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初始化项目，由ModelHub XC社区提供模型 Model: BeastyZ/e5-R-mistral-7b Source: Original Platform
2026-05-14 14:05:02 +08:00
---
library_name: transformers
license: apache-2.0
datasets:
- BeastyZ/E5-R
language:
- en
model-index:
- name: e5-R-mistral-7b
results:
- dataset:
config: default
name: MTEB ArguAna
revision: None
split: test
type: mteb/arguana
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:
config: default
name: MTEB CQADupstackRetrieval
revision: None
split: test
type: mteb/cqadupstack
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: 46.593999999999997
- 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: 46.593999999999997
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: None
split: test
type: mteb/climate-fever
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:
config: default
name: MTEB DBPedia
revision: None
split: test
type: mteb/dbpedia
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:
config: default
name: MTEB FEVER
revision: None
split: test
type: mteb/fever
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:
config: default
name: MTEB FiQA2018
revision: None
split: test
type: mteb/fiqa
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:
config: default
name: MTEB HotpotQA
revision: None
split: test
type: mteb/hotpotqa
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:
config: default
name: MTEB MSMARCO
revision: None
split: dev
type: mteb/msmarco
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:
config: default
name: MTEB NFCorpus
revision: None
split: test
type: mteb/nfcorpus
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:
config: default
name: MTEB NQ
revision: None
split: test
type: mteb/nq
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:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: mteb/quora
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:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: mteb/scidocs
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:
config: default
name: MTEB SciFact
revision: None
split: test
type: mteb/scifact
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:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: mteb/trec-covid
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:
config: default
name: MTEB Touche2020
revision: None
split: test
type: mteb/touche2020
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
task:
type: Retrieval
tags:
- mteb
---
# Model Card for e5-R-mistral-7b
<!-- Provide a quick summary of what the model is/does. -->
## Model Description
<!-- Provide a longer summary of what this model is. -->
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).
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