
value: 44.931
- type: mrr_at_100
value: 45.818999999999996
- type: mrr_at_1000
value: 45.864
- type: mrr_at_3
value: 42.618
- type: mrr_at_5
value: 43.736999999999995
- type: ndcg_at_1
value: 35.339999999999996
- type: ndcg_at_10
value: 37.852999999999994
- type: ndcg_at_100
value: 44.888
- type: ndcg_at_1000
value: 48.069
- type: ndcg_at_3
value: 34.127
- type: ndcg_at_5
value: 35.026
- type: precision_at_1
value: 35.339999999999996
- type: precision_at_10
value: 10.617
- type: precision_at_100
value: 1.7930000000000001
- type: precision_at_1000
value: 0.23600000000000002
- type: precision_at_3
value: 22.582
- type: precision_at_5
value: 16.605
- type: recall_at_1
value: 18.323
- type: recall_at_10
value: 44.948
- type: recall_at_100
value: 71.11800000000001
- type: recall_at_1000
value: 90.104
- type: recall_at_3
value: 31.661
- type: recall_at_5
value: 36.498000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA
revision: None
split: test
type: hotpotqa
metrics:
- type: map_at_1
value: 30.668
- type: map_at_10
value: 43.669999999999995
- type: map_at_100
value: 44.646
- type: map_at_1000
value: 44.731
- type: map_at_3
value: 40.897
- type: map_at_5
value: 42.559999999999995
- type: mrr_at_1
value: 61.336999999999996
- type: mrr_at_10
value: 68.496
- type: mrr_at_100
value: 68.916
- type: mrr_at_1000
value: 68.938
- type: mrr_at_3
value: 66.90700000000001
- type: mrr_at_5
value: 67.91199999999999
- type: ndcg_at_1
value: 61.336999999999996
- type: ndcg_at_10
value: 52.588
- type: ndcg_at_100
value: 56.389
- type: ndcg_at_1000
value: 58.187999999999995
- type: ndcg_at_3
value: 48.109
- type: ndcg_at_5
value: 50.498
- type: precision_at_1
value: 61.336999999999996
- type: precision_at_10
value: 11.033
- type: precision_at_100
value: 1.403
- type: precision_at_1000
value: 0.164
- type: precision_at_3
value: 30.105999999999998
- type: precision_at_5
value: 19.954
- type: recall_at_1
value: 30.668
- type: recall_at_10
value: 55.165
- type: recall_at_100
value: 70.169
- type: recall_at_1000
value: 82.12
- type: recall_at_3
value: 45.159
- type: recall_at_5
value: 49.885000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 78.542
- type: ap
value: 72.50692137216646
- type: f1
value: 78.40630687221642
task:
type: Classification
- dataset:
config: default
name: MTEB MSMARCO
revision: None
split: dev
type: msmarco
metrics:
- type: map_at_1
value: 18.613
- type: map_at_10
value: 29.98
- type: map_at_100
value: 31.136999999999997
- type: map_at_1000
value: 31.196
- type: map_at_3
value: 26.339000000000002
- type: map_at_5
value: 28.351
- type: mrr_at_1
value: 19.054
- type: mrr_at_10
value: 30.476
- type: mrr_at_100
value: 31.588
- type: mrr_at_1000
value: 31.641000000000002
- type: mrr_at_3
value: 26.834000000000003
- type: mrr_at_5
value: 28.849000000000004
- type: ndcg_at_1
value: 19.083
- type: ndcg_at_10
value: 36.541000000000004
- type: ndcg_at_100
value: 42.35
- type: ndcg_at_1000
value: 43.9
- type: ndcg_at_3
value: 29.015
- type: ndcg_at_5
value: 32.622
- type: precision_at_1
value: 19.083
- type: precision_at_10
value: 5.914
- type: precision_at_100
value: 0.889
- type: precision_at_1000
value: 0.10200000000000001
- type: precision_at_3
value: 12.483
- type: precision_at_5
value: 9.315
- type: recall_at_1
value: 18.613
- type: recall_at_10
value: 56.88999999999999
- type: recall_at_100
value: 84.207
- type: recall_at_1000
value: 96.20100000000001
- type: recall_at_3
value: 36.262
- type: recall_at_5
value: 44.925
task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 94.77656178750571
- type: f1
value: 94.37966073742972
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 77.72457820337438
- type: f1
value: 59.11327646329634
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 73.17753866846
- type: f1
value: 71.22604635414544
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 76.67787491593813
- type: f1
value: 76.87653151298177
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure
value: 33.3485843514749
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: v_measure
value: 29.792796913883617
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 31.310305659169963
- type: mrr
value: 32.38286775798406
task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus
revision: None
split: test
type: nfcorpus
metrics:
- type: map_at_1
value: 4.968
- type: map_at_10
value: 11.379
- type: map_at_100
value: 14.618999999999998
- type: map_at_1000
value: 16.055
- type: map_at_3
value: 8.34
- type: map_at_5
value: 9.690999999999999
- type: mrr_at_1
value: 43.034
- type: mrr_at_10
value: 51.019999999999996
- type: mrr_at_100
value: 51.63100000000001
- type: mrr_at_1000
value: 51.681
- type: mrr_at_3
value: 49.174
- type: mrr_at_5
value: 50.181
- type: ndcg_at_1
value: 41.176
- type: ndcg_at_10
value: 31.341
- type: ndcg_at_100
value: 29.451
- type: ndcg_at_1000
value: 38.007000000000005
- type: ndcg_at_3
value: 36.494
- type: ndcg_at_5
value: 34.499
- type: precision_at_1
value: 43.034
- type: precision_at_10
value: 23.375
- type: precision_at_100
value: 7.799
- type: precision_at_1000
value: 2.059
- type: precision_at_3
value: 34.675
- type: precision_at_5
value: 30.154999999999998
- type: recall_at_1
value: 4.968
- type: recall_at_10
value: 15.104999999999999
- type: recall_at_100
value: 30.741000000000003
- type: recall_at_1000
value: 61.182
- type: recall_at_3
value: 9.338000000000001
- type: recall_at_5
value: 11.484
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ
revision: None
split: test
type: nq
metrics:
- type: map_at_1
value: 23.716
- type: map_at_10
value: 38.32
- type: map_at_100
value: 39.565
- type: map_at_1000
value: 39.602
- type: map_at_3
value: 33.848
- type: map_at_5
value: 36.471
- type: mrr_at_1
value: 26.912000000000003
- type: mrr_at_10
value: 40.607
- type: mrr_at_100
value: 41.589
- type: mrr_at_1000
value: 41.614000000000004
- type: mrr_at_3
value: 36.684
- type: mrr_at_5
value: 39.036
- type: ndcg_at_1
value: 26.883000000000003
- type: ndcg_at_10
value: 46.096
- type: ndcg_at_100
value: 51.513
- type: ndcg_at_1000
value: 52.366
- type: ndcg_at_3
value: 37.549
- type: ndcg_at_5
value: 41.971000000000004
- type: precision_at_1
value: 26.883000000000003
- type: precision_at_10
value: 8.004
- type: precision_at_100
value: 1.107
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 17.516000000000002
- type: precision_at_5
value: 13.019
- type: recall_at_1
value: 23.716
- type: recall_at_10
value: 67.656
- type: recall_at_100
value: 91.413
- type: recall_at_1000
value: 97.714
- type: recall_at_3
value: 45.449
- type: recall_at_5
value: 55.598000000000006
task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: quora
metrics:
- type: map_at_1
value: 70.486
- type: map_at_10
value: 84.292
- type: map_at_100
value: 84.954
- type: map_at_1000
value: 84.969
- type: map_at_3
value: 81.295
- type: map_at_5
value: 83.165
- type: mrr_at_1
value: 81.16
- type: mrr_at_10
value: 87.31
- type: mrr_at_100
value: 87.423
- type: mrr_at_1000
value: 87.423
- type: mrr_at_3
value: 86.348
- type: mrr_at_5
value: 86.991
- type: ndcg_at_1
value: 81.17
- type: ndcg_at_10
value: 88.067
- type: ndcg_at_100
value: 89.34
- type: ndcg_at_1000
value: 89.43900000000001
- type: ndcg_at_3
value: 85.162
- type: ndcg_at_5
value: 86.752
- type: precision_at_1
value: 81.17
- type: precision_at_10
value: 13.394
- type: precision_at_100
value: 1.5310000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.193
- type: precision_at_5
value: 24.482
- type: recall_at_1
value: 70.486
- type: recall_at_10
value: 95.184
- type: recall_at_100
value: 99.53999999999999
- type: recall_at_1000
value: 99.98700000000001
- type: recall_at_3
value: 86.89
- type: recall_at_5
value: 91.365
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 44.118229475102154
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 48.68049097629063
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: scidocs
metrics:
- type: map_at_1
value: 4.888
- type: map_at_10
value: 12.770999999999999
- type: map_at_100
value: 15.238
- type: map_at_1000
value: 15.616
- type: map_at_3
value: 8.952
- type: map_at_5
value: 10.639999999999999
- type: mrr_at_1
value: 24.099999999999998
- type: mrr_at_10
value: 35.375
- type: mrr_at_100
value: 36.442
- type: mrr_at_1000
value: 36.488
- type: mrr_at_3
value: 31.717000000000002
- type: mrr_at_5
value: 33.722
- type: ndcg_at_1
value: 24.099999999999998
- type: ndcg_at_10
value: 21.438
- type: ndcg_at_100
value: 30.601
- type: ndcg_at_1000
value: 36.678
- type: ndcg_at_3
value: 19.861
- type: ndcg_at_5
value: 17.263
- type: precision_at_1
value: 24.099999999999998
- type: precision_at_10
value: 11.4
- type: precision_at_100
value: 2.465
- type: precision_at_1000
value: 0.392
- type: precision_at_3
value: 18.733
- type: precision_at_5
value: 15.22
- type: recall_at_1
value: 4.888
- type: recall_at_10
value: 23.118
- type: recall_at_100
value: 49.995
- type: recall_at_1000
value: 79.577
- type: recall_at_3
value: 11.398
- type: recall_at_5
value: 15.428
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
metrics:
- type: cos_sim_pearson
value: 85.33198632617024
- type: cos_sim_spearman
value: 79.09232997136625
- type: euclidean_pearson
value: 81.49986011523868
- type: euclidean_spearman
value: 77.03530620283338
- type: manhattan_pearson
value: 81.4741227286667
- type: manhattan_spearman
value: 76.98641133116311
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cos_sim_pearson
value: 84.60103674582464
- type: cos_sim_spearman
value: 75.03945035801914
- type: euclidean_pearson
value: 80.82455267481467
- type: euclidean_spearman
value: 70.3317366248871
- type: manhattan_pearson
value: 80.8928091531445
- type: manhattan_spearman
value: 70.43207370945672
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cos_sim_pearson
value: 82.52453177109315
- type: cos_sim_spearman
value: 83.26431569305103
- type: euclidean_pearson
value: 82.10494657997404
- type: euclidean_spearman
value: 83.41028425949024
- type: manhattan_pearson
value: 82.08669822983934
- type: manhattan_spearman
value: 83.39959776442115
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cos_sim_pearson
value: 82.67472020277681
- type: cos_sim_spearman
value: 78.61877889763109
- type: euclidean_pearson
value: 80.07878012437722
- type: euclidean_spearman
value: 77.44374494215397
- type: manhattan_pearson
value: 79.95988483102258
- type: manhattan_spearman
value: 77.36018101061366
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cos_sim_pearson
value: 85.55450610494437
- type: cos_sim_spearman
value: 87.03494331841401
- type: euclidean_pearson
value: 81.4319784394287
- type: euclidean_spearman
value: 82.47893040599372
- type: manhattan_pearson
value: 81.32627203699644
- type: manhattan_spearman
value: 82.40660565070675
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cos_sim_pearson
value: 81.51576965454805
- type: cos_sim_spearman
value: 83.0062959588245
- type: euclidean_pearson
value: 79.98888882568556
- type: euclidean_spearman
value: 81.08948911791873
- type: manhattan_pearson
value: 79.77952719568583
- type: manhattan_spearman
value: 80.79471040445408
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 87.28313046682885
- type: cos_sim_spearman
value: 87.35865211085007
- type: euclidean_pearson
value: 84.11501613667811
- type: euclidean_spearman
value: 82.82038954956121
- type: manhattan_pearson
value: 83.891278147302
- type: manhattan_spearman
value: 82.59947685165902
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 67.80653738006102
- type: cos_sim_spearman
value: 68.11259151179601
- type: euclidean_pearson
value: 43.16707985094242
- type: euclidean_spearman
value: 58.96200382968696
- type: manhattan_pearson
value: 43.84146858566507
- type: manhattan_spearman
value: 59.05193977207514
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cos_sim_pearson
value: 82.62068205073571
- type: cos_sim_spearman
value: 84.40071593577095
- type: euclidean_pearson
value: 80.90824726252514
- type: euclidean_spearman
value: 80.54974812534094
- type: manhattan_pearson
value: 80.6759008187939
- type: manhattan_spearman
value: 80.31149103896973
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 87.13774787530915
- type: mrr
value: 96.22233793802422
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: None
split: test
type: scifact
metrics:
- type: map_at_1
value: 49.167
- type: map_at_10
value: 59.852000000000004
- type: map_at_100
value: 60.544
- type: map_at_1000
value: 60.577000000000005
- type: map_at_3
value: 57.242000000000004
- type: map_at_5
value: 58.704
- type: mrr_at_1
value: 51
- type: mrr_at_10
value: 60.575
- type: mrr_at_100
value: 61.144
- type: mrr_at_1000
value: 61.175000000000004
- type: mrr_at_3
value: 58.667
- type: mrr_at_5
value: 59.599999999999994
- type: ndcg_at_1
value: 51
- type: ndcg_at_10
value: 64.398
- type: ndcg_at_100
value: 67.581
- type: ndcg_at_1000
value: 68.551
- type: ndcg_at_3
value: 59.928000000000004
- type: ndcg_at_5
value: 61.986
- type: precision_at_1
value: 51
- type: precision_at_10
value: 8.7
- type: precision_at_100
value: 1.047
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 23.666999999999998
- type: precision_at_5
value: 15.6
- type: recall_at_1
value: 49.167
- type: recall_at_10
value: 77.333
- type: recall_at_100
value: 91.833
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 65.594
- type: recall_at_5
value: 70.52199999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_accuracy
value: 99.77227722772277
- type: cos_sim_ap
value: 94.14261011689366
- type: cos_sim_f1
value: 88.37209302325581
- type: cos_sim_precision
value: 89.36605316973414
- type: cos_sim_recall
value: 87.4
- type: dot_accuracy
value: 99.07128712871287
- type: dot_ap
value: 27.325649239129486
- type: dot_f1
value: 33.295838020247466
- type: dot_precision
value: 38.04627249357326
- type: dot_recall
value: 29.599999999999998
- type: euclidean_accuracy
value: 99.74158415841585
- type: euclidean_ap
value: 92.32695359979576
- type: euclidean_f1
value: 86.90534575772439
- type: euclidean_precision
value: 85.27430221366699
- type: euclidean_recall
value: 88.6
- type: manhattan_accuracy
value: 99.74257425742574
- type: manhattan_ap
value: 92.40335687760499
- type: manhattan_f1
value: 86.96507624200687
- type: manhattan_precision
value: 85.57599225556632
- type: manhattan_recall
value: 88.4
- type: max_accuracy
value: 99.77227722772277
- type: max_ap
value: 94.14261011689366
- type: max_f1
value: 88.37209302325581
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 53.113809982945035
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 33.90915908471812
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 50.36481271702464
- type: mrr
value: 51.05628236142942
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cos_sim_pearson
value: 30.311305530381826
- type: cos_sim_spearman
value: 31.22029657606254
- type: dot_pearson
value: 12.157032445910177
- type: dot_spearman
value: 13.275185888551805
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: trec-covid
metrics:
- type: map_at_1
value: 0.167
- type: map_at_10
value: 1.113
- type: map_at_100
value: 5.926
- type: map_at_1000
value: 15.25
- type: map_at_3
value: 0.414
- type: map_at_5
value: 0.633
- type: mrr_at_1
value: 64
- type: mrr_at_10
value: 74.444
- type: mrr_at_100
value: 74.667
- type: mrr_at_1000
value: 74.679
- type: mrr_at_3
value: 72
- type: mrr_at_5
value: 74
- type: ndcg_at_1
value: 59
- type: ndcg_at_10
value: 51.468
- type: ndcg_at_100
value: 38.135000000000005
- type: ndcg_at_1000
value: 36.946
- type: ndcg_at_3
value: 55.827000000000005
- type: ndcg_at_5
value: 53.555
- type: precision_at_1
value: 64
- type: precision_at_10
value: 54.400000000000006
- type: precision_at_100
value: 39.08
- type: precision_at_1000
value: 16.618
- type: precision_at_3
value: 58.667
- type: precision_at_5
value: 56.8
- type: recall_at_1
value: 0.167
- type: recall_at_10
value: 1.38
- type: recall_at_100
value: 9.189
- type: recall_at_1000
value: 35.737
- type: recall_at_3
value: 0.455
- type: recall_at_5
value: 0.73
task:
type: Retrieval
- dataset:
config: default
name: MTEB Touche2020
revision: None
split: test
type: webis-touche2020
metrics:
- type: map_at_1
value: 2.4299999999999997
- type: map_at_10
value: 8.539
- type: map_at_100
value: 14.155999999999999
- type: map_at_1000
value: 15.684999999999999
- type: map_at_3
value: 3.857
- type: map_at_5
value: 5.583
- type: mrr_at_1
value: 26.531
- type: mrr_at_10
value: 40.489999999999995
- type: mrr_at_100
value: 41.772999999999996
- type: mrr_at_1000
value: 41.772999999999996
- type: mrr_at_3
value: 35.034
- type: mrr_at_5
value: 38.81
- type: ndcg_at_1
value: 21.429000000000002
- type: ndcg_at_10
value: 20.787
- type: ndcg_at_100
value: 33.202
- type: ndcg_at_1000
value: 45.167
- type: ndcg_at_3
value: 18.233
- type: ndcg_at_5
value: 19.887
- type: precision_at_1
value: 26.531
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.4079999999999995
- type: precision_at_1000
value: 1.5310000000000001
- type: precision_at_3
value: 19.728
- type: precision_at_5
value: 21.633
- type: recall_at_1
value: 2.4299999999999997
- type: recall_at_10
value: 14.901
- type: recall_at_100
value: 46.422000000000004
- type: recall_at_1000
value: 82.83500000000001
- type: recall_at_3
value: 4.655
- type: recall_at_5
value: 8.092
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 72.90140000000001
- type: ap
value: 15.138716624430662
- type: f1
value: 56.08803013269606
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 59.85285795132994
- type: f1
value: 60.17575819903709
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 41.125150148437065
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_accuracy
value: 84.96751505036657
- type: cos_sim_ap
value: 70.45642872444971
- type: cos_sim_f1
value: 65.75274793133259
- type: cos_sim_precision
value: 61.806361736707686
- type: cos_sim_recall
value: 70.23746701846966
- type: dot_accuracy
value: 77.84466829588126
- type: dot_ap
value: 32.49904328313596
- type: dot_f1
value: 37.903122189387126
- type: dot_precision
value: 25.050951086956523
- type: dot_recall
value: 77.83641160949868
- type: euclidean_accuracy
value: 84.5920009536866
- type: euclidean_ap
value: 68.83700633574043
- type: euclidean_f1
value: 64.92803542871202
- type: euclidean_precision
value: 60.820465545056464
- type: euclidean_recall
value: 69.63060686015831
- type: manhattan_accuracy
value: 84.52643500029802
- type: manhattan_ap
value: 68.63286046599892
- type: manhattan_f1
value: 64.7476540705047
- type: manhattan_precision
value: 62.3291015625
- type: manhattan_recall
value: 67.36147757255937
- type: max_accuracy
value: 84.96751505036657
- type: max_ap
value: 70.45642872444971
- type: max_f1
value: 65.75274793133259
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cos_sim_accuracy
value: 88.65603291031164
- type: cos_sim_ap
value: 85.58148320880878
- type: cos_sim_f1
value: 77.63202920041064
- type: cos_sim_precision
value: 76.68444377675957
- type: cos_sim_recall
value: 78.60332614721281
- type: dot_accuracy
value: 79.71048239996895
- type: dot_ap
value: 59.31114839296281
- type: dot_f1
value: 57.13895527483783
- type: dot_precision
value: 51.331125015335545
- type: dot_recall
value: 64.4287034185402
- type: euclidean_accuracy
value: 86.99305312997244
- type: euclidean_ap
value: 81.87075965254876
- type: euclidean_f1
value: 73.53543008715421
- type: euclidean_precision
value: 72.39964184450082
- type: euclidean_recall
value: 74.70742223591007
- type: manhattan_accuracy
value: 87.04156479217605
- type: manhattan_ap
value: 81.7850497283247
- type: manhattan_f1
value: 73.52951955143475
- type: manhattan_precision
value: 70.15875236030492
- type: manhattan_recall
value: 77.2405297197413
- type: max_accuracy
value: 88.65603291031164
- type: max_ap
value: 85.58148320880878
- type: max_f1
value: 77.63202920041064
task:
type: PairClassification
model_creator: avsolatorio
model_name: GIST-all-MiniLM-L6-v2
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- feature-extraction
- mteb
- sentence-similarity
- sentence-transformers
- gguf
- ggml
- quantized
GIST-all-MiniLM-L6-v2-GGUF
Quantized GGUF model files for GIST-all-MiniLM-L6-v2 from avsolatorio
Original Model Card:
GIST Embedding v0 - all-MiniLM-L6-v2
GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning
The model is fine-tuned on top of the sentence-transformers/all-MiniLM-L6-v2 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task).
The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
Technical paper: GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning
Data
The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
- Dataset: avsolatorio/medi-data-mteb_avs_triplets
- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
The dataset contains a task_type key, which can be used to select only the mteb classification tasks (prefixed with mteb_).
The MEDI Dataset is published in the following paper: One Embedder, Any Task: Instruction-Finetuned Text Embeddings.
The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance.
Usage
The model can be easily loaded using the Sentence Transformers library.
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
revision = None # Replace with the specific revision to ensure reproducibility if the model is updated.
model = SentenceTransformer("avsolatorio/GIST-all-MiniLM-L6-v2", revision=revision)
texts = [
"Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
"Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
"As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
]
# Compute embeddings
embeddings = model.encode(texts, convert_to_tensor=True)
# Compute cosine-similarity for each pair of sentences
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
print(scores.cpu().numpy())
Training Parameters
Below are the training parameters used to fine-tune the model:
Epochs = 40
Warmup ratio = 0.1
Learning rate = 5e-6
Batch size = 16
Checkpoint step = 102000
Contrastive loss temperature = 0.01
Evaluation
The model was evaluated using the MTEB Evaluation suite.
Citation
Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗
@article{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
journal={arXiv preprint arXiv:2402.16829},
year={2024},
URL={https://arxiv.org/abs/2402.16829}
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Acknowledgements
This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the Knowledge for Change Program (KCP) of the World Bank - RA-P503405-RESE-TF0C3444.
The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.