sentence-transformers pytorch safetensors roberta feature-extraction sentence-similarity transformers mteb pl arxiv:2402.13350 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
language: pl
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
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.
model-index:
- name: mmlw-roberta-large
  results:
  - task:
      type: Clustering
    dataset:
      name: MTEB 8TagsClustering
      type: PL-MTEB/8tags-clustering
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 31.16472823814849
  - task:
      type: Classification
    dataset:
      name: MTEB AllegroReviews
      type: PL-MTEB/allegro-reviews
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 47.48508946322067
    - type: f1
      value: 42.33327527584009
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna-PL
      type: 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:
      name: MTEB CBD
      type: PL-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:
      name: MTEB CDSC-E
      type: PL-MTEB/cdsce-pairclassification
      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:
      name: MTEB CDSC-R
      type: PL-MTEB/cdscr-sts
      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:
      name: MTEB DBPedia-PL
      type: 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:
      name: MTEB FiQA-PL
      type: 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: 25.0
    - 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:
      name: MTEB HotpotQA-PL
      type: 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:
      name: MTEB MSMARCO-PL
      type: 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:
      name: MTEB MassiveIntentClassification (pl)
      type: mteb/amazon_massive_intent
      config: pl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 74.8117014122394
    - type: f1
      value: 72.0259730121889
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (pl)
      type: mteb/amazon_massive_scenario
      config: pl
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 77.84465366509752
    - type: f1
      value: 77.73439218970051
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus-PL
      type: 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:
      name: MTEB NQ-PL
      type: 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:
      name: MTEB PAC
      type: laugustyniak/abusive-clauses-pl
      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:
      name: MTEB PPC
      type: PL-MTEB/ppc-pairclassification
      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:
      name: MTEB PSC
      type: PL-MTEB/psc-pairclassification
      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:
      name: MTEB PolEmo2.0-IN
      type: PL-MTEB/polemo2_in
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 76.84210526315789
    - type: f1
      value: 75.49713789106988
  - task:
      type: Classification
    dataset:
      name: MTEB PolEmo2.0-OUT
      type: PL-MTEB/polemo2_out
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 53.7246963562753
    - type: f1
      value: 43.060592194322986
  - task:
      type: Retrieval
    dataset:
      name: MTEB Quora-PL
      type: 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: 87.0
    - 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:
      name: MTEB SCIDOCS-PL
      type: 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:
      name: MTEB SICK-E-PL
      type: PL-MTEB/sicke-pl-pairclassification
      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:
      name: MTEB SICK-R-PL
      type: PL-MTEB/sickr-pl-sts
      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:
      name: MTEB STS22 (pl)
      type: mteb/sts22-crosslingual-sts
      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:
      name: MTEB SciFact-PL
      type: 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: 59.0
    - 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: 59.0
    - 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: 59.0
    - 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: 100.0
    - type: recall_at_3
      value: 71.822
    - type: recall_at_5
      value: 76.483
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID-PL
      type: trec-covid-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: 80.0
    - 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: 77.0
    - 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: 80.0
    - type: precision_at_10
      value: 75.0
    - type: precision_at_100
      value: 53.879999999999995
    - type: precision_at_1000
      value: 20.002
    - type: precision_at_3
      value: 80.0
    - 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
---

<h1 align="center">MMLW-roberta-large</h1>

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](https://aclanthology.org/2020.emnlp-main.365/) on a diverse corpus of 60 million Polish-English text pairs. We utilised [English FlagEmbeddings (BGE)](https://huggingface.co/BAAI/bge-base-en) 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](https://www.SBERT.net):

```python
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](https://huggingface.co/spaces/mteb/leaderboard) for detailed results.
- The model achieves **NDCG@10** of **55.95** on the Polish Information Retrieval Benchmark. See [PIRB Leaderboard](https://huggingface.co/spaces/sdadas/pirb) 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

```bibtex
@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}
}
```