README.md

metadata

tags:
  - mteb
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:2560000
  - loss:MultipleNegativesRankingLoss
model-index:
  - name: XLM-RoBERTa-base-MSMARCO-WebFAQ
    results:
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (ar)
          config: ar
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 25.374000000000002
          - type: map_at_10
            value: 40.863
          - type: map_at_100
            value: 42.667
          - type: map_at_1000
            value: 42.754999999999995
          - type: map_at_20
            value: 41.948
          - type: map_at_3
            value: 35.512
          - type: map_at_5
            value: 38.385999999999996
          - type: mrr_at_1
            value: 38.6
          - type: mrr_at_10
            value: 50.489
          - type: mrr_at_100
            value: 51.229
          - type: mrr_at_1000
            value: 51.248000000000005
          - type: mrr_at_20
            value: 50.980000000000004
          - type: mrr_at_3
            value: 46.933
          - type: mrr_at_5
            value: 48.983
          - type: ndcg_at_1
            value: 38.6
          - type: ndcg_at_10
            value: 49.257
          - type: ndcg_at_100
            value: 55.611999999999995
          - type: ndcg_at_1000
            value: 56.946
          - type: ndcg_at_20
            value: 52.10399999999999
          - type: ndcg_at_3
            value: 41.501
          - type: ndcg_at_5
            value: 44.729
          - type: precision_at_1
            value: 38.6
          - type: precision_at_10
            value: 11.540000000000001
          - type: precision_at_100
            value: 1.702
          - type: precision_at_1000
            value: 0.189
          - type: precision_at_20
            value: 6.795
          - type: precision_at_3
            value: 24.3
          - type: precision_at_5
            value: 18.16
          - type: recall_at_1
            value: 25.374000000000002
          - type: recall_at_10
            value: 63.474
          - type: recall_at_100
            value: 87.902
          - type: recall_at_1000
            value: 96.301
          - type: recall_at_20
            value: 72.538
          - type: recall_at_3
            value: 43.144
          - type: recall_at_5
            value: 51.548
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (arabic)
          config: arabic
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 26.056
          - type: map_at_10
            value: 37.684
          - type: map_at_100
            value: 38.616
          - type: map_at_1000
            value: 38.66
          - type: map_at_20
            value: 38.281
          - type: map_at_3
            value: 34.348
          - type: map_at_5
            value: 36.181999999999995
          - type: mrr_at_1
            value: 27.845
          - type: mrr_at_10
            value: 39.275
          - type: mrr_at_100
            value: 40.083
          - type: mrr_at_1000
            value: 40.115
          - type: mrr_at_20
            value: 39.822
          - type: mrr_at_3
            value: 36.124
          - type: mrr_at_5
            value: 37.885999999999996
          - type: ndcg_at_1
            value: 27.845
          - type: ndcg_at_10
            value: 44.153
          - type: ndcg_at_100
            value: 48.291000000000004
          - type: ndcg_at_1000
            value: 49.443
          - type: ndcg_at_20
            value: 46.188
          - type: ndcg_at_3
            value: 37.448
          - type: ndcg_at_5
            value: 40.685
          - type: precision_at_1
            value: 27.845
          - type: precision_at_10
            value: 6.938
          - type: precision_at_100
            value: 0.9209999999999999
          - type: precision_at_1000
            value: 0.10300000000000001
          - type: precision_at_20
            value: 3.936
          - type: precision_at_3
            value: 16.158
          - type: precision_at_5
            value: 11.452
          - type: recall_at_1
            value: 26.056
          - type: recall_at_10
            value: 62.627
          - type: recall_at_100
            value: 80.759
          - type: recall_at_1000
            value: 89.547
          - type: recall_at_20
            value: 70.21300000000001
          - type: recall_at_3
            value: 44.681
          - type: recall_at_5
            value: 52.344
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (bn)
          config: bn
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 23.083000000000002
          - type: map_at_10
            value: 37.285000000000004
          - type: map_at_100
            value: 38.834
          - type: map_at_1000
            value: 38.948
          - type: map_at_20
            value: 38.195
          - type: map_at_3
            value: 31.893
          - type: map_at_5
            value: 34.755
          - type: mrr_at_1
            value: 36.496
          - type: mrr_at_10
            value: 48.486000000000004
          - type: mrr_at_100
            value: 49.248
          - type: mrr_at_1000
            value: 49.28
          - type: mrr_at_20
            value: 48.998000000000005
          - type: mrr_at_3
            value: 44.85
          - type: mrr_at_5
            value: 47.028
          - type: ndcg_at_1
            value: 36.496
          - type: ndcg_at_10
            value: 45.96
          - type: ndcg_at_100
            value: 51.64
          - type: ndcg_at_1000
            value: 53.542
          - type: ndcg_at_20
            value: 48.543
          - type: ndcg_at_3
            value: 37.964999999999996
          - type: ndcg_at_5
            value: 41.44
          - type: precision_at_1
            value: 36.496
          - type: precision_at_10
            value: 11.29
          - type: precision_at_100
            value: 1.6199999999999999
          - type: precision_at_1000
            value: 0.191
          - type: precision_at_20
            value: 6.582000000000001
          - type: precision_at_3
            value: 22.871
          - type: precision_at_5
            value: 17.324
          - type: recall_at_1
            value: 23.083000000000002
          - type: recall_at_10
            value: 59.414
          - type: recall_at_100
            value: 81.08
          - type: recall_at_1000
            value: 92.793
          - type: recall_at_20
            value: 67.634
          - type: recall_at_3
            value: 39.001000000000005
          - type: recall_at_5
            value: 47.612
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (bengali)
          config: bengali
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 23.874000000000002
          - type: map_at_10
            value: 38.235
          - type: map_at_100
            value: 39.428000000000004
          - type: map_at_1000
            value: 39.458
          - type: map_at_20
            value: 39.119
          - type: map_at_3
            value: 34.233999999999995
          - type: map_at_5
            value: 36.577
          - type: mrr_at_1
            value: 28.829
          - type: mrr_at_10
            value: 41.032999999999994
          - type: mrr_at_100
            value: 42.077999999999996
          - type: mrr_at_1000
            value: 42.104
          - type: mrr_at_20
            value: 41.855
          - type: mrr_at_3
            value: 37.688
          - type: mrr_at_5
            value: 39.985
          - type: ndcg_at_1
            value: 28.829
          - type: ndcg_at_10
            value: 45.214999999999996
          - type: ndcg_at_100
            value: 49.986999999999995
          - type: ndcg_at_1000
            value: 50.67700000000001
          - type: ndcg_at_20
            value: 48.291000000000004
          - type: ndcg_at_3
            value: 37.822
          - type: ndcg_at_5
            value: 41.9
          - type: precision_at_1
            value: 28.829
          - type: precision_at_10
            value: 7.477
          - type: precision_at_100
            value: 0.9820000000000001
          - type: precision_at_1000
            value: 0.105
          - type: precision_at_20
            value: 4.414
          - type: precision_at_3
            value: 17.416999999999998
          - type: precision_at_5
            value: 12.613
          - type: recall_at_1
            value: 23.874000000000002
          - type: recall_at_10
            value: 63.964
          - type: recall_at_100
            value: 83.784
          - type: recall_at_1000
            value: 88.739
          - type: recall_at_20
            value: 75.676
          - type: recall_at_3
            value: 45.045
          - type: recall_at_5
            value: 54.505
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (de)
          config: de
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 12.524
          - type: map_at_10
            value: 25.932
          - type: map_at_100
            value: 29.215999999999998
          - type: map_at_1000
            value: 29.342000000000002
          - type: map_at_20
            value: 27.515
          - type: map_at_3
            value: 20.196
          - type: map_at_5
            value: 23.311999999999998
          - type: mrr_at_1
            value: 28.525
          - type: mrr_at_10
            value: 41.854
          - type: mrr_at_100
            value: 42.977
          - type: mrr_at_1000
            value: 42.998
          - type: mrr_at_20
            value: 42.628
          - type: mrr_at_3
            value: 38.251000000000005
          - type: mrr_at_5
            value: 40.514
          - type: ndcg_at_1
            value: 28.525
          - type: ndcg_at_10
            value: 35.831
          - type: ndcg_at_100
            value: 47.410000000000004
          - type: ndcg_at_1000
            value: 48.931000000000004
          - type: ndcg_at_20
            value: 40.021
          - type: ndcg_at_3
            value: 28.614
          - type: ndcg_at_5
            value: 31.427
          - type: precision_at_1
            value: 28.525
          - type: precision_at_10
            value: 11.934000000000001
          - type: precision_at_100
            value: 2.37
          - type: precision_at_1000
            value: 0.261
          - type: precision_at_20
            value: 7.704999999999999
          - type: precision_at_3
            value: 21.421
          - type: precision_at_5
            value: 17.574
          - type: recall_at_1
            value: 12.524
          - type: recall_at_10
            value: 47.558
          - type: recall_at_100
            value: 90.068
          - type: recall_at_1000
            value: 98.68900000000001
          - type: recall_at_20
            value: 59.943999999999996
          - type: recall_at_3
            value: 26.889999999999997
          - type: recall_at_5
            value: 36.152
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (en)
          config: en
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 14.431
          - type: map_at_10
            value: 27.194000000000003
          - type: map_at_100
            value: 30.575000000000003
          - type: map_at_1000
            value: 30.75
          - type: map_at_20
            value: 28.938999999999997
          - type: map_at_3
            value: 21.69
          - type: map_at_5
            value: 24.657999999999998
          - type: mrr_at_1
            value: 31.163999999999998
          - type: mrr_at_10
            value: 43.417
          - type: mrr_at_100
            value: 44.48
          - type: mrr_at_1000
            value: 44.499
          - type: mrr_at_20
            value: 44.112
          - type: mrr_at_3
            value: 39.57
          - type: mrr_at_5
            value: 41.692
          - type: ndcg_at_1
            value: 31.163999999999998
          - type: ndcg_at_10
            value: 36.925000000000004
          - type: ndcg_at_100
            value: 48.15
          - type: ndcg_at_1000
            value: 50.131
          - type: ndcg_at_20
            value: 41.308
          - type: ndcg_at_3
            value: 30.291
          - type: ndcg_at_5
            value: 32.731
          - type: precision_at_1
            value: 31.163999999999998
          - type: precision_at_10
            value: 12.065
          - type: precision_at_100
            value: 2.469
          - type: precision_at_1000
            value: 0.28500000000000003
          - type: precision_at_20
            value: 7.997
          - type: precision_at_3
            value: 21.443
          - type: precision_at_5
            value: 17.447
          - type: recall_at_1
            value: 14.431
          - type: recall_at_10
            value: 48.275
          - type: recall_at_100
            value: 87.19200000000001
          - type: recall_at_1000
            value: 97.79299999999999
          - type: recall_at_20
            value: 60.870000000000005
          - type: recall_at_3
            value: 27.679
          - type: recall_at_5
            value: 36.479
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (english)
          config: english
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 13.642000000000001
          - type: map_at_10
            value: 22.744
          - type: map_at_100
            value: 24.083
          - type: map_at_1000
            value: 24.157
          - type: map_at_20
            value: 23.592
          - type: map_at_3
            value: 19.134999999999998
          - type: map_at_5
            value: 21.448999999999998
          - type: mrr_at_1
            value: 16.667
          - type: mrr_at_10
            value: 25.77
          - type: mrr_at_100
            value: 26.878
          - type: mrr_at_1000
            value: 26.938000000000002
          - type: mrr_at_20
            value: 26.503
          - type: mrr_at_3
            value: 22.512999999999998
          - type: mrr_at_5
            value: 24.516
          - type: ndcg_at_1
            value: 16.667
          - type: ndcg_at_10
            value: 28.652
          - type: ndcg_at_100
            value: 34.716
          - type: ndcg_at_1000
            value: 36.649
          - type: ndcg_at_20
            value: 31.508000000000003
          - type: ndcg_at_3
            value: 21.748
          - type: ndcg_at_5
            value: 25.647
          - type: precision_at_1
            value: 16.667
          - type: precision_at_10
            value: 5.3629999999999995
          - type: precision_at_100
            value: 0.886
          - type: precision_at_1000
            value: 0.107
          - type: precision_at_20
            value: 3.36
          - type: precision_at_3
            value: 10.529
          - type: precision_at_5
            value: 8.522
          - type: recall_at_1
            value: 13.642000000000001
          - type: recall_at_10
            value: 43.884
          - type: recall_at_100
            value: 70.744
          - type: recall_at_1000
            value: 85.372
          - type: recall_at_20
            value: 54.48
          - type: recall_at_3
            value: 26.142
          - type: recall_at_5
            value: 35.17
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (fa)
          config: fa
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 20.752000000000002
          - type: map_at_10
            value: 34.931
          - type: map_at_100
            value: 37.132
          - type: map_at_1000
            value: 37.230999999999995
          - type: map_at_20
            value: 36.144
          - type: map_at_3
            value: 29.692
          - type: map_at_5
            value: 32.536
          - type: mrr_at_1
            value: 32.437
          - type: mrr_at_10
            value: 44.41
          - type: mrr_at_100
            value: 45.324
          - type: mrr_at_1000
            value: 45.348
          - type: mrr_at_20
            value: 44.945
          - type: mrr_at_3
            value: 41.482
          - type: mrr_at_5
            value: 43.183
          - type: ndcg_at_1
            value: 32.437
          - type: ndcg_at_10
            value: 43.018
          - type: ndcg_at_100
            value: 50.805
          - type: ndcg_at_1000
            value: 52.245
          - type: ndcg_at_20
            value: 46.215
          - type: ndcg_at_3
            value: 36.269
          - type: ndcg_at_5
            value: 39.101
          - type: precision_at_1
            value: 32.437
          - type: precision_at_10
            value: 11.013
          - type: precision_at_100
            value: 1.799
          - type: precision_at_1000
            value: 0.201
          - type: precision_at_20
            value: 6.7250000000000005
          - type: precision_at_3
            value: 22.416
          - type: precision_at_5
            value: 17.152
          - type: recall_at_1
            value: 20.752000000000002
          - type: recall_at_10
            value: 56.150999999999996
          - type: recall_at_100
            value: 85.735
          - type: recall_at_1000
            value: 94.599
          - type: recall_at_20
            value: 66.237
          - type: recall_at_3
            value: 37.551
          - type: recall_at_5
            value: 45.629
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (fi)
          config: fi
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 28.829
          - type: map_at_10
            value: 46.749
          - type: map_at_100
            value: 48.457
          - type: map_at_1000
            value: 48.516
          - type: map_at_20
            value: 47.798
          - type: map_at_3
            value: 40.449
          - type: map_at_5
            value: 44.3
          - type: mrr_at_1
            value: 46.5
          - type: mrr_at_10
            value: 58.619
          - type: mrr_at_100
            value: 59.294999999999995
          - type: mrr_at_1000
            value: 59.307
          - type: mrr_at_20
            value: 59.071
          - type: mrr_at_3
            value: 55.75
          - type: mrr_at_5
            value: 57.605
          - type: ndcg_at_1
            value: 46.5
          - type: ndcg_at_10
            value: 55.933
          - type: ndcg_at_100
            value: 61.732
          - type: ndcg_at_1000
            value: 62.651
          - type: ndcg_at_20
            value: 58.679
          - type: ndcg_at_3
            value: 46.866
          - type: ndcg_at_5
            value: 51.625
          - type: precision_at_1
            value: 46.5
          - type: precision_at_10
            value: 12.920000000000002
          - type: precision_at_100
            value: 1.745
          - type: precision_at_1000
            value: 0.187
          - type: precision_at_20
            value: 7.37
          - type: precision_at_3
            value: 28.166999999999998
          - type: precision_at_5
            value: 21.2
          - type: recall_at_1
            value: 28.829
          - type: recall_at_10
            value: 70.075
          - type: recall_at_100
            value: 92.098
          - type: recall_at_1000
            value: 97.813
          - type: recall_at_20
            value: 78.975
          - type: recall_at_3
            value: 48.635
          - type: recall_at_5
            value: 59.202999999999996
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (finnish)
          config: finnish
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 20.654
          - type: map_at_10
            value: 32.103
          - type: map_at_100
            value: 33.129
          - type: map_at_1000
            value: 33.184999999999995
          - type: map_at_20
            value: 32.739000000000004
          - type: map_at_3
            value: 28.338
          - type: map_at_5
            value: 30.564000000000004
          - type: mrr_at_1
            value: 22.567999999999998
          - type: mrr_at_10
            value: 33.949
          - type: mrr_at_100
            value: 34.804
          - type: mrr_at_1000
            value: 34.849999999999994
          - type: mrr_at_20
            value: 34.496
          - type: mrr_at_3
            value: 30.581999999999997
          - type: mrr_at_5
            value: 32.5
          - type: ndcg_at_1
            value: 22.567999999999998
          - type: ndcg_at_10
            value: 38.681
          - type: ndcg_at_100
            value: 43.367
          - type: ndcg_at_1000
            value: 44.836999999999996
          - type: ndcg_at_20
            value: 40.815
          - type: ndcg_at_3
            value: 31.328
          - type: ndcg_at_5
            value: 35.083
          - type: precision_at_1
            value: 22.567999999999998
          - type: precision_at_10
            value: 6.483
          - type: precision_at_100
            value: 0.906
          - type: precision_at_1000
            value: 0.104
          - type: precision_at_20
            value: 3.7319999999999998
          - type: precision_at_3
            value: 14.035
          - type: precision_at_5
            value: 10.51
          - type: recall_at_1
            value: 20.654
          - type: recall_at_10
            value: 57.297
          - type: recall_at_100
            value: 78.363
          - type: recall_at_1000
            value: 89.673
          - type: recall_at_20
            value: 65.28399999999999
          - type: recall_at_3
            value: 37.799
          - type: recall_at_5
            value: 46.518
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (fr)
          config: fr
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 15.484
          - type: map_at_10
            value: 29.692
          - type: map_at_100
            value: 32.604
          - type: map_at_1000
            value: 32.668
          - type: map_at_20
            value: 31.397000000000002
          - type: map_at_3
            value: 23.469
          - type: map_at_5
            value: 26.454
          - type: mrr_at_1
            value: 28.863
          - type: mrr_at_10
            value: 41.03
          - type: mrr_at_100
            value: 42.134
          - type: mrr_at_1000
            value: 42.149
          - type: mrr_at_20
            value: 41.730000000000004
          - type: mrr_at_3
            value: 36.394999999999996
          - type: mrr_at_5
            value: 38.814
          - type: ndcg_at_1
            value: 28.863
          - type: ndcg_at_10
            value: 39.523
          - type: ndcg_at_100
            value: 49.496
          - type: ndcg_at_1000
            value: 50.375
          - type: ndcg_at_20
            value: 43.923
          - type: ndcg_at_3
            value: 29.309
          - type: ndcg_at_5
            value: 33.077
          - type: precision_at_1
            value: 28.863
          - type: precision_at_10
            value: 11.254
          - type: precision_at_100
            value: 1.997
          - type: precision_at_1000
            value: 0.212
          - type: precision_at_20
            value: 7.172000000000001
          - type: precision_at_3
            value: 19.631
          - type: precision_at_5
            value: 15.568999999999999
          - type: recall_at_1
            value: 15.484
          - type: recall_at_10
            value: 56.279999999999994
          - type: recall_at_100
            value: 93.714
          - type: recall_at_1000
            value: 99.125
          - type: recall_at_20
            value: 70.215
          - type: recall_at_3
            value: 29.688
          - type: recall_at_5
            value: 39.329
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (hi)
          config: hi
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 16.442999999999998
          - type: map_at_10
            value: 29.224
          - type: map_at_100
            value: 31.386999999999997
          - type: map_at_1000
            value: 31.529
          - type: map_at_20
            value: 30.54
          - type: map_at_3
            value: 24.521
          - type: map_at_5
            value: 26.979
          - type: mrr_at_1
            value: 33.428999999999995
          - type: mrr_at_10
            value: 42.951
          - type: mrr_at_100
            value: 43.832
          - type: mrr_at_1000
            value: 43.891000000000005
          - type: mrr_at_20
            value: 43.494
          - type: mrr_at_3
            value: 40.048
          - type: mrr_at_5
            value: 41.819
          - type: ndcg_at_1
            value: 33.428999999999995
          - type: ndcg_at_10
            value: 37.462
          - type: ndcg_at_100
            value: 45.123000000000005
          - type: ndcg_at_1000
            value: 47.805
          - type: ndcg_at_20
            value: 40.739999999999995
          - type: ndcg_at_3
            value: 31.89
          - type: ndcg_at_5
            value: 33.934999999999995
          - type: precision_at_1
            value: 33.428999999999995
          - type: precision_at_10
            value: 10.343
          - type: precision_at_100
            value: 1.714
          - type: precision_at_1000
            value: 0.20500000000000002
          - type: precision_at_20
            value: 6.4
          - type: precision_at_3
            value: 20.952
          - type: precision_at_5
            value: 15.886
          - type: recall_at_1
            value: 16.442999999999998
          - type: recall_at_10
            value: 47.987
          - type: recall_at_100
            value: 77.628
          - type: recall_at_1000
            value: 95.673
          - type: recall_at_20
            value: 58.199999999999996
          - type: recall_at_3
            value: 30.904999999999998
          - type: recall_at_5
            value: 38.705
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (id)
          config: id
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 14.551
          - type: map_at_10
            value: 27.144000000000002
          - type: map_at_100
            value: 29.918
          - type: map_at_1000
            value: 30.14
          - type: map_at_20
            value: 28.665000000000003
          - type: map_at_3
            value: 22.004
          - type: map_at_5
            value: 24.851
          - type: mrr_at_1
            value: 33.542
          - type: mrr_at_10
            value: 45.576
          - type: mrr_at_100
            value: 46.402
          - type: mrr_at_1000
            value: 46.432
          - type: mrr_at_20
            value: 46.082
          - type: mrr_at_3
            value: 42.483
          - type: mrr_at_5
            value: 44.399
          - type: ndcg_at_1
            value: 33.542
          - type: ndcg_at_10
            value: 36.287000000000006
          - type: ndcg_at_100
            value: 45.253
          - type: ndcg_at_1000
            value: 48.33
          - type: ndcg_at_20
            value: 39.855000000000004
          - type: ndcg_at_3
            value: 31.69
          - type: ndcg_at_5
            value: 33.332
          - type: precision_at_1
            value: 33.542
          - type: precision_at_10
            value: 12.333
          - type: precision_at_100
            value: 2.322
          - type: precision_at_1000
            value: 0.292
          - type: precision_at_20
            value: 7.875
          - type: precision_at_3
            value: 23.125
          - type: precision_at_5
            value: 18.583
          - type: recall_at_1
            value: 14.551
          - type: recall_at_10
            value: 43.636
          - type: recall_at_100
            value: 73.603
          - type: recall_at_1000
            value: 90.596
          - type: recall_at_20
            value: 53.559
          - type: recall_at_3
            value: 27.383999999999997
          - type: recall_at_5
            value: 34.997
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (indonesian)
          config: indonesian
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 28.669
          - type: map_at_10
            value: 41.611
          - type: map_at_100
            value: 42.522999999999996
          - type: map_at_1000
            value: 42.552
          - type: map_at_20
            value: 42.211
          - type: map_at_3
            value: 37.719
          - type: map_at_5
            value: 40.182
          - type: mrr_at_1
            value: 31.846000000000004
          - type: mrr_at_10
            value: 43.695
          - type: mrr_at_100
            value: 44.45
          - type: mrr_at_1000
            value: 44.474000000000004
          - type: mrr_at_20
            value: 44.214999999999996
          - type: mrr_at_3
            value: 40.31
          - type: mrr_at_5
            value: 42.469
          - type: ndcg_at_1
            value: 31.846000000000004
          - type: ndcg_at_10
            value: 48.416
          - type: ndcg_at_100
            value: 52.464999999999996
          - type: ndcg_at_1000
            value: 53.234
          - type: ndcg_at_20
            value: 50.468999999999994
          - type: ndcg_at_3
            value: 40.973
          - type: ndcg_at_5
            value: 45.163
          - type: precision_at_1
            value: 31.846000000000004
          - type: precision_at_10
            value: 7.768
          - type: precision_at_100
            value: 0.992
          - type: precision_at_1000
            value: 0.106
          - type: precision_at_20
            value: 4.343
          - type: precision_at_3
            value: 18.134
          - type: precision_at_5
            value: 13.221
          - type: recall_at_1
            value: 28.669
          - type: recall_at_10
            value: 67.29
          - type: recall_at_100
            value: 85.324
          - type: recall_at_1000
            value: 91.27499999999999
          - type: recall_at_20
            value: 75.111
          - type: recall_at_3
            value: 47.869
          - type: recall_at_5
            value: 57.620000000000005
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (ja)
          config: ja
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 16.499
          - type: map_at_10
            value: 29.566
          - type: map_at_100
            value: 31.846999999999998
          - type: map_at_1000
            value: 31.968999999999998
          - type: map_at_20
            value: 30.758000000000003
          - type: map_at_3
            value: 24.954
          - type: map_at_5
            value: 27.195000000000004
          - type: mrr_at_1
            value: 27.093
          - type: mrr_at_10
            value: 40.861999999999995
          - type: mrr_at_100
            value: 41.926
          - type: mrr_at_1000
            value: 41.942
          - type: mrr_at_20
            value: 41.55
          - type: mrr_at_3
            value: 37.151
          - type: mrr_at_5
            value: 38.958999999999996
          - type: ndcg_at_1
            value: 27.093
          - type: ndcg_at_10
            value: 38.544
          - type: ndcg_at_100
            value: 47.143
          - type: ndcg_at_1000
            value: 48.802
          - type: ndcg_at_20
            value: 41.896
          - type: ndcg_at_3
            value: 31.249
          - type: ndcg_at_5
            value: 33.873999999999995
          - type: precision_at_1
            value: 27.093
          - type: precision_at_10
            value: 9.814
          - type: precision_at_100
            value: 1.737
          - type: precision_at_1000
            value: 0.2
          - type: precision_at_20
            value: 6.064
          - type: precision_at_3
            value: 19.147
          - type: precision_at_5
            value: 14.465
          - type: recall_at_1
            value: 16.499
          - type: recall_at_10
            value: 53.580000000000005
          - type: recall_at_100
            value: 86.792
          - type: recall_at_1000
            value: 96.61999999999999
          - type: recall_at_20
            value: 64.302
          - type: recall_at_3
            value: 33.588
          - type: recall_at_5
            value: 40.993
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (japanese)
          config: japanese
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 16.644000000000002
          - type: map_at_10
            value: 25.667
          - type: map_at_100
            value: 26.926
          - type: map_at_1000
            value: 27.003
          - type: map_at_20
            value: 26.468000000000004
          - type: map_at_3
            value: 22.808999999999997
          - type: map_at_5
            value: 24.392
          - type: mrr_at_1
            value: 20.278
          - type: mrr_at_10
            value: 28.683999999999997
          - type: mrr_at_100
            value: 29.785
          - type: mrr_at_1000
            value: 29.839
          - type: mrr_at_20
            value: 29.391000000000002
          - type: mrr_at_3
            value: 26.25
          - type: mrr_at_5
            value: 27.479
          - type: ndcg_at_1
            value: 20.278
          - type: ndcg_at_10
            value: 31.130000000000003
          - type: ndcg_at_100
            value: 36.954
          - type: ndcg_at_1000
            value: 38.805
          - type: ndcg_at_20
            value: 33.856
          - type: ndcg_at_3
            value: 25.590000000000003
          - type: ndcg_at_5
            value: 28.136
          - type: precision_at_1
            value: 20.278
          - type: precision_at_10
            value: 5.611
          - type: precision_at_100
            value: 0.899
          - type: precision_at_1000
            value: 0.109
          - type: precision_at_20
            value: 3.465
          - type: precision_at_3
            value: 12.5
          - type: precision_at_5
            value: 8.972
          - type: recall_at_1
            value: 16.644000000000002
          - type: recall_at_10
            value: 44.931
          - type: recall_at_100
            value: 70.741
          - type: recall_at_1000
            value: 84.282
          - type: recall_at_20
            value: 55.022999999999996
          - type: recall_at_3
            value: 30.37
          - type: recall_at_5
            value: 36.134
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (ko)
          config: ko
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 20.192
          - type: map_at_10
            value: 32.281
          - type: map_at_100
            value: 34.396
          - type: map_at_1000
            value: 34.604
          - type: map_at_20
            value: 33.495000000000005
          - type: map_at_3
            value: 27.489
          - type: map_at_5
            value: 30.022
          - type: mrr_at_1
            value: 34.742
          - type: mrr_at_10
            value: 46.117999999999995
          - type: mrr_at_100
            value: 47.066
          - type: mrr_at_1000
            value: 47.095
          - type: mrr_at_20
            value: 46.867
          - type: mrr_at_3
            value: 41.862
          - type: mrr_at_5
            value: 44.702999999999996
          - type: ndcg_at_1
            value: 34.742
          - type: ndcg_at_10
            value: 41.193999999999996
          - type: ndcg_at_100
            value: 48.691
          - type: ndcg_at_1000
            value: 51.364
          - type: ndcg_at_20
            value: 44.592999999999996
          - type: ndcg_at_3
            value: 35.004000000000005
          - type: ndcg_at_5
            value: 37.608000000000004
          - type: precision_at_1
            value: 34.742
          - type: precision_at_10
            value: 10.61
          - type: precision_at_100
            value: 1.8450000000000002
          - type: precision_at_1000
            value: 0.24
          - type: precision_at_20
            value: 6.5729999999999995
          - type: precision_at_3
            value: 20.657
          - type: precision_at_5
            value: 16.150000000000002
          - type: recall_at_1
            value: 20.192
          - type: recall_at_10
            value: 54.154
          - type: recall_at_100
            value: 80.49199999999999
          - type: recall_at_1000
            value: 94.61699999999999
          - type: recall_at_20
            value: 64.74
          - type: recall_at_3
            value: 34.288000000000004
          - type: recall_at_5
            value: 43.401
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (korean)
          config: korean
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 22.288
          - type: map_at_10
            value: 30.096
          - type: map_at_100
            value: 30.866
          - type: map_at_1000
            value: 30.939
          - type: map_at_20
            value: 30.496000000000002
          - type: map_at_3
            value: 27.672
          - type: map_at_5
            value: 28.866000000000003
          - type: mrr_at_1
            value: 23.990000000000002
          - type: mrr_at_10
            value: 32.017
          - type: mrr_at_100
            value: 32.665
          - type: mrr_at_1000
            value: 32.726
          - type: mrr_at_20
            value: 32.348
          - type: mrr_at_3
            value: 29.572
          - type: mrr_at_5
            value: 30.808000000000003
          - type: ndcg_at_1
            value: 23.990000000000002
          - type: ndcg_at_10
            value: 34.823
          - type: ndcg_at_100
            value: 38.625
          - type: ndcg_at_1000
            value: 40.760999999999996
          - type: ndcg_at_20
            value: 36.138
          - type: ndcg_at_3
            value: 29.744999999999997
          - type: ndcg_at_5
            value: 31.884
          - type: precision_at_1
            value: 23.990000000000002
          - type: precision_at_10
            value: 5.297000000000001
          - type: precision_at_100
            value: 0.753
          - type: precision_at_1000
            value: 0.095
          - type: precision_at_20
            value: 2.981
          - type: precision_at_3
            value: 12.272
          - type: precision_at_5
            value: 8.551
          - type: recall_at_1
            value: 22.288
          - type: recall_at_10
            value: 47.743
          - type: recall_at_100
            value: 65.202
          - type: recall_at_1000
            value: 82.106
          - type: recall_at_20
            value: 52.534000000000006
          - type: recall_at_3
            value: 33.927
          - type: recall_at_5
            value: 38.915
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (ru)
          config: ru
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 16.445
          - type: map_at_10
            value: 29.376
          - type: map_at_100
            value: 32.425
          - type: map_at_1000
            value: 32.589
          - type: map_at_20
            value: 30.973
          - type: map_at_3
            value: 24.026
          - type: map_at_5
            value: 26.831
          - type: mrr_at_1
            value: 31.8
          - type: mrr_at_10
            value: 44.454
          - type: mrr_at_100
            value: 45.534
          - type: mrr_at_1000
            value: 45.552
          - type: mrr_at_20
            value: 45.192
          - type: mrr_at_3
            value: 41.117
          - type: mrr_at_5
            value: 43.132
          - type: ndcg_at_1
            value: 31.8
          - type: ndcg_at_10
            value: 38.487
          - type: ndcg_at_100
            value: 48.705999999999996
          - type: ndcg_at_1000
            value: 50.732
          - type: ndcg_at_20
            value: 42.646
          - type: ndcg_at_3
            value: 31.922
          - type: ndcg_at_5
            value: 34.512
          - type: precision_at_1
            value: 31.8
          - type: precision_at_10
            value: 12.01
          - type: precision_at_100
            value: 2.325
          - type: precision_at_1000
            value: 0.27
          - type: precision_at_20
            value: 7.8549999999999995
          - type: precision_at_3
            value: 21.867
          - type: precision_at_5
            value: 17.46
          - type: recall_at_1
            value: 16.445
          - type: recall_at_10
            value: 48.691
          - type: recall_at_100
            value: 84.084
          - type: recall_at_1000
            value: 95.318
          - type: recall_at_20
            value: 60.873
          - type: recall_at_3
            value: 30.169
          - type: recall_at_5
            value: 38.3
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (russian)
          config: russian
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 16.516000000000002
          - type: map_at_10
            value: 24.461
          - type: map_at_100
            value: 25.499
          - type: map_at_1000
            value: 25.569999999999997
          - type: map_at_20
            value: 25.063000000000002
          - type: map_at_3
            value: 21.669
          - type: map_at_5
            value: 23.285
          - type: mrr_at_1
            value: 18.09
          - type: mrr_at_10
            value: 26.311
          - type: mrr_at_100
            value: 27.195999999999998
          - type: mrr_at_1000
            value: 27.255000000000003
          - type: mrr_at_20
            value: 26.823000000000004
          - type: mrr_at_3
            value: 23.618
          - type: mrr_at_5
            value: 25.156
          - type: ndcg_at_1
            value: 18.09
          - type: ndcg_at_10
            value: 29.453000000000003
          - type: ndcg_at_100
            value: 34.44
          - type: ndcg_at_1000
            value: 36.336
          - type: ndcg_at_20
            value: 31.482
          - type: ndcg_at_3
            value: 23.830000000000002
          - type: ndcg_at_5
            value: 26.666
          - type: precision_at_1
            value: 18.09
          - type: precision_at_10
            value: 4.925
          - type: precision_at_100
            value: 0.768
          - type: precision_at_1000
            value: 0.094
          - type: precision_at_20
            value: 2.9250000000000003
          - type: precision_at_3
            value: 10.586
          - type: precision_at_5
            value: 7.839
          - type: recall_at_1
            value: 16.516000000000002
          - type: recall_at_10
            value: 43.166
          - type: recall_at_100
            value: 66.11399999999999
          - type: recall_at_1000
            value: 80.771
          - type: recall_at_20
            value: 50.804
          - type: recall_at_3
            value: 28.191
          - type: recall_at_5
            value: 34.925
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (es)
          config: es
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 12.144
          - type: map_at_10
            value: 28.499999999999996
          - type: map_at_100
            value: 34.048
          - type: map_at_1000
            value: 34.268
          - type: map_at_20
            value: 31.401
          - type: map_at_3
            value: 20.459
          - type: map_at_5
            value: 24.245
          - type: mrr_at_1
            value: 41.975
          - type: mrr_at_10
            value: 54.525
          - type: mrr_at_100
            value: 55.359
          - type: mrr_at_1000
            value: 55.367
          - type: mrr_at_20
            value: 55.1
          - type: mrr_at_3
            value: 51.929
          - type: mrr_at_5
            value: 53.418
          - type: ndcg_at_1
            value: 41.975
          - type: ndcg_at_10
            value: 40.117000000000004
          - type: ndcg_at_100
            value: 54.102
          - type: ndcg_at_1000
            value: 56.191
          - type: ndcg_at_20
            value: 45.67
          - type: ndcg_at_3
            value: 37.505
          - type: ndcg_at_5
            value: 36.968
          - type: precision_at_1
            value: 41.975
          - type: precision_at_10
            value: 19.707
          - type: precision_at_100
            value: 4.003
          - type: precision_at_1000
            value: 0.44799999999999995
          - type: precision_at_20
            value: 13.272
          - type: precision_at_3
            value: 31.790000000000003
          - type: precision_at_5
            value: 26.667
          - type: recall_at_1
            value: 12.144
          - type: recall_at_10
            value: 44.92
          - type: recall_at_100
            value: 86.141
          - type: recall_at_1000
            value: 96.234
          - type: recall_at_20
            value: 58.194
          - type: recall_at_3
            value: 24.733
          - type: recall_at_5
            value: 32.385000000000005
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (sw)
          config: sw
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 21.401
          - type: map_at_10
            value: 34.205000000000005
          - type: map_at_100
            value: 35.571000000000005
          - type: map_at_1000
            value: 35.682
          - type: map_at_20
            value: 35.077000000000005
          - type: map_at_3
            value: 30.706
          - type: map_at_5
            value: 32.902
          - type: mrr_at_1
            value: 32.78
          - type: mrr_at_10
            value: 44.205
          - type: mrr_at_100
            value: 45.013
          - type: mrr_at_1000
            value: 45.055
          - type: mrr_at_20
            value: 44.726
          - type: mrr_at_3
            value: 41.909
          - type: mrr_at_5
            value: 43.434
          - type: ndcg_at_1
            value: 32.78
          - type: ndcg_at_10
            value: 41.385
          - type: ndcg_at_100
            value: 46.568
          - type: ndcg_at_1000
            value: 48.881
          - type: ndcg_at_20
            value: 43.872
          - type: ndcg_at_3
            value: 36.634
          - type: ndcg_at_5
            value: 38.964999999999996
          - type: precision_at_1
            value: 32.78
          - type: precision_at_10
            value: 8.859
          - type: precision_at_100
            value: 1.32
          - type: precision_at_1000
            value: 0.16199999999999998
          - type: precision_at_20
            value: 5.27
          - type: precision_at_3
            value: 20.608999999999998
          - type: precision_at_5
            value: 14.979000000000001
          - type: recall_at_1
            value: 21.401
          - type: recall_at_10
            value: 52.471000000000004
          - type: recall_at_100
            value: 72.069
          - type: recall_at_1000
            value: 87.996
          - type: recall_at_20
            value: 60.589999999999996
          - type: recall_at_3
            value: 39.28
          - type: recall_at_5
            value: 46.015
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (swahili)
          config: swahili
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 29.005
          - type: map_at_10
            value: 40.907
          - type: map_at_100
            value: 41.557
          - type: map_at_1000
            value: 41.604
          - type: map_at_20
            value: 41.234
          - type: map_at_3
            value: 38.114
          - type: map_at_5
            value: 39.6
          - type: mrr_at_1
            value: 30.597
          - type: mrr_at_10
            value: 42
          - type: mrr_at_100
            value: 42.557
          - type: mrr_at_1000
            value: 42.601
          - type: mrr_at_20
            value: 42.272999999999996
          - type: mrr_at_3
            value: 39.378
          - type: mrr_at_5
            value: 40.759
          - type: ndcg_at_1
            value: 30.597
          - type: ndcg_at_10
            value: 46.864
          - type: ndcg_at_100
            value: 50.099000000000004
          - type: ndcg_at_1000
            value: 51.354
          - type: ndcg_at_20
            value: 47.94
          - type: ndcg_at_3
            value: 41.234
          - type: ndcg_at_5
            value: 43.822
          - type: precision_at_1
            value: 30.597
          - type: precision_at_10
            value: 6.984999999999999
          - type: precision_at_100
            value: 0.8750000000000001
          - type: precision_at_1000
            value: 0.099
          - type: precision_at_20
            value: 3.7310000000000003
          - type: precision_at_3
            value: 17.463
          - type: precision_at_5
            value: 11.91
          - type: recall_at_1
            value: 29.005
          - type: recall_at_10
            value: 64.20400000000001
          - type: recall_at_100
            value: 79.403
          - type: recall_at_1000
            value: 89.104
          - type: recall_at_20
            value: 68.234
          - type: recall_at_3
            value: 49.055
          - type: recall_at_5
            value: 55.149
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (te)
          config: te
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 40.781
          - type: map_at_10
            value: 53.428
          - type: map_at_100
            value: 54.118
          - type: map_at_1000
            value: 54.135999999999996
          - type: map_at_20
            value: 53.873000000000005
          - type: map_at_3
            value: 50.205
          - type: map_at_5
            value: 52.356
          - type: mrr_at_1
            value: 41.184
          - type: mrr_at_10
            value: 53.74100000000001
          - type: mrr_at_100
            value: 54.388999999999996
          - type: mrr_at_1000
            value: 54.406
          - type: mrr_at_20
            value: 54.151
          - type: mrr_at_3
            value: 50.664
          - type: mrr_at_5
            value: 52.717000000000006
          - type: ndcg_at_1
            value: 41.184
          - type: ndcg_at_10
            value: 59.614999999999995
          - type: ndcg_at_100
            value: 62.875
          - type: ndcg_at_1000
            value: 63.368
          - type: ndcg_at_20
            value: 61.168
          - type: ndcg_at_3
            value: 53.322
          - type: ndcg_at_5
            value: 57.079
          - type: precision_at_1
            value: 41.184
          - type: precision_at_10
            value: 8.043
          - type: precision_at_100
            value: 0.963
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_20
            value: 4.342
          - type: precision_at_3
            value: 20.934
          - type: precision_at_5
            value: 14.469000000000001
          - type: recall_at_1
            value: 40.781
          - type: recall_at_10
            value: 78.523
          - type: recall_at_100
            value: 93.639
          - type: recall_at_1000
            value: 97.504
          - type: recall_at_20
            value: 84.56099999999999
          - type: recall_at_3
            value: 61.895999999999994
          - type: recall_at_5
            value: 70.79299999999999
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (telugu)
          config: telugu
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 43.344
          - type: map_at_10
            value: 56.452000000000005
          - type: map_at_100
            value: 57.108000000000004
          - type: map_at_1000
            value: 57.131
          - type: map_at_20
            value: 56.86600000000001
          - type: map_at_3
            value: 54.154
          - type: map_at_5
            value: 55.57
          - type: mrr_at_1
            value: 44.427
          - type: mrr_at_10
            value: 57.16
          - type: mrr_at_100
            value: 57.764
          - type: mrr_at_1000
            value: 57.785
          - type: mrr_at_20
            value: 57.548
          - type: mrr_at_3
            value: 55.031
          - type: mrr_at_5
            value: 56.330999999999996
          - type: ndcg_at_1
            value: 44.427
          - type: ndcg_at_10
            value: 62.208
          - type: ndcg_at_100
            value: 65.33099999999999
          - type: ndcg_at_1000
            value: 65.96
          - type: ndcg_at_20
            value: 63.671
          - type: ndcg_at_3
            value: 57.68600000000001
          - type: ndcg_at_5
            value: 60.126999999999995
          - type: precision_at_1
            value: 44.427
          - type: precision_at_10
            value: 8.251
          - type: precision_at_100
            value: 0.98
          - type: precision_at_1000
            value: 0.10300000000000001
          - type: precision_at_20
            value: 4.42
          - type: precision_at_3
            value: 23.116999999999997
          - type: precision_at_5
            value: 15.076999999999998
          - type: recall_at_1
            value: 43.344
          - type: recall_at_10
            value: 79.10199999999999
          - type: recall_at_100
            value: 93.57600000000001
          - type: recall_at_1000
            value: 98.529
          - type: recall_at_20
            value: 84.83000000000001
          - type: recall_at_3
            value: 67.02799999999999
          - type: recall_at_5
            value: 72.833
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (th)
          config: th
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 27.747
          - type: map_at_10
            value: 43.586999999999996
          - type: map_at_100
            value: 45.256
          - type: map_at_1000
            value: 45.339
          - type: map_at_20
            value: 44.628
          - type: map_at_3
            value: 38.751999999999995
          - type: map_at_5
            value: 41.510000000000005
          - type: mrr_at_1
            value: 40.791
          - type: mrr_at_10
            value: 53.551
          - type: mrr_at_100
            value: 54.31
          - type: mrr_at_1000
            value: 54.32599999999999
          - type: mrr_at_20
            value: 54.057
          - type: mrr_at_3
            value: 50.637
          - type: mrr_at_5
            value: 52.525999999999996
          - type: ndcg_at_1
            value: 40.791
          - type: ndcg_at_10
            value: 52.144999999999996
          - type: ndcg_at_100
            value: 57.977000000000004
          - type: ndcg_at_1000
            value: 59.24
          - type: ndcg_at_20
            value: 54.864999999999995
          - type: ndcg_at_3
            value: 45.074
          - type: ndcg_at_5
            value: 48.504999999999995
          - type: precision_at_1
            value: 40.791
          - type: precision_at_10
            value: 11.350999999999999
          - type: precision_at_100
            value: 1.6039999999999999
          - type: precision_at_1000
            value: 0.178
          - type: precision_at_20
            value: 6.589
          - type: precision_at_3
            value: 25.239
          - type: precision_at_5
            value: 18.526999999999997
          - type: recall_at_1
            value: 27.747
          - type: recall_at_10
            value: 66.752
          - type: recall_at_100
            value: 89.51400000000001
          - type: recall_at_1000
            value: 97.485
          - type: recall_at_20
            value: 75.658
          - type: recall_at_3
            value: 48.393
          - type: recall_at_5
            value: 56.977
      - task:
          type: retrieval
        dataset:
          type: mteb/mrtydi
          name: MTEB MrTydiRetrieval (thai)
          config: thai
          split: test
          revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7
        metrics:
          - type: map_at_1
            value: 28.810000000000002
          - type: map_at_10
            value: 41.803000000000004
          - type: map_at_100
            value: 42.818
          - type: map_at_1000
            value: 42.861
          - type: map_at_20
            value: 42.487
          - type: map_at_3
            value: 37.719
          - type: map_at_5
            value: 40.143
          - type: mrr_at_1
            value: 31.596999999999998
          - type: mrr_at_10
            value: 43.784
          - type: mrr_at_100
            value: 44.647
          - type: mrr_at_1000
            value: 44.681
          - type: mrr_at_20
            value: 44.385000000000005
          - type: mrr_at_3
            value: 40.266000000000005
          - type: mrr_at_5
            value: 42.266
          - type: ndcg_at_1
            value: 31.596999999999998
          - type: ndcg_at_10
            value: 48.874
          - type: ndcg_at_100
            value: 53.285000000000004
          - type: ndcg_at_1000
            value: 54.398
          - type: ndcg_at_20
            value: 51.188
          - type: ndcg_at_3
            value: 41.010000000000005
          - type: ndcg_at_5
            value: 45.054
          - type: precision_at_1
            value: 31.596999999999998
          - type: precision_at_10
            value: 7.747999999999999
          - type: precision_at_100
            value: 1.008
          - type: precision_at_1000
            value: 0.11100000000000002
          - type: precision_at_20
            value: 4.382
          - type: precision_at_3
            value: 17.619
          - type: precision_at_5
            value: 12.806999999999999
          - type: recall_at_1
            value: 28.810000000000002
          - type: recall_at_10
            value: 68.88
          - type: recall_at_100
            value: 88.263
          - type: recall_at_1000
            value: 96.765
          - type: recall_at_20
            value: 77.647
          - type: recall_at_3
            value: 48.137
          - type: recall_at_5
            value: 57.577
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (yo)
          config: yo
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 16.387
          - type: map_at_10
            value: 23.988
          - type: map_at_100
            value: 24.65
          - type: map_at_1000
            value: 24.725
          - type: map_at_20
            value: 24.310000000000002
          - type: map_at_3
            value: 21.23
          - type: map_at_5
            value: 23.093
          - type: mrr_at_1
            value: 17.647
          - type: mrr_at_10
            value: 26.144000000000002
          - type: mrr_at_100
            value: 26.751
          - type: mrr_at_1000
            value: 26.804
          - type: mrr_at_20
            value: 26.43
          - type: mrr_at_3
            value: 23.669
          - type: mrr_at_5
            value: 25.392
          - type: ndcg_at_1
            value: 17.647
          - type: ndcg_at_10
            value: 28.583
          - type: ndcg_at_100
            value: 31.790000000000003
          - type: ndcg_at_1000
            value: 33.705
          - type: ndcg_at_20
            value: 29.669
          - type: ndcg_at_3
            value: 23.271
          - type: ndcg_at_5
            value: 26.509
          - type: precision_at_1
            value: 17.647
          - type: precision_at_10
            value: 4.79
          - type: precision_at_100
            value: 0.655
          - type: precision_at_1000
            value: 0.08499999999999999
          - type: precision_at_20
            value: 2.6470000000000002
          - type: precision_at_3
            value: 10.363999999999999
          - type: precision_at_5
            value: 7.899000000000001
          - type: recall_at_1
            value: 16.387
          - type: recall_at_10
            value: 40.476
          - type: recall_at_100
            value: 55.11200000000001
          - type: recall_at_1000
            value: 69.538
          - type: recall_at_20
            value: 44.538
          - type: recall_at_3
            value: 26.540999999999997
          - type: recall_at_5
            value: 34.384
      - task:
          type: retrieval
        dataset:
          type: mteb/miracl-hard-negatives
          name: MTEB MIRACLRetrievalHardNegatives (zh)
          config: zh
          split: dev
          revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb
        metrics:
          - type: map_at_1
            value: 13.971
          - type: map_at_10
            value: 27.076
          - type: map_at_100
            value: 30.128
          - type: map_at_1000
            value: 30.25
          - type: map_at_20
            value: 28.731
          - type: map_at_3
            value: 21.029999999999998
          - type: map_at_5
            value: 23.769000000000002
          - type: mrr_at_1
            value: 26.717999999999996
          - type: mrr_at_10
            value: 40.331
          - type: mrr_at_100
            value: 41.448
          - type: mrr_at_1000
            value: 41.461999999999996
          - type: mrr_at_20
            value: 41.103
          - type: mrr_at_3
            value: 36.302
          - type: mrr_at_5
            value: 38.414
          - type: ndcg_at_1
            value: 26.717999999999996
          - type: ndcg_at_10
            value: 36.744
          - type: ndcg_at_100
            value: 47.361
          - type: ndcg_at_1000
            value: 48.869
          - type: ndcg_at_20
            value: 41.097
          - type: ndcg_at_3
            value: 28.322000000000003
          - type: ndcg_at_5
            value: 30.875999999999998
          - type: precision_at_1
            value: 26.717999999999996
          - type: precision_at_10
            value: 11.679
          - type: precision_at_100
            value: 2.2159999999999997
          - type: precision_at_1000
            value: 0.248
          - type: precision_at_20
            value: 7.545
          - type: precision_at_3
            value: 20.102
          - type: precision_at_5
            value: 16.131999999999998
          - type: recall_at_1
            value: 13.971
          - type: recall_at_10
            value: 50.763999999999996
          - type: recall_at_100
            value: 89.666
          - type: recall_at_1000
            value: 98.038
          - type: recall_at_20
            value: 64.1
          - type: recall_at_3
            value: 27.16
          - type: recall_at_5
            value: 35.022999999999996
widget:
  - source_sentence: ما هي أفضل الفنادق في ايبوهبالقرب من Ipoh Parade Shopping Centre؟
    sentences:
      - >-
        Bei ORION gibt es eine Sale-Rubrik, in der alle reduzierten Artikel zu
        finden sind. Wenn du also auf der Suche nach einem Schnäppchen bist,
        weißt du, an welcher Stelle auf der Webseite du fündig wirst. Der Sale
        umfasst viele verschiedene Produke. Von Toys bis hin zu Dessous und
        Drogerieartikel - es spielt keine Rolle, wonach du suchst. Aufgrund der
        Produktvielfalt ist die Chance, dass du im Sale den passenden Gegenstand
        findest, groß.
      - عادة ما يكون لأصحاب النفوذ الجزئي ما بين 10000 و 100000 متابع.
      - >-
        المسافرون الموثّقون إلى مدينة ايبوه الذين أقاموا قرب Ipoh Parade
        Shopping Centre أعطوا أعلى التقييمات لـفندق فايل ، Zone Hotel (Ipoh)
        وGolden Roof Hotel Ampang Ipoh.
  - source_sentence: Habe ich Vorteile, wenn ich früh in das Projekt einsteige?
    sentences:
      - >-
        نعم لدينا خصومات مُتعددة على جميع أعمال السواتر والمظلات والجلسات ففي
        فصل الشتاء والصيف هناك خصومات مُتعددة في الأعياد والمناسبات
      - >-
        Der Vorteil einer frühen Mitgliedschaft besteht in der Möglichkeit der
        Mitgestaltung des Projektes. Alle später Hinzukommenden müssen die bis
        dahin getroffenen Entscheidungen akzeptieren. Zudem entscheidet unter
        anderem auch das Eintrittsdatum in die eG und das Engagement während des
        Projektverlaufes über die spätere Reihenfolge der Vergabe der Wohnungen.
      - >-
        Средняя оценка Registered от клиентов – 4 на основе 227 оценок и
        отзывов. Заходите на сайт и прочитайте реальные отзывы о Registered.
  - source_sentence: В какое время доступны ваши технические услуги?
    sentences:
      - >-
        Наша команда технической поддержки обеспечивает круглосуточное
        обслуживание в случае чрезвычайных ситуаций. Вы можете связаться с нами
        в любой день недели, в любое время суток и получить поддержку для ваших
        холодильных систем. Услуги по плановому техническому обслуживанию и
        ремонту предоставляются в обычное рабочее время, а услуги
        предоставляются в экстренных случаях, в том числе в ночное время и в
        выходные дни.
      - >-
        این سوال کاملا به علاقه و مهارت شما بستگی دارد. اگر به درس شیمی علاقه
        زیادی دارید این رشته بهترین انتخاب برای تحصیل در دانشگاه برای شما محسوب
        می‌شود.
      - |-
        eo光は10Gを提供している光回線です。
        提供エリアは、通常プランと変わらず関西地方と福井県です。しかし、一部の利用できないエリアもあるので契約前に確認しましょう。
        関連記事
        eo光の10Gプランの評判口コミ
  - source_sentence: Supertotobet redtiger oyun çeşitleri hangileri?
    sentences:
      - >-
        Supertotobet redtiger oyunları arasında gold star, golden tsar, golden
        lotus, blood suckers ve redtiger slot gibi çeşitli oyunlar vardır. Bu
        oyun seçeneklerini kullanabilmek için oyunlar hakkında bilgi sahibi
        olmalısınız.
      - >-
        Время выполнения проекта зависит от его сложности и размера. Обычно,
        время выполнения проекта составляет несколько месяцев.
      - >-
        Wer kein Homeoffice während des Coronavirus anbieten kann, ist dazu
        verpflichtet, Schutzmaßnahmen zu ergreifen.

        Die Arbeitsschutzbehörden der Länder sind befugt, Corona-Schutzmaßnahmen
        in Betrieben zu kontrollieren und Fehlverhalten zu bestrafen. Bei
        Verstößen sind Bußgelder in Höhe von bis zu 30.000 Euro möglich.
        Wiederholen sich schwere Verstöße, droht den Verantwortlichen sogar bis
        zu einem Jahr Freiheitsstrafe.

        Arbeitgeber, die die Vorschriften missachten, könnten zudem dafür
        haften, wenn Mitarbeiter durch eine Corona-Infektion gesundheitliche
        Schäden erleiden.
  - source_sentence: Muss der Deckel der TipBox beim Autoklavieren geöffnet werden?
    sentences:
      - >-
        ВВП (валовый внутренний продукт) - это общая стоимость всех товаров и
        услуг, произведенных в стране за определенный период времени. Он
        является ключевым экономическим показателем, который отражает общий
        уровень экономической активности и роста. Инвесторы следят за ВВП, чтобы
        оценить состояние и перспективы экономики, потенциал для роста и
        возможности для инвестиций
      - >-
        برآمدگی های بیضه ممکن است نشان دهنده مشکلی در بیضه ها باشد. ممکن است به
        دلیل صدمه ای به وجود آمده یا ممکن است یک مشکل پزشکی جدی باشد.
      - >-
        Nein, das ist nicht notwendig. Die neue TipBox kann bei 121°C im
        geschlossenen Zustand autoklaviert werden.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
license: mit

SentenceTransformer

This is a sentence-transformers model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: XLM-RoBERTa-base-MSMARCO
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset: WebFAQ Retrieval Dataset
• License: MIT

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("PaDaS-Lab/xlm-roberta-base-msmarco-webfaq")
# Run inference
sentences = [
    'Muss der Deckel der TipBox beim Autoklavieren geöffnet werden?',
    'Nein, das ist nicht notwendig. Die neue TipBox kann bei 121°C im geschlossenen Zustand autoklaviert werden.',
    'برآمدگی های بیضه ممکن است نشان دهنده مشکلی در بیضه ها باشد. ممکن است به دلیل صدمه ای به وجود آمده یا ممکن است یک مشکل پزشکی جدی باشد.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Training Details

Training Dataset

Unnamed Dataset

• Size: 2,560,000 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 1000 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 6 tokens mean: 15.02 tokens max: 86 tokens	min: 11 tokens mean: 66.82 tokens max: 512 tokens

• Samples:

  sentence_0	sentence_1
  Hat myTime ein großes Produktsortiment?	Das Sortiment von myTime umfasst mehr als 13.000 Lebensmittel. Du findest alle Produkte, die du auch im Supermarkt findest, darunter Obst und Gemüse, trockene Lebensmittel wie Pasta und Reis, Backwaren, Snacks und Tiefkühlkost. Auch Getränke wie Kaffee, Alkohol und Soda findest du im Online-Supermarkt.
  Gibt es eine Tigerspin App?	Tigerspin verzichtet auf eine mobile App. Wenn Sie ein paar Runden spielen möchten, öffnen Sie einfach die Webseite des Casinos und starten die Spiele im Browser.
  Bietet ihr auch maschinelle Übersetzungen an? Wenn ja, wann eignet sich diese und wann nicht?	Maschinelle Übersetzungen sind ein span­nen­des Thema, auch aktuell bei techtrans. Unter maschineller Übersetzung (MÜ) ver­steht man die auto­mati­sierte Über­tra­gung eines Aus­gangs­textes in die Ziel­sprache mittels einer so­ge­nannten Über­set­zungs­engine. Eine solche Engine kann nach re­gel­ba­sier­ten, statis­tischen oder neu­ro­nalen Prin­zipien auf­ge­baut sein. Ob­wohl es maschinelle Über­set­zungs­engines schon seit einigen Jahr­zehn­ten gibt, ist erst mit der Ein­führung der neu­ro­nalen Engines (NMT) ca. ab dem Jahre 2015 die Output-Qualität ge­stie­gen. Nam­hafte Engine Provider sind zum Bei­spiel Google, DeepL, Microsoft, Amazon AWS und SDL. So ist es kaum ver­wunder­lich, dass diese Tech­no­logie zu­neh­mend Ein­zug sowohl in unseren All­tag als auch in den Über­set­zungs­pro­zess findet.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• num_train_epochs: 1
• fp16: True
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• prediction_loss_only: True
• per_device_train_batch_size: 128
• per_device_eval_batch_size: 128
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1
• num_train_epochs: 1
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.0
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	Training Loss
0.025	500	0.1999
0.05	1000	0.0279
0.075	1500	0.0234
0.1	2000	0.0203
0.125	2500	0.0179
0.15	3000	0.0171
0.175	3500	0.0153
0.2	4000	0.015
0.225	4500	0.0143
0.25	5000	0.014
0.275	5500	0.0128
0.3	6000	0.013
0.325	6500	0.0129
0.35	7000	0.0124
0.375	7500	0.012
0.4	8000	0.0121
0.425	8500	0.0115
0.45	9000	0.0113
0.475	9500	0.0106
0.5	10000	0.0107
0.525	10500	0.011
0.55	11000	0.0108
0.575	11500	0.0103
0.6	12000	0.0097
0.625	12500	0.01
0.65	13000	0.0104
0.675	13500	0.0096
0.7	14000	0.0096
0.725	14500	0.0097
0.75	15000	0.0097
0.775	15500	0.0089
0.8	16000	0.0089
0.825	16500	0.0091
0.85	17000	0.0085
0.875	17500	0.0084
0.9	18000	0.0089
0.925	18500	0.0087
0.95	19000	0.0087
0.975	19500	0.0088
1.0	20000	0.0089

Framework Versions

• Python: 3.11.5
• Sentence Transformers: 3.4.0
• Transformers: 4.48.0
• PyTorch: 2.5.1+cu124
• Accelerate: 1.2.1
• Datasets: 2.21.0
• Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

WebFAQ Dataset

@misc{dinzinger2025webfaq,
    title={WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval},
    author={Michael Dinzinger and Laura Caspari and Kanishka Ghosh Dastidar and Jelena Mitrović and Michael Granitzer},
    year={2025},
    eprint={2502.20936},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}