metadata
base_model: baai/bge-base-en-v1.5
language:
- en
library_name: model2vec
license: mit
model_name: brown-beetle-small-v1.1
tags:
- embeddings
- static-embeddings
- sentence-transformers
- mteb
model-index:
- name: brown-beetle-small-v1.1
results:
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 64.17541229385307
- type: ap
value: 16.60204697801977
- type: ap_weighted
value: 16.60204697801977
- type: f1
value: 52.85158979871349
- type: f1_weighted
value: 71.01772161371149
- type: main_score
value: 64.17541229385307
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 67.95522388059702
- type: ap
value: 30.57070988929934
- type: ap_weighted
value: 30.57070988929934
- type: f1
value: 61.90881643158208
- type: f1_weighted
value: 71.10993639617122
- type: main_score
value: 67.95522388059702
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 68.56845
- type: ap
value: 63.30584262469745
- type: ap_weighted
value: 63.30584262469745
- type: f1
value: 68.21389686056138
- type: f1_weighted
value: 68.21389686056136
- type: main_score
value: 68.56845
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 31.15
- type: f1
value: 30.66951259375703
- type: f1_weighted
value: 30.66951259375703
- type: main_score
value: 31.15
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
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value: 39.376
- type: map_at_1
value: 19.772000000000002
- type: map_at_10
value: 32.135000000000005
- type: map_at_100
value: 33.376
- type: map_at_1000
value: 33.414
- type: map_at_20
value: 32.945
- type: map_at_3
value: 28.294999999999998
- type: map_at_5
value: 30.226
- type: mrr_at_1
value: 20.34139402560455
- type: mrr_at_10
value: 32.33633069159379
- type: mrr_at_100
value: 33.58379647152725
- type: mrr_at_1000
value: 33.62159311523655
- type: mrr_at_20
value: 33.15193936028619
- type: mrr_at_3
value: 28.485064011379745
- type: mrr_at_5
value: 30.433854907539025
- type: nauc_map_at_1000_diff1
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- type: nauc_map_at_1000_std
value: 5.934864951031507
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value: 8.326065851380307
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value: 5.989633199631491
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value: 5.683690460635193
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value: 8.93189875813997
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value: -3.616457512351117
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value: 2.1414345673803385
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value: 4.919465780723746
- type: nauc_mrr_at_1000_diff1
value: 6.5322883708653
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value: 5.808593834461153
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value: 8.130679116211944
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- type: nauc_recall_at_1000_std
value: 64.85081531801451
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value: 52.51582113211646
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value: 12.93850053308348
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value: 16.63967790523289
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value: 17.521014546132687
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value: 8.93189875813997
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value: -3.616457512351117
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value: 20.784747001364558
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value: 28.169118488092863
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value: 3.7981721833670807
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value: 8.13067911621197
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value: 8.525156659630733
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value: 8.235240001481452
- type: nauc_recall_at_5_std
value: 10.337250476001598
- type: ndcg_at_1
value: 19.772000000000002
- type: ndcg_at_10
value: 39.376
- type: ndcg_at_100
value: 45.394
- type: ndcg_at_1000
value: 46.382
- type: ndcg_at_20
value: 42.325
- type: ndcg_at_3
value: 31.295
- type: ndcg_at_5
value: 34.778
- type: precision_at_1
value: 19.772000000000002
- type: precision_at_10
value: 6.265999999999999
- type: precision_at_100
value: 0.9079999999999999
- type: precision_at_1000
value: 0.099
- type: precision_at_20
value: 3.7159999999999997
- type: precision_at_3
value: 13.347999999999999
- type: precision_at_5
value: 9.701
- type: recall_at_1
value: 19.772000000000002
- type: recall_at_10
value: 62.660000000000004
- type: recall_at_100
value: 90.754
- type: recall_at_1000
value: 98.506
- type: recall_at_20
value: 74.324
- type: recall_at_3
value: 40.043
- type: recall_at_5
value: 48.506
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: main_score
value: 32.80230807469564
- type: v_measure
value: 32.80230807469564
- type: v_measure_std
value: 14.21338818541074
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: main_score
value: 21.248453313024708
- type: v_measure
value: 21.248453313024708
- type: v_measure_std
value: 15.433670233573585
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: main_score
value: 53.30705219507139
- type: map
value: 53.30705219507139
- type: mrr
value: 67.55584135778041
- type: nAUC_map_diff1
value: 5.977242825014382
- type: nAUC_map_max
value: 14.494667647754635
- type: nAUC_map_std
value: 6.908970811447951
- type: nAUC_mrr_diff1
value: 9.755994572083837
- type: nAUC_mrr_max
value: 19.919368404049635
- type: nAUC_mrr_std
value: 11.331919744344269
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 77.2402358434998
- type: cosine_spearman
value: 73.53300080051118
- type: euclidean_pearson
value: 52.172219986452426
- type: euclidean_spearman
value: 52.57408950255944
- type: main_score
value: 73.53300080051118
- type: manhattan_pearson
value: 51.68816829817331
- type: manhattan_spearman
value: 52.64276576618988
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 74.56818181818183
- type: f1
value: 74.61226338355016
- type: f1_weighted
value: 74.61226338355017
- type: main_score
value: 74.56818181818183
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: main_score
value: 27.44009290643918
- type: v_measure
value: 27.44009290643918
- type: v_measure_std
value: 0.9289374483657263
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: main_score
value: 14.416522378472521
- type: v_measure
value: 14.416522378472521
- type: v_measure_std
value: 0.998944687002706
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval (default)
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
metrics:
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value: 29.559
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- type: map_at_10
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- type: map_at_100
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task:
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name: MTEB CQADupstackRetrieval (default)
revision: CQADupstackRetrieval_is_a_combined_dataset
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task:
type: Retrieval
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config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
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value: 84.39580483356133
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value: 83.44868295994982
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value: 84.62451137330042
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value: 84.39580483356133
task:
type: Classification
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config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
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value: 57.49886000911992
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value: 40.4415476797798
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value: 61.62208163727965
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value: 57.49886000911992
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 59.83187626092804
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value: 59.13403345594064
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value: 60.63671199667425
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value: 59.83187626092804
task:
type: Classification
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config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 65.26437007515528
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value: 65.8253382410501
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value: 65.42703429724277
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: 25.727269855956525
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task:
type: Clustering
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config: default
name: MTEB MedrxivClusteringS2S (default)
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
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value: 20.215145846542196
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task:
type: Clustering
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config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
metrics:
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task:
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config: default
name: MTEB NFCorpus (default)
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
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task:
type: Retrieval
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config: default
name: MTEB NQ (default)
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
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type: mteb/nq
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- type: nauc_recall_at_1000_max
value: 17.996576770868053
- type: nauc_recall_at_1000_std
value: 32.80342100287712
- type: nauc_recall_at_100_diff1
value: 5.430758469249528
- type: nauc_recall_at_100_max
value: 14.268961540014038
- type: nauc_recall_at_100_std
value: 27.166326235119115
- type: nauc_recall_at_10_diff1
value: 6.617506438998378
- type: nauc_recall_at_10_max
value: 11.314056709334377
- type: nauc_recall_at_10_std
value: 16.426078332040536
- type: nauc_recall_at_1_diff1
value: 21.528872289406724
- type: nauc_recall_at_1_max
value: 9.599915129165018
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value: 7.4189719966979695
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value: 8.254005259259117
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value: 14.556520143078655
- type: nauc_recall_at_20_std
value: 19.511702371470943
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value: 11.978386540088236
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value: 9.401559647087673
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value: 9.853537735534156
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value: 13.600000000000001
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value: 11.755
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value: 17.377000000000002
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value: 22.474
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value: 13.453999999999999
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value: 10.813
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value: 9.232
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value: 13.600000000000001
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value: 6.279999999999999
- type: precision_at_100
value: 1.459
- type: precision_at_1000
value: 0.269
- type: precision_at_20
value: 4.125
- type: precision_at_3
value: 10.033
- type: precision_at_5
value: 7.9399999999999995
- type: recall_at_1
value: 2.768
- type: recall_at_10
value: 12.717999999999998
- type: recall_at_100
value: 29.585
- type: recall_at_1000
value: 54.62200000000001
- type: recall_at_20
value: 16.698
- type: recall_at_3
value: 6.093
- type: recall_at_5
value: 8.043
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 74.58146532711963
- type: cosine_spearman
value: 63.636039988054506
- type: euclidean_pearson
value: 64.59092438482887
- type: euclidean_spearman
value: 57.73224863369497
- type: main_score
value: 63.636039988054506
- type: manhattan_pearson
value: 64.63728666058486
- type: manhattan_spearman
value: 58.223485178516476
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: main_score
value: 70.06178724296457
- type: map
value: 70.06178724296457
- type: mrr
value: 89.71142873103656
- type: nAUC_map_diff1
value: 10.154741326485558
- type: nAUC_map_max
value: 54.74815645280038
- type: nAUC_map_std
value: 66.54847292479904
- type: nAUC_mrr_diff1
value: 46.233982215309005
- type: nAUC_mrr_max
value: 74.6598844145684
- type: nAUC_mrr_std
value: 71.47617560630509
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
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value: 46.796
- type: map_at_1
value: 34.556
- type: map_at_10
value: 42.120000000000005
- type: map_at_100
value: 43.116
- type: map_at_1000
value: 43.177
- type: map_at_20
value: 42.763
- type: map_at_3
value: 39.287
- type: map_at_5
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- type: nauc_map_at_1_std
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value: 38.032966545915066
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value: -2.036405590684688
- type: nauc_ndcg_at_3_diff1
value: 49.063873089396935
- type: nauc_ndcg_at_3_max
value: 36.390876752154995
- type: nauc_ndcg_at_3_std
value: -5.12186312913497
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- type: nauc_ndcg_at_5_max
value: 37.37379928370081
- type: nauc_ndcg_at_5_std
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- type: nauc_precision_at_1000_diff1
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- type: nauc_precision_at_1000_max
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- type: nauc_precision_at_1000_std
value: 41.57893149114604
- type: nauc_precision_at_100_diff1
value: 25.37133445101328
- type: nauc_precision_at_100_max
value: 41.134052962342054
- type: nauc_precision_at_100_std
value: 34.441699265664674
- type: nauc_precision_at_10_diff1
value: 35.03840828990103
- type: nauc_precision_at_10_max
value: 40.73464723049954
- type: nauc_precision_at_10_std
value: 15.374821189563962
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- type: nauc_precision_at_1_std
value: -5.524381776657941
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value: 31.014953347186534
- type: nauc_precision_at_20_max
value: 39.726194841706366
- type: nauc_precision_at_20_std
value: 23.58350649879802
- type: nauc_precision_at_3_diff1
value: 46.44907298087332
- type: nauc_precision_at_3_max
value: 39.80848932930549
- type: nauc_precision_at_3_std
value: 6.335648506265601
- type: nauc_precision_at_5_diff1
value: 42.35642787046128
- type: nauc_precision_at_5_max
value: 41.37742885181555
- type: nauc_precision_at_5_std
value: 12.847105004906794
- type: nauc_recall_at_1000_diff1
value: 57.410701506880244
- type: nauc_recall_at_1000_max
value: 72.5585611373502
- type: nauc_recall_at_1000_std
value: 22.19121225132394
- type: nauc_recall_at_100_diff1
value: 41.836461147395376
- type: nauc_recall_at_100_max
value: 51.21326527192921
- type: nauc_recall_at_100_std
value: 15.283098953388274
- type: nauc_recall_at_10_diff1
value: 41.98734464823052
- type: nauc_recall_at_10_max
value: 43.11005961389504
- type: nauc_recall_at_10_std
value: 2.7618009527587675
- type: nauc_recall_at_1_diff1
value: 50.47494116455279
- type: nauc_recall_at_1_max
value: 32.16298630084698
- type: nauc_recall_at_1_std
value: -11.024801178030241
- type: nauc_recall_at_20_diff1
value: 39.55898073995315
- type: nauc_recall_at_20_max
value: 45.262556530962094
- type: nauc_recall_at_20_std
value: 6.638551472438822
- type: nauc_recall_at_3_diff1
value: 46.788698070628854
- type: nauc_recall_at_3_max
value: 38.14319201368208
- type: nauc_recall_at_3_std
value: -3.174965712818214
- type: nauc_recall_at_5_diff1
value: 45.246595282349695
- type: nauc_recall_at_5_max
value: 40.561651416937245
- type: nauc_recall_at_5_std
value: 2.8901060710485122
- type: ndcg_at_1
value: 36.333
- type: ndcg_at_10
value: 46.796
- type: ndcg_at_100
value: 51.690999999999995
- type: ndcg_at_1000
value: 53.315999999999995
- type: ndcg_at_20
value: 49.143
- type: ndcg_at_3
value: 41.411
- type: ndcg_at_5
value: 43.932
- type: precision_at_1
value: 36.333
- type: precision_at_10
value: 6.7
- type: precision_at_100
value: 0.9299999999999999
- type: precision_at_1000
value: 0.108
- type: precision_at_20
value: 3.9
- type: precision_at_3
value: 16.444
- type: precision_at_5
value: 11.333
- type: recall_at_1
value: 34.556
- type: recall_at_10
value: 60.094
- type: recall_at_100
value: 82.806
- type: recall_at_1000
value: 95.433
- type: recall_at_20
value: 69.272
- type: recall_at_3
value: 45.417
- type: recall_at_5
value: 51.456
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cosine_accuracy
value: 99.6920792079208
- type: cosine_accuracy_threshold
value: 78.24471592903137
- type: cosine_ap
value: 88.99087156661658
- type: cosine_f1
value: 83.70179948586119
- type: cosine_f1_threshold
value: 76.73087120056152
- type: cosine_precision
value: 86.13756613756614
- type: cosine_recall
value: 81.39999999999999
- type: dot_accuracy
value: 99.05049504950495
- type: dot_accuracy_threshold
value: 59690.91796875
- type: dot_ap
value: 29.244403878099977
- type: dot_f1
value: 34.292972057578325
- type: dot_f1_threshold
value: 37022.113037109375
- type: dot_precision
value: 29.735682819383257
- type: dot_recall
value: 40.5
- type: euclidean_accuracy
value: 99.44554455445544
- type: euclidean_accuracy_threshold
value: 1234.984016418457
- type: euclidean_ap
value: 70.64704777963355
- type: euclidean_f1
value: 69.04138292299635
- type: euclidean_f1_threshold
value: 1326.447582244873
- type: euclidean_precision
value: 72.4972497249725
- type: euclidean_recall
value: 65.9
- type: main_score
value: 88.99087156661658
- type: manhattan_accuracy
value: 99.43861386138614
- type: manhattan_accuracy_threshold
value: 15389.85595703125
- type: manhattan_ap
value: 70.18426845437493
- type: manhattan_f1
value: 68.78306878306879
- type: manhattan_f1_threshold
value: 16085.997009277344
- type: manhattan_precision
value: 73.03370786516854
- type: manhattan_recall
value: 65
- type: max_accuracy
value: 99.6920792079208
- type: max_ap
value: 88.99087156661658
- type: max_f1
value: 83.70179948586119
- type: max_precision
value: 86.13756613756614
- type: max_recall
value: 81.39999999999999
- type: similarity_accuracy
value: 99.6920792079208
- type: similarity_accuracy_threshold
value: 78.24471592903137
- type: similarity_ap
value: 88.99087156661658
- type: similarity_f1
value: 83.70179948586119
- type: similarity_f1_threshold
value: 76.73087120056152
- type: similarity_precision
value: 86.13756613756614
- type: similarity_recall
value: 81.39999999999999
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: main_score
value: 39.18863241150203
- type: v_measure
value: 39.18863241150203
- type: v_measure_std
value: 4.7194058271386705
task:
type: Clustering
🪲 brown-beetle-small-v1.1 Model Card
Beetles are some of the most diverse and interesting creatures on Earth. They are found in every environment, from the deepest oceans to the highest mountains. They are also known for their ability to adapt to a wide range of habitats and lifestyles. They are small, fast and powerful!
The beetle series of models are made as good starting points for Static Embedding training (via TokenLearn or Fine-tuning), as well as decent Static Embedding models. Each beetle model is made to be an improvement over the original M2V_base_output model in some way, and that's the threshold we set for each model (except the brown beetle series, which is the original model).
This model has been distilled from baai/bge-base-en-v1.5, with PCA with 256 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-base-v1.1 model.
The brown beetle series is made for convinience in loading and using the model instead of having to run it, though it is pretty fast to reproduce anyways. If you want to use the original model by the folks from the Minish Lab, you can use the M2V_base_output model.
Version Information
- brown-beetle-base-v0: The original model, without using PCA or Zipf. The lack of PCA and Zipf also makes this a decent model for further training.
- brown-beetle-base-v0.1: The original model, with PCA but of the same size as the original model. This model is great if you want to experiment with Zipf or other weighting methods.
- brown-beetle-base-v1: The original model, with PCA and Zipf.
- brown-beetle-small-v1: A smaller version of the original model, with PCA and Zipf. Equivalent to M2V_base_output.
- brown-beetle-tiny-v1: A tiny version of the original model, with PCA and Zipf.
- brown-beetle-base-v1.1: The original model, with PCA with 768 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model.
- brown-beetle-small-v1.1: A smaller version of the original model, with PCA with 256 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-base-v1.1 model.
- brown-beetle-tiny-v1.1: A tiny version of the original model, with PCA with 128 dimensions, applying Zipf and applying SIF re-weighting, learnt from a subset of the C4 corpus. This model is significantly better than the M2V_base_output model but slightly worse than the brown-beetle-small-v1.1 model.
Installation
Install model2vec using pip:
pip install model2vec
Usage
Load this model using the from_pretrained method:
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("bhavnicksm/brown-beetle-small-v1.1")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
Read more about the Model2Vec library here.
Comparison with other models
Coming soon...
Acknowledgements
This model is made using the Model2Vec library. Credit goes to the Minish Lab team for developing this library.
Citation
Please cite the Model2Vec repository if you use this model in your work.
@software{minishlab2024model2vec,
authors = {Stephan Tulkens, Thomas van Dongen},
title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model},
year = {2024},
url = {https://github.com/MinishLab/model2vec},
}