---
base_model: baai/bge-base-en-v1.5
language:
- en
library_name: model2vec
license: mit
model_name: brown-fairy-base-v0
tags:
- embeddings
- static-embeddings
- sentence-transformers
- mteb
model-index:
- name: bhavnicksm/brown-fairy-base-v0
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 69.52239999999999
- type: f1
value: 63.4127
- type: f1_weighted
value: 72.48599999999999
- type: ap
value: 31.8446
- type: ap_weighted
value: 31.8446
- type: main_score
value: 69.52239999999999
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 68.709
- type: f1
value: 68.2583
- type: f1_weighted
value: 68.2583
- type: ap
value: 63.728899999999996
- type: ap_weighted
value: 63.728899999999996
- type: main_score
value: 68.709
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 34.014
- type: f1
value: 33.4588
- type: f1_weighted
value: 33.4588
- type: main_score
value: 34.014
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: ndcg_at_1
value: 20.341
- type: ndcg_at_3
value: 30.547
- type: ndcg_at_5
value: 34.963
- type: ndcg_at_10
value: 39.805
- type: ndcg_at_20
value: 42.397
- type: ndcg_at_100
value: 45.216
- type: ndcg_at_1000
value: 46.339999999999996
- type: map_at_1
value: 20.341
- type: map_at_3
value: 27.962999999999997
- type: map_at_5
value: 30.409999999999997
- type: map_at_10
value: 32.4
- type: map_at_20
value: 33.113
- type: map_at_100
value: 33.512
- type: map_at_1000
value: 33.556000000000004
- type: recall_at_1
value: 20.341
- type: recall_at_3
value: 38.051
- type: recall_at_5
value: 48.791000000000004
- type: recall_at_10
value: 63.798
- type: recall_at_20
value: 74.03999999999999
- type: recall_at_100
value: 89.118
- type: recall_at_1000
value: 97.866
- type: precision_at_1
value: 20.341
- type: precision_at_3
value: 12.684000000000001
- type: precision_at_5
value: 9.758
- type: precision_at_10
value: 6.38
- type: precision_at_20
value: 3.702
- type: precision_at_100
value: 0.8909999999999999
- type: precision_at_1000
value: 0.098
- type: mrr_at_1
value: 20.6259
- type: mrr_at_3
value: 28.058300000000003
- type: mrr_at_5
value: 30.4979
- type: mrr_at_10
value: 32.5131
- type: mrr_at_20
value: 33.222699999999996
- type: mrr_at_100
value: 33.6243
- type: mrr_at_1000
value: 33.6687
- type: nauc_ndcg_at_1_max
value: -6.208
- type: nauc_ndcg_at_1_std
value: 0.6887
- type: nauc_ndcg_at_1_diff1
value: 5.5123
- type: nauc_ndcg_at_3_max
value: -1.8608
- type: nauc_ndcg_at_3_std
value: 3.7832999999999997
- type: nauc_ndcg_at_3_diff1
value: 7.5778
- type: nauc_ndcg_at_5_max
value: 0.0929
- type: nauc_ndcg_at_5_std
value: 5.8453
- type: nauc_ndcg_at_5_diff1
value: 9.316
- type: nauc_ndcg_at_10_max
value: 0.557
- type: nauc_ndcg_at_10_std
value: 5.8692
- type: nauc_ndcg_at_10_diff1
value: 8.3828
- type: nauc_ndcg_at_20_max
value: 1.567
- type: nauc_ndcg_at_20_std
value: 8.2355
- type: nauc_ndcg_at_20_diff1
value: 9.1907
- type: nauc_ndcg_at_100_max
value: 1.0833000000000002
- type: nauc_ndcg_at_100_std
value: 8.6248
- type: nauc_ndcg_at_100_diff1
value: 9.0073
- type: nauc_ndcg_at_1000_max
value: -0.166
- type: nauc_ndcg_at_1000_std
value: 7.394100000000001
- type: nauc_ndcg_at_1000_diff1
value: 8.1955
- type: nauc_map_at_1_max
value: -6.208
- type: nauc_map_at_1_std
value: 0.6887
- type: nauc_map_at_1_diff1
value: 5.5123
- type: nauc_map_at_3_max
value: -3.0332999999999997
- type: nauc_map_at_3_std
value: 2.9010000000000002
- type: nauc_map_at_3_diff1
value: 6.8088
- type: nauc_map_at_5_max
value: -1.9215
- type: nauc_map_at_5_std
value: 4.023000000000001
- type: nauc_map_at_5_diff1
value: 7.8248999999999995
- type: nauc_map_at_10_max
value: -1.8037
- type: nauc_map_at_10_std
value: 3.9838
- type: nauc_map_at_10_diff1
value: 7.3617
- type: nauc_map_at_20_max
value: -1.5614
- type: nauc_map_at_20_std
value: 4.6065000000000005
- type: nauc_map_at_20_diff1
value: 7.5846
- type: nauc_map_at_100_max
value: -1.6330999999999998
- type: nauc_map_at_100_std
value: 4.693
- type: nauc_map_at_100_diff1
value: 7.5309
- type: nauc_map_at_1000_max
value: -1.6847999999999999
- type: nauc_map_at_1000_std
value: 4.6508
- type: nauc_map_at_1000_diff1
value: 7.5036000000000005
- type: nauc_recall_at_1_max
value: -6.208
- type: nauc_recall_at_1_std
value: 0.6887
- type: nauc_recall_at_1_diff1
value: 5.5123
- type: nauc_recall_at_3_max
value: 1.2662
- type: nauc_recall_at_3_std
value: 6.1506
- type: nauc_recall_at_3_diff1
value: 9.6919
- type: nauc_recall_at_5_max
value: 5.7511
- type: nauc_recall_at_5_std
value: 11.0652
- type: nauc_recall_at_5_diff1
value: 13.5713
- type: nauc_recall_at_10_max
value: 8.5342
- type: nauc_recall_at_10_std
value: 12.2161
- type: nauc_recall_at_10_diff1
value: 11.6188
- type: nauc_recall_at_20_max
value: 15.7488
- type: nauc_recall_at_20_std
value: 25.6755
- type: nauc_recall_at_20_diff1
value: 16.3568
- type: nauc_recall_at_100_max
value: 24.424799999999998
- type: nauc_recall_at_100_std
value: 47.6945
- type: nauc_recall_at_100_diff1
value: 22.4622
- type: nauc_recall_at_1000_max
value: 3.0951
- type: nauc_recall_at_1000_std
value: 84.10419999999999
- type: nauc_recall_at_1000_diff1
value: -2.6364
- type: nauc_precision_at_1_max
value: -6.208
- type: nauc_precision_at_1_std
value: 0.6887
- type: nauc_precision_at_1_diff1
value: 5.5123
- type: nauc_precision_at_3_max
value: 1.2662
- type: nauc_precision_at_3_std
value: 6.1506
- type: nauc_precision_at_3_diff1
value: 9.6919
- type: nauc_precision_at_5_max
value: 5.7511
- type: nauc_precision_at_5_std
value: 11.0652
- type: nauc_precision_at_5_diff1
value: 13.5713
- type: nauc_precision_at_10_max
value: 8.5342
- type: nauc_precision_at_10_std
value: 12.2161
- type: nauc_precision_at_10_diff1
value: 11.6188
- type: nauc_precision_at_20_max
value: 15.7488
- type: nauc_precision_at_20_std
value: 25.6755
- type: nauc_precision_at_20_diff1
value: 16.3568
- type: nauc_precision_at_100_max
value: 24.424799999999998
- type: nauc_precision_at_100_std
value: 47.6945
- type: nauc_precision_at_100_diff1
value: 22.4622
- type: nauc_precision_at_1000_max
value: 3.0951
- type: nauc_precision_at_1000_std
value: 84.10419999999999
- type: nauc_precision_at_1000_diff1
value: -2.6364
- type: nauc_mrr_at_1_max
value: -5.611800000000001
- type: nauc_mrr_at_1_std
value: 0.2596
- type: nauc_mrr_at_1_diff1
value: 4.5101
- type: nauc_mrr_at_3_max
value: -3.1917
- type: nauc_mrr_at_3_std
value: 2.7559
- type: nauc_mrr_at_3_diff1
value: 5.756
- type: nauc_mrr_at_5_max
value: -2.1292999999999997
- type: nauc_mrr_at_5_std
value: 3.7653
- type: nauc_mrr_at_5_diff1
value: 6.7995
- type: nauc_mrr_at_10_max
value: -1.8915000000000002
- type: nauc_mrr_at_10_std
value: 3.778
- type: nauc_mrr_at_10_diff1
value: 6.4253
- type: nauc_mrr_at_20_max
value: -1.6753
- type: nauc_mrr_at_20_std
value: 4.389
- type: nauc_mrr_at_20_diff1
value: 6.6081
- type: nauc_mrr_at_100_max
value: -1.7302000000000002
- type: nauc_mrr_at_100_std
value: 4.4796000000000005
- type: nauc_mrr_at_100_diff1
value: 6.563199999999999
- type: nauc_mrr_at_1000_max
value: -1.7819000000000003
- type: nauc_mrr_at_1000_std
value: 4.4372
- type: nauc_mrr_at_1000_diff1
value: 6.5346
- type: main_score
value: 39.805
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 30.9023
- type: v_measure_std
value: 14.6095
- type: main_score
value: 30.9023
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 19.1012
- type: v_measure_std
value: 15.511800000000001
- type: main_score
value: 19.1012
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 54.0474
- type: mrr
value: 67.00150000000001
- type: nAUC_map_max
value: 14.266100000000002
- type: nAUC_map_std
value: 11.7906
- type: nAUC_map_diff1
value: 7.5044
- type: nAUC_mrr_max
value: 20.1721
- type: nAUC_mrr_std
value: 13.1225
- type: nAUC_mrr_diff1
value: 14.3512
- type: main_score
value: 54.0474
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: pearson
value: 73.3465
- type: spearman
value: 69.6932
- type: cosine_pearson
value: 73.3465
- type: cosine_spearman
value: 69.6932
- type: manhattan_pearson
value: 54.115899999999996
- type: manhattan_spearman
value: 54.1759
- type: euclidean_pearson
value: 54.2153
- type: euclidean_spearman
value: 54.0488
- type: main_score
value: 69.6932
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 74.2987
- type: f1
value: 73.85119999999999
- type: f1_weighted
value: 73.85119999999999
- type: main_score
value: 74.2987
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 29.8415
- type: v_measure_std
value: 0.7605
- type: main_score
value: 29.8415
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 16.4917
- type: v_measure_std
value: 1.2364
- type: main_score
value: 16.4917
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackRetrieval (default)
revision: '1'
split: test
type: CQADupstackRetrieval_is_a_combined_dataset
metrics:
- type: ndcg_at_10
value: 21.9561
- type: main_score
value: 21.9561
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER (default)
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
metrics:
- type: ndcg_at_1
value: 18.826999999999998
- type: ndcg_at_3
value: 16.482
- type: ndcg_at_5
value: 17.9
- type: ndcg_at_10
value: 20.948
- type: ndcg_at_20
value: 23.665
- type: ndcg_at_100
value: 28.192
- type: ndcg_at_1000
value: 31.846999999999998
- type: map_at_1
value: 8.221
- type: map_at_3
value: 11.72
- type: map_at_5
value: 12.844
- type: map_at_10
value: 14.17
- type: map_at_20
value: 15.043000000000001
- type: map_at_100
value: 15.842
- type: map_at_1000
value: 16.04
- type: recall_at_1
value: 8.221
- type: recall_at_3
value: 15.214
- type: recall_at_5
value: 19.185
- type: recall_at_10
value: 26.14
- type: recall_at_20
value: 33.931
- type: recall_at_100
value: 51.429
- type: recall_at_1000
value: 72.269
- type: precision_at_1
value: 18.826999999999998
- type: precision_at_3
value: 12.4
- type: precision_at_5
value: 9.707
- type: precision_at_10
value: 6.84
- type: precision_at_20
value: 4.557
- type: precision_at_100
value: 1.461
- type: precision_at_1000
value: 0.212
- type: mrr_at_1
value: 18.8274
- type: mrr_at_3
value: 25.2226
- type: mrr_at_5
value: 27.163999999999998
- type: mrr_at_10
value: 28.6116
- type: mrr_at_20
value: 29.3082
- type: mrr_at_100
value: 29.7302
- type: mrr_at_1000
value: 29.786600000000004
- type: nauc_ndcg_at_1_max
value: 23.3019
- type: nauc_ndcg_at_1_std
value: 14.4153
- type: nauc_ndcg_at_1_diff1
value: 21.8879
- type: nauc_ndcg_at_3_max
value: 22.2746
- type: nauc_ndcg_at_3_std
value: 15.487300000000001
- type: nauc_ndcg_at_3_diff1
value: 17.8275
- type: nauc_ndcg_at_5_max
value: 23.0993
- type: nauc_ndcg_at_5_std
value: 16.4617
- type: nauc_ndcg_at_5_diff1
value: 16.7855
- type: nauc_ndcg_at_10_max
value: 24.7783
- type: nauc_ndcg_at_10_std
value: 20.1484
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value: 17.0753
- type: nauc_ndcg_at_20_max
value: 26.1465
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value: 27.9877
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value: 16.223499999999998
- type: nauc_map_at_1_max
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- type: nauc_map_at_1_std
value: 9.546000000000001
- type: nauc_map_at_1_diff1
value: 29.008499999999998
- type: nauc_map_at_3_max
value: 22.0196
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value: 11.7774
- type: nauc_map_at_3_diff1
value: 21.7038
- type: nauc_map_at_5_max
value: 22.7222
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value: 12.8126
- type: nauc_map_at_5_diff1
value: 20.288
- type: nauc_map_at_10_max
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- type: nauc_map_at_10_std
value: 14.8877
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value: 19.9221
- type: nauc_map_at_20_max
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value: 15.9395
- type: nauc_map_at_20_diff1
value: 19.6606
- type: nauc_map_at_100_max
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value: 16.8474
- type: nauc_map_at_100_diff1
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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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value: 29.008499999999998
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value: 15.7
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value: 21.5474
- type: nauc_precision_at_3_diff1
value: 10.6452
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value: 30.1734
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- type: nauc_precision_at_20_std
value: 33.1933
- type: nauc_precision_at_20_diff1
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- type: nauc_precision_at_100_std
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value: 2.2525
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- type: nauc_precision_at_1000_std
value: 39.4567
- type: nauc_precision_at_1000_diff1
value: -1.2073
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value: 23.3019
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value: 14.4153
- type: nauc_mrr_at_1_diff1
value: 21.8879
- type: nauc_mrr_at_3_max
value: 23.340700000000002
- type: nauc_mrr_at_3_std
value: 18.1166
- type: nauc_mrr_at_3_diff1
value: 16.4821
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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: 82.4669
- type: f1
value: 81.3346
- type: f1_weighted
value: 82.6885
- type: main_score
value: 82.4669
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 58.1145
- type: f1
value: 40.7841
- type: f1_weighted
value: 62.343
- type: main_score
value: 58.1145
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 60.24549999999999
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value: 59.534
- type: f1_weighted
value: 60.47670000000001
- type: main_score
value: 60.24549999999999
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 66.32820000000001
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value: 65.2929
- type: f1_weighted
value: 66.51979999999999
- type: main_score
value: 66.32820000000001
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P (default)
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
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value: 25.8495
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value: 1.6320000000000001
- type: main_score
value: 25.8495
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: 20.0754
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value: 1.3306
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value: 20.0754
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
metrics:
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value: -20.8019
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value: -5.307300000000001
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value: 20.6483
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value: -2.9508
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value: 18.6743
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value: 28.5611
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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task:
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config: default
name: MTEB RedditClustering (default)
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
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task:
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revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
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type: mteb/reddit-clustering-p2p
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task:
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name: MTEB SCIDOCS (default)
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
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name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
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task:
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config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
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task:
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name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
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- type: cosine_pearson
value: 76.4727
- type: cosine_spearman
value: 76.983
- type: manhattan_pearson
value: 49.4803
- type: manhattan_spearman
value: 51.1301
- type: euclidean_pearson
value: 49.4542
- type: euclidean_spearman
value: 51.19669999999999
- type: main_score
value: 76.983
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: pearson
value: 75.777
- type: spearman
value: 71.2099
- type: cosine_pearson
value: 75.777
- type: cosine_spearman
value: 71.2099
- type: manhattan_pearson
value: 52.475899999999996
- type: manhattan_spearman
value: 53.8072
- type: euclidean_pearson
value: 52.416799999999995
- type: euclidean_spearman
value: 53.725500000000004
- type: main_score
value: 71.2099
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: pearson
value: 80.1072
- type: spearman
value: 80.735
- type: cosine_pearson
value: 80.1072
- type: cosine_spearman
value: 80.7349
- type: manhattan_pearson
value: 50.711600000000004
- type: manhattan_spearman
value: 53.491299999999995
- type: euclidean_pearson
value: 50.6255
- type: euclidean_spearman
value: 53.47539999999999
- type: main_score
value: 80.7349
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: pearson
value: 73.1658
- type: spearman
value: 74.2121
- type: cosine_pearson
value: 73.1658
- type: cosine_spearman
value: 74.2121
- type: manhattan_pearson
value: 43.4074
- type: manhattan_spearman
value: 47.193200000000004
- type: euclidean_pearson
value: 43.438300000000005
- type: euclidean_spearman
value: 47.2757
- type: main_score
value: 74.2121
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: 81.8156
- type: spearman
value: 81.9457
- type: cosine_pearson
value: 81.8156
- type: cosine_spearman
value: 81.9457
- type: manhattan_pearson
value: 59.4332
- type: manhattan_spearman
value: 60.5687
- type: euclidean_pearson
value: 59.2942
- type: euclidean_spearman
value: 60.39679999999999
- type: main_score
value: 81.9457
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 48.9285
- type: spearman
value: 55.862500000000004
- type: cosine_pearson
value: 48.9285
- type: cosine_spearman
value: 55.862500000000004
- type: manhattan_pearson
value: 43.082300000000004
- type: manhattan_spearman
value: 51.1876
- type: euclidean_pearson
value: 43.2313
- type: euclidean_spearman
value: 51.094899999999996
- type: main_score
value: 55.862500000000004
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: pearson
value: 73.44380000000001
- type: spearman
value: 71.9343
- type: cosine_pearson
value: 73.44380000000001
- type: cosine_spearman
value: 71.9345
- type: manhattan_pearson
value: 52.233799999999995
- type: manhattan_spearman
value: 51.7687
- type: euclidean_pearson
value: 52.2753
- type: euclidean_spearman
value: 51.845
- type: main_score
value: 71.9345
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 71.4557
- type: mrr
value: 90.6219
- type: nAUC_map_max
value: 54.74830000000001
- type: nAUC_map_std
value: 65.2558
- type: nAUC_map_diff1
value: 10.2936
- type: nAUC_mrr_max
value: 75.10900000000001
- type: nAUC_mrr_std
value: 69.6523
- type: nAUC_mrr_diff1
value: 49.4991
- type: main_score
value: 71.4557
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: ndcg_at_1
value: 43.667
- type: ndcg_at_3
value: 52.102000000000004
- type: ndcg_at_5
value: 54.751000000000005
- type: ndcg_at_10
value: 57.422
- type: ndcg_at_20
value: 59.425
- type: ndcg_at_100
value: 61.166
- type: ndcg_at_1000
value: 62.244
- type: map_at_1
value: 41.888999999999996
- type: map_at_3
value: 49.435
- type: map_at_5
value: 51.029
- type: map_at_10
value: 52.190000000000005
- type: map_at_20
value: 52.797000000000004
- type: map_at_100
value: 53.03
- type: map_at_1000
value: 53.069
- type: recall_at_1
value: 41.888999999999996
- type: recall_at_3
value: 57.916999999999994
- type: recall_at_5
value: 64.372
- type: recall_at_10
value: 72.311
- type: recall_at_20
value: 79.97800000000001
- type: recall_at_100
value: 89.333
- type: recall_at_1000
value: 97.867
- type: precision_at_1
value: 43.667
- type: precision_at_3
value: 20.778
- type: precision_at_5
value: 14.066999999999998
- type: precision_at_10
value: 8.033
- type: precision_at_20
value: 4.45
- type: precision_at_100
value: 1.0030000000000001
- type: precision_at_1000
value: 0.11
- type: mrr_at_1
value: 43.666700000000006
- type: mrr_at_3
value: 50.9444
- type: mrr_at_5
value: 52.3444
- type: mrr_at_10
value: 53.3852
- type: mrr_at_20
value: 53.8864
- type: mrr_at_100
value: 54.0887
- type: mrr_at_1000
value: 54.11749999999999
- type: nauc_ndcg_at_1_max
value: 36.6444
- type: nauc_ndcg_at_1_std
value: -7.4722
- type: nauc_ndcg_at_1_diff1
value: 63.631099999999996
- type: nauc_ndcg_at_3_max
value: 37.2859
- type: nauc_ndcg_at_3_std
value: -11.2775
- type: nauc_ndcg_at_3_diff1
value: 56.352999999999994
- type: nauc_ndcg_at_5_max
value: 36.7832
- type: nauc_ndcg_at_5_std
value: -12.310699999999999
- type: nauc_ndcg_at_5_diff1
value: 55.41740000000001
- type: nauc_ndcg_at_10_max
value: 37.9586
- type: nauc_ndcg_at_10_std
value: -9.7483
- type: nauc_ndcg_at_10_diff1
value: 56.8082
- type: nauc_ndcg_at_20_max
value: 38.4072
- type: nauc_ndcg_at_20_std
value: -7.473299999999999
- type: nauc_ndcg_at_20_diff1
value: 56.4974
- type: nauc_ndcg_at_100_max
value: 38.5583
- type: nauc_ndcg_at_100_std
value: -5.521100000000001
- type: nauc_ndcg_at_100_diff1
value: 56.8808
- type: nauc_ndcg_at_1000_max
value: 38.580999999999996
- type: nauc_ndcg_at_1000_std
value: -6.6578
- type: nauc_ndcg_at_1000_diff1
value: 57.3412
- type: nauc_map_at_1_max
value: 35.4069
- type: nauc_map_at_1_std
value: -11.9598
- type: nauc_map_at_1_diff1
value: 62.351299999999995
- type: nauc_map_at_3_max
value: 36.3612
- type: nauc_map_at_3_std
value: -12.6999
- type: nauc_map_at_3_diff1
value: 57.918099999999995
- type: nauc_map_at_5_max
value: 36.268299999999996
- type: nauc_map_at_5_std
value: -12.921199999999999
- type: nauc_map_at_5_diff1
value: 57.496
- type: nauc_map_at_10_max
value: 36.918099999999995
- type: nauc_map_at_10_std
value: -11.6299
- type: nauc_map_at_10_diff1
value: 58.1148
- type: nauc_map_at_20_max
value: 37.060900000000004
- type: nauc_map_at_20_std
value: -10.8228
- type: nauc_map_at_20_diff1
value: 58.0205
- type: nauc_map_at_100_max
value: 37.085499999999996
- type: nauc_map_at_100_std
value: -10.5358
- type: nauc_map_at_100_diff1
value: 58.095
- type: nauc_map_at_1000_max
value: 37.1083
- type: nauc_map_at_1000_std
value: -10.5578
- type: nauc_map_at_1000_diff1
value: 58.1224
- type: nauc_recall_at_1_max
value: 35.4069
- type: nauc_recall_at_1_std
value: -11.9598
- type: nauc_recall_at_1_diff1
value: 62.351299999999995
- type: nauc_recall_at_3_max
value: 37.6511
- type: nauc_recall_at_3_std
value: -13.3993
- type: nauc_recall_at_3_diff1
value: 50.4572
- type: nauc_recall_at_5_max
value: 35.8548
- type: nauc_recall_at_5_std
value: -16.1098
- type: nauc_recall_at_5_diff1
value: 47.2106
- type: nauc_recall_at_10_max
value: 38.9793
- type: nauc_recall_at_10_std
value: -8.1869
- type: nauc_recall_at_10_diff1
value: 50.5379
- type: nauc_recall_at_20_max
value: 42.3127
- type: nauc_recall_at_20_std
value: 4.1918999999999995
- type: nauc_recall_at_20_diff1
value: 47.5366
- type: nauc_recall_at_100_max
value: 48.4392
- type: nauc_recall_at_100_std
value: 37.5486
- type: nauc_recall_at_100_diff1
value: 46.853699999999996
- type: nauc_recall_at_1000_max
value: 70.1389
- type: nauc_recall_at_1000_std
value: 81.7519
- type: nauc_recall_at_1000_diff1
value: 46.0741
- type: nauc_precision_at_1_max
value: 36.6444
- type: nauc_precision_at_1_std
value: -7.4722
- type: nauc_precision_at_1_diff1
value: 63.631099999999996
- type: nauc_precision_at_3_max
value: 37.9141
- type: nauc_precision_at_3_std
value: -2.6281
- type: nauc_precision_at_3_diff1
value: 45.406600000000005
- type: nauc_precision_at_5_max
value: 35.0402
- type: nauc_precision_at_5_std
value: 0.7128
- type: nauc_precision_at_5_diff1
value: 36.686099999999996
- type: nauc_precision_at_10_max
value: 37.4825
- type: nauc_precision_at_10_std
value: 15.613199999999999
- type: nauc_precision_at_10_diff1
value: 33.1716
- type: nauc_precision_at_20_max
value: 36.1575
- type: nauc_precision_at_20_std
value: 30.4446
- type: nauc_precision_at_20_diff1
value: 23.3224
- type: nauc_precision_at_100_max
value: 29.5019
- type: nauc_precision_at_100_std
value: 52.942
- type: nauc_precision_at_100_diff1
value: 9.0284
- type: nauc_precision_at_1000_max
value: 20.350099999999998
- type: nauc_precision_at_1000_std
value: 52.2915
- type: nauc_precision_at_1000_diff1
value: -8.6009
- type: nauc_mrr_at_1_max
value: 36.6444
- type: nauc_mrr_at_1_std
value: -7.4722
- type: nauc_mrr_at_1_diff1
value: 63.631099999999996
- type: nauc_mrr_at_3_max
value: 38.016299999999994
- type: nauc_mrr_at_3_std
value: -8.0229
- type: nauc_mrr_at_3_diff1
value: 58.757400000000004
- type: nauc_mrr_at_5_max
value: 37.433899999999994
- type: nauc_mrr_at_5_std
value: -8.1996
- type: nauc_mrr_at_5_diff1
value: 58.235899999999994
- type: nauc_mrr_at_10_max
value: 37.7997
- type: nauc_mrr_at_10_std
value: -7.542699999999999
- type: nauc_mrr_at_10_diff1
value: 58.8486
- type: nauc_mrr_at_20_max
value: 37.8879
- type: nauc_mrr_at_20_std
value: -7.133000000000001
- type: nauc_mrr_at_20_diff1
value: 58.834900000000005
- type: nauc_mrr_at_100_max
value: 37.8627
- type: nauc_mrr_at_100_std
value: -6.9667
- type: nauc_mrr_at_100_diff1
value: 58.880900000000004
- type: nauc_mrr_at_1000_max
value: 37.8675
- type: nauc_mrr_at_1000_std
value: -6.9817
- type: nauc_mrr_at_1000_diff1
value: 58.904500000000006
- type: main_score
value: 57.422
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: similarity_accuracy
value: 99.6703
- type: similarity_accuracy_threshold
value: 81.69669999999999
- type: similarity_f1
value: 82.5479
- type: similarity_f1_threshold
value: 80.97919999999999
- type: similarity_precision
value: 85.6069
- type: similarity_recall
value: 79.7
- type: similarity_ap
value: 87.6918
- type: cosine_accuracy
value: 99.6703
- type: cosine_accuracy_threshold
value: 81.69669999999999
- type: cosine_f1
value: 82.5479
- type: cosine_f1_threshold
value: 80.97919999999999
- type: cosine_precision
value: 85.6069
- type: cosine_recall
value: 79.7
- type: cosine_ap
value: 87.6918
- type: manhattan_accuracy
value: 99.4327
- type: manhattan_accuracy_threshold
value: 2292.4838999999997
- type: manhattan_f1
value: 66.0851
- type: manhattan_f1_threshold
value: 2517.333
- type: manhattan_precision
value: 72.6619
- type: manhattan_recall
value: 60.6
- type: manhattan_ap
value: 68.1683
- type: euclidean_accuracy
value: 99.4327
- type: euclidean_accuracy_threshold
value: 105.6427
- type: euclidean_f1
value: 66.1605
- type: euclidean_f1_threshold
value: 114.9346
- type: euclidean_precision
value: 72.2749
- type: euclidean_recall
value: 61.0
- type: euclidean_ap
value: 68.2419
- type: dot_accuracy
value: 99.0168
- type: dot_accuracy_threshold
value: 1011.5417000000001
- type: dot_f1
value: 18.6459
- type: dot_f1_threshold
value: 554.0581999999999
- type: dot_precision
value: 20.9476
- type: dot_recall
value: 16.8
- type: dot_ap
value: 11.5838
- type: max_accuracy
value: 99.6703
- type: max_f1
value: 82.5479
- type: max_precision
value: 85.6069
- type: max_recall
value: 79.7
- type: max_ap
value: 87.6918
- type: main_score
value: 87.6918
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 27.147700000000004
- type: v_measure_std
value: 4.3151
- type: main_score
value: 27.147700000000004
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P (default)
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 28.9253
- type: v_measure_std
value: 1.6500000000000001
- type: main_score
value: 28.9253
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 42.7933
- type: mrr
value: 43.2531
- type: nAUC_map_max
value: 15.137400000000001
- type: nAUC_map_std
value: 4.6048
- type: nAUC_map_diff1
value: 31.665100000000002
- type: nAUC_mrr_max
value: 16.429299999999998
- type: nAUC_mrr_std
value: 4.943899999999999
- type: nAUC_mrr_diff1
value: 30.8849
- type: main_score
value: 42.7933
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: pearson
value: 31.8891
- type: spearman
value: 30.635299999999997
- type: cosine_spearman
value: 30.635299999999997
- type: cosine_pearson
value: 31.8891
- type: dot_spearman
value: 23.1495
- type: dot_pearson
value: 20.2811
- type: main_score
value: 30.635299999999997
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID (default)
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
split: test
type: mteb/trec-covid
metrics:
- type: ndcg_at_1
value: 60.0
- type: ndcg_at_3
value: 56.592
- type: ndcg_at_5
value: 52.15
- type: ndcg_at_10
value: 48.264
- type: ndcg_at_20
value: 43.568
- type: ndcg_at_100
value: 31.196
- type: ndcg_at_1000
value: 26.101000000000003
- type: map_at_1
value: 0.153
- type: map_at_3
value: 0.4
- type: map_at_5
value: 0.601
- type: map_at_10
value: 1.016
- type: map_at_20
value: 1.6099999999999999
- type: map_at_100
value: 4.169
- type: map_at_1000
value: 9.733
- type: recall_at_1
value: 0.153
- type: recall_at_3
value: 0.42300000000000004
- type: recall_at_5
value: 0.6629999999999999
- type: recall_at_10
value: 1.201
- type: recall_at_20
value: 2.022
- type: recall_at_100
value: 6.5409999999999995
- type: recall_at_1000
value: 24.422
- type: precision_at_1
value: 64.0
- type: precision_at_3
value: 58.667
- type: precision_at_5
value: 54.0
- type: precision_at_10
value: 49.8
- type: precision_at_20
value: 44.3
- type: precision_at_100
value: 31.180000000000003
- type: precision_at_1000
value: 12.21
- type: mrr_at_1
value: 64.0
- type: mrr_at_3
value: 68.6667
- type: mrr_at_5
value: 69.9667
- type: mrr_at_10
value: 71.2222
- type: mrr_at_20
value: 71.3651
- type: mrr_at_100
value: 71.4965
- type: mrr_at_1000
value: 71.51429999999999
- type: nauc_ndcg_at_1_max
value: 37.0018
- type: nauc_ndcg_at_1_std
value: 3.0042
- type: nauc_ndcg_at_1_diff1
value: 1.0129000000000001
- type: nauc_ndcg_at_3_max
value: 42.3179
- type: nauc_ndcg_at_3_std
value: 1.1211
- type: nauc_ndcg_at_3_diff1
value: -1.3197999999999999
- type: nauc_ndcg_at_5_max
value: 38.2867
- type: nauc_ndcg_at_5_std
value: 1.436
- type: nauc_ndcg_at_5_diff1
value: -0.635
- type: nauc_ndcg_at_10_max
value: 36.545100000000005
- type: nauc_ndcg_at_10_std
value: 9.4313
- type: nauc_ndcg_at_10_diff1
value: 0.7185
- type: nauc_ndcg_at_20_max
value: 28.841499999999996
- type: nauc_ndcg_at_20_std
value: 14.584
- type: nauc_ndcg_at_20_diff1
value: 0.2278
- type: nauc_ndcg_at_100_max
value: 22.2284
- type: nauc_ndcg_at_100_std
value: 30.9548
- type: nauc_ndcg_at_100_diff1
value: 1.7124000000000001
- type: nauc_ndcg_at_1000_max
value: 7.9275
- type: nauc_ndcg_at_1000_std
value: 43.918
- type: nauc_ndcg_at_1000_diff1
value: 1.1608
- type: nauc_map_at_1_max
value: 16.718700000000002
- type: nauc_map_at_1_std
value: -14.5026
- type: nauc_map_at_1_diff1
value: 6.9494
- type: nauc_map_at_3_max
value: 26.3749
- type: nauc_map_at_3_std
value: -14.2379
- type: nauc_map_at_3_diff1
value: 2.6883
- type: nauc_map_at_5_max
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value: -0.7799999999999999
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value: 14.5538
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value: 29.851499999999998
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value: -1.5013
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value: 6.609800000000001
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value: 50.8853
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value: 2.2463
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value: 26.313
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- type: nauc_recall_at_5_diff1
value: -2.2484
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value: -7.2991
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value: 0.35729999999999995
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value: 24.1923
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value: 0.3075
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value: 48.264
task:
type: Retrieval
- dataset:
config: default
name: MTEB Touche2020 (default)
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
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value: 16.817
- type: ndcg_at_5
value: 17.718999999999998
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value: 4.37
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value: 29.251700000000003
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value: 32.9252
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- type: mrr_at_1000
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value: 11.7185
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value: -27.8755
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value: 6.2789
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- type: nauc_precision_at_10_std
value: -45.101200000000006
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value: 18.4024
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value: 29.625899999999998
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value: 45.918
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- type: nauc_mrr_at_1_max
value: -36.7404
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value: -20.7164
- type: nauc_mrr_at_1_diff1
value: -24.9514
- type: nauc_mrr_at_3_max
value: -34.8748
- type: nauc_mrr_at_3_std
value: -11.2167
- type: nauc_mrr_at_3_diff1
value: -14.4811
- type: nauc_mrr_at_5_max
value: -39.5232
- type: nauc_mrr_at_5_std
value: -18.9591
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value: -13.2719
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value: -41.7821
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value: -18.368399999999998
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value: -13.4359
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value: -42.8581
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value: -18.6052
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value: -13.6098
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value: -42.0696
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value: -18.1447
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value: -14.102500000000001
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value: -42.0696
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value: -18.1447
- type: nauc_mrr_at_1000_diff1
value: -14.102500000000001
- type: main_score
value: 17.318
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 74.0283
- type: f1
value: 54.813100000000006
- type: f1_weighted
value: 79.4125
- type: ap
value: 12.750800000000002
- type: ap_weighted
value: 12.750800000000002
- type: main_score
value: 74.0283
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 52.818299999999994
- type: f1
value: 52.8999
- type: f1_weighted
value: 52.223299999999995
- type: main_score
value: 52.818299999999994
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 14.5905
- type: v_measure_std
value: 1.0532
- type: main_score
value: 14.5905
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
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value: 80.3481
- type: similarity_accuracy_threshold
value: 85.3551
- type: similarity_f1
value: 51.27850000000001
- type: similarity_f1_threshold
value: 75.8966
- type: similarity_precision
value: 45.8247
- type: similarity_recall
value: 58.205799999999996
- type: similarity_ap
value: 52.295100000000005
- type: cosine_accuracy
value: 80.3481
- type: cosine_accuracy_threshold
value: 85.3551
- type: cosine_f1
value: 51.27850000000001
- type: cosine_f1_threshold
value: 75.8966
- type: cosine_precision
value: 45.8247
- type: cosine_recall
value: 58.205799999999996
- type: cosine_ap
value: 52.295199999999994
- type: manhattan_accuracy
value: 78.9712
- type: manhattan_accuracy_threshold
value: 3046.9002
- type: manhattan_f1
value: 44.784600000000005
- type: manhattan_f1_threshold
value: 4624.7635
- type: manhattan_precision
value: 35.5133
- type: manhattan_recall
value: 60.606899999999996
- type: manhattan_ap
value: 44.4155
- type: euclidean_accuracy
value: 78.9772
- type: euclidean_accuracy_threshold
value: 141.3014
- type: euclidean_f1
value: 44.8638
- type: euclidean_f1_threshold
value: 210.8781
- type: euclidean_precision
value: 35.3191
- type: euclidean_recall
value: 61.477599999999995
- type: euclidean_ap
value: 44.3973
- type: dot_accuracy
value: 77.4095
- type: dot_accuracy_threshold
value: 3833.3893000000003
- type: dot_f1
value: 41.7116
- type: dot_f1_threshold
value: 336.5812
- type: dot_precision
value: 28.259600000000002
- type: dot_recall
value: 79.6042
- type: dot_ap
value: 30.7809
- type: max_accuracy
value: 80.3481
- type: max_f1
value: 51.27850000000001
- type: max_precision
value: 45.8247
- type: max_recall
value: 79.6042
- type: max_ap
value: 52.295199999999994
- type: main_score
value: 52.295199999999994
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: similarity_accuracy
value: 85.9025
- type: similarity_accuracy_threshold
value: 71.6078
- type: similarity_f1
value: 70.9832
- type: similarity_f1_threshold
value: 66.4079
- type: similarity_precision
value: 68.9871
- type: similarity_recall
value: 73.0982
- type: similarity_ap
value: 79.2622
- type: cosine_accuracy
value: 85.9025
- type: cosine_accuracy_threshold
value: 71.6078
- type: cosine_f1
value: 70.9832
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value: 66.4079
- type: cosine_precision
value: 68.9871
- type: cosine_recall
value: 73.0982
- type: cosine_ap
value: 79.2622
- type: manhattan_accuracy
value: 81.8954
- type: manhattan_accuracy_threshold
value: 2754.9084000000003
- type: manhattan_f1
value: 58.4303
- type: manhattan_f1_threshold
value: 3301.9608
- type: manhattan_precision
value: 56.1511
- type: manhattan_recall
value: 60.9024
- type: manhattan_ap
value: 66.2046
- type: euclidean_accuracy
value: 81.8974
- type: euclidean_accuracy_threshold
value: 122.74810000000001
- type: euclidean_f1
value: 58.455
- type: euclidean_f1_threshold
value: 151.3654
- type: euclidean_precision
value: 55.0722
- type: euclidean_recall
value: 62.2806
- type: euclidean_ap
value: 66.22019999999999
- type: dot_accuracy
value: 78.7402
- type: dot_accuracy_threshold
value: 317.0264
- type: dot_f1
value: 58.2905
- type: dot_f1_threshold
value: 187.0591
- type: dot_precision
value: 48.1454
- type: dot_recall
value: 73.8528
- type: dot_ap
value: 58.116
- type: max_accuracy
value: 85.9025
- type: max_f1
value: 70.9832
- type: max_precision
value: 68.9871
- type: max_recall
value: 73.8528
- type: max_ap
value: 79.2622
- type: main_score
value: 79.2622
task:
type: PairClassification
---
# 🧚🏻♀️ brown-fairy-base-v0 Model Card
> [!TIP]
> Fairies are among the most enchanting and magical beings in folklore and mythology. They appear across countless cultures and stories, from ancient forests to modern gardens. They are celebrated for their ability to bridge the mundane and magical realms, known for their ethereal grace and transformative powers. Fairies are tiny, higher-dimensional beings that can interact with the world in ways that are beyond our understanding.
The fairy series of models are an attempt to tune the beetle series of models to be more suitable for downstream tasks. These models are meant to fully open experiments at making state-of-the-art static embeddings.
The brown-fairy-base-v0 model is a distillation of the `baai/bge-base-en-v1.5` model into the `brown-beetle-base-v0` model. There was no PCA or Zipf applied to this model.
## Installation
Install model2vec using pip:
```bash
pip install model2vec
```
## Usage
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("bhavnicksm/brown-fairy-base-v0")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
Read more about the Model2Vec library [here](https://github.com/MinishLab/model2vec).
## Reproduce this model
This model was trained on a subset of the 2 Million texts from the [FineWeb-Edu](https://huggingface.co/datasets/mixedbread-ai/fineweb-edu) dataset, which was labeled by the `baai/bge-base-en-v1.5` model.
Training Code
Note: The datasets need to me made seperately and loaded with the `datasets` library.
```python
static_embedding = StaticEmbedding.from_model2vec("bhavnicksm/brown-beetle-base-v0")
model = SentenceTransformer(
modules=[static_embedding]
)
loss = MSELoss(model)
run_name = "brown-fairy-base-v0"
args = SentenceTransformerTrainingArguments(
# Required parameter:
output_dir=f"output/{run_name}",
# Optional training parameters:
num_train_epochs=1,
per_device_train_batch_size=2048,
per_device_eval_batch_size=2048,
learning_rate=1e-1,
warmup_ratio=0.1,
fp16=False, # Set to False if you get an error that your GPU can't run on FP16
bf16=True, # Set to True if you have a GPU that supports BF16
batch_sampler=BatchSamplers.NO_DUPLICATES,
# Optional tracking/debugging parameters:
eval_strategy="steps",
eval_steps=50,
save_strategy="steps",
save_steps=50,
save_total_limit=5,
logging_steps=50,
logging_first_step=True,
run_name=run_name,
)
evaluator = NanoBEIREvaluator()
evaluator(model)
trainer = SentenceTransformerTrainer(
model=model,
args=args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
loss=loss,
evaluator=evaluator,
)
trainer.train()
evaluator(model)
model.save_pretrained(f"output/{run_name}")
```
## Comparison with other models
Coming soon...
## Acknowledgements
This model is based on the [Model2Vec](https://github.com/MinishLab/model2vec) library. Credit goes to the [Minish Lab](https://github.com/MinishLab) team for developing this library.
## Citation
This model builds on work done by Minish Lab. Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```bibtex
@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},
}
```