BeastyZ_-_e5-R-mistral-7b-gguf

RichardErkhov/BeastyZ_-_e5-R-mistral-7b-gguf

BeastyZ_-_e5-R-mistral-7b-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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At a glance

Access: Public
Created: Sep 20, 2024
Updated: Sep 20, 2024
SHA: 1bc49b3e

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From the Hugging Face model README

Quantization made by Richard Erkhov.

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e5-R-mistral-7b - GGUF

Model creator: https://huggingface.co/BeastyZ/
Original model: https://huggingface.co/BeastyZ/e5-R-mistral-7b/

Name	Quant method	Size
e5-R-mistral-7b.Q2_K.gguf	Q2_K	2.53GB
e5-R-mistral-7b.IQ3_XS.gguf	IQ3_XS	2.81GB
e5-R-mistral-7b.IQ3_S.gguf	IQ3_S	2.96GB
e5-R-mistral-7b.Q3_K_S.gguf	Q3_K_S	2.95GB
e5-R-mistral-7b.IQ3_M.gguf	IQ3_M	3.06GB
e5-R-mistral-7b.Q3_K.gguf	Q3_K	3.28GB
e5-R-mistral-7b.Q3_K_M.gguf	Q3_K_M	3.28GB
e5-R-mistral-7b.Q3_K_L.gguf	Q3_K_L	3.56GB
e5-R-mistral-7b.IQ4_XS.gguf	IQ4_XS	3.67GB
e5-R-mistral-7b.Q4_0.gguf	Q4_0	3.83GB
e5-R-mistral-7b.IQ4_NL.gguf	IQ4_NL	3.87GB
e5-R-mistral-7b.Q4_K_S.gguf	Q4_K_S	3.86GB
e5-R-mistral-7b.Q4_K.gguf	Q4_K	4.07GB
e5-R-mistral-7b.Q4_K_M.gguf	Q4_K_M	4.07GB
e5-R-mistral-7b.Q4_1.gguf	Q4_1	4.24GB
e5-R-mistral-7b.Q5_0.gguf	Q5_0	4.65GB
e5-R-mistral-7b.Q5_K_S.gguf	Q5_K_S	4.65GB
e5-R-mistral-7b.Q5_K.gguf	Q5_K	4.78GB
e5-R-mistral-7b.Q5_K_M.gguf	Q5_K_M	4.78GB
e5-R-mistral-7b.Q5_1.gguf	Q5_1	5.07GB
e5-R-mistral-7b.Q6_K.gguf	Q6_K	5.53GB
e5-R-mistral-7b.Q8_0.gguf	Q8_0	7.17GB

Original model description:

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

Model Description

e5-R-mistral-7b is a LLM retriever fine-tuned from mistralai/Mistral-7B-v0.1.

Model type: CausalLM
Repository: Welcome to our GitHub repository to obtain code
Training dataset: Dataset used for fine-tuning e5-R-mistral-7b is available here.

Created: Sep 20, 2024
Updated: Sep 20, 2024