metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: mmlw-roberta-large
results:
- task:
type: Clustering
dataset:
type: PL-MTEB/8tags-clustering
name: MTEB 8TagsClustering
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 31.16472823814849
- task:
type: Classification
dataset:
type: PL-MTEB/allegro-reviews
name: MTEB AllegroReviews
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 47.48508946322067
- type: f1
value: 42.33327527584009
- task:
type: Retrieval
dataset:
type: arguana-pl
name: MTEB ArguAna-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.834
- type: map_at_10
value: 55.22899999999999
- type: map_at_100
value: 55.791999999999994
- type: map_at_1000
value: 55.794
- type: map_at_3
value: 51.233
- type: map_at_5
value: 53.772
- type: mrr_at_1
value: 39.687
- type: mrr_at_10
value: 55.596000000000004
- type: mrr_at_100
value: 56.157000000000004
- type: mrr_at_1000
value: 56.157999999999994
- type: mrr_at_3
value: 51.66
- type: mrr_at_5
value: 54.135
- type: ndcg_at_1
value: 38.834
- type: ndcg_at_10
value: 63.402
- type: ndcg_at_100
value: 65.78
- type: ndcg_at_1000
value: 65.816
- type: ndcg_at_3
value: 55.349000000000004
- type: ndcg_at_5
value: 59.892
- type: precision_at_1
value: 38.834
- type: precision_at_10
value: 8.905000000000001
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 22.428
- type: precision_at_5
value: 15.647
- type: recall_at_1
value: 38.834
- type: recall_at_10
value: 89.047
- type: recall_at_100
value: 99.36
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 67.283
- type: recall_at_5
value: 78.236
- task:
type: Classification
dataset:
type: PL-MTEB/cbd
name: MTEB CBD
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 69.33
- type: ap
value: 22.972409521444508
- type: f1
value: 58.91072163784952
- task:
type: PairClassification
dataset:
type: PL-MTEB/cdsce-pairclassification
name: MTEB CDSC-E
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 89.8
- type: cos_sim_ap
value: 79.87039801032493
- type: cos_sim_f1
value: 68.53932584269663
- type: cos_sim_precision
value: 73.49397590361446
- type: cos_sim_recall
value: 64.21052631578948
- type: dot_accuracy
value: 86.1
- type: dot_ap
value: 63.684975861694035
- type: dot_f1
value: 63.61746361746362
- type: dot_precision
value: 52.57731958762887
- type: dot_recall
value: 80.52631578947368
- type: euclidean_accuracy
value: 89.8
- type: euclidean_ap
value: 79.7527126811392
- type: euclidean_f1
value: 68.46361185983827
- type: euclidean_precision
value: 70.1657458563536
- type: euclidean_recall
value: 66.84210526315789
- type: manhattan_accuracy
value: 89.7
- type: manhattan_ap
value: 79.64632771093657
- type: manhattan_f1
value: 68.4931506849315
- type: manhattan_precision
value: 71.42857142857143
- type: manhattan_recall
value: 65.78947368421053
- type: max_accuracy
value: 89.8
- type: max_ap
value: 79.87039801032493
- type: max_f1
value: 68.53932584269663
- task:
type: STS
dataset:
type: PL-MTEB/cdscr-sts
name: MTEB CDSC-R
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 92.1088892402831
- type: cos_sim_spearman
value: 92.54126377343101
- type: euclidean_pearson
value: 91.99022371986013
- type: euclidean_spearman
value: 92.55235973775511
- type: manhattan_pearson
value: 91.92170171331357
- type: manhattan_spearman
value: 92.47797623672449
- task:
type: Retrieval
dataset:
type: dbpedia-pl
name: MTEB DBPedia-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.683
- type: map_at_10
value: 18.9
- type: map_at_100
value: 26.933
- type: map_at_1000
value: 28.558
- type: map_at_3
value: 13.638
- type: map_at_5
value: 15.9
- type: mrr_at_1
value: 63.74999999999999
- type: mrr_at_10
value: 73.566
- type: mrr_at_100
value: 73.817
- type: mrr_at_1000
value: 73.824
- type: mrr_at_3
value: 71.875
- type: mrr_at_5
value: 73.2
- type: ndcg_at_1
value: 53.125
- type: ndcg_at_10
value: 40.271
- type: ndcg_at_100
value: 45.51
- type: ndcg_at_1000
value: 52.968
- type: ndcg_at_3
value: 45.122
- type: ndcg_at_5
value: 42.306
- type: precision_at_1
value: 63.74999999999999
- type: precision_at_10
value: 31.55
- type: precision_at_100
value: 10.440000000000001
- type: precision_at_1000
value: 2.01
- type: precision_at_3
value: 48.333
- type: precision_at_5
value: 40.5
- type: recall_at_1
value: 8.683
- type: recall_at_10
value: 24.63
- type: recall_at_100
value: 51.762
- type: recall_at_1000
value: 75.64999999999999
- type: recall_at_3
value: 15.136
- type: recall_at_5
value: 18.678
- task:
type: Retrieval
dataset:
type: fiqa-pl
name: MTEB FiQA-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.872999999999998
- type: map_at_10
value: 32.923
- type: map_at_100
value: 34.819
- type: map_at_1000
value: 34.99
- type: map_at_3
value: 28.500999999999998
- type: map_at_5
value: 31.087999999999997
- type: mrr_at_1
value: 40.432
- type: mrr_at_10
value: 49.242999999999995
- type: mrr_at_100
value: 50.014
- type: mrr_at_1000
value: 50.05500000000001
- type: mrr_at_3
value: 47.144999999999996
- type: mrr_at_5
value: 48.171
- type: ndcg_at_1
value: 40.586
- type: ndcg_at_10
value: 40.887
- type: ndcg_at_100
value: 47.701
- type: ndcg_at_1000
value: 50.624
- type: ndcg_at_3
value: 37.143
- type: ndcg_at_5
value: 38.329
- type: precision_at_1
value: 40.586
- type: precision_at_10
value: 11.497
- type: precision_at_100
value: 1.838
- type: precision_at_1000
value: 0.23700000000000002
- type: precision_at_3
value: 25
- type: precision_at_5
value: 18.549
- type: recall_at_1
value: 19.872999999999998
- type: recall_at_10
value: 48.073
- type: recall_at_100
value: 73.473
- type: recall_at_1000
value: 90.94
- type: recall_at_3
value: 33.645
- type: recall_at_5
value: 39.711
- task:
type: Retrieval
dataset:
type: hotpotqa-pl
name: MTEB HotpotQA-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.399
- type: map_at_10
value: 62.604000000000006
- type: map_at_100
value: 63.475
- type: map_at_1000
value: 63.534
- type: map_at_3
value: 58.870999999999995
- type: map_at_5
value: 61.217
- type: mrr_at_1
value: 78.758
- type: mrr_at_10
value: 84.584
- type: mrr_at_100
value: 84.753
- type: mrr_at_1000
value: 84.759
- type: mrr_at_3
value: 83.65700000000001
- type: mrr_at_5
value: 84.283
- type: ndcg_at_1
value: 78.798
- type: ndcg_at_10
value: 71.04
- type: ndcg_at_100
value: 74.048
- type: ndcg_at_1000
value: 75.163
- type: ndcg_at_3
value: 65.862
- type: ndcg_at_5
value: 68.77600000000001
- type: precision_at_1
value: 78.798
- type: precision_at_10
value: 14.949000000000002
- type: precision_at_100
value: 1.7309999999999999
- type: precision_at_1000
value: 0.188
- type: precision_at_3
value: 42.237
- type: precision_at_5
value: 27.634999999999998
- type: recall_at_1
value: 39.399
- type: recall_at_10
value: 74.747
- type: recall_at_100
value: 86.529
- type: recall_at_1000
value: 93.849
- type: recall_at_3
value: 63.356
- type: recall_at_5
value: 69.08800000000001
- task:
type: Retrieval
dataset:
type: msmarco-pl
name: MTEB MSMARCO-PL
config: default
split: validation
revision: None
metrics:
- type: map_at_1
value: 19.598
- type: map_at_10
value: 30.453999999999997
- type: map_at_100
value: 31.601000000000003
- type: map_at_1000
value: 31.66
- type: map_at_3
value: 27.118
- type: map_at_5
value: 28.943
- type: mrr_at_1
value: 20.1
- type: mrr_at_10
value: 30.978
- type: mrr_at_100
value: 32.057
- type: mrr_at_1000
value: 32.112
- type: mrr_at_3
value: 27.679
- type: mrr_at_5
value: 29.493000000000002
- type: ndcg_at_1
value: 20.158
- type: ndcg_at_10
value: 36.63
- type: ndcg_at_100
value: 42.291000000000004
- type: ndcg_at_1000
value: 43.828
- type: ndcg_at_3
value: 29.744999999999997
- type: ndcg_at_5
value: 33.024
- type: precision_at_1
value: 20.158
- type: precision_at_10
value: 5.811999999999999
- type: precision_at_100
value: 0.868
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 12.689
- type: precision_at_5
value: 9.295
- type: recall_at_1
value: 19.598
- type: recall_at_10
value: 55.596999999999994
- type: recall_at_100
value: 82.143
- type: recall_at_1000
value: 94.015
- type: recall_at_3
value: 36.720000000000006
- type: recall_at_5
value: 44.606
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (pl)
config: pl
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.8117014122394
- type: f1
value: 72.0259730121889
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (pl)
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.84465366509752
- type: f1
value: 77.73439218970051
- task:
type: Retrieval
dataset:
type: nfcorpus-pl
name: MTEB NFCorpus-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.604
- type: map_at_10
value: 12.684000000000001
- type: map_at_100
value: 16.274
- type: map_at_1000
value: 17.669
- type: map_at_3
value: 9.347
- type: map_at_5
value: 10.752
- type: mrr_at_1
value: 43.963
- type: mrr_at_10
value: 52.94
- type: mrr_at_100
value: 53.571000000000005
- type: mrr_at_1000
value: 53.613
- type: mrr_at_3
value: 51.032
- type: mrr_at_5
value: 52.193
- type: ndcg_at_1
value: 41.486000000000004
- type: ndcg_at_10
value: 33.937
- type: ndcg_at_100
value: 31.726
- type: ndcg_at_1000
value: 40.331
- type: ndcg_at_3
value: 39.217
- type: ndcg_at_5
value: 36.521
- type: precision_at_1
value: 43.034
- type: precision_at_10
value: 25.324999999999996
- type: precision_at_100
value: 8.022
- type: precision_at_1000
value: 2.0629999999999997
- type: precision_at_3
value: 36.945
- type: precision_at_5
value: 31.517
- type: recall_at_1
value: 5.604
- type: recall_at_10
value: 16.554
- type: recall_at_100
value: 33.113
- type: recall_at_1000
value: 62.832
- type: recall_at_3
value: 10.397
- type: recall_at_5
value: 12.629999999999999
- task:
type: Retrieval
dataset:
type: nq-pl
name: MTEB NQ-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.642
- type: map_at_10
value: 40.367999999999995
- type: map_at_100
value: 41.487
- type: map_at_1000
value: 41.528
- type: map_at_3
value: 36.292
- type: map_at_5
value: 38.548
- type: mrr_at_1
value: 30.156
- type: mrr_at_10
value: 42.853
- type: mrr_at_100
value: 43.742
- type: mrr_at_1000
value: 43.772
- type: mrr_at_3
value: 39.47
- type: mrr_at_5
value: 41.366
- type: ndcg_at_1
value: 30.214000000000002
- type: ndcg_at_10
value: 47.620000000000005
- type: ndcg_at_100
value: 52.486
- type: ndcg_at_1000
value: 53.482
- type: ndcg_at_3
value: 39.864
- type: ndcg_at_5
value: 43.645
- type: precision_at_1
value: 30.214000000000002
- type: precision_at_10
value: 8.03
- type: precision_at_100
value: 1.0739999999999998
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 18.183
- type: precision_at_5
value: 13.105
- type: recall_at_1
value: 26.642
- type: recall_at_10
value: 67.282
- type: recall_at_100
value: 88.632
- type: recall_at_1000
value: 96.109
- type: recall_at_3
value: 47.048
- type: recall_at_5
value: 55.791000000000004
- task:
type: Classification
dataset:
type: laugustyniak/abusive-clauses-pl
name: MTEB PAC
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 64.69446857804807
- type: ap
value: 75.58028779280512
- type: f1
value: 62.3610392963539
- task:
type: PairClassification
dataset:
type: PL-MTEB/ppc-pairclassification
name: MTEB PPC
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 88.4
- type: cos_sim_ap
value: 93.56462741831817
- type: cos_sim_f1
value: 90.73634204275535
- type: cos_sim_precision
value: 86.94992412746586
- type: cos_sim_recall
value: 94.86754966887418
- type: dot_accuracy
value: 75.3
- type: dot_ap
value: 83.06945936688015
- type: dot_f1
value: 81.50887573964496
- type: dot_precision
value: 73.66310160427807
- type: dot_recall
value: 91.22516556291392
- type: euclidean_accuracy
value: 88.8
- type: euclidean_ap
value: 93.53974198044985
- type: euclidean_f1
value: 90.87947882736157
- type: euclidean_precision
value: 89.42307692307693
- type: euclidean_recall
value: 92.3841059602649
- type: manhattan_accuracy
value: 88.8
- type: manhattan_ap
value: 93.54209967780366
- type: manhattan_f1
value: 90.85072231139645
- type: manhattan_precision
value: 88.1619937694704
- type: manhattan_recall
value: 93.70860927152319
- type: max_accuracy
value: 88.8
- type: max_ap
value: 93.56462741831817
- type: max_f1
value: 90.87947882736157
- task:
type: PairClassification
dataset:
type: PL-MTEB/psc-pairclassification
name: MTEB PSC
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 97.03153988868274
- type: cos_sim_ap
value: 98.63208302459417
- type: cos_sim_f1
value: 95.06172839506173
- type: cos_sim_precision
value: 96.25
- type: cos_sim_recall
value: 93.90243902439023
- type: dot_accuracy
value: 86.82745825602969
- type: dot_ap
value: 83.77450133931302
- type: dot_f1
value: 79.3053545586107
- type: dot_precision
value: 75.48209366391184
- type: dot_recall
value: 83.53658536585365
- type: euclidean_accuracy
value: 97.03153988868274
- type: euclidean_ap
value: 98.80678168225653
- type: euclidean_f1
value: 95.20958083832335
- type: euclidean_precision
value: 93.52941176470588
- type: euclidean_recall
value: 96.95121951219512
- type: manhattan_accuracy
value: 97.21706864564007
- type: manhattan_ap
value: 98.82279484224186
- type: manhattan_f1
value: 95.44072948328268
- type: manhattan_precision
value: 95.15151515151516
- type: manhattan_recall
value: 95.73170731707317
- type: max_accuracy
value: 97.21706864564007
- type: max_ap
value: 98.82279484224186
- type: max_f1
value: 95.44072948328268
- task:
type: Classification
dataset:
type: PL-MTEB/polemo2_in
name: MTEB PolEmo2.0-IN
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 76.84210526315789
- type: f1
value: 75.49713789106988
- task:
type: Classification
dataset:
type: PL-MTEB/polemo2_out
name: MTEB PolEmo2.0-OUT
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 53.7246963562753
- type: f1
value: 43.060592194322986
- task:
type: Retrieval
dataset:
type: quora-pl
name: MTEB Quora-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 67.021
- type: map_at_10
value: 81.362
- type: map_at_100
value: 82.06700000000001
- type: map_at_1000
value: 82.084
- type: map_at_3
value: 78.223
- type: map_at_5
value: 80.219
- type: mrr_at_1
value: 77.17
- type: mrr_at_10
value: 84.222
- type: mrr_at_100
value: 84.37599999999999
- type: mrr_at_1000
value: 84.379
- type: mrr_at_3
value: 83.003
- type: mrr_at_5
value: 83.834
- type: ndcg_at_1
value: 77.29
- type: ndcg_at_10
value: 85.506
- type: ndcg_at_100
value: 87
- type: ndcg_at_1000
value: 87.143
- type: ndcg_at_3
value: 82.17
- type: ndcg_at_5
value: 84.057
- type: precision_at_1
value: 77.29
- type: precision_at_10
value: 13.15
- type: precision_at_100
value: 1.522
- type: precision_at_1000
value: 0.156
- type: precision_at_3
value: 36.173
- type: precision_at_5
value: 23.988
- type: recall_at_1
value: 67.021
- type: recall_at_10
value: 93.943
- type: recall_at_100
value: 99.167
- type: recall_at_1000
value: 99.929
- type: recall_at_3
value: 84.55799999999999
- type: recall_at_5
value: 89.697
- task:
type: Retrieval
dataset:
type: scidocs-pl
name: MTEB SCIDOCS-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.523
- type: map_at_10
value: 11.584
- type: map_at_100
value: 13.705
- type: map_at_1000
value: 14.038999999999998
- type: map_at_3
value: 8.187999999999999
- type: map_at_5
value: 9.922
- type: mrr_at_1
value: 22.1
- type: mrr_at_10
value: 32.946999999999996
- type: mrr_at_100
value: 34.11
- type: mrr_at_1000
value: 34.163
- type: mrr_at_3
value: 29.633
- type: mrr_at_5
value: 31.657999999999998
- type: ndcg_at_1
value: 22.2
- type: ndcg_at_10
value: 19.466
- type: ndcg_at_100
value: 27.725
- type: ndcg_at_1000
value: 33.539
- type: ndcg_at_3
value: 18.26
- type: ndcg_at_5
value: 16.265
- type: precision_at_1
value: 22.2
- type: precision_at_10
value: 10.11
- type: precision_at_100
value: 2.204
- type: precision_at_1000
value: 0.36
- type: precision_at_3
value: 17.1
- type: precision_at_5
value: 14.44
- type: recall_at_1
value: 4.523
- type: recall_at_10
value: 20.497
- type: recall_at_100
value: 44.757000000000005
- type: recall_at_1000
value: 73.14699999999999
- type: recall_at_3
value: 10.413
- type: recall_at_5
value: 14.638000000000002
- task:
type: PairClassification
dataset:
type: PL-MTEB/sicke-pl-pairclassification
name: MTEB SICK-E-PL
config: default
split: test
revision: None
metrics:
- type: cos_sim_accuracy
value: 87.4235629841011
- type: cos_sim_ap
value: 84.46531935663157
- type: cos_sim_f1
value: 77.18910963944077
- type: cos_sim_precision
value: 79.83257229832572
- type: cos_sim_recall
value: 74.71509971509973
- type: dot_accuracy
value: 81.10476966979209
- type: dot_ap
value: 71.12231750543143
- type: dot_f1
value: 68.13455657492355
- type: dot_precision
value: 59.69989281886387
- type: dot_recall
value: 79.34472934472934
- type: euclidean_accuracy
value: 87.21973094170403
- type: euclidean_ap
value: 84.33077991405355
- type: euclidean_f1
value: 76.81931132410365
- type: euclidean_precision
value: 76.57466383581033
- type: euclidean_recall
value: 77.06552706552706
- type: manhattan_accuracy
value: 87.21973094170403
- type: manhattan_ap
value: 84.35651252115137
- type: manhattan_f1
value: 76.87004481213376
- type: manhattan_precision
value: 74.48229792919172
- type: manhattan_recall
value: 79.41595441595442
- type: max_accuracy
value: 87.4235629841011
- type: max_ap
value: 84.46531935663157
- type: max_f1
value: 77.18910963944077
- task:
type: STS
dataset:
type: PL-MTEB/sickr-pl-sts
name: MTEB SICK-R-PL
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 83.05629619004273
- type: cos_sim_spearman
value: 79.90632583043678
- type: euclidean_pearson
value: 81.56426663515931
- type: euclidean_spearman
value: 80.05439220131294
- type: manhattan_pearson
value: 81.52958181013108
- type: manhattan_spearman
value: 80.0387467163383
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (pl)
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 35.93847200513348
- type: cos_sim_spearman
value: 39.31543525546526
- type: euclidean_pearson
value: 30.19743936591465
- type: euclidean_spearman
value: 39.966612599252095
- type: manhattan_pearson
value: 30.195614462473387
- type: manhattan_spearman
value: 39.822552043685754
- task:
type: Retrieval
dataset:
type: scifact-pl
name: MTEB SciFact-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 56.05
- type: map_at_10
value: 65.93299999999999
- type: map_at_100
value: 66.571
- type: map_at_1000
value: 66.60000000000001
- type: map_at_3
value: 63.489
- type: map_at_5
value: 64.91799999999999
- type: mrr_at_1
value: 59
- type: mrr_at_10
value: 67.026
- type: mrr_at_100
value: 67.559
- type: mrr_at_1000
value: 67.586
- type: mrr_at_3
value: 65.444
- type: mrr_at_5
value: 66.278
- type: ndcg_at_1
value: 59
- type: ndcg_at_10
value: 70.233
- type: ndcg_at_100
value: 72.789
- type: ndcg_at_1000
value: 73.637
- type: ndcg_at_3
value: 66.40700000000001
- type: ndcg_at_5
value: 68.206
- type: precision_at_1
value: 59
- type: precision_at_10
value: 9.367
- type: precision_at_100
value: 1.06
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.222
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 56.05
- type: recall_at_10
value: 82.089
- type: recall_at_100
value: 93.167
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 71.822
- type: recall_at_5
value: 76.483
- task:
type: Retrieval
dataset:
type: trec-covid-pl
name: MTEB TRECCOVID-PL
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.21
- type: map_at_10
value: 1.7680000000000002
- type: map_at_100
value: 9.447999999999999
- type: map_at_1000
value: 21.728
- type: map_at_3
value: 0.603
- type: map_at_5
value: 0.9610000000000001
- type: mrr_at_1
value: 80
- type: mrr_at_10
value: 88.667
- type: mrr_at_100
value: 88.667
- type: mrr_at_1000
value: 88.667
- type: mrr_at_3
value: 87.667
- type: mrr_at_5
value: 88.667
- type: ndcg_at_1
value: 77
- type: ndcg_at_10
value: 70.814
- type: ndcg_at_100
value: 52.532000000000004
- type: ndcg_at_1000
value: 45.635999999999996
- type: ndcg_at_3
value: 76.542
- type: ndcg_at_5
value: 73.24000000000001
- type: precision_at_1
value: 80
- type: precision_at_10
value: 75
- type: precision_at_100
value: 53.879999999999995
- type: precision_at_1000
value: 20.002
- type: precision_at_3
value: 80
- type: precision_at_5
value: 76.4
- type: recall_at_1
value: 0.21
- type: recall_at_10
value: 2.012
- type: recall_at_100
value: 12.781999999999998
- type: recall_at_1000
value: 42.05
- type: recall_at_3
value: 0.644
- type: recall_at_5
value: 1.04
language: pl
license: apache-2.0
widget:
- source_sentence: 'zapytanie: Jak dożyć 100 lat?'
sentences:
- Trzeba zdrowo się odżywiać i uprawiać sport.
- Trzeba pić alkohol, imprezować i jeździć szybkimi autami.
- >-
Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem
niedzielnego handlu.
MMLW-roberta-large
MMLW (muszę mieć lepszą wiadomość) are neural text encoders for Polish. This is a distilled model that can be used to generate embeddings applicable to many tasks such as semantic similarity, clustering, information retrieval. The model can also serve as a base for further fine-tuning. It transforms texts to 1024 dimensional vectors. The model was initialized with Polish RoBERTa checkpoint, and then trained with multilingual knowledge distillation method on a diverse corpus of 60 million Polish-English text pairs. We utilised English FlagEmbeddings (BGE) as teacher models for distillation.
Usage (Sentence-Transformers)
⚠️ Our embedding models require the use of specific prefixes and suffixes when encoding texts. For this model, each query should be preceded by the prefix "zapytanie: " ⚠️
You can use the model like this with sentence-transformers:
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
query_prefix = "zapytanie: "
answer_prefix = ""
queries = [query_prefix + "Jak dożyć 100 lat?"]
answers = [
answer_prefix + "Trzeba zdrowo się odżywiać i uprawiać sport.",
answer_prefix + "Trzeba pić alkohol, imprezować i jeździć szybkimi autami.",
answer_prefix + "Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem niedzielnego handlu."
]
model = SentenceTransformer("sdadas/mmlw-roberta-large")
queries_emb = model.encode(queries, convert_to_tensor=True, show_progress_bar=False)
answers_emb = model.encode(answers, convert_to_tensor=True, show_progress_bar=False)
best_answer = cos_sim(queries_emb, answers_emb).argmax().item()
print(answers[best_answer])
# Trzeba zdrowo się odżywiać i uprawiać sport.
Evaluation Results
- The model achieves an Average Score of 63.23 on the Polish Massive Text Embedding Benchmark (MTEB). See MTEB Leaderboard for detailed results.
- The model achieves NDCG@10 of 55.95 on the Polish Information Retrieval Benchmark. See PIRB Leaderboard for detailed results.
Acknowledgements
This model was trained with the A100 GPU cluster support delivered by the Gdansk University of Technology within the TASK center initiative.
Citation
@article{dadas2024pirb,
title={{PIRB}: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methods},
author={Sławomir Dadas and Michał Perełkiewicz and Rafał Poświata},
year={2024},
eprint={2402.13350},
archivePrefix={arXiv},
primaryClass={cs.CL}
}