sentence-transformers pytorch safetensors bert 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: 'query: Jak dożyć 100 lat?'
  sentences:
  - 'passage: Trzeba zdrowo się odżywiać i uprawiać sport.'
  - 'passage: Trzeba pić alkohol, imprezować i jeździć szybkimi autami.'
  - 'passage: Gdy trwała kampania politycy zapewniali, że rozprawią się z zakazem
    niedzielnego handlu.'
model-index:
- name: mmlw-e5-small
  results:
  - task:
      type: Clustering
    dataset:
      name: MTEB 8TagsClustering
      type: PL-MTEB/8tags-clustering
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 31.772224277808153
  - task:
      type: Classification
    dataset:
      name: MTEB AllegroReviews
      type: PL-MTEB/allegro-reviews
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 33.03180914512922
    - type: f1
      value: 29.800304217426167
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna-PL
      type: arguana-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 28.804999999999996
    - type: map_at_10
      value: 45.327
    - type: map_at_100
      value: 46.17
    - type: map_at_1000
      value: 46.177
    - type: map_at_3
      value: 40.528999999999996
    - type: map_at_5
      value: 43.335
    - type: mrr_at_1
      value: 30.299
    - type: mrr_at_10
      value: 45.763
    - type: mrr_at_100
      value: 46.641
    - type: mrr_at_1000
      value: 46.648
    - type: mrr_at_3
      value: 41.074
    - type: mrr_at_5
      value: 43.836999999999996
    - type: ndcg_at_1
      value: 28.804999999999996
    - type: ndcg_at_10
      value: 54.308
    - type: ndcg_at_100
      value: 57.879000000000005
    - type: ndcg_at_1000
      value: 58.048
    - type: ndcg_at_3
      value: 44.502
    - type: ndcg_at_5
      value: 49.519000000000005
    - type: precision_at_1
      value: 28.804999999999996
    - type: precision_at_10
      value: 8.286
    - type: precision_at_100
      value: 0.984
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 18.682000000000002
    - type: precision_at_5
      value: 13.627
    - type: recall_at_1
      value: 28.804999999999996
    - type: recall_at_10
      value: 82.85900000000001
    - type: recall_at_100
      value: 98.36399999999999
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 56.04599999999999
    - type: recall_at_5
      value: 68.137
  - task:
      type: Classification
    dataset:
      name: MTEB CBD
      type: PL-MTEB/cbd
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 64.24
    - type: ap
      value: 17.967103105024705
    - type: f1
      value: 52.97375416129459
  - 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: 88.8
    - type: cos_sim_ap
      value: 76.68028778789487
    - type: cos_sim_f1
      value: 66.82352941176471
    - type: cos_sim_precision
      value: 60.42553191489362
    - type: cos_sim_recall
      value: 74.73684210526315
    - type: dot_accuracy
      value: 88.1
    - type: dot_ap
      value: 72.04910086070551
    - type: dot_f1
      value: 66.66666666666667
    - type: dot_precision
      value: 69.31818181818183
    - type: dot_recall
      value: 64.21052631578948
    - type: euclidean_accuracy
      value: 88.8
    - type: euclidean_ap
      value: 76.63591858340688
    - type: euclidean_f1
      value: 67.13286713286713
    - type: euclidean_precision
      value: 60.25104602510461
    - type: euclidean_recall
      value: 75.78947368421053
    - type: manhattan_accuracy
      value: 88.9
    - type: manhattan_ap
      value: 76.54552849815124
    - type: manhattan_f1
      value: 66.66666666666667
    - type: manhattan_precision
      value: 60.51502145922747
    - type: manhattan_recall
      value: 74.21052631578947
    - type: max_accuracy
      value: 88.9
    - type: max_ap
      value: 76.68028778789487
    - type: max_f1
      value: 67.13286713286713
  - 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: 91.64169404461497
    - type: cos_sim_spearman
      value: 91.9755161377078
    - type: euclidean_pearson
      value: 90.87481478491249
    - type: euclidean_spearman
      value: 91.92362666383987
    - type: manhattan_pearson
      value: 90.8415510499638
    - type: manhattan_spearman
      value: 91.85927127194698
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia-PL
      type: dbpedia-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.148
    - type: map_at_10
      value: 12.870999999999999
    - type: map_at_100
      value: 18.04
    - type: map_at_1000
      value: 19.286
    - type: map_at_3
      value: 9.156
    - type: map_at_5
      value: 10.857999999999999
    - type: mrr_at_1
      value: 53.25
    - type: mrr_at_10
      value: 61.016999999999996
    - type: mrr_at_100
      value: 61.48400000000001
    - type: mrr_at_1000
      value: 61.507999999999996
    - type: mrr_at_3
      value: 58.75
    - type: mrr_at_5
      value: 60.375
    - type: ndcg_at_1
      value: 41.0
    - type: ndcg_at_10
      value: 30.281000000000002
    - type: ndcg_at_100
      value: 33.955999999999996
    - type: ndcg_at_1000
      value: 40.77
    - type: ndcg_at_3
      value: 34.127
    - type: ndcg_at_5
      value: 32.274
    - type: precision_at_1
      value: 52.5
    - type: precision_at_10
      value: 24.525
    - type: precision_at_100
      value: 8.125
    - type: precision_at_1000
      value: 1.728
    - type: precision_at_3
      value: 37.083
    - type: precision_at_5
      value: 32.15
    - type: recall_at_1
      value: 6.148
    - type: recall_at_10
      value: 17.866
    - type: recall_at_100
      value: 39.213
    - type: recall_at_1000
      value: 61.604000000000006
    - type: recall_at_3
      value: 10.084
    - type: recall_at_5
      value: 13.333999999999998
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA-PL
      type: fiqa-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 14.643
    - type: map_at_10
      value: 23.166
    - type: map_at_100
      value: 24.725
    - type: map_at_1000
      value: 24.92
    - type: map_at_3
      value: 20.166
    - type: map_at_5
      value: 22.003
    - type: mrr_at_1
      value: 29.630000000000003
    - type: mrr_at_10
      value: 37.632
    - type: mrr_at_100
      value: 38.512
    - type: mrr_at_1000
      value: 38.578
    - type: mrr_at_3
      value: 35.391
    - type: mrr_at_5
      value: 36.857
    - type: ndcg_at_1
      value: 29.166999999999998
    - type: ndcg_at_10
      value: 29.749
    - type: ndcg_at_100
      value: 35.983
    - type: ndcg_at_1000
      value: 39.817
    - type: ndcg_at_3
      value: 26.739
    - type: ndcg_at_5
      value: 27.993000000000002
    - type: precision_at_1
      value: 29.166999999999998
    - type: precision_at_10
      value: 8.333
    - type: precision_at_100
      value: 1.448
    - type: precision_at_1000
      value: 0.213
    - type: precision_at_3
      value: 17.747
    - type: precision_at_5
      value: 13.58
    - type: recall_at_1
      value: 14.643
    - type: recall_at_10
      value: 35.247
    - type: recall_at_100
      value: 59.150999999999996
    - type: recall_at_1000
      value: 82.565
    - type: recall_at_3
      value: 24.006
    - type: recall_at_5
      value: 29.383
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA-PL
      type: hotpotqa-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 32.627
    - type: map_at_10
      value: 48.041
    - type: map_at_100
      value: 49.008
    - type: map_at_1000
      value: 49.092999999999996
    - type: map_at_3
      value: 44.774
    - type: map_at_5
      value: 46.791
    - type: mrr_at_1
      value: 65.28
    - type: mrr_at_10
      value: 72.53500000000001
    - type: mrr_at_100
      value: 72.892
    - type: mrr_at_1000
      value: 72.909
    - type: mrr_at_3
      value: 71.083
    - type: mrr_at_5
      value: 71.985
    - type: ndcg_at_1
      value: 65.253
    - type: ndcg_at_10
      value: 57.13700000000001
    - type: ndcg_at_100
      value: 60.783
    - type: ndcg_at_1000
      value: 62.507000000000005
    - type: ndcg_at_3
      value: 52.17
    - type: ndcg_at_5
      value: 54.896
    - type: precision_at_1
      value: 65.253
    - type: precision_at_10
      value: 12.088000000000001
    - type: precision_at_100
      value: 1.496
    - type: precision_at_1000
      value: 0.172
    - type: precision_at_3
      value: 32.96
    - type: precision_at_5
      value: 21.931
    - type: recall_at_1
      value: 32.627
    - type: recall_at_10
      value: 60.439
    - type: recall_at_100
      value: 74.80799999999999
    - type: recall_at_1000
      value: 86.219
    - type: recall_at_3
      value: 49.44
    - type: recall_at_5
      value: 54.827999999999996
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO-PL
      type: msmarco-pl
      config: default
      split: validation
      revision: None
    metrics:
    - type: map_at_1
      value: 13.150999999999998
    - type: map_at_10
      value: 21.179000000000002
    - type: map_at_100
      value: 22.227
    - type: map_at_1000
      value: 22.308
    - type: map_at_3
      value: 18.473
    - type: map_at_5
      value: 19.942999999999998
    - type: mrr_at_1
      value: 13.467
    - type: mrr_at_10
      value: 21.471
    - type: mrr_at_100
      value: 22.509
    - type: mrr_at_1000
      value: 22.585
    - type: mrr_at_3
      value: 18.789
    - type: mrr_at_5
      value: 20.262
    - type: ndcg_at_1
      value: 13.539000000000001
    - type: ndcg_at_10
      value: 25.942999999999998
    - type: ndcg_at_100
      value: 31.386999999999997
    - type: ndcg_at_1000
      value: 33.641
    - type: ndcg_at_3
      value: 20.368
    - type: ndcg_at_5
      value: 23.003999999999998
    - type: precision_at_1
      value: 13.539000000000001
    - type: precision_at_10
      value: 4.249
    - type: precision_at_100
      value: 0.7040000000000001
    - type: precision_at_1000
      value: 0.09
    - type: precision_at_3
      value: 8.782
    - type: precision_at_5
      value: 6.6049999999999995
    - type: recall_at_1
      value: 13.150999999999998
    - type: recall_at_10
      value: 40.698
    - type: recall_at_100
      value: 66.71000000000001
    - type: recall_at_1000
      value: 84.491
    - type: recall_at_3
      value: 25.452
    - type: recall_at_5
      value: 31.791000000000004
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (pl)
      type: mteb/amazon_massive_intent
      config: pl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 67.3537323470074
    - type: f1
      value: 64.67852047603644
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (pl)
      type: mteb/amazon_massive_scenario
      config: pl
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 72.12508406186953
    - type: f1
      value: 71.55887309568853
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus-PL
      type: nfcorpus-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.18
    - type: map_at_10
      value: 9.524000000000001
    - type: map_at_100
      value: 12.272
    - type: map_at_1000
      value: 13.616
    - type: map_at_3
      value: 6.717
    - type: map_at_5
      value: 8.172
    - type: mrr_at_1
      value: 37.152
    - type: mrr_at_10
      value: 45.068000000000005
    - type: mrr_at_100
      value: 46.026
    - type: mrr_at_1000
      value: 46.085
    - type: mrr_at_3
      value: 43.344
    - type: mrr_at_5
      value: 44.412
    - type: ndcg_at_1
      value: 34.52
    - type: ndcg_at_10
      value: 27.604
    - type: ndcg_at_100
      value: 26.012999999999998
    - type: ndcg_at_1000
      value: 35.272
    - type: ndcg_at_3
      value: 31.538
    - type: ndcg_at_5
      value: 30.165999999999997
    - type: precision_at_1
      value: 36.223
    - type: precision_at_10
      value: 21.053
    - type: precision_at_100
      value: 7.08
    - type: precision_at_1000
      value: 1.9929999999999999
    - type: precision_at_3
      value: 30.031000000000002
    - type: precision_at_5
      value: 26.997
    - type: recall_at_1
      value: 4.18
    - type: recall_at_10
      value: 12.901000000000002
    - type: recall_at_100
      value: 27.438000000000002
    - type: recall_at_1000
      value: 60.768
    - type: recall_at_3
      value: 7.492
    - type: recall_at_5
      value: 10.05
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ-PL
      type: nq-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 17.965
    - type: map_at_10
      value: 28.04
    - type: map_at_100
      value: 29.217
    - type: map_at_1000
      value: 29.285
    - type: map_at_3
      value: 24.818
    - type: map_at_5
      value: 26.617
    - type: mrr_at_1
      value: 20.22
    - type: mrr_at_10
      value: 30.148000000000003
    - type: mrr_at_100
      value: 31.137999999999998
    - type: mrr_at_1000
      value: 31.19
    - type: mrr_at_3
      value: 27.201999999999998
    - type: mrr_at_5
      value: 28.884999999999998
    - type: ndcg_at_1
      value: 20.365
    - type: ndcg_at_10
      value: 33.832
    - type: ndcg_at_100
      value: 39.33
    - type: ndcg_at_1000
      value: 41.099999999999994
    - type: ndcg_at_3
      value: 27.46
    - type: ndcg_at_5
      value: 30.584
    - type: precision_at_1
      value: 20.365
    - type: precision_at_10
      value: 5.849
    - type: precision_at_100
      value: 0.8959999999999999
    - type: precision_at_1000
      value: 0.107
    - type: precision_at_3
      value: 12.64
    - type: precision_at_5
      value: 9.334000000000001
    - type: recall_at_1
      value: 17.965
    - type: recall_at_10
      value: 49.503
    - type: recall_at_100
      value: 74.351
    - type: recall_at_1000
      value: 87.766
    - type: recall_at_3
      value: 32.665
    - type: recall_at_5
      value: 39.974
  - task:
      type: Classification
    dataset:
      name: MTEB PAC
      type: laugustyniak/abusive-clauses-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 63.11323486823051
    - type: ap
      value: 74.53486257377787
    - type: f1
      value: 60.631005373417736
  - task:
      type: PairClassification
    dataset:
      name: MTEB PPC
      type: PL-MTEB/ppc-pairclassification
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 80.10000000000001
    - type: cos_sim_ap
      value: 89.69526236458292
    - type: cos_sim_f1
      value: 83.37468982630274
    - type: cos_sim_precision
      value: 83.30578512396694
    - type: cos_sim_recall
      value: 83.44370860927152
    - type: dot_accuracy
      value: 77.8
    - type: dot_ap
      value: 87.72366051496104
    - type: dot_f1
      value: 82.83752860411899
    - type: dot_precision
      value: 76.80339462517681
    - type: dot_recall
      value: 89.90066225165563
    - type: euclidean_accuracy
      value: 80.10000000000001
    - type: euclidean_ap
      value: 89.61317191870039
    - type: euclidean_f1
      value: 83.40214698596202
    - type: euclidean_precision
      value: 83.19604612850083
    - type: euclidean_recall
      value: 83.6092715231788
    - type: manhattan_accuracy
      value: 79.60000000000001
    - type: manhattan_ap
      value: 89.48363786968471
    - type: manhattan_f1
      value: 82.96296296296296
    - type: manhattan_precision
      value: 82.48772504091653
    - type: manhattan_recall
      value: 83.44370860927152
    - type: max_accuracy
      value: 80.10000000000001
    - type: max_ap
      value: 89.69526236458292
    - type: max_f1
      value: 83.40214698596202
  - task:
      type: PairClassification
    dataset:
      name: MTEB PSC
      type: PL-MTEB/psc-pairclassification
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 96.93877551020408
    - type: cos_sim_ap
      value: 98.86489482248999
    - type: cos_sim_f1
      value: 95.11111111111113
    - type: cos_sim_precision
      value: 92.507204610951
    - type: cos_sim_recall
      value: 97.86585365853658
    - type: dot_accuracy
      value: 95.73283858998145
    - type: dot_ap
      value: 97.8261652492545
    - type: dot_f1
      value: 93.21533923303835
    - type: dot_precision
      value: 90.28571428571428
    - type: dot_recall
      value: 96.34146341463415
    - type: euclidean_accuracy
      value: 96.93877551020408
    - type: euclidean_ap
      value: 98.84837797066623
    - type: euclidean_f1
      value: 95.11111111111113
    - type: euclidean_precision
      value: 92.507204610951
    - type: euclidean_recall
      value: 97.86585365853658
    - type: manhattan_accuracy
      value: 96.84601113172542
    - type: manhattan_ap
      value: 98.78659090944161
    - type: manhattan_f1
      value: 94.9404761904762
    - type: manhattan_precision
      value: 92.73255813953489
    - type: manhattan_recall
      value: 97.2560975609756
    - type: max_accuracy
      value: 96.93877551020408
    - type: max_ap
      value: 98.86489482248999
    - type: max_f1
      value: 95.11111111111113
  - task:
      type: Classification
    dataset:
      name: MTEB PolEmo2.0-IN
      type: PL-MTEB/polemo2_in
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 63.961218836565095
    - type: f1
      value: 64.3979989243291
  - task:
      type: Classification
    dataset:
      name: MTEB PolEmo2.0-OUT
      type: PL-MTEB/polemo2_out
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 40.32388663967612
    - type: f1
      value: 32.339117999015755
  - task:
      type: Retrieval
    dataset:
      name: MTEB Quora-PL
      type: quora-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 62.757
    - type: map_at_10
      value: 76.55999999999999
    - type: map_at_100
      value: 77.328
    - type: map_at_1000
      value: 77.35499999999999
    - type: map_at_3
      value: 73.288
    - type: map_at_5
      value: 75.25500000000001
    - type: mrr_at_1
      value: 72.28
    - type: mrr_at_10
      value: 79.879
    - type: mrr_at_100
      value: 80.121
    - type: mrr_at_1000
      value: 80.12700000000001
    - type: mrr_at_3
      value: 78.40700000000001
    - type: mrr_at_5
      value: 79.357
    - type: ndcg_at_1
      value: 72.33000000000001
    - type: ndcg_at_10
      value: 81.151
    - type: ndcg_at_100
      value: 83.107
    - type: ndcg_at_1000
      value: 83.397
    - type: ndcg_at_3
      value: 77.3
    - type: ndcg_at_5
      value: 79.307
    - type: precision_at_1
      value: 72.33000000000001
    - type: precision_at_10
      value: 12.587000000000002
    - type: precision_at_100
      value: 1.488
    - type: precision_at_1000
      value: 0.155
    - type: precision_at_3
      value: 33.943
    - type: precision_at_5
      value: 22.61
    - type: recall_at_1
      value: 62.757
    - type: recall_at_10
      value: 90.616
    - type: recall_at_100
      value: 97.905
    - type: recall_at_1000
      value: 99.618
    - type: recall_at_3
      value: 79.928
    - type: recall_at_5
      value: 85.30499999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS-PL
      type: scidocs-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 3.313
    - type: map_at_10
      value: 8.559999999999999
    - type: map_at_100
      value: 10.177999999999999
    - type: map_at_1000
      value: 10.459999999999999
    - type: map_at_3
      value: 6.094
    - type: map_at_5
      value: 7.323
    - type: mrr_at_1
      value: 16.3
    - type: mrr_at_10
      value: 25.579
    - type: mrr_at_100
      value: 26.717000000000002
    - type: mrr_at_1000
      value: 26.799
    - type: mrr_at_3
      value: 22.583000000000002
    - type: mrr_at_5
      value: 24.298000000000002
    - type: ndcg_at_1
      value: 16.3
    - type: ndcg_at_10
      value: 14.789
    - type: ndcg_at_100
      value: 21.731
    - type: ndcg_at_1000
      value: 27.261999999999997
    - type: ndcg_at_3
      value: 13.74
    - type: ndcg_at_5
      value: 12.199
    - type: precision_at_1
      value: 16.3
    - type: precision_at_10
      value: 7.779999999999999
    - type: precision_at_100
      value: 1.79
    - type: precision_at_1000
      value: 0.313
    - type: precision_at_3
      value: 12.933
    - type: precision_at_5
      value: 10.86
    - type: recall_at_1
      value: 3.313
    - type: recall_at_10
      value: 15.772
    - type: recall_at_100
      value: 36.392
    - type: recall_at_1000
      value: 63.525
    - type: recall_at_3
      value: 7.863
    - type: recall_at_5
      value: 11.003
  - 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: 81.7977986139421
    - type: cos_sim_ap
      value: 73.21294750778902
    - type: cos_sim_f1
      value: 66.57391304347826
    - type: cos_sim_precision
      value: 65.05778382053025
    - type: cos_sim_recall
      value: 68.16239316239316
    - type: dot_accuracy
      value: 78.67916836526702
    - type: dot_ap
      value: 63.61943815978181
    - type: dot_f1
      value: 62.45014245014245
    - type: dot_precision
      value: 52.04178537511871
    - type: dot_recall
      value: 78.06267806267806
    - type: euclidean_accuracy
      value: 81.7774154097024
    - type: euclidean_ap
      value: 73.25053778387148
    - type: euclidean_f1
      value: 66.55064392620953
    - type: euclidean_precision
      value: 65.0782845473111
    - type: euclidean_recall
      value: 68.09116809116809
    - type: manhattan_accuracy
      value: 81.63473298002447
    - type: manhattan_ap
      value: 72.99781945530033
    - type: manhattan_f1
      value: 66.3623595505618
    - type: manhattan_precision
      value: 65.4432132963989
    - type: manhattan_recall
      value: 67.3076923076923
    - type: max_accuracy
      value: 81.7977986139421
    - type: max_ap
      value: 73.25053778387148
    - type: max_f1
      value: 66.57391304347826
  - 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: 79.62332929388755
    - type: cos_sim_spearman
      value: 73.70598290849304
    - type: euclidean_pearson
      value: 77.3603286710006
    - type: euclidean_spearman
      value: 73.74420279933932
    - type: manhattan_pearson
      value: 77.12735032552482
    - type: manhattan_spearman
      value: 73.53014836690127
  - 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: 37.696942928686724
    - type: cos_sim_spearman
      value: 40.6271445245692
    - type: euclidean_pearson
      value: 30.212734461370832
    - type: euclidean_spearman
      value: 40.66643376699638
    - type: manhattan_pearson
      value: 29.90223716230108
    - type: manhattan_spearman
      value: 40.35576319091178
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact-PL
      type: scifact-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 43.528
    - type: map_at_10
      value: 53.290000000000006
    - type: map_at_100
      value: 54.342
    - type: map_at_1000
      value: 54.376999999999995
    - type: map_at_3
      value: 50.651999999999994
    - type: map_at_5
      value: 52.248000000000005
    - type: mrr_at_1
      value: 46.666999999999994
    - type: mrr_at_10
      value: 55.286
    - type: mrr_at_100
      value: 56.094
    - type: mrr_at_1000
      value: 56.125
    - type: mrr_at_3
      value: 53.222
    - type: mrr_at_5
      value: 54.339000000000006
    - type: ndcg_at_1
      value: 46.0
    - type: ndcg_at_10
      value: 58.142
    - type: ndcg_at_100
      value: 62.426
    - type: ndcg_at_1000
      value: 63.395999999999994
    - type: ndcg_at_3
      value: 53.53
    - type: ndcg_at_5
      value: 55.842000000000006
    - type: precision_at_1
      value: 46.0
    - type: precision_at_10
      value: 7.9670000000000005
    - type: precision_at_100
      value: 1.023
    - type: precision_at_1000
      value: 0.11100000000000002
    - type: precision_at_3
      value: 21.444
    - type: precision_at_5
      value: 14.333000000000002
    - type: recall_at_1
      value: 43.528
    - type: recall_at_10
      value: 71.511
    - type: recall_at_100
      value: 89.93299999999999
    - type: recall_at_1000
      value: 97.667
    - type: recall_at_3
      value: 59.067
    - type: recall_at_5
      value: 64.789
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID-PL
      type: trec-covid-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.22699999999999998
    - type: map_at_10
      value: 1.3379999999999999
    - type: map_at_100
      value: 6.965000000000001
    - type: map_at_1000
      value: 17.135
    - type: map_at_3
      value: 0.53
    - type: map_at_5
      value: 0.799
    - type: mrr_at_1
      value: 84.0
    - type: mrr_at_10
      value: 88.083
    - type: mrr_at_100
      value: 88.432
    - type: mrr_at_1000
      value: 88.432
    - type: mrr_at_3
      value: 87.333
    - type: mrr_at_5
      value: 87.833
    - type: ndcg_at_1
      value: 76.0
    - type: ndcg_at_10
      value: 58.199
    - type: ndcg_at_100
      value: 43.230000000000004
    - type: ndcg_at_1000
      value: 39.751
    - type: ndcg_at_3
      value: 63.743
    - type: ndcg_at_5
      value: 60.42999999999999
    - type: precision_at_1
      value: 84.0
    - type: precision_at_10
      value: 62.0
    - type: precision_at_100
      value: 44.519999999999996
    - type: precision_at_1000
      value: 17.746000000000002
    - type: precision_at_3
      value: 67.333
    - type: precision_at_5
      value: 63.2
    - type: recall_at_1
      value: 0.22699999999999998
    - type: recall_at_10
      value: 1.627
    - type: recall_at_100
      value: 10.600999999999999
    - type: recall_at_1000
      value: 37.532
    - type: recall_at_3
      value: 0.547
    - type: recall_at_5
      value: 0.864
---

<h1 align="center">MMLW-e5-small</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 384 dimensional vectors. 
The model was initialized with multilingual E5 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, queries should be prefixed with **"query: "** and passages with **"passage: "** ⚠️

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 = "query: "
answer_prefix = "passage: "
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-e5-small")
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 **55.84** 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 **47.64** 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}
}
```