mmlw-roberta-large / README.md
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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}
}