metadata
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- language
- granite
- embeddings
- mteb
- transformers
model-index:
- name: ibm-granite/granite-embedding-125m-english
results:
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 67.3613
- type: f1
value: 55.0794
- type: f1_weighted
value: 73.55120000000001
- type: ap
value: 17.643900000000002
- type: ap_weighted
value: 17.643900000000002
- type: main_score
value: 67.3613
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 63.403
- type: f1
value: 57.4178
- type: f1_weighted
value: 66.9704
- type: ap
value: 26.892300000000002
- type: ap_weighted
value: 26.892300000000002
- type: main_score
value: 63.403
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 64.5872
- type: f1
value: 64.33330000000001
- type: f1_weighted
value: 64.33330000000001
- type: ap
value: 59.602
- type: ap_weighted
value: 59.602
- type: main_score
value: 64.5872
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 33.534000000000006
- type: f1
value: 32.5389
- type: f1_weighted
value: 32.5389
- type: main_score
value: 33.534000000000006
task:
type: Classification
- dataset:
config: default
name: MTEB AppsRetrieval (default)
revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5
split: test
type: CoIR-Retrieval/apps
metrics:
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value: 6.932
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value: 9.577
- type: ndcg_at_5
value: 10.597
- type: ndcg_at_10
value: 11.787
- type: ndcg_at_20
value: 12.863
- type: ndcg_at_100
value: 15.573999999999998
- type: ndcg_at_1000
value: 19.772000000000002
- type: map_at_1
value: 6.932
- type: map_at_3
value: 8.938
- type: map_at_5
value: 9.506
- type: map_at_10
value: 10
- type: map_at_20
value: 10.296
- type: map_at_100
value: 10.644
- type: map_at_1000
value: 10.771
- type: recall_at_1
value: 6.932
- type: recall_at_3
value: 11.421000000000001
- type: recall_at_5
value: 13.891
- type: recall_at_10
value: 17.556
- type: recall_at_20
value: 21.806
- type: recall_at_100
value: 36.839
- type: recall_at_1000
value: 71.71300000000001
- type: precision_at_1
value: 6.932
- type: precision_at_3
value: 3.807
- type: precision_at_5
value: 2.778
- type: precision_at_10
value: 1.756
- type: precision_at_20
value: 1.09
- type: precision_at_100
value: 0.368
- type: precision_at_1000
value: 0.07200000000000001
- type: mrr_at_1
value: 6.9323
- type: mrr_at_3
value: 8.9376
- type: mrr_at_5
value: 9.506
- type: mrr_at_10
value: 9.9999
- type: mrr_at_20
value: 10.2957
- type: mrr_at_100
value: 10.643600000000001
- type: mrr_at_1000
value: 10.7707
- type: nauc_ndcg_at_1_max
value: 27.327299999999997
- type: nauc_ndcg_at_1_std
value: 9.6266
- type: nauc_ndcg_at_1_diff1
value: 39.4451
- type: nauc_ndcg_at_3_max
value: 22.9053
- type: nauc_ndcg_at_3_std
value: 10.123
- type: nauc_ndcg_at_3_diff1
value: 27.742099999999997
- type: nauc_ndcg_at_5_max
value: 21.7041
- type: nauc_ndcg_at_5_std
value: 9.661100000000001
- type: nauc_ndcg_at_5_diff1
value: 25.0689
- type: nauc_ndcg_at_10_max
value: 21.0966
- type: nauc_ndcg_at_10_std
value: 10.4106
- type: nauc_ndcg_at_10_diff1
value: 23.4219
- type: nauc_ndcg_at_20_max
value: 20.0575
- type: nauc_ndcg_at_20_std
value: 10.89
- type: nauc_ndcg_at_20_diff1
value: 22.6143
- type: nauc_ndcg_at_100_max
value: 19.4243
- type: nauc_ndcg_at_100_std
value: 11.5431
- type: nauc_ndcg_at_100_diff1
value: 21.013
- type: nauc_ndcg_at_1000_max
value: 20.6057
- type: nauc_ndcg_at_1000_std
value: 13.0027
- type: nauc_ndcg_at_1000_diff1
value: 20.988799999999998
- type: nauc_map_at_1_max
value: 27.327299999999997
- type: nauc_map_at_1_std
value: 9.6266
- type: nauc_map_at_1_diff1
value: 39.4451
- type: nauc_map_at_3_max
value: 23.6991
- type: nauc_map_at_3_std
value: 9.9287
- type: nauc_map_at_3_diff1
value: 29.909799999999997
- type: nauc_map_at_5_max
value: 22.9242
- type: nauc_map_at_5_std
value: 9.640600000000001
- type: nauc_map_at_5_diff1
value: 28.228199999999998
- type: nauc_map_at_10_max
value: 22.612199999999998
- type: nauc_map_at_10_std
value: 10.0051
- type: nauc_map_at_10_diff1
value: 27.3942
- type: nauc_map_at_20_max
value: 22.236
- type: nauc_map_at_20_std
value: 10.168000000000001
- type: nauc_map_at_20_diff1
value: 27.0258
- type: nauc_map_at_100_max
value: 22.1373
- type: nauc_map_at_100_std
value: 10.2741
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value: 22.1829
- type: nauc_map_at_1000_std
value: 10.3395
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value: 26.7158
- type: nauc_recall_at_1_max
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- type: nauc_recall_at_1_std
value: 9.6266
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value: 39.4451
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value: 21.0841
- type: nauc_recall_at_3_std
value: 10.6057
- type: nauc_recall_at_3_diff1
value: 22.745
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value: 19.0389
- type: nauc_recall_at_5_std
value: 9.697899999999999
- type: nauc_recall_at_5_diff1
value: 18.137600000000003
- type: nauc_recall_at_10_max
value: 18.0668
- type: nauc_recall_at_10_std
value: 11.326799999999999
- type: nauc_recall_at_10_diff1
value: 15.423
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value: 15.798100000000002
- type: nauc_recall_at_20_std
value: 12.4585
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value: 14.509500000000001
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value: 14.2836
- type: nauc_recall_at_100_std
value: 14.2989
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value: 10.7304
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- type: nauc_recall_at_1000_std
value: 24.5691
- type: nauc_recall_at_1000_diff1
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- type: nauc_precision_at_1_max
value: 27.327299999999997
- type: nauc_precision_at_1_std
value: 9.6266
- type: nauc_precision_at_1_diff1
value: 39.4451
- type: nauc_precision_at_3_max
value: 21.0841
- type: nauc_precision_at_3_std
value: 10.6057
- type: nauc_precision_at_3_diff1
value: 22.745
- type: nauc_precision_at_5_max
value: 19.0389
- type: nauc_precision_at_5_std
value: 9.697899999999999
- type: nauc_precision_at_5_diff1
value: 18.137600000000003
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value: 18.0668
- type: nauc_precision_at_10_std
value: 11.326799999999999
- type: nauc_precision_at_10_diff1
value: 15.423
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value: 15.798100000000002
- type: nauc_precision_at_20_std
value: 12.4585
- type: nauc_precision_at_20_diff1
value: 14.509500000000001
- type: nauc_precision_at_100_max
value: 14.2836
- type: nauc_precision_at_100_std
value: 14.2989
- type: nauc_precision_at_100_diff1
value: 10.7304
- type: nauc_precision_at_1000_max
value: 19.728299999999997
- type: nauc_precision_at_1000_std
value: 24.5691
- type: nauc_precision_at_1000_diff1
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- type: nauc_mrr_at_1_max
value: 27.327299999999997
- type: nauc_mrr_at_1_std
value: 9.6266
- type: nauc_mrr_at_1_diff1
value: 39.4451
- type: nauc_mrr_at_3_max
value: 23.6991
- type: nauc_mrr_at_3_std
value: 9.9287
- type: nauc_mrr_at_3_diff1
value: 29.909799999999997
- type: nauc_mrr_at_5_max
value: 22.9242
- type: nauc_mrr_at_5_std
value: 9.640600000000001
- type: nauc_mrr_at_5_diff1
value: 28.228199999999998
- type: nauc_mrr_at_10_max
value: 22.612199999999998
- type: nauc_mrr_at_10_std
value: 10.0051
- type: nauc_mrr_at_10_diff1
value: 27.3942
- type: nauc_mrr_at_20_max
value: 22.236
- type: nauc_mrr_at_20_std
value: 10.168000000000001
- type: nauc_mrr_at_20_diff1
value: 27.0258
- type: nauc_mrr_at_100_max
value: 22.1372
- type: nauc_mrr_at_100_std
value: 10.2743
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value: 22.1828
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value: 10.3397
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value: 26.7157
- type: main_score
value: 11.787
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: ndcg_at_1
value: 33.642
- type: ndcg_at_3
value: 48.825
- type: ndcg_at_5
value: 53.689
- type: ndcg_at_10
value: 58.401
- type: ndcg_at_20
value: 60.78
- type: ndcg_at_100
value: 61.57
- type: ndcg_at_1000
value: 61.608
- type: map_at_1
value: 33.642
- type: map_at_3
value: 45.057
- type: map_at_5
value: 47.774
- type: map_at_10
value: 49.716
- type: map_at_20
value: 50.400999999999996
- type: map_at_100
value: 50.519000000000005
- type: map_at_1000
value: 50.52100000000001
- type: recall_at_1
value: 33.642
- type: recall_at_3
value: 59.744
- type: recall_at_5
value: 71.479
- type: recall_at_10
value: 86.06
- type: recall_at_20
value: 95.235
- type: recall_at_100
value: 99.36
- type: recall_at_1000
value: 99.644
- type: precision_at_1
value: 33.642
- type: precision_at_3
value: 19.915
- type: precision_at_5
value: 14.296000000000001
- type: precision_at_10
value: 8.606
- type: precision_at_20
value: 4.7620000000000005
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 34.495
- type: mrr_at_3
value: 45.2821
- type: mrr_at_5
value: 48.1128
- type: mrr_at_10
value: 50.036199999999994
- type: mrr_at_20
value: 50.7172
- type: mrr_at_100
value: 50.83259999999999
- type: mrr_at_1000
value: 50.8343
- type: nauc_ndcg_at_1_max
value: -11.838999999999999
- type: nauc_ndcg_at_1_std
value: -11.8923
- type: nauc_ndcg_at_1_diff1
value: 18.2163
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- type: nauc_ndcg_at_3_std
value: -12.2408
- type: nauc_ndcg_at_3_diff1
value: 12.4326
- type: nauc_ndcg_at_5_max
value: -11.2332
- type: nauc_ndcg_at_5_std
value: -10.99
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value: 11.4272
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value: -9.7581
- type: nauc_ndcg_at_10_std
value: -10.6279
- type: nauc_ndcg_at_10_diff1
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value: 18.2163
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- type: nauc_mrr_at_5_max
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value: -11.3205
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value: 11.6499
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value: 58.401
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P (default)
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 48.3018
- type: v_measure_std
value: 13.845199999999998
- type: main_score
value: 48.3018
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S (default)
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 44.837900000000005
- type: v_measure_std
value: 14.089599999999999
- type: main_score
value: 44.837900000000005
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 66.4838
- type: mrr
value: 79.3195
- type: nAUC_map_max
value: 23.2658
- type: nAUC_map_std
value: 17.5795
- type: nAUC_map_diff1
value: 11.5539
- type: nAUC_mrr_max
value: 35.565400000000004
- type: nAUC_mrr_std
value: 23.7189
- type: nAUC_mrr_diff1
value: 15.962299999999999
- type: main_score
value: 66.4838
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: pearson
value: 90.1203
- type: spearman
value: 87.8424
- type: cosine_pearson
value: 90.1203
- type: cosine_spearman
value: 87.8424
- type: manhattan_pearson
value: 88.1164
- type: manhattan_spearman
value: 87.752
- type: euclidean_pearson
value: 88.3146
- type: euclidean_spearman
value: 87.8424
- type: main_score
value: 87.8424
task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 77.9156
- type: f1
value: 76.9641
- type: f1_weighted
value: 76.9641
- type: main_score
value: 77.9156
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P (default)
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 38.3582
- type: v_measure_std
value: 1.1436
- type: main_score
value: 38.3582
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S (default)
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 36.2911
- type: v_measure_std
value: 0.44339999999999996
- type: main_score
value: 36.2911
task:
type: Clustering
- dataset:
config: python
name: MTEB COIRCodeSearchNetRetrieval (python)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
metrics:
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value: 76.351
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value: 82.116
- type: ndcg_at_5
value: 83.231
- type: ndcg_at_10
value: 84.301
- type: ndcg_at_20
value: 84.83800000000001
- type: ndcg_at_100
value: 85.462
- type: ndcg_at_1000
value: 85.706
- type: map_at_1
value: 76.351
- type: map_at_3
value: 80.744
- type: map_at_5
value: 81.365
- type: map_at_10
value: 81.812
- type: map_at_20
value: 81.96
- type: map_at_100
value: 82.05
- type: map_at_1000
value: 82.06
- type: recall_at_1
value: 76.351
- type: recall_at_3
value: 86.071
- type: recall_at_5
value: 88.765
- type: recall_at_10
value: 92.04299999999999
- type: recall_at_20
value: 94.16799999999999
- type: recall_at_100
value: 97.466
- type: recall_at_1000
value: 99.383
- type: precision_at_1
value: 76.351
- type: precision_at_3
value: 28.689999999999998
- type: precision_at_5
value: 17.753
- type: precision_at_10
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- type: precision_at_20
value: 4.707999999999999
- type: precision_at_100
value: 0.975
- type: precision_at_1000
value: 0.099
- type: mrr_at_1
value: 76.3507
- type: mrr_at_3
value: 80.7436
- type: mrr_at_5
value: 81.3647
- type: mrr_at_10
value: 81.8121
- type: mrr_at_20
value: 81.9598
- type: mrr_at_100
value: 82.0504
- type: mrr_at_1000
value: 82.0597
- type: nauc_ndcg_at_1_max
value: 73.2541
- type: nauc_ndcg_at_1_std
value: -0.8352
- type: nauc_ndcg_at_1_diff1
value: 85.1422
- type: nauc_ndcg_at_3_max
value: 75.9862
- type: nauc_ndcg_at_3_std
value: 0.14100000000000001
- type: nauc_ndcg_at_3_diff1
value: 82.4674
- type: nauc_ndcg_at_5_max
value: 75.7513
- type: nauc_ndcg_at_5_std
value: 0.614
- type: nauc_ndcg_at_5_diff1
value: 82.2885
- type: nauc_ndcg_at_10_max
value: 75.6282
- type: nauc_ndcg_at_10_std
value: 0.6251
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value: 82.3616
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value: 75.7286
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value: 0.9792000000000001
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value: 82.6106
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value: 75.58840000000001
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value: 1.0781
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value: 82.82969999999999
- type: nauc_ndcg_at_1000_max
value: 75.4705
- type: nauc_ndcg_at_1000_std
value: 0.8326
- type: nauc_ndcg_at_1000_diff1
value: 82.889
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value: 73.2541
- type: nauc_map_at_1_std
value: -0.8352
- type: nauc_map_at_1_diff1
value: 85.1422
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value: 75.2756
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value: -0.145
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value: 83.15780000000001
- type: nauc_map_at_5_max
value: 75.1281
- type: nauc_map_at_5_std
value: 0.0837
- type: nauc_map_at_5_diff1
value: 83.08250000000001
- type: nauc_map_at_10_max
value: 75.05579999999999
- type: nauc_map_at_10_std
value: 0.068
- type: nauc_map_at_10_diff1
value: 83.1206
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value: 75.0708
- type: nauc_map_at_20_std
value: 0.13749999999999998
- type: nauc_map_at_20_diff1
value: 83.1861
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value: 75.0491
- type: nauc_map_at_100_std
value: 0.1411
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value: 75.04570000000001
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value: 0.1359
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value: 83.2179
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value: 73.2541
- type: nauc_recall_at_1_std
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- type: nauc_recall_at_1_diff1
value: 85.1422
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value: 79.8732
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value: 78.46
- type: nauc_recall_at_5_std
value: 3.1027
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value: 78.7509
- type: nauc_recall_at_10_max
value: 78.9542
- type: nauc_recall_at_10_std
value: 4.2138
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- type: nauc_recall_at_20_max
value: 81.2016
- type: nauc_recall_at_20_std
value: 9.092500000000001
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value: 78.6045
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value: 84.5044
- type: nauc_recall_at_100_std
value: 22.6368
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- type: nauc_recall_at_1000_std
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value: 73.2541
- type: nauc_precision_at_1_std
value: -0.8352
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value: 85.1422
- type: nauc_precision_at_3_max
value: 78.65990000000001
- type: nauc_precision_at_3_std
value: 1.2368000000000001
- type: nauc_precision_at_3_diff1
value: 79.8732
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value: 78.46
- type: nauc_precision_at_5_std
value: 3.1027
- type: nauc_precision_at_5_diff1
value: 78.7509
- type: nauc_precision_at_10_max
value: 78.9542
- type: nauc_precision_at_10_std
value: 4.2138
- type: nauc_precision_at_10_diff1
value: 77.8697
- type: nauc_precision_at_20_max
value: 81.2016
- type: nauc_precision_at_20_std
value: 9.092500000000001
- type: nauc_precision_at_20_diff1
value: 78.6045
- type: nauc_precision_at_100_max
value: 84.5044
- type: nauc_precision_at_100_std
value: 22.6368
- type: nauc_precision_at_100_diff1
value: 79.553
- type: nauc_precision_at_1000_max
value: 91.4393
- type: nauc_precision_at_1000_std
value: 44.0261
- type: nauc_precision_at_1000_diff1
value: 78.6859
- type: nauc_mrr_at_1_max
value: 73.2541
- type: nauc_mrr_at_1_std
value: -0.8352
- type: nauc_mrr_at_1_diff1
value: 85.1422
- type: nauc_mrr_at_3_max
value: 75.2756
- type: nauc_mrr_at_3_std
value: -0.145
- type: nauc_mrr_at_3_diff1
value: 83.15780000000001
- type: nauc_mrr_at_5_max
value: 75.1281
- type: nauc_mrr_at_5_std
value: 0.0837
- type: nauc_mrr_at_5_diff1
value: 83.08250000000001
- type: nauc_mrr_at_10_max
value: 75.05579999999999
- type: nauc_mrr_at_10_std
value: 0.068
- type: nauc_mrr_at_10_diff1
value: 83.1206
- type: nauc_mrr_at_20_max
value: 75.0708
- type: nauc_mrr_at_20_std
value: 0.13749999999999998
- type: nauc_mrr_at_20_diff1
value: 83.1861
- type: nauc_mrr_at_100_max
value: 75.0491
- type: nauc_mrr_at_100_std
value: 0.1411
- type: nauc_mrr_at_100_diff1
value: 83.21539999999999
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value: 75.04570000000001
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value: 0.1359
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value: 83.2179
- type: main_score
value: 84.301
task:
type: Retrieval
- dataset:
config: javascript
name: MTEB COIRCodeSearchNetRetrieval (javascript)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
metrics:
- type: ndcg_at_1
value: 34.154
- type: ndcg_at_3
value: 41.637
- type: ndcg_at_5
value: 43.775
- type: ndcg_at_10
value: 46.093
- type: ndcg_at_20
value: 47.659
- type: ndcg_at_100
value: 49.975
- type: ndcg_at_1000
value: 51.652
- type: map_at_1
value: 34.154
- type: map_at_3
value: 39.811
- type: map_at_5
value: 40.996
- type: map_at_10
value: 41.945
- type: map_at_20
value: 42.375
- type: map_at_100
value: 42.693999999999996
- type: map_at_1000
value: 42.752
- type: recall_at_1
value: 34.154
- type: recall_at_3
value: 46.916000000000004
- type: recall_at_5
value: 52.112
- type: recall_at_10
value: 59.313
- type: recall_at_20
value: 65.512
- type: recall_at_100
value: 78.001
- type: recall_at_1000
value: 91.49199999999999
- type: precision_at_1
value: 34.154
- type: precision_at_3
value: 15.639
- type: precision_at_5
value: 10.421999999999999
- type: precision_at_10
value: 5.931
- type: precision_at_20
value: 3.276
- type: precision_at_100
value: 0.7799999999999999
- type: precision_at_1000
value: 0.091
- type: mrr_at_1
value: 34.153800000000004
- type: mrr_at_3
value: 39.8106
- type: mrr_at_5
value: 40.995599999999996
- type: mrr_at_10
value: 41.9454
- type: mrr_at_20
value: 42.375099999999996
- type: mrr_at_100
value: 42.6943
- type: mrr_at_1000
value: 42.7521
- type: nauc_ndcg_at_1_max
value: 43.9354
- type: nauc_ndcg_at_1_std
value: -3.6563
- type: nauc_ndcg_at_1_diff1
value: 63.9034
- type: nauc_ndcg_at_3_max
value: 45.9224
- type: nauc_ndcg_at_3_std
value: -1.1915
- type: nauc_ndcg_at_3_diff1
value: 56.65599999999999
- type: nauc_ndcg_at_5_max
value: 45.7943
- type: nauc_ndcg_at_5_std
value: -0.7263000000000001
- type: nauc_ndcg_at_5_diff1
value: 55.4796
- type: nauc_ndcg_at_10_max
value: 45.4291
- type: nauc_ndcg_at_10_std
value: 0.12290000000000001
- type: nauc_ndcg_at_10_diff1
value: 54.7952
- type: nauc_ndcg_at_20_max
value: 45.7072
- type: nauc_ndcg_at_20_std
value: 1.3283
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value: 54.8465
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value: 45.8073
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value: 1.8653
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value: 54.9886
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- type: nauc_ndcg_at_1000_std
value: 1.2590999999999999
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value: 55.374500000000005
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value: 43.9354
- type: nauc_map_at_1_std
value: -3.6563
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value: 63.9034
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value: 58.3822
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value: -1.2034
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value: -0.8769000000000001
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value: 43.9354
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value: -3.6563
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value: 63.9034
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value: 45.4465
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value: 58.3822
- type: nauc_mrr_at_5_max
value: 45.3588
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value: -1.5449
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value: 57.737
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value: 45.2115
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value: -1.2034
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value: 57.4859
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value: 45.29
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task:
type: Retrieval
- dataset:
config: go
name: MTEB COIRCodeSearchNetRetrieval (go)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
metrics:
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value: 43.105
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value: 52.758
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value: 55.284
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value: 57.557
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value: 58.885
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value: 60.803
- type: ndcg_at_1000
value: 61.855000000000004
- type: map_at_1
value: 43.105
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value: 50.38399999999999
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value: 51.783
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value: 52.727999999999994
- type: map_at_20
value: 53.095000000000006
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- type: map_at_1000
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- type: recall_at_1
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- type: recall_at_3
value: 59.628
- type: recall_at_5
value: 65.77199999999999
- type: recall_at_10
value: 72.765
- type: recall_at_20
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- type: recall_at_100
value: 88.31599999999999
- type: recall_at_1000
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- type: precision_at_1
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- type: precision_at_10
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- type: precision_at_20
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- type: precision_at_100
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- type: precision_at_1000
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- type: mrr_at_3
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- type: mrr_at_5
value: 51.783
- type: mrr_at_10
value: 52.727900000000005
- type: mrr_at_20
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- type: mrr_at_1000
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- type: nauc_ndcg_at_1_max
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value: -2.3253
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value: 60.0465
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value: 38.2665
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value: -2.7671
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value: 54.8964
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value: -2.5911
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value: 53.9601
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name: MTEB COIRCodeSearchNetRetrieval (ruby)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
type: CoIR-Retrieval/CodeSearchNet
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config: java
name: MTEB COIRCodeSearchNetRetrieval (java)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
split: test
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name: MTEB COIRCodeSearchNetRetrieval (php)
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3
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revision: f46a197baaae43b4f621051089b82a364682dfeb
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type: mteb/cqadupstack-android
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task:
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config: default
name: MTEB CQADupstackEnglishRetrieval (default)
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
split: test
type: mteb/cqadupstack-english
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revision: CQADupstackRetrieval_is_a_combined_dataset
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task:
type: Retrieval
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config: python
name: MTEB CodeSearchNetCCRetrieval (python)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
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task:
type: Retrieval
- dataset:
config: javascript
name: MTEB CodeSearchNetCCRetrieval (javascript)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
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task:
type: Retrieval
- dataset:
config: go
name: MTEB CodeSearchNetCCRetrieval (go)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
split: test
type: CoIR-Retrieval/CodeSearchNet-ccr
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revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
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type: CoIR-Retrieval/CodeSearchNet-ccr
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name: MTEB CodeSearchNetCCRetrieval (java)
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8
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type: CoIR-Retrieval/CodeSearchNet-ccr
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name: MTEB CodeSearchNetRetrieval (python)
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
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type: code-search-net/code_search_net
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task:
type: Retrieval
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config: javascript
name: MTEB CodeSearchNetRetrieval (javascript)
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
split: test
type: code-search-net/code_search_net
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revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
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name: MTEB CodeSearchNetRetrieval (java)
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
split: test
type: code-search-net/code_search_net
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revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759
split: test
type: code-search-net/code_search_net
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task:
type: Retrieval
- dataset:
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name: MTEB CodeTransOceanContest (default)
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task:
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name: MTEB CodeTransOceanDL (default)
revision: 281562cb8a1265ab5c0824bfa6ddcd9b0a15618f
split: test
type: CoIR-Retrieval/codetrans-dl
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name: MTEB CosQA (default)
revision: bc5efb7e9d437246ce393ed19d772e08e4a79535
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type: CoIR-Retrieval/cosqa
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revision: ab518f4d6fcca38d87c25209f94beba119d02014
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revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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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: 91.31099999999999
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value: 90.9331
- type: f1_weighted
value: 91.2787
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value: 91.31099999999999
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 54.9362
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value: 38.364399999999996
- type: f1_weighted
value: 57.1133
- type: main_score
value: 54.9362
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 64.5461
- type: f1
value: 60.8751
- type: f1_weighted
value: 63.248599999999996
- type: main_score
value: 64.5461
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: 71.6476
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value: 71.03110000000001
- type: f1_weighted
value: 71.3832
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value: 71.6476
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: 32.3037
- type: v_measure_std
value: 1.4981
- type: main_score
value: 32.3037
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: 31.9128
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value: 1.4597
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value: 31.9128
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
metrics:
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value: 33.4843
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value: -17.8061
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value: -1.1424
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value: 14.106
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task:
type: Reranking
- dataset:
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
split: test
type: mteb/nq
metrics:
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- type: nauc_precision_at_10_max
value: 34.4474
- type: nauc_precision_at_10_std
value: 23.5843
- type: nauc_precision_at_10_diff1
value: 14.9385
- type: nauc_precision_at_20_max
value: 31.4376
- type: nauc_precision_at_20_std
value: 27.7123
- type: nauc_precision_at_20_diff1
value: 8.6083
- type: nauc_precision_at_100_max
value: 29.401300000000003
- type: nauc_precision_at_100_std
value: 40.5942
- type: nauc_precision_at_100_diff1
value: 7.6172
- type: nauc_precision_at_1000_max
value: 25.2832
- type: nauc_precision_at_1000_std
value: 40.9653
- type: nauc_precision_at_1000_diff1
value: 10.3534
- type: nauc_mrr_at_1_max
value: 21.2698
- type: nauc_mrr_at_1_std
value: 8.8522
- type: nauc_mrr_at_1_diff1
value: 21.6443
- type: nauc_mrr_at_3_max
value: 26.8557
- type: nauc_mrr_at_3_std
value: 12.482600000000001
- type: nauc_mrr_at_3_diff1
value: 19.3542
- type: nauc_mrr_at_5_max
value: 28.0333
- type: nauc_mrr_at_5_std
value: 13.4664
- type: nauc_mrr_at_5_diff1
value: 20.0372
- type: nauc_mrr_at_10_max
value: 28.0659
- type: nauc_mrr_at_10_std
value: 13.791999999999998
- type: nauc_mrr_at_10_diff1
value: 20.7022
- type: nauc_mrr_at_20_max
value: 27.886499999999998
- type: nauc_mrr_at_20_std
value: 13.952700000000002
- type: nauc_mrr_at_20_diff1
value: 20.5573
- type: nauc_mrr_at_100_max
value: 27.714299999999998
- type: nauc_mrr_at_100_std
value: 13.863700000000001
- type: nauc_mrr_at_100_diff1
value: 20.5074
- type: nauc_mrr_at_1000_max
value: 27.700599999999998
- type: nauc_mrr_at_1000_std
value: 13.8399
- type: nauc_mrr_at_1000_diff1
value: 20.5031
- type: main_score
value: 24.146
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: pearson
value: 78.6926
- type: spearman
value: 71.2001
- type: cosine_pearson
value: 78.6926
- type: cosine_spearman
value: 71.2001
- type: manhattan_pearson
value: 75.264
- type: manhattan_spearman
value: 71.1303
- type: euclidean_pearson
value: 75.3261
- type: euclidean_spearman
value: 71.2001
- type: main_score
value: 71.2001
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: pearson
value: 71.0057
- type: spearman
value: 65.9247
- type: cosine_pearson
value: 71.0057
- type: cosine_spearman
value: 65.9247
- type: manhattan_pearson
value: 67.392
- type: manhattan_spearman
value: 65.8026
- type: euclidean_pearson
value: 67.5888
- type: euclidean_spearman
value: 65.92479999999999
- type: main_score
value: 65.9247
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: pearson
value: 81.67649999999999
- type: spearman
value: 81.7525
- type: cosine_pearson
value: 81.67649999999999
- type: cosine_spearman
value: 81.7525
- type: manhattan_pearson
value: 81.0327
- type: manhattan_spearman
value: 81.6717
- type: euclidean_pearson
value: 81.10000000000001
- type: euclidean_spearman
value: 81.7526
- type: main_score
value: 81.7525
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: pearson
value: 79.47579999999999
- type: spearman
value: 74.2305
- type: cosine_pearson
value: 79.47579999999999
- type: cosine_spearman
value: 74.2305
- type: manhattan_pearson
value: 77.8846
- type: manhattan_spearman
value: 74.1908
- type: euclidean_pearson
value: 77.9333
- type: euclidean_spearman
value: 74.2305
- type: main_score
value: 74.2305
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: pearson
value: 82.90180000000001
- type: spearman
value: 84.1271
- type: cosine_pearson
value: 82.90180000000001
- type: cosine_spearman
value: 84.1271
- type: manhattan_pearson
value: 83.6431
- type: manhattan_spearman
value: 84.1091
- type: euclidean_pearson
value: 83.6388
- type: euclidean_spearman
value: 84.127
- type: main_score
value: 84.1271
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: pearson
value: 80.19810000000001
- type: spearman
value: 81.6627
- type: cosine_pearson
value: 80.19810000000001
- type: cosine_spearman
value: 81.6627
- type: manhattan_pearson
value: 81.4605
- type: manhattan_spearman
value: 81.62819999999999
- type: euclidean_pearson
value: 81.5043
- type: euclidean_spearman
value: 81.6627
- type: main_score
value: 81.6627
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 47.9276
- type: spearman
value: 50.0286
- type: cosine_pearson
value: 47.9276
- type: cosine_spearman
value: 50.0286
- type: manhattan_pearson
value: 48.5188
- type: manhattan_spearman
value: 50.432
- type: euclidean_pearson
value: 48.1655
- type: euclidean_spearman
value: 50.0286
- type: main_score
value: 50.0286
task:
type: STS
- dataset:
config: en-tr
name: MTEB STS17 (en-tr)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 24.4119
- type: spearman
value: 22.1195
- type: cosine_pearson
value: 24.4119
- type: cosine_spearman
value: 22.1195
- type: manhattan_pearson
value: 25.873800000000003
- type: manhattan_spearman
value: 23.6049
- type: euclidean_pearson
value: 24.3693
- type: euclidean_spearman
value: 22.1195
- type: main_score
value: 22.1195
task:
type: STS
- dataset:
config: en-ar
name: MTEB STS17 (en-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 22.656200000000002
- type: spearman
value: 22.5445
- type: cosine_pearson
value: 22.656200000000002
- type: cosine_spearman
value: 22.5445
- type: manhattan_pearson
value: 22.414
- type: manhattan_spearman
value: 22.1601
- type: euclidean_pearson
value: 22.7736
- type: euclidean_spearman
value: 22.5445
- type: main_score
value: 22.5445
task:
type: STS
- dataset:
config: nl-en
name: MTEB STS17 (nl-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 44.4998
- type: spearman
value: 43.1984
- type: cosine_pearson
value: 44.4998
- type: cosine_spearman
value: 43.1984
- type: manhattan_pearson
value: 43.3837
- type: manhattan_spearman
value: 43.1122
- type: euclidean_pearson
value: 44.1642
- type: euclidean_spearman
value: 43.1984
- type: main_score
value: 43.1984
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: 82.3891
- type: spearman
value: 83.9634
- type: cosine_pearson
value: 82.3891
- type: cosine_spearman
value: 83.9634
- type: manhattan_pearson
value: 83.1481
- type: manhattan_spearman
value: 83.9743
- type: euclidean_pearson
value: 83.2767
- type: euclidean_spearman
value: 83.9634
- type: main_score
value: 83.9634
task:
type: STS
- dataset:
config: it-en
name: MTEB STS17 (it-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 35.3106
- type: spearman
value: 30.7572
- type: cosine_pearson
value: 35.3106
- type: cosine_spearman
value: 30.7572
- type: manhattan_pearson
value: 35.6552
- type: manhattan_spearman
value: 31.596000000000004
- type: euclidean_pearson
value: 35.4393
- type: euclidean_spearman
value: 30.7572
- type: main_score
value: 30.7572
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 36.9322
- type: spearman
value: 37.7137
- type: cosine_pearson
value: 36.9322
- type: cosine_spearman
value: 37.7137
- type: manhattan_pearson
value: 36.0714
- type: manhattan_spearman
value: 36.9979
- type: euclidean_pearson
value: 36.784800000000004
- type: euclidean_spearman
value: 37.7137
- type: main_score
value: 37.7137
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: pearson
value: 39.963300000000004
- type: spearman
value: 38.9248
- type: cosine_pearson
value: 39.963300000000004
- type: cosine_spearman
value: 38.9248
- type: manhattan_pearson
value: 39.539699999999996
- type: manhattan_spearman
value: 38.191900000000004
- type: euclidean_pearson
value: 39.8596
- type: euclidean_spearman
value: 38.9248
- type: main_score
value: 38.9248
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 56.0924
- type: spearman
value: 54.1844
- type: cosine_pearson
value: 56.0924
- type: cosine_spearman
value: 54.1844
- type: manhattan_pearson
value: 56.938100000000006
- type: manhattan_spearman
value: 53.9407
- type: euclidean_pearson
value: 57.9844
- type: euclidean_spearman
value: 54.1844
- type: main_score
value: 54.1844
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 69.3771
- type: spearman
value: 69.3609
- type: cosine_pearson
value: 69.3771
- type: cosine_spearman
value: 69.3609
- type: manhattan_pearson
value: 70.8762
- type: manhattan_spearman
value: 69.1889
- type: euclidean_pearson
value: 70.9433
- type: euclidean_spearman
value: 69.3609
- type: main_score
value: 69.3609
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 74.11609999999999
- type: spearman
value: 71.63340000000001
- type: cosine_pearson
value: 74.11609999999999
- type: cosine_spearman
value: 71.63340000000001
- type: manhattan_pearson
value: 73.2348
- type: manhattan_spearman
value: 71.1802
- type: euclidean_pearson
value: 73.284
- type: euclidean_spearman
value: 71.63340000000001
- type: main_score
value: 71.63340000000001
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 70.08879999999999
- type: spearman
value: 73.79
- type: cosine_pearson
value: 70.08879999999999
- type: cosine_spearman
value: 73.79
- type: manhattan_pearson
value: 71.5415
- type: manhattan_spearman
value: 73.6588
- type: euclidean_pearson
value: 71.621
- type: euclidean_spearman
value: 73.79
- type: main_score
value: 73.79
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: pearson
value: 37.5935
- type: spearman
value: 39.5919
- type: cosine_pearson
value: 37.5935
- type: cosine_spearman
value: 39.5919
- type: manhattan_pearson
value: 37.1717
- type: manhattan_spearman
value: 38.6974
- type: euclidean_pearson
value: 37.5632
- type: euclidean_spearman
value: 39.5919
- type: main_score
value: 39.5919
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: pearson
value: 79.9453
- type: spearman
value: 79.6569
- type: cosine_pearson
value: 79.9453
- type: cosine_spearman
value: 79.6569
- type: manhattan_pearson
value: 79.8923
- type: manhattan_spearman
value: 79.58370000000001
- type: euclidean_pearson
value: 79.9829
- type: euclidean_spearman
value: 79.6569
- type: main_score
value: 79.6569
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 88.09949999999999
- type: mrr
value: 96.6455
- type: nAUC_map_max
value: 53.3622
- type: nAUC_map_std
value: 70.3532
- type: nAUC_map_diff1
value: -0.21419999999999997
- type: nAUC_mrr_max
value: 88.893
- type: nAUC_mrr_std
value: 85.4516
- type: nAUC_mrr_diff1
value: 43.6847
- type: main_score
value: 88.09949999999999
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact (default)
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: ndcg_at_1
value: 62.666999999999994
- type: ndcg_at_3
value: 69.77600000000001
- type: ndcg_at_5
value: 71.964
- type: ndcg_at_10
value: 74.72
- type: ndcg_at_20
value: 76.154
- type: ndcg_at_100
value: 76.961
- type: ndcg_at_1000
value: 77.294
- type: map_at_1
value: 60.011
- type: map_at_3
value: 67.135
- type: map_at_5
value: 68.78
- type: map_at_10
value: 70.101
- type: map_at_20
value: 70.56099999999999
- type: map_at_100
value: 70.687
- type: map_at_1000
value: 70.699
- type: recall_at_1
value: 60.011
- type: recall_at_3
value: 74.839
- type: recall_at_5
value: 80.028
- type: recall_at_10
value: 87.8
- type: recall_at_20
value: 93.10000000000001
- type: recall_at_100
value: 97.333
- type: recall_at_1000
value: 100
- type: precision_at_1
value: 62.666999999999994
- type: precision_at_3
value: 27
- type: precision_at_5
value: 17.8
- type: precision_at_10
value: 9.933
- type: precision_at_20
value: 5.283
- type: precision_at_100
value: 1.103
- type: precision_at_1000
value: 0.11299999999999999
- type: mrr_at_1
value: 62.6667
- type: mrr_at_3
value: 68.9444
- type: mrr_at_5
value: 69.9611
- type: mrr_at_10
value: 71.02199999999999
- type: mrr_at_20
value: 71.3777
- type: mrr_at_100
value: 71.4841
- type: mrr_at_1000
value: 71.4961
- type: nauc_ndcg_at_1_max
value: 55.4562
- type: nauc_ndcg_at_1_std
value: -9.3317
- type: nauc_ndcg_at_1_diff1
value: 71.1878
- type: nauc_ndcg_at_3_max
value: 55.3473
- type: nauc_ndcg_at_3_std
value: -14.341400000000002
- type: nauc_ndcg_at_3_diff1
value: 69.11880000000001
- type: nauc_ndcg_at_5_max
value: 55.5531
- type: nauc_ndcg_at_5_std
value: -13.448699999999999
- type: nauc_ndcg_at_5_diff1
value: 67.4611
- type: nauc_ndcg_at_10_max
value: 59.5974
- type: nauc_ndcg_at_10_std
value: -10.262
- type: nauc_ndcg_at_10_diff1
value: 68.3408
- type: nauc_ndcg_at_20_max
value: 58.586499999999994
- type: nauc_ndcg_at_20_std
value: -9.8438
- type: nauc_ndcg_at_20_diff1
value: 68.4434
- type: nauc_ndcg_at_100_max
value: 58.28489999999999
- type: nauc_ndcg_at_100_std
value: -8.7782
- type: nauc_ndcg_at_100_diff1
value: 68.585
- type: nauc_ndcg_at_1000_max
value: 58.0138
- type: nauc_ndcg_at_1000_std
value: -9.4827
- type: nauc_ndcg_at_1000_diff1
value: 69.0467
- type: nauc_map_at_1_max
value: 49.434
- type: nauc_map_at_1_std
value: -17.0503
- type: nauc_map_at_1_diff1
value: 71.80290000000001
- type: nauc_map_at_3_max
value: 52.8035
- type: nauc_map_at_3_std
value: -16.2138
- type: nauc_map_at_3_diff1
value: 69.81739999999999
- type: nauc_map_at_5_max
value: 54.644400000000005
- type: nauc_map_at_5_std
value: -13.910900000000002
- type: nauc_map_at_5_diff1
value: 68.8879
- type: nauc_map_at_10_max
value: 56.550999999999995
- type: nauc_map_at_10_std
value: -12.126900000000001
- type: nauc_map_at_10_diff1
value: 69.2326
- type: nauc_map_at_20_max
value: 56.299699999999994
- type: nauc_map_at_20_std
value: -11.8978
- type: nauc_map_at_20_diff1
value: 69.3387
- type: nauc_map_at_100_max
value: 56.295300000000005
- type: nauc_map_at_100_std
value: -11.6546
- type: nauc_map_at_100_diff1
value: 69.3881
- type: nauc_map_at_1000_max
value: 56.2905
- type: nauc_map_at_1000_std
value: -11.666400000000001
- type: nauc_map_at_1000_diff1
value: 69.4106
- type: nauc_recall_at_1_max
value: 49.434
- type: nauc_recall_at_1_std
value: -17.0503
- type: nauc_recall_at_1_diff1
value: 71.80290000000001
- type: nauc_recall_at_3_max
value: 53.6504
- type: nauc_recall_at_3_std
value: -20.3796
- type: nauc_recall_at_3_diff1
value: 66.0397
- type: nauc_recall_at_5_max
value: 54.45140000000001
- type: nauc_recall_at_5_std
value: -17.8965
- type: nauc_recall_at_5_diff1
value: 60.6996
- type: nauc_recall_at_10_max
value: 72.7183
- type: nauc_recall_at_10_std
value: -7.3393
- type: nauc_recall_at_10_diff1
value: 62.0422
- type: nauc_recall_at_20_max
value: 70.7849
- type: nauc_recall_at_20_std
value: -3.1933000000000002
- type: nauc_recall_at_20_diff1
value: 58.146
- type: nauc_recall_at_100_max
value: 75.43769999999999
- type: nauc_recall_at_100_std
value: 36.5488
- type: nauc_recall_at_100_diff1
value: 46.3177
- type: nauc_recall_at_1000_max
value: .nan
- type: nauc_recall_at_1000_std
value: .nan
- type: nauc_recall_at_1000_diff1
value: .nan
- type: nauc_precision_at_1_max
value: 55.4562
- type: nauc_precision_at_1_std
value: -9.3317
- type: nauc_precision_at_1_diff1
value: 71.1878
- type: nauc_precision_at_3_max
value: 52.548300000000005
- type: nauc_precision_at_3_std
value: 6.719899999999999
- type: nauc_precision_at_3_diff1
value: 42.6315
- type: nauc_precision_at_5_max
value: 47.9921
- type: nauc_precision_at_5_std
value: 21.9242
- type: nauc_precision_at_5_diff1
value: 23.0825
- type: nauc_precision_at_10_max
value: 47.517399999999995
- type: nauc_precision_at_10_std
value: 44.4913
- type: nauc_precision_at_10_diff1
value: 5.4589
- type: nauc_precision_at_20_max
value: 36.0675
- type: nauc_precision_at_20_std
value: 53.9269
- type: nauc_precision_at_20_diff1
value: -7.0865
- type: nauc_precision_at_100_max
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- type: nauc_precision_at_100_std
value: 66.17920000000001
- type: nauc_precision_at_100_diff1
value: -19.653000000000002
- type: nauc_precision_at_1000_max
value: 22.470100000000002
- type: nauc_precision_at_1000_std
value: 69.6725
- type: nauc_precision_at_1000_diff1
value: -27.430500000000002
- type: nauc_mrr_at_1_max
value: 55.4562
- type: nauc_mrr_at_1_std
value: -9.3317
- type: nauc_mrr_at_1_diff1
value: 71.1878
- type: nauc_mrr_at_3_max
value: 57.4634
- type: nauc_mrr_at_3_std
value: -10.6496
- type: nauc_mrr_at_3_diff1
value: 69.881
- type: nauc_mrr_at_5_max
value: 56.8667
- type: nauc_mrr_at_5_std
value: -10.2421
- type: nauc_mrr_at_5_diff1
value: 69.0777
- type: nauc_mrr_at_10_max
value: 58.06289999999999
- type: nauc_mrr_at_10_std
value: -9.8724
- type: nauc_mrr_at_10_diff1
value: 69.5505
- type: nauc_mrr_at_20_max
value: 57.740700000000004
- type: nauc_mrr_at_20_std
value: -10.0261
- type: nauc_mrr_at_20_diff1
value: 69.5455
- type: nauc_mrr_at_100_max
value: 57.735499999999995
- type: nauc_mrr_at_100_std
value: -9.8413
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value: 69.5846
- type: nauc_mrr_at_1000_max
value: 57.7313
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value: -9.8523
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value: 69.6076
- type: main_score
value: 74.72
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: similarity_accuracy
value: 99.798
- type: similarity_accuracy_threshold
value: 92.7546
- type: similarity_f1
value: 89.441
- type: similarity_f1_threshold
value: 92.7546
- type: similarity_precision
value: 92.70389999999999
- type: similarity_recall
value: 86.4
- type: similarity_ap
value: 95.40729999999999
- type: cosine_accuracy
value: 99.798
- type: cosine_accuracy_threshold
value: 92.7546
- type: cosine_f1
value: 89.441
- type: cosine_f1_threshold
value: 92.7546
- type: cosine_precision
value: 92.70389999999999
- type: cosine_recall
value: 86.4
- type: cosine_ap
value: 95.40729999999999
- type: manhattan_accuracy
value: 99.795
- type: manhattan_accuracy_threshold
value: 851.3785
- type: manhattan_f1
value: 89.5464
- type: manhattan_f1_threshold
value: 902.8005999999999
- type: manhattan_precision
value: 88.3268
- type: manhattan_recall
value: 90.8
- type: manhattan_ap
value: 95.3814
- type: euclidean_accuracy
value: 99.798
- type: euclidean_accuracy_threshold
value: 38.0669
- type: euclidean_f1
value: 89.441
- type: euclidean_f1_threshold
value: 38.0669
- type: euclidean_precision
value: 92.70389999999999
- type: euclidean_recall
value: 86.4
- type: euclidean_ap
value: 95.4074
- type: dot_accuracy
value: 99.798
- type: dot_accuracy_threshold
value: 92.7546
- type: dot_f1
value: 89.441
- type: dot_f1_threshold
value: 92.7546
- type: dot_precision
value: 92.70389999999999
- type: dot_recall
value: 86.4
- type: dot_ap
value: 95.4074
- type: max_accuracy
value: 99.798
- type: max_f1
value: 89.5464
- type: max_precision
value: 92.70389999999999
- type: max_recall
value: 90.8
- type: max_ap
value: 95.4074
- type: main_score
value: 95.4074
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 70.3156
- type: v_measure_std
value: 3.9677
- type: main_score
value: 70.3156
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P (default)
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 35.4198
- type: v_measure_std
value: 1.5537
- type: main_score
value: 35.4198
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions (default)
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 54.522099999999995
- type: mrr
value: 55.500099999999996
- type: nAUC_map_max
value: 7.9342
- type: nAUC_map_std
value: 6.8542000000000005
- type: nAUC_map_diff1
value: 38.738099999999996
- type: nAUC_mrr_max
value: 8.862
- type: nAUC_mrr_std
value: 7.2187
- type: nAUC_mrr_diff1
value: 38.5236
- type: main_score
value: 54.522099999999995
task:
type: Reranking
- dataset:
config: default
name: MTEB StackOverflowQA (default)
revision: db8f169f3894c14a00251061f957b2063eef2bd5
split: test
type: CoIR-Retrieval/stackoverflow-qa
metrics:
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value: 83.2
- type: ndcg_at_3
value: 88.397
- type: ndcg_at_5
value: 89.202
- type: ndcg_at_10
value: 89.846
- type: ndcg_at_20
value: 90.235
- type: ndcg_at_100
value: 90.55199999999999
- type: ndcg_at_1000
value: 90.654
- type: map_at_1
value: 83.2
- type: map_at_3
value: 87.17
- type: map_at_5
value: 87.616
- type: map_at_10
value: 87.889
- type: map_at_20
value: 87.994
- type: map_at_100
value: 88.041
- type: map_at_1000
value: 88.045
- type: recall_at_1
value: 83.2
- type: recall_at_3
value: 91.926
- type: recall_at_5
value: 93.882
- type: recall_at_10
value: 95.838
- type: recall_at_20
value: 97.392
- type: recall_at_100
value: 99.047
- type: recall_at_1000
value: 99.85000000000001
- type: precision_at_1
value: 83.2
- type: precision_at_3
value: 30.642000000000003
- type: precision_at_5
value: 18.776
- type: precision_at_10
value: 9.584
- type: precision_at_20
value: 4.87
- type: precision_at_100
value: 0.9900000000000001
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 83.19959999999999
- type: mrr_at_3
value: 87.1698
- type: mrr_at_5
value: 87.6162
- type: mrr_at_10
value: 87.8891
- type: mrr_at_20
value: 87.99369999999999
- type: mrr_at_100
value: 88.0412
- type: mrr_at_1000
value: 88.045
- type: nauc_ndcg_at_1_max
value: 78.6007
- type: nauc_ndcg_at_1_std
value: -0.0095
- type: nauc_ndcg_at_1_diff1
value: 88.7762
- type: nauc_ndcg_at_3_max
value: 81.4239
- type: nauc_ndcg_at_3_std
value: 1.4683
- type: nauc_ndcg_at_3_diff1
value: 86.54220000000001
- type: nauc_ndcg_at_5_max
value: 80.8469
- type: nauc_ndcg_at_5_std
value: -0.5089
- type: nauc_ndcg_at_5_diff1
value: 86.7397
- type: nauc_ndcg_at_10_max
value: 80.60730000000001
- type: nauc_ndcg_at_10_std
value: 1.2302
- type: nauc_ndcg_at_10_diff1
value: 86.5722
- type: nauc_ndcg_at_20_max
value: 80.5133
- type: nauc_ndcg_at_20_std
value: 1.0021
- type: nauc_ndcg_at_20_diff1
value: 86.6381
- type: nauc_ndcg_at_100_max
value: 80.4389
- type: nauc_ndcg_at_100_std
value: 0.33
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value: 86.993
- type: nauc_ndcg_at_1000_max
value: 80.3736
- type: nauc_ndcg_at_1000_std
value: 0.582
- type: nauc_ndcg_at_1000_diff1
value: 86.9238
- type: nauc_map_at_1_max
value: 78.6007
- type: nauc_map_at_1_std
value: -0.0095
- type: nauc_map_at_1_diff1
value: 88.7762
- type: nauc_map_at_3_max
value: 80.6167
- type: nauc_map_at_3_std
value: 0.8933
- type: nauc_map_at_3_diff1
value: 87.07629999999999
- type: nauc_map_at_5_max
value: 80.3056
- type: nauc_map_at_5_std
value: -0.1035
- type: nauc_map_at_5_diff1
value: 87.1974
- type: nauc_map_at_10_max
value: 80.1979
- type: nauc_map_at_10_std
value: 0.4875
- type: nauc_map_at_10_diff1
value: 87.1597
- type: nauc_map_at_20_max
value: 80.1758
- type: nauc_map_at_20_std
value: 0.4484
- type: nauc_map_at_20_diff1
value: 87.1785
- type: nauc_map_at_100_max
value: 80.1598
- type: nauc_map_at_100_std
value: 0.3517
- type: nauc_map_at_100_diff1
value: 87.2128
- type: nauc_map_at_1000_max
value: 80.1585
- type: nauc_map_at_1000_std
value: 0.3646
- type: nauc_map_at_1000_diff1
value: 87.2108
- type: nauc_recall_at_1_max
value: 78.6007
- type: nauc_recall_at_1_std
value: -0.0095
- type: nauc_recall_at_1_diff1
value: 88.7762
- type: nauc_recall_at_3_max
value: 84.951
- type: nauc_recall_at_3_std
value: 4.0854
- type: nauc_recall_at_3_diff1
value: 84.2801
- type: nauc_recall_at_5_max
value: 83.68339999999999
- type: nauc_recall_at_5_std
value: -3.1815
- type: nauc_recall_at_5_diff1
value: 84.33619999999999
- type: nauc_recall_at_10_max
value: 83.4402
- type: nauc_recall_at_10_std
value: 8.585700000000001
- type: nauc_recall_at_10_diff1
value: 81.84320000000001
- type: nauc_recall_at_20_max
value: 83.6935
- type: nauc_recall_at_20_std
value: 9.088799999999999
- type: nauc_recall_at_20_diff1
value: 80.01
- type: nauc_recall_at_100_max
value: 86.5116
- type: nauc_recall_at_100_std
value: -7.6839
- type: nauc_recall_at_100_diff1
value: 88.1354
- type: nauc_recall_at_1000_max
value: 86.3848
- type: nauc_recall_at_1000_std
value: 52.8467
- type: nauc_recall_at_1000_diff1
value: 61.4995
- type: nauc_precision_at_1_max
value: 78.6007
- type: nauc_precision_at_1_std
value: -0.0095
- type: nauc_precision_at_1_diff1
value: 88.7762
- type: nauc_precision_at_3_max
value: 84.951
- type: nauc_precision_at_3_std
value: 4.0854
- type: nauc_precision_at_3_diff1
value: 84.2801
- type: nauc_precision_at_5_max
value: 83.68339999999999
- type: nauc_precision_at_5_std
value: -3.1815
- type: nauc_precision_at_5_diff1
value: 84.33619999999999
- type: nauc_precision_at_10_max
value: 83.4402
- type: nauc_precision_at_10_std
value: 8.585700000000001
- type: nauc_precision_at_10_diff1
value: 81.84320000000001
- type: nauc_precision_at_20_max
value: 83.6935
- type: nauc_precision_at_20_std
value: 9.088799999999999
- type: nauc_precision_at_20_diff1
value: 80.01
- type: nauc_precision_at_100_max
value: 86.5116
- type: nauc_precision_at_100_std
value: -7.6839
- type: nauc_precision_at_100_diff1
value: 88.1354
- type: nauc_precision_at_1000_max
value: 86.3848
- type: nauc_precision_at_1000_std
value: 52.8467
- type: nauc_precision_at_1000_diff1
value: 61.4995
- type: nauc_mrr_at_1_max
value: 78.6007
- type: nauc_mrr_at_1_std
value: -0.0095
- type: nauc_mrr_at_1_diff1
value: 88.7762
- type: nauc_mrr_at_3_max
value: 80.6167
- type: nauc_mrr_at_3_std
value: 0.8933
- type: nauc_mrr_at_3_diff1
value: 87.07629999999999
- type: nauc_mrr_at_5_max
value: 80.3056
- type: nauc_mrr_at_5_std
value: -0.1035
- type: nauc_mrr_at_5_diff1
value: 87.1974
- type: nauc_mrr_at_10_max
value: 80.1979
- type: nauc_mrr_at_10_std
value: 0.4875
- type: nauc_mrr_at_10_diff1
value: 87.1597
- type: nauc_mrr_at_20_max
value: 80.1758
- type: nauc_mrr_at_20_std
value: 0.4484
- type: nauc_mrr_at_20_diff1
value: 87.1785
- type: nauc_mrr_at_100_max
value: 80.1598
- type: nauc_mrr_at_100_std
value: 0.3517
- type: nauc_mrr_at_100_diff1
value: 87.2128
- type: nauc_mrr_at_1000_max
value: 80.1585
- type: nauc_mrr_at_1000_std
value: 0.3646
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value: 87.2108
- type: main_score
value: 89.846
task:
type: Retrieval
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
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value: 30.709999999999997
- type: spearman
value: 31.841199999999997
- type: cosine_spearman
value: 31.841199999999997
- type: cosine_pearson
value: 30.709999999999997
- type: dot_spearman
value: 31.841199999999997
- type: dot_pearson
value: 30.709999999999997
- type: main_score
value: 31.841199999999997
task:
type: Summarization
- dataset:
config: default
name: MTEB SyntheticText2SQL (default)
revision: 686b87296c3a0191b5d9415a00526c62db9fce09
split: test
type: CoIR-Retrieval/synthetic-text2sql
metrics:
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value: 3.692
- type: ndcg_at_3
value: 42.481
- type: ndcg_at_5
value: 45.909
- type: ndcg_at_10
value: 48.41
- type: ndcg_at_20
value: 49.845
- type: ndcg_at_100
value: 51.358000000000004
- type: ndcg_at_1000
value: 51.739999999999995
- type: map_at_1
value: 3.692
- type: map_at_3
value: 33.82
- type: map_at_5
value: 35.727
- type: map_at_10
value: 36.768
- type: map_at_20
value: 37.162
- type: map_at_100
value: 37.377
- type: map_at_1000
value: 37.391999999999996
- type: recall_at_1
value: 3.692
- type: recall_at_3
value: 67.18499999999999
- type: recall_at_5
value: 75.491
- type: recall_at_10
value: 83.182
- type: recall_at_20
value: 88.857
- type: recall_at_100
value: 96.92399999999999
- type: recall_at_1000
value: 99.88
- type: precision_at_1
value: 3.692
- type: precision_at_3
value: 22.395
- type: precision_at_5
value: 15.098
- type: precision_at_10
value: 8.318
- type: precision_at_20
value: 4.443
- type: precision_at_100
value: 0.9690000000000001
- type: precision_at_1000
value: 0.1
- type: mrr_at_1
value: 31.4647
- type: mrr_at_3
value: 49.3391
- type: mrr_at_5
value: 50.9842
- type: mrr_at_10
value: 51.902499999999996
- type: mrr_at_20
value: 52.2801
- type: mrr_at_100
value: 52.4906
- type: mrr_at_1000
value: 52.506
- type: nauc_ndcg_at_1_max
value: 5.9474
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value: -15.6036
- type: nauc_ndcg_at_1_diff1
value: 74.4115
- type: nauc_ndcg_at_3_max
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- type: nauc_ndcg_at_3_std
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task:
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name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
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task:
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name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
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metrics:
- type: accuracy
value: 53.3192
- type: f1
value: 53.505100000000006
- type: f1_weighted
value: 52.726600000000005
- type: main_score
value: 53.3192
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 48.3133
- type: v_measure_std
value: 1.6674000000000002
- type: main_score
value: 48.3133
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: similarity_accuracy
value: 82.2972
- type: similarity_accuracy_threshold
value: 92.5986
- type: similarity_f1
value: 58.2994
- type: similarity_f1_threshold
value: 89.689
- type: similarity_precision
value: 53.3772
- type: similarity_recall
value: 64.2216
- type: similarity_ap
value: 60.9374
- type: cosine_accuracy
value: 82.2972
- type: cosine_accuracy_threshold
value: 92.5986
- type: cosine_f1
value: 58.2994
- type: cosine_f1_threshold
value: 89.689
- type: cosine_precision
value: 53.3772
- type: cosine_recall
value: 64.2216
- type: cosine_ap
value: 60.9374
- type: manhattan_accuracy
value: 82.2912
- type: manhattan_accuracy_threshold
value: 839.1809000000001
- type: manhattan_f1
value: 58.2447
- type: manhattan_f1_threshold
value: 996.9049
- type: manhattan_precision
value: 53.74830000000001
- type: manhattan_recall
value: 63.562
- type: manhattan_ap
value: 60.8808
- type: euclidean_accuracy
value: 82.2972
- type: euclidean_accuracy_threshold
value: 38.4743
- type: euclidean_f1
value: 58.2994
- type: euclidean_f1_threshold
value: 45.4114
- type: euclidean_precision
value: 53.3772
- type: euclidean_recall
value: 64.2216
- type: euclidean_ap
value: 60.9374
- type: dot_accuracy
value: 82.2972
- type: dot_accuracy_threshold
value: 92.5986
- type: dot_f1
value: 58.2994
- type: dot_f1_threshold
value: 89.689
- type: dot_precision
value: 53.3772
- type: dot_recall
value: 64.2216
- type: dot_ap
value: 60.9374
- type: max_accuracy
value: 82.2972
- type: max_f1
value: 58.2994
- type: max_precision
value: 53.74830000000001
- type: max_recall
value: 64.2216
- type: max_ap
value: 60.9374
- type: main_score
value: 60.9374
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: similarity_accuracy
value: 87.2162
- type: similarity_accuracy_threshold
value: 91.6164
- type: similarity_f1
value: 74.8086
- type: similarity_f1_threshold
value: 90.18260000000001
- type: similarity_precision
value: 69.3065
- type: similarity_recall
value: 81.25959999999999
- type: similarity_ap
value: 82.53160000000001
- type: cosine_accuracy
value: 87.2162
- type: cosine_accuracy_threshold
value: 91.6164
- type: cosine_f1
value: 74.8086
- type: cosine_f1_threshold
value: 90.18260000000001
- type: cosine_precision
value: 69.3065
- type: cosine_recall
value: 81.25959999999999
- type: cosine_ap
value: 82.53160000000001
- type: manhattan_accuracy
value: 87.21039999999999
- type: manhattan_accuracy_threshold
value: 899.2865999999999
- type: manhattan_f1
value: 74.77510000000001
- type: manhattan_f1_threshold
value: 962.114
- type: manhattan_precision
value: 70.6927
- type: manhattan_recall
value: 79.3579
- type: manhattan_ap
value: 82.5262
- type: euclidean_accuracy
value: 87.2162
- type: euclidean_accuracy_threshold
value: 40.9478
- type: euclidean_f1
value: 74.8086
- type: euclidean_f1_threshold
value: 44.3112
- type: euclidean_precision
value: 69.3065
- type: euclidean_recall
value: 81.25959999999999
- type: euclidean_ap
value: 82.53160000000001
- type: dot_accuracy
value: 87.2162
- type: dot_accuracy_threshold
value: 91.6164
- type: dot_f1
value: 74.8086
- type: dot_f1_threshold
value: 90.18260000000001
- type: dot_precision
value: 69.3065
- type: dot_recall
value: 81.25959999999999
- type: dot_ap
value: 82.53160000000001
- type: max_accuracy
value: 87.2162
- type: max_f1
value: 74.8086
- type: max_precision
value: 70.6927
- type: max_recall
value: 81.25959999999999
- type: max_ap
value: 82.53160000000001
- type: main_score
value: 82.53160000000001
task:
type: PairClassification
pipeline_tag: sentence-similarity
Granite-Embedding-125m-English
News: Granite Embedding R2 models with 8192 context length released.
- granite-embedding-english-r2 (149M parameters): with an output embedding size of 768, replacing granite-embedding-125m-english.
- granite-embedding-small-english-r2 (47M parameters): A first-of-its-kind reduced-size model, with fewer layers and a smaller output embedding size (384), replacing granite-embedding-30m-english.
Model Summary: Granite-Embedding-125m-English is a 125M parameter dense biencoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation.
- Developers: Granite Embedding Team, IBM
- GitHub Repository: ibm-granite/granite-embedding-models
- Website: Granite Docs
- Paper: Technical Report
- Release Date: December 18th, 2024
- License: Apache 2.0
Supported Languages: English.
Intended use: The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications.
Usage with Sentence Transformers: The model is compatible with SentenceTransformer library and is very easy to use:
First, install the sentence transformers library
pip install sentence_transformers
The model can then be used to encode pairs of text and find the similarity between their representations
from sentence_transformers import SentenceTransformer, util
model_path = "ibm-granite/granite-embedding-125m-english"
# Load the Sentence Transformer model
model = SentenceTransformer(model_path)
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
input_passages = [
"Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
# encode queries and passages
query_embeddings = model.encode(input_queries)
passage_embeddings = model.encode(input_passages)
# calculate cosine similarity
print(util.cos_sim(query_embeddings, passage_embeddings))
Usage with Huggingface Transformers: This is a simple example of how to use the Granite-Embedding-125m-English model with the Transformers library and PyTorch.
First, install the required libraries
pip install transformers torch
The model can then be used to encode pairs of text
import torch
from transformers import AutoModel, AutoTokenizer
model_path = "ibm-granite/granite-embedding-125m-english"
# Load the model and tokenizer
model = AutoModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()
input_queries = [
' Who made the song My achy breaky heart? ',
'summit define'
]
# tokenize inputs
tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')
# encode queries
with torch.no_grad():
# Queries
model_output = model(**tokenized_queries)
# Perform pooling. granite-embedding-125m-english uses CLS Pooling
query_embeddings = model_output[0][:, 0]
# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)
Evaluation:
The performance of the Granite-Embedding-125M-English model on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below.
| Model | Paramters (M) | Embedding Dimension | MTEB Retrieval (15) | CoIR (10) |
|---|---|---|---|---|
| granite-embedding-125m-english | 125 | 768 | 52.3 | 50.3 |
Model Architecture: Granite-Embedding-125m-English is based on an encoder-only RoBERTa like transformer architecture, trained internally at IBM Research.
| Model | granite-embedding-30m-english | granite-embedding-125m-english | granite-embedding-107m-multilingual | granite-embedding-278m-multilingual |
|---|---|---|---|---|
| Embedding size | 384 | 768 | 384 | 768 |
| Number of layers | 6 | 12 | 6 | 12 |
| Number of attention heads | 12 | 12 | 12 | 12 |
| Intermediate size | 1536 | 3072 | 1536 | 3072 |
| Activation Function | GeLU | GeLU | GeLU | GeLU |
| Vocabulary Size | 50265 | 50265 | 250002 | 250002 |
| Max. Sequence Length | 512 | 512 | 512 | 512 |
| # Parameters | 30M | 125M | 107M | 278M |
Training Data: Overall, the training data consists of four key sources: (1) unsupervised title-body paired data scraped from the web, (2) publicly available paired with permissive, enterprise-friendly license, (3) IBM-internal paired data targetting specific technical domains, and (4) IBM-generated synthetic data. The data is listed below:
| Dataset | Num. Pairs |
|---|---|
| SPECTER citation triplets | 684,100 |
| Stack Exchange Duplicate questions (titles) | 304,525 |
| Stack Exchange Duplicate questions (bodies) | 250,519 |
| Stack Exchange Duplicate questions (titles+bodies) | 250,460 |
| Natural Questions (NQ) | 100,231 |
| SQuAD2.0 | 87,599 |
| PAQ (Question, Answer) pairs | 64,371,441 |
| Stack Exchange (Title, Answer) pairs | 4,067,139 |
| Stack Exchange (Title, Body) pairs | 23,978,013 |
| Stack Exchange (Title+Body, Answer) pairs | 187,195 |
| S2ORC Citation pairs (Titles) | 52,603,982 |
| S2ORC (Title, Abstract) | 41,769,185 |
| S2ORC (Citations, abstracts) | 52,603,982 |
| WikiAnswers Duplicate question pairs | 77,427,422 |
| SearchQA | 582,261 |
| HotpotQA | 85,000 |
| Fever | 109,810 |
| Arxiv | 2,358,545 |
| Wikipedia | 20,745,403 |
| PubMed | 20,000,000 |
| Miracl En Pairs | 9,016 |
| DBPedia Title-Body Pairs | 4,635,922 |
| Synthetic: Query-Wikipedia Passage | 1,879,093 |
| Synthetic: Fact Verification | 9,888 |
| IBM Internal Triples | 40,290 |
| IBM Internal Title-Body Pairs | 1,524,586 |
Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license, while other open-source models train on this dataset due to its high quality.
Infrastructure: We train Granite Embedding Models using IBM's computing cluster, Cognitive Compute Cluster, which is outfitted with NVIDIA A100 80gb GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.
Ethical Considerations and Limitations: The data used to train the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-Embedding-125m-English is trained only for English texts, and has a context length of 512 tokens (longer texts will be truncated to this size).
Resources
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources