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-base
  results:
  - task:
      type: Clustering
    dataset:
      name: MTEB 8TagsClustering
      type: PL-MTEB/8tags-clustering
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 33.08463724780795
  - task:
      type: Classification
    dataset:
      name: MTEB AllegroReviews
      type: PL-MTEB/allegro-reviews
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 40.25844930417495
    - type: f1
      value: 35.59685265418916
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna-PL
      type: arguana-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 33.073
    - type: map_at_10
      value: 50.223
    - type: map_at_100
      value: 50.942
    - type: map_at_1000
      value: 50.94499999999999
    - type: map_at_3
      value: 45.721000000000004
    - type: map_at_5
      value: 48.413000000000004
    - type: mrr_at_1
      value: 34.424
    - type: mrr_at_10
      value: 50.68899999999999
    - type: mrr_at_100
      value: 51.437999999999995
    - type: mrr_at_1000
      value: 51.441
    - type: mrr_at_3
      value: 46.219
    - type: mrr_at_5
      value: 48.921
    - type: ndcg_at_1
      value: 33.073
    - type: ndcg_at_10
      value: 59.021
    - type: ndcg_at_100
      value: 61.902
    - type: ndcg_at_1000
      value: 61.983999999999995
    - type: ndcg_at_3
      value: 49.818
    - type: ndcg_at_5
      value: 54.644999999999996
    - type: precision_at_1
      value: 33.073
    - type: precision_at_10
      value: 8.684
    - type: precision_at_100
      value: 0.9900000000000001
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 20.555
    - type: precision_at_5
      value: 14.666
    - type: recall_at_1
      value: 33.073
    - type: recall_at_10
      value: 86.842
    - type: recall_at_100
      value: 99.004
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 61.663999999999994
    - type: recall_at_5
      value: 73.329
  - task:
      type: Classification
    dataset:
      name: MTEB CBD
      type: PL-MTEB/cbd
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 68.11
    - type: ap
      value: 20.916633959031266
    - type: f1
      value: 56.85804802205465
  - 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: 89.2
    - type: cos_sim_ap
      value: 79.1041156765933
    - type: cos_sim_f1
      value: 70.0
    - type: cos_sim_precision
      value: 74.11764705882354
    - type: cos_sim_recall
      value: 66.3157894736842
    - type: dot_accuracy
      value: 88.2
    - type: dot_ap
      value: 72.57183688228149
    - type: dot_f1
      value: 67.16417910447761
    - type: dot_precision
      value: 63.67924528301887
    - type: dot_recall
      value: 71.05263157894737
    - type: euclidean_accuracy
      value: 89.3
    - type: euclidean_ap
      value: 79.01345533432428
    - type: euclidean_f1
      value: 70.19498607242339
    - type: euclidean_precision
      value: 74.55621301775149
    - type: euclidean_recall
      value: 66.3157894736842
    - type: manhattan_accuracy
      value: 89.3
    - type: manhattan_ap
      value: 79.01671381791259
    - type: manhattan_f1
      value: 70.0280112044818
    - type: manhattan_precision
      value: 74.8502994011976
    - type: manhattan_recall
      value: 65.78947368421053
    - type: max_accuracy
      value: 89.3
    - type: max_ap
      value: 79.1041156765933
    - type: max_f1
      value: 70.19498607242339
  - 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: 91.79559442663039
    - type: cos_sim_spearman
      value: 92.5438168962641
    - type: euclidean_pearson
      value: 92.02981265332856
    - type: euclidean_spearman
      value: 92.5548245733484
    - type: manhattan_pearson
      value: 91.95296287979178
    - type: manhattan_spearman
      value: 92.50279516120241
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia-PL
      type: dbpedia-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 7.829999999999999
    - type: map_at_10
      value: 16.616
    - type: map_at_100
      value: 23.629
    - type: map_at_1000
      value: 25.235999999999997
    - type: map_at_3
      value: 12.485
    - type: map_at_5
      value: 14.077
    - type: mrr_at_1
      value: 61.75000000000001
    - type: mrr_at_10
      value: 69.852
    - type: mrr_at_100
      value: 70.279
    - type: mrr_at_1000
      value: 70.294
    - type: mrr_at_3
      value: 68.375
    - type: mrr_at_5
      value: 69.187
    - type: ndcg_at_1
      value: 49.75
    - type: ndcg_at_10
      value: 36.217
    - type: ndcg_at_100
      value: 41.235
    - type: ndcg_at_1000
      value: 48.952
    - type: ndcg_at_3
      value: 41.669
    - type: ndcg_at_5
      value: 38.285000000000004
    - type: precision_at_1
      value: 61.5
    - type: precision_at_10
      value: 28.499999999999996
    - type: precision_at_100
      value: 9.572
    - type: precision_at_1000
      value: 2.025
    - type: precision_at_3
      value: 44.083
    - type: precision_at_5
      value: 36.3
    - type: recall_at_1
      value: 7.829999999999999
    - type: recall_at_10
      value: 21.462999999999997
    - type: recall_at_100
      value: 47.095
    - type: recall_at_1000
      value: 71.883
    - type: recall_at_3
      value: 13.891
    - type: recall_at_5
      value: 16.326999999999998
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA-PL
      type: fiqa-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 16.950000000000003
    - type: map_at_10
      value: 27.422
    - type: map_at_100
      value: 29.146
    - type: map_at_1000
      value: 29.328
    - type: map_at_3
      value: 23.735999999999997
    - type: map_at_5
      value: 25.671
    - type: mrr_at_1
      value: 33.796
    - type: mrr_at_10
      value: 42.689
    - type: mrr_at_100
      value: 43.522
    - type: mrr_at_1000
      value: 43.563
    - type: mrr_at_3
      value: 40.226
    - type: mrr_at_5
      value: 41.685
    - type: ndcg_at_1
      value: 33.642
    - type: ndcg_at_10
      value: 35.008
    - type: ndcg_at_100
      value: 41.839
    - type: ndcg_at_1000
      value: 45.035
    - type: ndcg_at_3
      value: 31.358999999999998
    - type: ndcg_at_5
      value: 32.377
    - type: precision_at_1
      value: 33.642
    - type: precision_at_10
      value: 9.937999999999999
    - type: precision_at_100
      value: 1.685
    - type: precision_at_1000
      value: 0.22699999999999998
    - type: precision_at_3
      value: 21.142
    - type: precision_at_5
      value: 15.586
    - type: recall_at_1
      value: 16.950000000000003
    - type: recall_at_10
      value: 42.286
    - type: recall_at_100
      value: 68.51899999999999
    - type: recall_at_1000
      value: 87.471
    - type: recall_at_3
      value: 28.834
    - type: recall_at_5
      value: 34.274
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA-PL
      type: hotpotqa-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 37.711
    - type: map_at_10
      value: 57.867999999999995
    - type: map_at_100
      value: 58.77
    - type: map_at_1000
      value: 58.836999999999996
    - type: map_at_3
      value: 54.400999999999996
    - type: map_at_5
      value: 56.564
    - type: mrr_at_1
      value: 75.449
    - type: mrr_at_10
      value: 81.575
    - type: mrr_at_100
      value: 81.783
    - type: mrr_at_1000
      value: 81.792
    - type: mrr_at_3
      value: 80.50399999999999
    - type: mrr_at_5
      value: 81.172
    - type: ndcg_at_1
      value: 75.422
    - type: ndcg_at_10
      value: 66.635
    - type: ndcg_at_100
      value: 69.85
    - type: ndcg_at_1000
      value: 71.179
    - type: ndcg_at_3
      value: 61.648
    - type: ndcg_at_5
      value: 64.412
    - type: precision_at_1
      value: 75.422
    - type: precision_at_10
      value: 13.962
    - type: precision_at_100
      value: 1.649
    - type: precision_at_1000
      value: 0.183
    - type: precision_at_3
      value: 39.172000000000004
    - type: precision_at_5
      value: 25.691000000000003
    - type: recall_at_1
      value: 37.711
    - type: recall_at_10
      value: 69.811
    - type: recall_at_100
      value: 82.471
    - type: recall_at_1000
      value: 91.29
    - type: recall_at_3
      value: 58.757999999999996
    - type: recall_at_5
      value: 64.227
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO-PL
      type: msmarco-pl
      config: default
      split: validation
      revision: None
    metrics:
    - type: map_at_1
      value: 17.033
    - type: map_at_10
      value: 27.242
    - type: map_at_100
      value: 28.451999999999998
    - type: map_at_1000
      value: 28.515
    - type: map_at_3
      value: 24.046
    - type: map_at_5
      value: 25.840999999999998
    - type: mrr_at_1
      value: 17.493
    - type: mrr_at_10
      value: 27.67
    - type: mrr_at_100
      value: 28.823999999999998
    - type: mrr_at_1000
      value: 28.881
    - type: mrr_at_3
      value: 24.529999999999998
    - type: mrr_at_5
      value: 26.27
    - type: ndcg_at_1
      value: 17.479
    - type: ndcg_at_10
      value: 33.048
    - type: ndcg_at_100
      value: 39.071
    - type: ndcg_at_1000
      value: 40.739999999999995
    - type: ndcg_at_3
      value: 26.493
    - type: ndcg_at_5
      value: 29.701
    - type: precision_at_1
      value: 17.479
    - type: precision_at_10
      value: 5.324
    - type: precision_at_100
      value: 0.8380000000000001
    - type: precision_at_1000
      value: 0.098
    - type: precision_at_3
      value: 11.408999999999999
    - type: precision_at_5
      value: 8.469999999999999
    - type: recall_at_1
      value: 17.033
    - type: recall_at_10
      value: 50.929
    - type: recall_at_100
      value: 79.262
    - type: recall_at_1000
      value: 92.239
    - type: recall_at_3
      value: 33.06
    - type: recall_at_5
      value: 40.747
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (pl)
      type: mteb/amazon_massive_intent
      config: pl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 72.31002017484867
    - type: f1
      value: 69.61603671063031
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (pl)
      type: mteb/amazon_massive_scenario
      config: pl
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 75.52790854068594
    - type: f1
      value: 75.4053872472259
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus-PL
      type: nfcorpus-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 5.877000000000001
    - type: map_at_10
      value: 12.817
    - type: map_at_100
      value: 16.247
    - type: map_at_1000
      value: 17.683
    - type: map_at_3
      value: 9.334000000000001
    - type: map_at_5
      value: 10.886999999999999
    - type: mrr_at_1
      value: 45.201
    - type: mrr_at_10
      value: 52.7
    - type: mrr_at_100
      value: 53.425999999999995
    - type: mrr_at_1000
      value: 53.461000000000006
    - type: mrr_at_3
      value: 50.464
    - type: mrr_at_5
      value: 51.827
    - type: ndcg_at_1
      value: 41.949999999999996
    - type: ndcg_at_10
      value: 34.144999999999996
    - type: ndcg_at_100
      value: 31.556
    - type: ndcg_at_1000
      value: 40.265
    - type: ndcg_at_3
      value: 38.07
    - type: ndcg_at_5
      value: 36.571
    - type: precision_at_1
      value: 44.272
    - type: precision_at_10
      value: 25.697
    - type: precision_at_100
      value: 8.077
    - type: precision_at_1000
      value: 2.084
    - type: precision_at_3
      value: 36.016999999999996
    - type: precision_at_5
      value: 31.703
    - type: recall_at_1
      value: 5.877000000000001
    - type: recall_at_10
      value: 16.986
    - type: recall_at_100
      value: 32.719
    - type: recall_at_1000
      value: 63.763000000000005
    - type: recall_at_3
      value: 10.292
    - type: recall_at_5
      value: 12.886000000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ-PL
      type: nq-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 25.476
    - type: map_at_10
      value: 38.67
    - type: map_at_100
      value: 39.784000000000006
    - type: map_at_1000
      value: 39.831
    - type: map_at_3
      value: 34.829
    - type: map_at_5
      value: 37.025000000000006
    - type: mrr_at_1
      value: 28.621000000000002
    - type: mrr_at_10
      value: 41.13
    - type: mrr_at_100
      value: 42.028
    - type: mrr_at_1000
      value: 42.059999999999995
    - type: mrr_at_3
      value: 37.877
    - type: mrr_at_5
      value: 39.763999999999996
    - type: ndcg_at_1
      value: 28.563
    - type: ndcg_at_10
      value: 45.654
    - type: ndcg_at_100
      value: 50.695
    - type: ndcg_at_1000
      value: 51.873999999999995
    - type: ndcg_at_3
      value: 38.359
    - type: ndcg_at_5
      value: 42.045
    - type: precision_at_1
      value: 28.563
    - type: precision_at_10
      value: 7.6450000000000005
    - type: precision_at_100
      value: 1.052
    - type: precision_at_1000
      value: 0.117
    - type: precision_at_3
      value: 17.458000000000002
    - type: precision_at_5
      value: 12.613
    - type: recall_at_1
      value: 25.476
    - type: recall_at_10
      value: 64.484
    - type: recall_at_100
      value: 86.96199999999999
    - type: recall_at_1000
      value: 95.872
    - type: recall_at_3
      value: 45.527
    - type: recall_at_5
      value: 54.029
  - task:
      type: Classification
    dataset:
      name: MTEB PAC
      type: laugustyniak/abusive-clauses-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 65.87315377932232
    - type: ap
      value: 76.41966964416534
    - type: f1
      value: 63.64417488639012
  - task:
      type: PairClassification
    dataset:
      name: MTEB PPC
      type: PL-MTEB/ppc-pairclassification
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 87.7
    - type: cos_sim_ap
      value: 92.81319372631636
    - type: cos_sim_f1
      value: 90.04048582995952
    - type: cos_sim_precision
      value: 88.11410459587957
    - type: cos_sim_recall
      value: 92.05298013245033
    - type: dot_accuracy
      value: 75.0
    - type: dot_ap
      value: 83.63089957943261
    - type: dot_f1
      value: 80.76923076923077
    - type: dot_precision
      value: 75.43103448275862
    - type: dot_recall
      value: 86.9205298013245
    - type: euclidean_accuracy
      value: 87.7
    - type: euclidean_ap
      value: 92.94772245932825
    - type: euclidean_f1
      value: 90.10458567980692
    - type: euclidean_precision
      value: 87.63693270735524
    - type: euclidean_recall
      value: 92.71523178807946
    - type: manhattan_accuracy
      value: 87.8
    - type: manhattan_ap
      value: 92.95330512127123
    - type: manhattan_f1
      value: 90.08130081300813
    - type: manhattan_precision
      value: 88.49840255591054
    - type: manhattan_recall
      value: 91.72185430463577
    - type: max_accuracy
      value: 87.8
    - type: max_ap
      value: 92.95330512127123
    - type: max_f1
      value: 90.10458567980692
  - task:
      type: PairClassification
    dataset:
      name: MTEB PSC
      type: PL-MTEB/psc-pairclassification
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 96.19666048237477
    - type: cos_sim_ap
      value: 98.61237969571302
    - type: cos_sim_f1
      value: 93.77845220030349
    - type: cos_sim_precision
      value: 93.35347432024169
    - type: cos_sim_recall
      value: 94.20731707317073
    - type: dot_accuracy
      value: 94.89795918367348
    - type: dot_ap
      value: 97.02853491357943
    - type: dot_f1
      value: 91.85185185185186
    - type: dot_precision
      value: 89.33717579250721
    - type: dot_recall
      value: 94.51219512195121
    - type: euclidean_accuracy
      value: 96.38218923933209
    - type: euclidean_ap
      value: 98.58145584134218
    - type: euclidean_f1
      value: 94.04580152671755
    - type: euclidean_precision
      value: 94.18960244648318
    - type: euclidean_recall
      value: 93.90243902439023
    - type: manhattan_accuracy
      value: 96.47495361781077
    - type: manhattan_ap
      value: 98.6108221024781
    - type: manhattan_f1
      value: 94.18960244648318
    - type: manhattan_precision
      value: 94.47852760736197
    - type: manhattan_recall
      value: 93.90243902439023
    - type: max_accuracy
      value: 96.47495361781077
    - type: max_ap
      value: 98.61237969571302
    - type: max_f1
      value: 94.18960244648318
  - task:
      type: Classification
    dataset:
      name: MTEB PolEmo2.0-IN
      type: PL-MTEB/polemo2_in
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 71.73130193905818
    - type: f1
      value: 71.17731918813324
  - task:
      type: Classification
    dataset:
      name: MTEB PolEmo2.0-OUT
      type: PL-MTEB/polemo2_out
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 46.59919028340081
    - type: f1
      value: 37.216392949948954
  - task:
      type: Retrieval
    dataset:
      name: MTEB Quora-PL
      type: quora-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 66.134
    - type: map_at_10
      value: 80.19
    - type: map_at_100
      value: 80.937
    - type: map_at_1000
      value: 80.95599999999999
    - type: map_at_3
      value: 77.074
    - type: map_at_5
      value: 79.054
    - type: mrr_at_1
      value: 75.88000000000001
    - type: mrr_at_10
      value: 83.226
    - type: mrr_at_100
      value: 83.403
    - type: mrr_at_1000
      value: 83.406
    - type: mrr_at_3
      value: 82.03200000000001
    - type: mrr_at_5
      value: 82.843
    - type: ndcg_at_1
      value: 75.94
    - type: ndcg_at_10
      value: 84.437
    - type: ndcg_at_100
      value: 86.13
    - type: ndcg_at_1000
      value: 86.29299999999999
    - type: ndcg_at_3
      value: 81.07799999999999
    - type: ndcg_at_5
      value: 83.0
    - type: precision_at_1
      value: 75.94
    - type: precision_at_10
      value: 12.953999999999999
    - type: precision_at_100
      value: 1.514
    - type: precision_at_1000
      value: 0.156
    - type: precision_at_3
      value: 35.61
    - type: precision_at_5
      value: 23.652
    - type: recall_at_1
      value: 66.134
    - type: recall_at_10
      value: 92.991
    - type: recall_at_100
      value: 99.003
    - type: recall_at_1000
      value: 99.86
    - type: recall_at_3
      value: 83.643
    - type: recall_at_5
      value: 88.81099999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS-PL
      type: scidocs-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.183
    - type: map_at_10
      value: 10.626
    - type: map_at_100
      value: 12.485
    - type: map_at_1000
      value: 12.793
    - type: map_at_3
      value: 7.531000000000001
    - type: map_at_5
      value: 9.037
    - type: mrr_at_1
      value: 20.5
    - type: mrr_at_10
      value: 30.175
    - type: mrr_at_100
      value: 31.356
    - type: mrr_at_1000
      value: 31.421
    - type: mrr_at_3
      value: 26.900000000000002
    - type: mrr_at_5
      value: 28.689999999999998
    - type: ndcg_at_1
      value: 20.599999999999998
    - type: ndcg_at_10
      value: 17.84
    - type: ndcg_at_100
      value: 25.518
    - type: ndcg_at_1000
      value: 31.137999999999998
    - type: ndcg_at_3
      value: 16.677
    - type: ndcg_at_5
      value: 14.641000000000002
    - type: precision_at_1
      value: 20.599999999999998
    - type: precision_at_10
      value: 9.3
    - type: precision_at_100
      value: 2.048
    - type: precision_at_1000
      value: 0.33999999999999997
    - type: precision_at_3
      value: 15.533
    - type: precision_at_5
      value: 12.839999999999998
    - type: recall_at_1
      value: 4.183
    - type: recall_at_10
      value: 18.862000000000002
    - type: recall_at_100
      value: 41.592
    - type: recall_at_1000
      value: 69.037
    - type: recall_at_3
      value: 9.443
    - type: recall_at_5
      value: 13.028
  - 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: 86.32286995515696
    - type: cos_sim_ap
      value: 82.04302619416443
    - type: cos_sim_f1
      value: 74.95572086432874
    - type: cos_sim_precision
      value: 74.55954897815363
    - type: cos_sim_recall
      value: 75.35612535612536
    - type: dot_accuracy
      value: 83.9176518548716
    - type: dot_ap
      value: 76.8608733580272
    - type: dot_f1
      value: 72.31936654569449
    - type: dot_precision
      value: 67.36324523663184
    - type: dot_recall
      value: 78.06267806267806
    - type: euclidean_accuracy
      value: 86.32286995515696
    - type: euclidean_ap
      value: 81.9648986659308
    - type: euclidean_f1
      value: 74.93796526054591
    - type: euclidean_precision
      value: 74.59421312632321
    - type: euclidean_recall
      value: 75.28490028490027
    - type: manhattan_accuracy
      value: 86.30248675091724
    - type: manhattan_ap
      value: 81.92853980116878
    - type: manhattan_f1
      value: 74.80968858131489
    - type: manhattan_precision
      value: 72.74562584118439
    - type: manhattan_recall
      value: 76.99430199430199
    - type: max_accuracy
      value: 86.32286995515696
    - type: max_ap
      value: 82.04302619416443
    - type: max_f1
      value: 74.95572086432874
  - 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: 83.07566183637853
    - type: cos_sim_spearman
      value: 79.20198022242548
    - type: euclidean_pearson
      value: 81.27875473517936
    - type: euclidean_spearman
      value: 79.21560102311153
    - type: manhattan_pearson
      value: 81.21559474880459
    - type: manhattan_spearman
      value: 79.1537846814979
  - 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: 36.39657573900194
    - type: cos_sim_spearman
      value: 40.36403461037013
    - type: euclidean_pearson
      value: 29.143416004776316
    - type: euclidean_spearman
      value: 40.43197841306375
    - type: manhattan_pearson
      value: 29.18632337290767
    - type: manhattan_spearman
      value: 40.50563343395481
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact-PL
      type: scifact-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 49.428
    - type: map_at_10
      value: 60.423
    - type: map_at_100
      value: 61.037
    - type: map_at_1000
      value: 61.065999999999995
    - type: map_at_3
      value: 56.989000000000004
    - type: map_at_5
      value: 59.041999999999994
    - type: mrr_at_1
      value: 52.666999999999994
    - type: mrr_at_10
      value: 61.746
    - type: mrr_at_100
      value: 62.273
    - type: mrr_at_1000
      value: 62.300999999999995
    - type: mrr_at_3
      value: 59.278
    - type: mrr_at_5
      value: 60.611000000000004
    - type: ndcg_at_1
      value: 52.333
    - type: ndcg_at_10
      value: 65.75
    - type: ndcg_at_100
      value: 68.566
    - type: ndcg_at_1000
      value: 69.314
    - type: ndcg_at_3
      value: 59.768
    - type: ndcg_at_5
      value: 62.808
    - type: precision_at_1
      value: 52.333
    - type: precision_at_10
      value: 9.167
    - type: precision_at_100
      value: 1.0630000000000002
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 23.778
    - type: precision_at_5
      value: 16.2
    - type: recall_at_1
      value: 49.428
    - type: recall_at_10
      value: 81.07799999999999
    - type: recall_at_100
      value: 93.93299999999999
    - type: recall_at_1000
      value: 99.667
    - type: recall_at_3
      value: 65.061
    - type: recall_at_5
      value: 72.667
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID-PL
      type: trec-covid-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.22100000000000003
    - type: map_at_10
      value: 1.788
    - type: map_at_100
      value: 9.937
    - type: map_at_1000
      value: 24.762999999999998
    - type: map_at_3
      value: 0.579
    - type: map_at_5
      value: 0.947
    - type: mrr_at_1
      value: 78.0
    - type: mrr_at_10
      value: 88.067
    - type: mrr_at_100
      value: 88.067
    - type: mrr_at_1000
      value: 88.067
    - type: mrr_at_3
      value: 87.667
    - type: mrr_at_5
      value: 88.067
    - type: ndcg_at_1
      value: 76.0
    - type: ndcg_at_10
      value: 71.332
    - type: ndcg_at_100
      value: 54.80500000000001
    - type: ndcg_at_1000
      value: 49.504999999999995
    - type: ndcg_at_3
      value: 73.693
    - type: ndcg_at_5
      value: 73.733
    - type: precision_at_1
      value: 82.0
    - type: precision_at_10
      value: 76.8
    - type: precision_at_100
      value: 56.68
    - type: precision_at_1000
      value: 22.236
    - type: precision_at_3
      value: 78.667
    - type: precision_at_5
      value: 79.2
    - type: recall_at_1
      value: 0.22100000000000003
    - type: recall_at_10
      value: 2.033
    - type: recall_at_100
      value: 13.431999999999999
    - type: recall_at_1000
      value: 46.913
    - type: recall_at_3
      value: 0.625
    - type: recall_at_5
      value: 1.052
---

<h1 align="center">MMLW-roberta-base</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 768 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-base")
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 **61.05** 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 **53.60** 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}
}
```