transformers safetensors mistral text-generation mteb en dataset:BeastyZ/E5-R license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
datasets:
- BeastyZ/E5-R
language:
- en
library_name: transformers
license: apache-2.0
tags:
- mteb
model-index:
- name: e5-R-mistral-7b
  results:
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: mteb/arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 33.57
    - type: map_at_10
      value: 49.952000000000005
    - type: map_at_100
      value: 50.673
    - type: map_at_1000
      value: 50.674
    - type: map_at_3
      value: 44.915
    - type: map_at_5
      value: 47.876999999999995
    - type: mrr_at_1
      value: 34.211000000000006
    - type: mrr_at_10
      value: 50.19
    - type: mrr_at_100
      value: 50.905
    - type: mrr_at_1000
      value: 50.906
    - type: mrr_at_3
      value: 45.128
    - type: mrr_at_5
      value: 48.097
    - type: ndcg_at_1
      value: 33.57
    - type: ndcg_at_10
      value: 58.994
    - type: ndcg_at_100
      value: 61.806000000000004
    - type: ndcg_at_1000
      value: 61.824999999999996
    - type: ndcg_at_3
      value: 48.681000000000004
    - type: ndcg_at_5
      value: 54.001
    - type: precision_at_1
      value: 33.57
    - type: precision_at_10
      value: 8.784
    - type: precision_at_100
      value: 0.9950000000000001
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 19.867
    - type: precision_at_5
      value: 14.495
    - type: recall_at_1
      value: 33.57
    - type: recall_at_10
      value: 87.83800000000001
    - type: recall_at_100
      value: 99.502
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 59.602
    - type: recall_at_5
      value: 72.475
    - type: main_score
      value: 58.994
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackRetrieval
      type: mteb/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 24.75
    - type: map_at_10
      value: 34.025
    - type: map_at_100
      value: 35.126000000000005
    - type: map_at_1000
      value: 35.219
    - type: map_at_3
      value: 31.607000000000003
    - type: map_at_5
      value: 32.962
    - type: mrr_at_1
      value: 27.357
    - type: mrr_at_10
      value: 36.370999999999995
    - type: mrr_at_100
      value: 37.364000000000004
    - type: mrr_at_1000
      value: 37.423
    - type: mrr_at_3
      value: 34.288000000000004
    - type: mrr_at_5
      value: 35.434
    - type: ndcg_at_1
      value: 27.357
    - type: ndcg_at_10
      value: 46.593999999999994
    - type: ndcg_at_100
      value: 44.317
    - type: ndcg_at_1000
      value: 46.475
    - type: ndcg_at_3
      value: 34.473
    - type: ndcg_at_5
      value: 36.561
    - type: precision_at_1
      value: 27.357
    - type: precision_at_10
      value: 6.081
    - type: precision_at_100
      value: 0.9299999999999999
    - type: precision_at_1000
      value: 0.124
    - type: precision_at_3
      value: 14.911
    - type: precision_at_5
      value: 10.24
    - type: recall_at_1
      value: 24.75
    - type: recall_at_10
      value: 51.856
    - type: recall_at_100
      value: 76.44300000000001
    - type: recall_at_1000
      value: 92.078
    - type: recall_at_3
      value: 39.427
    - type: recall_at_5
      value: 44.639
    - type: main_score
      value: 46.593999999999994
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: mteb/climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 16.436
    - type: map_at_10
      value: 29.693
    - type: map_at_100
      value: 32.179
    - type: map_at_1000
      value: 32.353
    - type: map_at_3
      value: 24.556
    - type: map_at_5
      value: 27.105
    - type: mrr_at_1
      value: 37.524
    - type: mrr_at_10
      value: 51.475
    - type: mrr_at_100
      value: 52.107000000000006
    - type: mrr_at_1000
      value: 52.123
    - type: mrr_at_3
      value: 48.35
    - type: mrr_at_5
      value: 50.249
    - type: ndcg_at_1
      value: 37.524
    - type: ndcg_at_10
      value: 40.258
    - type: ndcg_at_100
      value: 48.364000000000004
    - type: ndcg_at_1000
      value: 51.031000000000006
    - type: ndcg_at_3
      value: 33.359
    - type: ndcg_at_5
      value: 35.573
    - type: precision_at_1
      value: 37.524
    - type: precision_at_10
      value: 12.886000000000001
    - type: precision_at_100
      value: 2.169
    - type: precision_at_1000
      value: 0.268
    - type: precision_at_3
      value: 25.624000000000002
    - type: precision_at_5
      value: 19.453
    - type: recall_at_1
      value: 16.436
    - type: recall_at_10
      value: 47.77
    - type: recall_at_100
      value: 74.762
    - type: recall_at_1000
      value: 89.316
    - type: recall_at_3
      value: 30.508000000000003
    - type: recall_at_5
      value: 37.346000000000004
    - type: main_score
      value: 40.258
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: mteb/dbpedia
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 10.147
    - type: map_at_10
      value: 24.631
    - type: map_at_100
      value: 35.657
    - type: map_at_1000
      value: 37.824999999999996
    - type: map_at_3
      value: 16.423
    - type: map_at_5
      value: 19.666
    - type: mrr_at_1
      value: 76.5
    - type: mrr_at_10
      value: 82.793
    - type: mrr_at_100
      value: 83.015
    - type: mrr_at_1000
      value: 83.021
    - type: mrr_at_3
      value: 81.75
    - type: mrr_at_5
      value: 82.375
    - type: ndcg_at_1
      value: 64.75
    - type: ndcg_at_10
      value: 51.031000000000006
    - type: ndcg_at_100
      value: 56.005
    - type: ndcg_at_1000
      value: 63.068000000000005
    - type: ndcg_at_3
      value: 54.571999999999996
    - type: ndcg_at_5
      value: 52.66499999999999
    - type: precision_at_1
      value: 76.5
    - type: precision_at_10
      value: 42.15
    - type: precision_at_100
      value: 13.22
    - type: precision_at_1000
      value: 2.5989999999999998
    - type: precision_at_3
      value: 58.416999999999994
    - type: precision_at_5
      value: 52.2
    - type: recall_at_1
      value: 10.147
    - type: recall_at_10
      value: 30.786
    - type: recall_at_100
      value: 62.873000000000005
    - type: recall_at_1000
      value: 85.358
    - type: recall_at_3
      value: 17.665
    - type: recall_at_5
      value: 22.088
    - type: main_score
      value: 51.031000000000006
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: mteb/fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 78.52900000000001
    - type: map_at_10
      value: 87.24199999999999
    - type: map_at_100
      value: 87.446
    - type: map_at_1000
      value: 87.457
    - type: map_at_3
      value: 86.193
    - type: map_at_5
      value: 86.898
    - type: mrr_at_1
      value: 84.518
    - type: mrr_at_10
      value: 90.686
    - type: mrr_at_100
      value: 90.73
    - type: mrr_at_1000
      value: 90.731
    - type: mrr_at_3
      value: 90.227
    - type: mrr_at_5
      value: 90.575
    - type: ndcg_at_1
      value: 84.518
    - type: ndcg_at_10
      value: 90.324
    - type: ndcg_at_100
      value: 90.96300000000001
    - type: ndcg_at_1000
      value: 91.134
    - type: ndcg_at_3
      value: 88.937
    - type: ndcg_at_5
      value: 89.788
    - type: precision_at_1
      value: 84.518
    - type: precision_at_10
      value: 10.872
    - type: precision_at_100
      value: 1.1440000000000001
    - type: precision_at_1000
      value: 0.117
    - type: precision_at_3
      value: 34.108
    - type: precision_at_5
      value: 21.154999999999998
    - type: recall_at_1
      value: 78.52900000000001
    - type: recall_at_10
      value: 96.123
    - type: recall_at_100
      value: 98.503
    - type: recall_at_1000
      value: 99.518
    - type: recall_at_3
      value: 92.444
    - type: recall_at_5
      value: 94.609
    - type: main_score
      value: 90.324
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: mteb/fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 29.38
    - type: map_at_10
      value: 50.28
    - type: map_at_100
      value: 52.532999999999994
    - type: map_at_1000
      value: 52.641000000000005
    - type: map_at_3
      value: 43.556
    - type: map_at_5
      value: 47.617
    - type: mrr_at_1
      value: 56.79
    - type: mrr_at_10
      value: 65.666
    - type: mrr_at_100
      value: 66.211
    - type: mrr_at_1000
      value: 66.226
    - type: mrr_at_3
      value: 63.452
    - type: mrr_at_5
      value: 64.895
    - type: ndcg_at_1
      value: 56.79
    - type: ndcg_at_10
      value: 58.68
    - type: ndcg_at_100
      value: 65.22
    - type: ndcg_at_1000
      value: 66.645
    - type: ndcg_at_3
      value: 53.981
    - type: ndcg_at_5
      value: 55.95
    - type: precision_at_1
      value: 56.79
    - type: precision_at_10
      value: 16.311999999999998
    - type: precision_at_100
      value: 2.316
    - type: precision_at_1000
      value: 0.258
    - type: precision_at_3
      value: 36.214
    - type: precision_at_5
      value: 27.067999999999998
    - type: recall_at_1
      value: 29.38
    - type: recall_at_10
      value: 66.503
    - type: recall_at_100
      value: 89.885
    - type: recall_at_1000
      value: 97.954
    - type: recall_at_3
      value: 48.866
    - type: recall_at_5
      value: 57.60999999999999
    - type: main_score
      value: 58.68
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: mteb/hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 42.134
    - type: map_at_10
      value: 73.412
    - type: map_at_100
      value: 74.144
    - type: map_at_1000
      value: 74.181
    - type: map_at_3
      value: 70.016
    - type: map_at_5
      value: 72.174
    - type: mrr_at_1
      value: 84.267
    - type: mrr_at_10
      value: 89.18599999999999
    - type: mrr_at_100
      value: 89.29599999999999
    - type: mrr_at_1000
      value: 89.298
    - type: mrr_at_3
      value: 88.616
    - type: mrr_at_5
      value: 88.957
    - type: ndcg_at_1
      value: 84.267
    - type: ndcg_at_10
      value: 80.164
    - type: ndcg_at_100
      value: 82.52199999999999
    - type: ndcg_at_1000
      value: 83.176
    - type: ndcg_at_3
      value: 75.616
    - type: ndcg_at_5
      value: 78.184
    - type: precision_at_1
      value: 84.267
    - type: precision_at_10
      value: 16.916
    - type: precision_at_100
      value: 1.872
    - type: precision_at_1000
      value: 0.196
    - type: precision_at_3
      value: 49.71
    - type: precision_at_5
      value: 31.854
    - type: recall_at_1
      value: 42.134
    - type: recall_at_10
      value: 84.578
    - type: recall_at_100
      value: 93.606
    - type: recall_at_1000
      value: 97.86
    - type: recall_at_3
      value: 74.564
    - type: recall_at_5
      value: 79.635
    - type: main_score
      value: 80.164
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: mteb/msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 22.276
    - type: map_at_10
      value: 35.493
    - type: map_at_100
      value: 36.656
    - type: map_at_1000
      value: 36.699
    - type: map_at_3
      value: 31.320999999999998
    - type: map_at_5
      value: 33.772999999999996
    - type: mrr_at_1
      value: 22.966
    - type: mrr_at_10
      value: 36.074
    - type: mrr_at_100
      value: 37.183
    - type: mrr_at_1000
      value: 37.219
    - type: mrr_at_3
      value: 31.984
    - type: mrr_at_5
      value: 34.419
    - type: ndcg_at_1
      value: 22.966
    - type: ndcg_at_10
      value: 42.895
    - type: ndcg_at_100
      value: 48.453
    - type: ndcg_at_1000
      value: 49.464999999999996
    - type: ndcg_at_3
      value: 34.410000000000004
    - type: ndcg_at_5
      value: 38.78
    - type: precision_at_1
      value: 22.966
    - type: precision_at_10
      value: 6.88
    - type: precision_at_100
      value: 0.966
    - type: precision_at_1000
      value: 0.105
    - type: precision_at_3
      value: 14.785
    - type: precision_at_5
      value: 11.074
    - type: recall_at_1
      value: 22.276
    - type: recall_at_10
      value: 65.756
    - type: recall_at_100
      value: 91.34100000000001
    - type: recall_at_1000
      value: 98.957
    - type: recall_at_3
      value: 42.67
    - type: recall_at_5
      value: 53.161
    - type: main_score
      value: 42.895
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: mteb/nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 7.188999999999999
    - type: map_at_10
      value: 16.176
    - type: map_at_100
      value: 20.504
    - type: map_at_1000
      value: 22.203999999999997
    - type: map_at_3
      value: 11.766
    - type: map_at_5
      value: 13.655999999999999
    - type: mrr_at_1
      value: 55.418
    - type: mrr_at_10
      value: 62.791
    - type: mrr_at_100
      value: 63.339
    - type: mrr_at_1000
      value: 63.369
    - type: mrr_at_3
      value: 60.99099999999999
    - type: mrr_at_5
      value: 62.059
    - type: ndcg_at_1
      value: 53.715
    - type: ndcg_at_10
      value: 41.377
    - type: ndcg_at_100
      value: 37.999
    - type: ndcg_at_1000
      value: 46.726
    - type: ndcg_at_3
      value: 47.262
    - type: ndcg_at_5
      value: 44.708999999999996
    - type: precision_at_1
      value: 55.108000000000004
    - type: precision_at_10
      value: 30.154999999999998
    - type: precision_at_100
      value: 9.582
    - type: precision_at_1000
      value: 2.2720000000000002
    - type: precision_at_3
      value: 43.55
    - type: precision_at_5
      value: 38.204
    - type: recall_at_1
      value: 7.188999999999999
    - type: recall_at_10
      value: 20.655
    - type: recall_at_100
      value: 38.068000000000005
    - type: recall_at_1000
      value: 70.208
    - type: recall_at_3
      value: 12.601
    - type: recall_at_5
      value: 15.573999999999998
    - type: main_score
      value: 41.377
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: mteb/nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 46.017
    - type: map_at_10
      value: 62.910999999999994
    - type: map_at_100
      value: 63.526
    - type: map_at_1000
      value: 63.536
    - type: map_at_3
      value: 59.077999999999996
    - type: map_at_5
      value: 61.521
    - type: mrr_at_1
      value: 51.68000000000001
    - type: mrr_at_10
      value: 65.149
    - type: mrr_at_100
      value: 65.542
    - type: mrr_at_1000
      value: 65.55
    - type: mrr_at_3
      value: 62.49
    - type: mrr_at_5
      value: 64.178
    - type: ndcg_at_1
      value: 51.651
    - type: ndcg_at_10
      value: 69.83500000000001
    - type: ndcg_at_100
      value: 72.18
    - type: ndcg_at_1000
      value: 72.393
    - type: ndcg_at_3
      value: 63.168
    - type: ndcg_at_5
      value: 66.958
    - type: precision_at_1
      value: 51.651
    - type: precision_at_10
      value: 10.626
    - type: precision_at_100
      value: 1.195
    - type: precision_at_1000
      value: 0.121
    - type: precision_at_3
      value: 28.012999999999998
    - type: precision_at_5
      value: 19.09
    - type: recall_at_1
      value: 46.017
    - type: recall_at_10
      value: 88.345
    - type: recall_at_100
      value: 98.129
    - type: recall_at_1000
      value: 99.696
    - type: recall_at_3
      value: 71.531
    - type: recall_at_5
      value: 80.108
    - type: main_score
      value: 69.83500000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: mteb/quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 72.473
    - type: map_at_10
      value: 86.72800000000001
    - type: map_at_100
      value: 87.323
    - type: map_at_1000
      value: 87.332
    - type: map_at_3
      value: 83.753
    - type: map_at_5
      value: 85.627
    - type: mrr_at_1
      value: 83.39
    - type: mrr_at_10
      value: 89.149
    - type: mrr_at_100
      value: 89.228
    - type: mrr_at_1000
      value: 89.229
    - type: mrr_at_3
      value: 88.335
    - type: mrr_at_5
      value: 88.895
    - type: ndcg_at_1
      value: 83.39
    - type: ndcg_at_10
      value: 90.109
    - type: ndcg_at_100
      value: 91.09
    - type: ndcg_at_1000
      value: 91.13900000000001
    - type: ndcg_at_3
      value: 87.483
    - type: ndcg_at_5
      value: 88.942
    - type: precision_at_1
      value: 83.39
    - type: precision_at_10
      value: 13.711
    - type: precision_at_100
      value: 1.549
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 38.342999999999996
    - type: precision_at_5
      value: 25.188
    - type: recall_at_1
      value: 72.473
    - type: recall_at_10
      value: 96.57
    - type: recall_at_100
      value: 99.792
    - type: recall_at_1000
      value: 99.99900000000001
    - type: recall_at_3
      value: 88.979
    - type: recall_at_5
      value: 93.163
    - type: main_score
      value: 90.109
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: mteb/scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.598
    - type: map_at_10
      value: 11.405999999999999
    - type: map_at_100
      value: 13.447999999999999
    - type: map_at_1000
      value: 13.758999999999999
    - type: map_at_3
      value: 8.332
    - type: map_at_5
      value: 9.709
    - type: mrr_at_1
      value: 22.6
    - type: mrr_at_10
      value: 32.978
    - type: mrr_at_100
      value: 34.149
    - type: mrr_at_1000
      value: 34.213
    - type: mrr_at_3
      value: 29.7
    - type: mrr_at_5
      value: 31.485000000000003
    - type: ndcg_at_1
      value: 22.6
    - type: ndcg_at_10
      value: 19.259999999999998
    - type: ndcg_at_100
      value: 27.21
    - type: ndcg_at_1000
      value: 32.7
    - type: ndcg_at_3
      value: 18.445
    - type: ndcg_at_5
      value: 15.812000000000001
    - type: precision_at_1
      value: 22.6
    - type: precision_at_10
      value: 9.959999999999999
    - type: precision_at_100
      value: 2.139
    - type: precision_at_1000
      value: 0.345
    - type: precision_at_3
      value: 17.299999999999997
    - type: precision_at_5
      value: 13.719999999999999
    - type: recall_at_1
      value: 4.598
    - type: recall_at_10
      value: 20.186999999999998
    - type: recall_at_100
      value: 43.362
    - type: recall_at_1000
      value: 70.11800000000001
    - type: recall_at_3
      value: 10.543
    - type: recall_at_5
      value: 13.923
    - type: main_score
      value: 19.259999999999998
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: mteb/scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 65.467
    - type: map_at_10
      value: 74.935
    - type: map_at_100
      value: 75.395
    - type: map_at_1000
      value: 75.412
    - type: map_at_3
      value: 72.436
    - type: map_at_5
      value: 73.978
    - type: mrr_at_1
      value: 68.667
    - type: mrr_at_10
      value: 76.236
    - type: mrr_at_100
      value: 76.537
    - type: mrr_at_1000
      value: 76.55499999999999
    - type: mrr_at_3
      value: 74.722
    - type: mrr_at_5
      value: 75.639
    - type: ndcg_at_1
      value: 68.667
    - type: ndcg_at_10
      value: 78.92099999999999
    - type: ndcg_at_100
      value: 80.645
    - type: ndcg_at_1000
      value: 81.045
    - type: ndcg_at_3
      value: 75.19500000000001
    - type: ndcg_at_5
      value: 77.114
    - type: precision_at_1
      value: 68.667
    - type: precision_at_10
      value: 10.133000000000001
    - type: precision_at_100
      value: 1.0999999999999999
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 28.889
    - type: precision_at_5
      value: 18.8
    - type: recall_at_1
      value: 65.467
    - type: recall_at_10
      value: 89.517
    - type: recall_at_100
      value: 97
    - type: recall_at_1000
      value: 100
    - type: recall_at_3
      value: 79.72200000000001
    - type: recall_at_5
      value: 84.511
    - type: main_score
      value: 78.92099999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: mteb/trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.244
    - type: map_at_10
      value: 2.183
    - type: map_at_100
      value: 13.712
    - type: map_at_1000
      value: 33.147
    - type: map_at_3
      value: 0.7270000000000001
    - type: map_at_5
      value: 1.199
    - type: mrr_at_1
      value: 94
    - type: mrr_at_10
      value: 97
    - type: mrr_at_100
      value: 97
    - type: mrr_at_1000
      value: 97
    - type: mrr_at_3
      value: 97
    - type: mrr_at_5
      value: 97
    - type: ndcg_at_1
      value: 92
    - type: ndcg_at_10
      value: 84.399
    - type: ndcg_at_100
      value: 66.771
    - type: ndcg_at_1000
      value: 59.092
    - type: ndcg_at_3
      value: 89.173
    - type: ndcg_at_5
      value: 88.52600000000001
    - type: precision_at_1
      value: 94
    - type: precision_at_10
      value: 86.8
    - type: precision_at_100
      value: 68.24
    - type: precision_at_1000
      value: 26.003999999999998
    - type: precision_at_3
      value: 92.667
    - type: precision_at_5
      value: 92.4
    - type: recall_at_1
      value: 0.244
    - type: recall_at_10
      value: 2.302
    - type: recall_at_100
      value: 16.622
    - type: recall_at_1000
      value: 55.175
    - type: recall_at_3
      value: 0.748
    - type: recall_at_5
      value: 1.247
    - type: main_score
      value: 84.399
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: mteb/touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 2.707
    - type: map_at_10
      value: 10.917
    - type: map_at_100
      value: 16.308
    - type: map_at_1000
      value: 17.953
    - type: map_at_3
      value: 5.65
    - type: map_at_5
      value: 7.379
    - type: mrr_at_1
      value: 34.694
    - type: mrr_at_10
      value: 49.745
    - type: mrr_at_100
      value: 50.309000000000005
    - type: mrr_at_1000
      value: 50.32
    - type: mrr_at_3
      value: 44.897999999999996
    - type: mrr_at_5
      value: 48.061
    - type: ndcg_at_1
      value: 33.672999999999995
    - type: ndcg_at_10
      value: 26.894000000000002
    - type: ndcg_at_100
      value: 37.423
    - type: ndcg_at_1000
      value: 49.376999999999995
    - type: ndcg_at_3
      value: 30.456
    - type: ndcg_at_5
      value: 27.772000000000002
    - type: precision_at_1
      value: 34.694
    - type: precision_at_10
      value: 23.878
    - type: precision_at_100
      value: 7.489999999999999
    - type: precision_at_1000
      value: 1.555
    - type: precision_at_3
      value: 31.293
    - type: precision_at_5
      value: 26.939
    - type: recall_at_1
      value: 2.707
    - type: recall_at_10
      value: 18.104
    - type: recall_at_100
      value: 46.93
    - type: recall_at_1000
      value: 83.512
    - type: recall_at_3
      value: 6.622999999999999
    - type: recall_at_5
      value: 10.051
    - type: main_score
      value: 26.894000000000002
---

# Model Card for e5-R-mistral-7b

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## Model Description

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e5-R-mistral-7b is a LLM retriever fine-tuned from [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).

- **Model type:** CausalLM
- **Repository:** Welcome to our [GitHub](https://github.com/LeeSureman/E5-Retrieval-Reproduction) repository to obtain code
- **Training dataset:** Dataset used for fine-tuning e5-R-mistral-7b is available [here](https://huggingface.co/datasets/BeastyZ/E5-R).