VenkatNDivi77
/

e5-R-mistral-7b-Q4_K_M-GGUF

+---
+base_model: BeastyZ/e5-R-mistral-7b
+datasets:
+- BeastyZ/E5-R
+language:
+- en
+library_name: transformers
+license: apache-2.0
+tags:
+- mteb
+- llama-cpp
+- gguf-my-repo
+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
+---
+# VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF
+This model was converted to GGUF format from [`BeastyZ/e5-R-mistral-7b`](https://huggingface.co/BeastyZ/e5-R-mistral-7b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
+Refer to the [original model card](https://huggingface.co/BeastyZ/e5-R-mistral-7b) for more details on the model.
+## Use with llama.cpp
+Install llama.cpp through brew (works on Mac and Linux)
+```bash
+brew install llama.cpp
+```
+Invoke the llama.cpp server or the CLI.
+### CLI:
+```bash
+llama-cli --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+### Server:
+```bash
+llama-server --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -c 2048
+```
+Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
+Step 1: Clone llama.cpp from GitHub.
+```
+git clone https://github.com/ggerganov/llama.cpp
+```
+Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
+```
+cd llama.cpp && LLAMA_CURL=1 make
+```
+Step 3: Run inference through the main binary.
+```
+./llama-cli --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+or
+```
+./llama-server --hf-repo VenkatNDivi77/e5-R-mistral-7b-Q4_K_M-GGUF --hf-file e5-r-mistral-7b-q4_k_m.gguf -c 2048
+```