SFR-Embedding-2_R / README.md
yliu279's picture
Add proper Sentence Transformers integration (#1)
3388895 verified
metadata
tags:
  - mteb
  - sentence-transformers
  - transformers
model-index:
  - name: Salesforce/SFR-Embedding-2_R
    results:
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en)
          config: en
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 92.71641791044776
          - type: ap
            value: 69.47931007147756
          - type: f1
            value: 88.0252625393374
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 97.31075
          - type: ap
            value: 96.26693923450127
          - type: f1
            value: 97.31042448894502
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 61.040000000000006
          - type: f1
            value: 60.78646832640785
      - task:
          type: Retrieval
        dataset:
          type: mteb/arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
        metrics:
          - type: map_at_1
            value: 37.767
          - type: map_at_10
            value: 53.908
          - type: map_at_100
            value: 54.583000000000006
          - type: map_at_1000
            value: 54.583999999999996
          - type: map_at_20
            value: 54.50899999999999
          - type: map_at_3
            value: 49.514
          - type: map_at_5
            value: 52.059999999999995
          - type: mrr_at_1
            value: 38.26458036984353
          - type: mrr_at_10
            value: 54.120408001987066
          - type: mrr_at_100
            value: 54.780719904297406
          - type: mrr_at_1000
            value: 54.78174226698592
          - type: mrr_at_20
            value: 54.706604527160295
          - type: mrr_at_3
            value: 49.71550497866294
          - type: mrr_at_5
            value: 52.247510668563436
          - type: ndcg_at_1
            value: 37.767
          - type: ndcg_at_10
            value: 62.339999999999996
          - type: ndcg_at_100
            value: 64.89399999999999
          - type: ndcg_at_1000
            value: 64.914
          - type: ndcg_at_20
            value: 64.402
          - type: ndcg_at_3
            value: 53.33
          - type: ndcg_at_5
            value: 57.93899999999999
          - type: precision_at_1
            value: 37.767
          - type: precision_at_10
            value: 8.905000000000001
          - type: precision_at_100
            value: 0.9950000000000001
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_20
            value: 4.8469999999999995
          - type: precision_at_3
            value: 21.456
          - type: precision_at_5
            value: 15.121
          - type: recall_at_1
            value: 37.767
          - type: recall_at_10
            value: 89.047
          - type: recall_at_100
            value: 99.502
          - type: recall_at_1000
            value: 99.644
          - type: recall_at_20
            value: 96.942
          - type: recall_at_3
            value: 64.36699999999999
          - type: recall_at_5
            value: 75.605
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 54.024325012036314
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 48.817300846601675
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 66.71478959728732
          - type: mrr
            value: 79.07202216066482
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 88.79517914982239
          - type: cos_sim_spearman
            value: 87.60440576436838
          - type: euclidean_pearson
            value: 87.75596873521118
          - type: euclidean_spearman
            value: 87.60440576436838
          - type: manhattan_pearson
            value: 87.74113773865973
          - type: manhattan_spearman
            value: 87.50560833247899
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 90.02272727272727
          - type: f1
            value: 89.96681880265936
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 50.75930389699286
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 46.57286439805565
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
        metrics:
          - type: map_at_1
            value: 28.056666666666665
          - type: map_at_10
            value: 39.61749999999999
          - type: map_at_100
            value: 41.00666666666666
          - type: map_at_1000
            value: 41.11358333333334
          - type: map_at_20
            value: 40.410250000000005
          - type: map_at_3
            value: 35.98591666666667
          - type: map_at_5
            value: 38.02
          - type: mrr_at_1
            value: 33.73950708467142
          - type: mrr_at_10
            value: 44.0987162763402
          - type: mrr_at_100
            value: 44.94302678553521
          - type: mrr_at_1000
            value: 44.98758207055161
          - type: mrr_at_20
            value: 44.61156907536121
          - type: mrr_at_3
            value: 41.247253732468415
          - type: mrr_at_5
            value: 42.84859071071954
          - type: ndcg_at_1
            value: 33.739666666666665
          - type: ndcg_at_10
            value: 46.10683333333334
          - type: ndcg_at_100
            value: 51.49275000000001
          - type: ndcg_at_1000
            value: 53.2585
          - type: ndcg_at_20
            value: 48.349
          - type: ndcg_at_3
            value: 40.12416666666667
          - type: ndcg_at_5
            value: 42.94783333333333
          - type: precision_at_1
            value: 33.739666666666665
          - type: precision_at_10
            value: 8.46025
          - type: precision_at_100
            value: 1.3215833333333333
          - type: precision_at_1000
            value: 0.16524999999999998
          - type: precision_at_20
            value: 4.9935833333333335
          - type: precision_at_3
            value: 19.00516666666667
          - type: precision_at_5
            value: 13.72141666666667
          - type: recall_at_1
            value: 28.056666666666665
          - type: recall_at_10
            value: 60.68825000000001
          - type: recall_at_100
            value: 83.74433333333334
          - type: recall_at_1000
            value: 95.62299999999999
          - type: recall_at_20
            value: 68.77641666666668
          - type: recall_at_3
            value: 44.06991666666667
          - type: recall_at_5
            value: 51.324999999999996
      - task:
          type: Retrieval
        dataset:
          type: mteb/climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
        metrics:
          - type: map_at_1
            value: 15.609
          - type: map_at_10
            value: 25.584
          - type: map_at_100
            value: 27.291999999999998
          - type: map_at_1000
            value: 27.471
          - type: map_at_20
            value: 26.497
          - type: map_at_3
            value: 21.61
          - type: map_at_5
            value: 23.76
          - type: mrr_at_1
            value: 34.98371335504886
          - type: mrr_at_10
            value: 45.73747479447807
          - type: mrr_at_100
            value: 46.4973410206458
          - type: mrr_at_1000
            value: 46.53372527933685
          - type: mrr_at_20
            value: 46.19978503202757
          - type: mrr_at_3
            value: 42.85559174809991
          - type: mrr_at_5
            value: 44.65038002171556
          - type: ndcg_at_1
            value: 34.984
          - type: ndcg_at_10
            value: 34.427
          - type: ndcg_at_100
            value: 40.908
          - type: ndcg_at_1000
            value: 44.118
          - type: ndcg_at_20
            value: 36.885
          - type: ndcg_at_3
            value: 29.09
          - type: ndcg_at_5
            value: 30.979
          - type: precision_at_1
            value: 34.984
          - type: precision_at_10
            value: 10.476
          - type: precision_at_100
            value: 1.748
          - type: precision_at_1000
            value: 0.23500000000000001
          - type: precision_at_20
            value: 6.313000000000001
          - type: precision_at_3
            value: 21.39
          - type: precision_at_5
            value: 16.378
          - type: recall_at_1
            value: 15.609
          - type: recall_at_10
            value: 39.619
          - type: recall_at_100
            value: 61.952
          - type: recall_at_1000
            value: 79.861
          - type: recall_at_20
            value: 46.489000000000004
          - type: recall_at_3
            value: 26.134
          - type: recall_at_5
            value: 31.955
      - task:
          type: Retrieval
        dataset:
          type: mteb/dbpedia
          name: MTEB DBPedia
          config: default
          split: test
          revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
        metrics:
          - type: map_at_1
            value: 10.482
          - type: map_at_10
            value: 25.155
          - type: map_at_100
            value: 36.606
          - type: map_at_1000
            value: 38.617000000000004
          - type: map_at_20
            value: 29.676000000000002
          - type: map_at_3
            value: 16.881
          - type: map_at_5
            value: 20.043
          - type: mrr_at_1
            value: 76
          - type: mrr_at_10
            value: 82.5610119047619
          - type: mrr_at_100
            value: 82.74795937825128
          - type: mrr_at_1000
            value: 82.75526942226163
          - type: mrr_at_20
            value: 82.70580357142858
          - type: mrr_at_3
            value: 81.41666666666667
          - type: mrr_at_5
            value: 82.26666666666667
          - type: ndcg_at_1
            value: 63.625
          - type: ndcg_at_10
            value: 51.214000000000006
          - type: ndcg_at_100
            value: 56.411
          - type: ndcg_at_1000
            value: 63.429
          - type: ndcg_at_20
            value: 50.595
          - type: ndcg_at_3
            value: 54.989
          - type: ndcg_at_5
            value: 52.589
          - type: precision_at_1
            value: 76
          - type: precision_at_10
            value: 41.975
          - type: precision_at_100
            value: 13.26
          - type: precision_at_1000
            value: 2.493
          - type: precision_at_20
            value: 32.15
          - type: precision_at_3
            value: 59
          - type: precision_at_5
            value: 51.24999999999999
          - type: recall_at_1
            value: 10.482
          - type: recall_at_10
            value: 31.075000000000003
          - type: recall_at_100
            value: 63.119
          - type: recall_at_1000
            value: 85.32300000000001
          - type: recall_at_20
            value: 40.345
          - type: recall_at_3
            value: 17.916
          - type: recall_at_5
            value: 22.475
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 93.36500000000001
          - type: f1
            value: 89.89541440183861
      - task:
          type: Retrieval
        dataset:
          type: mteb/fever
          name: MTEB FEVER
          config: default
          split: test
          revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
        metrics:
          - type: map_at_1
            value: 81.948
          - type: map_at_10
            value: 89.47500000000001
          - type: map_at_100
            value: 89.66199999999999
          - type: map_at_1000
            value: 89.671
          - type: map_at_20
            value: 89.582
          - type: map_at_3
            value: 88.646
          - type: map_at_5
            value: 89.19
          - type: mrr_at_1
            value: 88.23882388238825
          - type: mrr_at_10
            value: 93.2122736083131
          - type: mrr_at_100
            value: 93.23908769526588
          - type: mrr_at_1000
            value: 93.23932393435209
          - type: mrr_at_20
            value: 93.23217832106207
          - type: mrr_at_3
            value: 92.98679867986787
          - type: mrr_at_5
            value: 93.16906690669056
          - type: ndcg_at_1
            value: 88.239
          - type: ndcg_at_10
            value: 92.155
          - type: ndcg_at_100
            value: 92.735
          - type: ndcg_at_1000
            value: 92.866
          - type: ndcg_at_20
            value: 92.39699999999999
          - type: ndcg_at_3
            value: 91.188
          - type: ndcg_at_5
            value: 91.754
          - type: precision_at_1
            value: 88.239
          - type: precision_at_10
            value: 10.903
          - type: precision_at_100
            value: 1.147
          - type: precision_at_1000
            value: 0.117
          - type: precision_at_20
            value: 5.5440000000000005
          - type: precision_at_3
            value: 34.598
          - type: precision_at_5
            value: 21.302
          - type: recall_at_1
            value: 81.948
          - type: recall_at_10
            value: 96.518
          - type: recall_at_100
            value: 98.646
          - type: recall_at_1000
            value: 99.399
          - type: recall_at_20
            value: 97.262
          - type: recall_at_3
            value: 93.89800000000001
          - type: recall_at_5
            value: 95.38600000000001
      - task:
          type: Retrieval
        dataset:
          type: mteb/fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: 27a168819829fe9bcd655c2df245fb19452e8e06
        metrics:
          - type: map_at_1
            value: 32.033
          - type: map_at_10
            value: 53.55
          - type: map_at_100
            value: 55.672
          - type: map_at_1000
            value: 55.764
          - type: map_at_20
            value: 54.87800000000001
          - type: map_at_3
            value: 46.761
          - type: map_at_5
            value: 50.529
          - type: mrr_at_1
            value: 60.95679012345679
          - type: mrr_at_10
            value: 68.70835782872815
          - type: mrr_at_100
            value: 69.21918402444501
          - type: mrr_at_1000
            value: 69.23608783148705
          - type: mrr_at_20
            value: 69.07497388036454
          - type: mrr_at_3
            value: 66.76954732510285
          - type: mrr_at_5
            value: 67.95781893004109
          - type: ndcg_at_1
            value: 60.956999999999994
          - type: ndcg_at_10
            value: 61.766
          - type: ndcg_at_100
            value: 67.652
          - type: ndcg_at_1000
            value: 68.94500000000001
          - type: ndcg_at_20
            value: 64.48700000000001
          - type: ndcg_at_3
            value: 57.25
          - type: ndcg_at_5
            value: 58.64
          - type: precision_at_1
            value: 60.956999999999994
          - type: precision_at_10
            value: 17.083000000000002
          - type: precision_at_100
            value: 2.346
          - type: precision_at_1000
            value: 0.257
          - type: precision_at_20
            value: 9.807
          - type: precision_at_3
            value: 38.477
          - type: precision_at_5
            value: 27.962999999999997
          - type: recall_at_1
            value: 32.033
          - type: recall_at_10
            value: 69.44
          - type: recall_at_100
            value: 90.17500000000001
          - type: recall_at_1000
            value: 97.90100000000001
          - type: recall_at_20
            value: 77.629
          - type: recall_at_3
            value: 51.664
          - type: recall_at_5
            value: 59.565
      - task:
          type: Retrieval
        dataset:
          type: mteb/hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: ab518f4d6fcca38d87c25209f94beba119d02014
        metrics:
          - type: map_at_1
            value: 42.741
          - type: map_at_10
            value: 74.811
          - type: map_at_100
            value: 75.508
          - type: map_at_1000
            value: 75.541
          - type: map_at_20
            value: 75.25699999999999
          - type: map_at_3
            value: 71.31
          - type: map_at_5
            value: 73.69
          - type: mrr_at_1
            value: 85.48278190411884
          - type: mrr_at_10
            value: 90.20347684425987
          - type: mrr_at_100
            value: 90.29734129342121
          - type: mrr_at_1000
            value: 90.30017606259217
          - type: mrr_at_20
            value: 90.27225310310567
          - type: mrr_at_3
            value: 89.67364393427842
          - type: mrr_at_5
            value: 90.02408282691847
          - type: ndcg_at_1
            value: 85.483
          - type: ndcg_at_10
            value: 81.361
          - type: ndcg_at_100
            value: 83.588
          - type: ndcg_at_1000
            value: 84.19
          - type: ndcg_at_20
            value: 82.42699999999999
          - type: ndcg_at_3
            value: 76.779
          - type: ndcg_at_5
            value: 79.581
          - type: precision_at_1
            value: 85.483
          - type: precision_at_10
            value: 17.113
          - type: precision_at_100
            value: 1.882
          - type: precision_at_1000
            value: 0.196
          - type: precision_at_20
            value: 8.899
          - type: precision_at_3
            value: 50.397999999999996
          - type: precision_at_5
            value: 32.443
          - type: recall_at_1
            value: 42.741
          - type: recall_at_10
            value: 85.564
          - type: recall_at_100
            value: 94.07799999999999
          - type: recall_at_1000
            value: 97.995
          - type: recall_at_20
            value: 88.98700000000001
          - type: recall_at_3
            value: 75.598
          - type: recall_at_5
            value: 81.107
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 96.80320000000002
          - type: ap
            value: 94.98856145360044
          - type: f1
            value: 96.80287885839178
      - task:
          type: Retrieval
        dataset:
          type: mteb/msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: c5a29a104738b98a9e76336939199e264163d4a0
        metrics:
          - type: map_at_1
            value: 22.539
          - type: map_at_10
            value: 35.109
          - type: map_at_100
            value: 36.287000000000006
          - type: map_at_1000
            value: 36.335
          - type: map_at_20
            value: 35.838
          - type: map_at_3
            value: 31.11
          - type: map_at_5
            value: 33.455
          - type: mrr_at_1
            value: 23.15186246418338
          - type: mrr_at_10
            value: 35.70532018920268
          - type: mrr_at_100
            value: 36.815167506137584
          - type: mrr_at_1000
            value: 36.85695349443505
          - type: mrr_at_20
            value: 36.39500867880642
          - type: mrr_at_3
            value: 31.81232091690535
          - type: mrr_at_5
            value: 34.096704871060155
          - type: ndcg_at_1
            value: 23.152
          - type: ndcg_at_10
            value: 42.181999999999995
          - type: ndcg_at_100
            value: 47.847
          - type: ndcg_at_1000
            value: 48.988
          - type: ndcg_at_20
            value: 44.767
          - type: ndcg_at_3
            value: 34.088
          - type: ndcg_at_5
            value: 38.257999999999996
          - type: precision_at_1
            value: 23.152
          - type: precision_at_10
            value: 6.678000000000001
          - type: precision_at_100
            value: 0.9530000000000001
          - type: precision_at_1000
            value: 0.105
          - type: precision_at_20
            value: 3.881
          - type: precision_at_3
            value: 14.518
          - type: precision_at_5
            value: 10.831
          - type: recall_at_1
            value: 22.539
          - type: recall_at_10
            value: 63.965
          - type: recall_at_100
            value: 90.129
          - type: recall_at_1000
            value: 98.721
          - type: recall_at_20
            value: 74.00999999999999
          - type: recall_at_3
            value: 42.004999999999995
          - type: recall_at_5
            value: 52.028
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 98.5750113999088
          - type: f1
            value: 98.41576079230245
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 91.29502963976289
          - type: f1
            value: 74.84400169335184
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 85.96839273705447
          - type: f1
            value: 82.43129186593926
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (en)
          config: en
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 90.60860793544047
          - type: f1
            value: 89.79415994859477
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 46.661892807041355
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 44.17598473858937
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
        metrics:
          - type: map
            value: 31.260919294024603
          - type: mrr
            value: 32.37049108835034
      - task:
          type: Retrieval
        dataset:
          type: mteb/nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
        metrics:
          - type: map_at_1
            value: 6.672000000000001
          - type: map_at_10
            value: 15.972
          - type: map_at_100
            value: 20.94
          - type: map_at_1000
            value: 22.877
          - type: map_at_20
            value: 17.986
          - type: map_at_3
            value: 11.161
          - type: map_at_5
            value: 13.293
          - type: mrr_at_1
            value: 53.56037151702786
          - type: mrr_at_10
            value: 61.915696103002595
          - type: mrr_at_100
            value: 62.4130902631107
          - type: mrr_at_1000
            value: 62.45228087711845
          - type: mrr_at_20
            value: 62.1983715004112
          - type: mrr_at_3
            value: 60.31991744066049
          - type: mrr_at_5
            value: 61.27966976264191
          - type: ndcg_at_1
            value: 50.929
          - type: ndcg_at_10
            value: 41.336
          - type: ndcg_at_100
            value: 38.586999999999996
          - type: ndcg_at_1000
            value: 48.155
          - type: ndcg_at_20
            value: 38.888
          - type: ndcg_at_3
            value: 47
          - type: ndcg_at_5
            value: 44.335
          - type: precision_at_1
            value: 53.251000000000005
          - type: precision_at_10
            value: 31.146
          - type: precision_at_100
            value: 10.040000000000001
          - type: precision_at_1000
            value: 2.432
          - type: precision_at_20
            value: 23.421
          - type: precision_at_3
            value: 45.098
          - type: precision_at_5
            value: 39.071
          - type: recall_at_1
            value: 6.672000000000001
          - type: recall_at_10
            value: 20.764
          - type: recall_at_100
            value: 40.759
          - type: recall_at_1000
            value: 75.015
          - type: recall_at_20
            value: 25.548
          - type: recall_at_3
            value: 12.328
          - type: recall_at_5
            value: 15.601999999999999
      - task:
          type: Retrieval
        dataset:
          type: mteb/nq
          name: MTEB NQ
          config: default
          split: test
          revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
        metrics:
          - type: map_at_1
            value: 50.944
          - type: map_at_10
            value: 67.565
          - type: map_at_100
            value: 68.10300000000001
          - type: map_at_1000
            value: 68.109
          - type: map_at_20
            value: 67.973
          - type: map_at_3
            value: 64.176
          - type: map_at_5
            value: 66.39699999999999
          - type: mrr_at_1
            value: 57.01042873696408
          - type: mrr_at_10
            value: 69.76629605105849
          - type: mrr_at_100
            value: 70.09927347130204
          - type: mrr_at_1000
            value: 70.10309675839956
          - type: mrr_at_20
            value: 70.02288627712392
          - type: mrr_at_3
            value: 67.46813441483191
          - type: mrr_at_5
            value: 68.93105446118189
          - type: ndcg_at_1
            value: 57.010000000000005
          - type: ndcg_at_10
            value: 73.956
          - type: ndcg_at_100
            value: 75.90299999999999
          - type: ndcg_at_1000
            value: 76.03999999999999
          - type: ndcg_at_20
            value: 75.17
          - type: ndcg_at_3
            value: 68.13900000000001
          - type: ndcg_at_5
            value: 71.532
          - type: precision_at_1
            value: 57.010000000000005
          - type: precision_at_10
            value: 10.91
          - type: precision_at_100
            value: 1.2
          - type: precision_at_1000
            value: 0.121
          - type: precision_at_20
            value: 5.753
          - type: precision_at_3
            value: 29.828
          - type: precision_at_5
            value: 19.971
          - type: recall_at_1
            value: 50.944
          - type: recall_at_10
            value: 90.754
          - type: recall_at_100
            value: 98.699
          - type: recall_at_1000
            value: 99.701
          - type: recall_at_20
            value: 95.148
          - type: recall_at_3
            value: 76.224
          - type: recall_at_5
            value: 83.872
      - task:
          type: Retrieval
        dataset:
          type: mteb/quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
        metrics:
          - type: map_at_1
            value: 71.856
          - type: map_at_10
            value: 86.077
          - type: map_at_100
            value: 86.696
          - type: map_at_1000
            value: 86.708
          - type: map_at_20
            value: 86.493
          - type: map_at_3
            value: 83.176
          - type: map_at_5
            value: 85.008
          - type: mrr_at_1
            value: 82.74000000000001
          - type: mrr_at_10
            value: 88.68947222222207
          - type: mrr_at_100
            value: 88.78196949571182
          - type: mrr_at_1000
            value: 88.78223256200576
          - type: mrr_at_20
            value: 88.76455636228219
          - type: mrr_at_3
            value: 87.85833333333316
          - type: mrr_at_5
            value: 88.43933333333311
          - type: ndcg_at_1
            value: 82.74000000000001
          - type: ndcg_at_10
            value: 89.583
          - type: ndcg_at_100
            value: 90.652
          - type: ndcg_at_1000
            value: 90.711
          - type: ndcg_at_20
            value: 90.203
          - type: ndcg_at_3
            value: 86.967
          - type: ndcg_at_5
            value: 88.43299999999999
          - type: precision_at_1
            value: 82.74000000000001
          - type: precision_at_10
            value: 13.617
          - type: precision_at_100
            value: 1.542
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_20
            value: 7.217999999999999
          - type: precision_at_3
            value: 38.163000000000004
          - type: precision_at_5
            value: 25.05
          - type: recall_at_1
            value: 71.856
          - type: recall_at_10
            value: 96.244
          - type: recall_at_100
            value: 99.773
          - type: recall_at_1000
            value: 99.99900000000001
          - type: recall_at_20
            value: 98.221
          - type: recall_at_3
            value: 88.715
          - type: recall_at_5
            value: 92.88499999999999
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 62.91969510127886
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
        metrics:
          - type: v_measure
            value: 72.74201090913765
      - task:
          type: Retrieval
        dataset:
          type: mteb/scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
        metrics:
          - type: map_at_1
            value: 5.8229999999999995
          - type: map_at_10
            value: 15.152
          - type: map_at_100
            value: 17.936
          - type: map_at_1000
            value: 18.292
          - type: map_at_20
            value: 16.526
          - type: map_at_3
            value: 10.294
          - type: map_at_5
            value: 12.794
          - type: mrr_at_1
            value: 28.599999999999998
          - type: mrr_at_10
            value: 40.68206349206347
          - type: mrr_at_100
            value: 41.673752995361795
          - type: mrr_at_1000
            value: 41.71500072915374
          - type: mrr_at_20
            value: 41.28552805166964
          - type: mrr_at_3
            value: 36.84999999999998
          - type: mrr_at_5
            value: 39.19999999999995
          - type: ndcg_at_1
            value: 28.599999999999998
          - type: ndcg_at_10
            value: 24.866
          - type: ndcg_at_100
            value: 34.597
          - type: ndcg_at_1000
            value: 39.994
          - type: ndcg_at_20
            value: 28.309
          - type: ndcg_at_3
            value: 22.749
          - type: ndcg_at_5
            value: 20.502000000000002
          - type: precision_at_1
            value: 28.599999999999998
          - type: precision_at_10
            value: 13.089999999999998
          - type: precision_at_100
            value: 2.7119999999999997
          - type: precision_at_1000
            value: 0.39899999999999997
          - type: precision_at_20
            value: 8.53
          - type: precision_at_3
            value: 21.099999999999998
          - type: precision_at_5
            value: 18.22
          - type: recall_at_1
            value: 5.8229999999999995
          - type: recall_at_10
            value: 26.522000000000002
          - type: recall_at_100
            value: 55.003
          - type: recall_at_1000
            value: 80.977
          - type: recall_at_20
            value: 34.618
          - type: recall_at_3
            value: 12.848
          - type: recall_at_5
            value: 18.477
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
        metrics:
          - type: cos_sim_pearson
            value: 80.72562067620224
          - type: cos_sim_spearman
            value: 77.00710192931953
          - type: euclidean_pearson
            value: 78.65843289108192
          - type: euclidean_spearman
            value: 77.00710077709005
          - type: manhattan_pearson
            value: 78.48859522905846
          - type: manhattan_spearman
            value: 76.8213740840866
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 81.15015325911659
          - type: cos_sim_spearman
            value: 75.67268325741222
          - type: euclidean_pearson
            value: 75.54004763633206
          - type: euclidean_spearman
            value: 75.67262179635058
          - type: manhattan_pearson
            value: 75.80681616893116
          - type: manhattan_spearman
            value: 75.93721016401406
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 81.71651874476737
          - type: cos_sim_spearman
            value: 82.39667472464997
          - type: euclidean_pearson
            value: 82.28256504757712
          - type: euclidean_spearman
            value: 82.39663674872656
          - type: manhattan_pearson
            value: 82.3192873176068
          - type: manhattan_spearman
            value: 82.41915252757059
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 81.222967367593
          - type: cos_sim_spearman
            value: 79.92685877403252
          - type: euclidean_pearson
            value: 79.95053542861498
          - type: euclidean_spearman
            value: 79.9268858850991
          - type: manhattan_pearson
            value: 79.90485851323321
          - type: manhattan_spearman
            value: 79.93878025669312
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 85.27539130156643
          - type: cos_sim_spearman
            value: 85.81645767911826
          - type: euclidean_pearson
            value: 85.5488615685444
          - type: euclidean_spearman
            value: 85.81647022566916
          - type: manhattan_pearson
            value: 85.6358149547879
          - type: manhattan_spearman
            value: 85.96347118567043
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 83.43727336154858
          - type: cos_sim_spearman
            value: 84.50468882202796
          - type: euclidean_pearson
            value: 83.23576727105372
          - type: euclidean_spearman
            value: 84.50468882202796
          - type: manhattan_pearson
            value: 83.28843314503176
          - type: manhattan_spearman
            value: 84.60383766214322
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (en-en)
          config: en-en
          split: test
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
        metrics:
          - type: cos_sim_pearson
            value: 88.86589365166874
          - type: cos_sim_spearman
            value: 88.93117996163835
          - type: euclidean_pearson
            value: 89.12271565981082
          - type: euclidean_spearman
            value: 88.93117996163835
          - type: manhattan_pearson
            value: 88.94419759325545
          - type: manhattan_spearman
            value: 88.63073561731899
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (en)
          config: en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 67.96578378422929
          - type: cos_sim_spearman
            value: 67.10257461424345
          - type: euclidean_pearson
            value: 67.51317866195149
          - type: euclidean_spearman
            value: 67.10257461424345
          - type: manhattan_pearson
            value: 67.74940912013754
          - type: manhattan_spearman
            value: 67.46694183937207
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 83.55433725920493
          - type: cos_sim_spearman
            value: 83.60373857254014
          - type: euclidean_pearson
            value: 83.08086082334839
          - type: euclidean_spearman
            value: 83.6036864776559
          - type: manhattan_pearson
            value: 83.2232267589246
          - type: manhattan_spearman
            value: 83.78923946962664
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 87.28566757174322
          - type: mrr
            value: 96.63827639317836
      - task:
          type: Retrieval
        dataset:
          type: mteb/scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
        metrics:
          - type: map_at_1
            value: 70.661
          - type: map_at_10
            value: 82.051
          - type: map_at_100
            value: 82.162
          - type: map_at_1000
            value: 82.167
          - type: map_at_20
            value: 82.122
          - type: map_at_3
            value: 79.919
          - type: map_at_5
            value: 81.368
          - type: mrr_at_1
            value: 74.33333333333333
          - type: mrr_at_10
            value: 82.98452380952381
          - type: mrr_at_100
            value: 83.09512420633841
          - type: mrr_at_1000
            value: 83.10026279387446
          - type: mrr_at_20
            value: 83.05460927960928
          - type: mrr_at_3
            value: 81.8888888888889
          - type: mrr_at_5
            value: 82.65555555555557
          - type: ndcg_at_1
            value: 74.333
          - type: ndcg_at_10
            value: 85.914
          - type: ndcg_at_100
            value: 86.473
          - type: ndcg_at_1000
            value: 86.602
          - type: ndcg_at_20
            value: 86.169
          - type: ndcg_at_3
            value: 83.047
          - type: ndcg_at_5
            value: 84.72
          - type: precision_at_1
            value: 74.333
          - type: precision_at_10
            value: 10.933
          - type: precision_at_100
            value: 1.1199999999999999
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_20
            value: 5.5169999999999995
          - type: precision_at_3
            value: 32.444
          - type: precision_at_5
            value: 20.8
          - type: recall_at_1
            value: 70.661
          - type: recall_at_10
            value: 96.333
          - type: recall_at_100
            value: 99
          - type: recall_at_1000
            value: 100
          - type: recall_at_20
            value: 97.333
          - type: recall_at_3
            value: 88.64999999999999
          - type: recall_at_5
            value: 93.089
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.89108910891089
          - type: cos_sim_ap
            value: 97.61815451002174
          - type: cos_sim_f1
            value: 94.51097804391219
          - type: cos_sim_precision
            value: 94.32270916334662
          - type: cos_sim_recall
            value: 94.69999999999999
          - type: dot_accuracy
            value: 99.89108910891089
          - type: dot_ap
            value: 97.61815451002175
          - type: dot_f1
            value: 94.51097804391219
          - type: dot_precision
            value: 94.32270916334662
          - type: dot_recall
            value: 94.69999999999999
          - type: euclidean_accuracy
            value: 99.89108910891089
          - type: euclidean_ap
            value: 97.61815534251431
          - type: euclidean_f1
            value: 94.51097804391219
          - type: euclidean_precision
            value: 94.32270916334662
          - type: euclidean_recall
            value: 94.69999999999999
          - type: manhattan_accuracy
            value: 99.8940594059406
          - type: manhattan_ap
            value: 97.66124472227202
          - type: manhattan_f1
            value: 94.65267366316841
          - type: manhattan_precision
            value: 94.60539460539461
          - type: manhattan_recall
            value: 94.69999999999999
          - type: max_accuracy
            value: 99.8940594059406
          - type: max_ap
            value: 97.66124472227202
          - type: max_f1
            value: 94.65267366316841
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 76.482776391195
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 48.29023235124473
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 55.3190739691685
          - type: mrr
            value: 56.40441972243442
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 31.98570594378664
          - type: cos_sim_spearman
            value: 30.712965330802174
          - type: dot_pearson
            value: 31.98570540209124
          - type: dot_spearman
            value: 30.712965330802174
      - task:
          type: Retrieval
        dataset:
          type: mteb/trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
        metrics:
          - type: map_at_1
            value: 0.25
          - type: map_at_10
            value: 2.2640000000000002
          - type: map_at_100
            value: 14.447
          - type: map_at_1000
            value: 35.452
          - type: map_at_20
            value: 4.163
          - type: map_at_3
            value: 0.715
          - type: map_at_5
            value: 1.1780000000000002
          - type: mrr_at_1
            value: 94
          - type: mrr_at_10
            value: 96.66666666666667
          - type: mrr_at_100
            value: 96.66666666666667
          - type: mrr_at_1000
            value: 96.66666666666667
          - type: mrr_at_20
            value: 96.66666666666667
          - type: mrr_at_3
            value: 96.66666666666667
          - type: mrr_at_5
            value: 96.66666666666667
          - type: ndcg_at_1
            value: 92
          - type: ndcg_at_10
            value: 87.26899999999999
          - type: ndcg_at_100
            value: 68.586
          - type: ndcg_at_1000
            value: 61.056999999999995
          - type: ndcg_at_20
            value: 83.452
          - type: ndcg_at_3
            value: 90.11200000000001
          - type: ndcg_at_5
            value: 89.103
          - type: precision_at_1
            value: 94
          - type: precision_at_10
            value: 91.2
          - type: precision_at_100
            value: 70.12
          - type: precision_at_1000
            value: 26.773999999999997
          - type: precision_at_20
            value: 87.3
          - type: precision_at_3
            value: 92.667
          - type: precision_at_5
            value: 92.4
          - type: recall_at_1
            value: 0.25
          - type: recall_at_10
            value: 2.3970000000000002
          - type: recall_at_100
            value: 17.233999999999998
          - type: recall_at_1000
            value: 57.879000000000005
          - type: recall_at_20
            value: 4.508
          - type: recall_at_3
            value: 0.734
          - type: recall_at_5
            value: 1.2269999999999999
      - task:
          type: Retrieval
        dataset:
          type: mteb/touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
        metrics:
          - type: map_at_1
            value: 2.806
          - type: map_at_10
            value: 11.369
          - type: map_at_100
            value: 17.791
          - type: map_at_1000
            value: 19.363
          - type: map_at_20
            value: 14.038999999999998
          - type: map_at_3
            value: 5.817
          - type: map_at_5
            value: 8.331
          - type: mrr_at_1
            value: 36.734693877551024
          - type: mrr_at_10
            value: 53.355199222546155
          - type: mrr_at_100
            value: 53.648197984932665
          - type: mrr_at_1000
            value: 53.648197984932665
          - type: mrr_at_20
            value: 53.500971817298336
          - type: mrr_at_3
            value: 48.63945578231292
          - type: mrr_at_5
            value: 51.29251700680272
          - type: ndcg_at_1
            value: 35.714
          - type: ndcg_at_10
            value: 28.18
          - type: ndcg_at_100
            value: 39.22
          - type: ndcg_at_1000
            value: 50.807
          - type: ndcg_at_20
            value: 28.979
          - type: ndcg_at_3
            value: 31.114000000000004
          - type: ndcg_at_5
            value: 29.687
          - type: precision_at_1
            value: 36.735
          - type: precision_at_10
            value: 24.898
          - type: precision_at_100
            value: 7.918
          - type: precision_at_1000
            value: 1.5779999999999998
          - type: precision_at_20
            value: 18.878
          - type: precision_at_3
            value: 31.293
          - type: precision_at_5
            value: 29.387999999999998
          - type: recall_at_1
            value: 2.806
          - type: recall_at_10
            value: 17.776
          - type: recall_at_100
            value: 49.41
          - type: recall_at_1000
            value: 84.97200000000001
          - type: recall_at_20
            value: 26.589000000000002
          - type: recall_at_3
            value: 6.866999999999999
          - type: recall_at_5
            value: 10.964
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
        metrics:
          - type: accuracy
            value: 91.1376953125
          - type: ap
            value: 40.51219896084815
          - type: f1
            value: 77.5195445434559
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 79.69722693831352
          - type: f1
            value: 80.02969178591319
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 66.42427742893598
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 87.81069321094355
          - type: cos_sim_ap
            value: 78.57014017906349
          - type: cos_sim_f1
            value: 72.38883143743536
          - type: cos_sim_precision
            value: 70.95793208312215
          - type: cos_sim_recall
            value: 73.87862796833772
          - type: dot_accuracy
            value: 87.81069321094355
          - type: dot_ap
            value: 78.5701399541226
          - type: dot_f1
            value: 72.38883143743536
          - type: dot_precision
            value: 70.95793208312215
          - type: dot_recall
            value: 73.87862796833772
          - type: euclidean_accuracy
            value: 87.81069321094355
          - type: euclidean_ap
            value: 78.57015336777854
          - type: euclidean_f1
            value: 72.38883143743536
          - type: euclidean_precision
            value: 70.95793208312215
          - type: euclidean_recall
            value: 73.87862796833772
          - type: manhattan_accuracy
            value: 87.57227156225785
          - type: manhattan_ap
            value: 78.19109731614216
          - type: manhattan_f1
            value: 71.87819856704198
          - type: manhattan_precision
            value: 69.77148534525584
          - type: manhattan_recall
            value: 74.1160949868074
          - type: max_accuracy
            value: 87.81069321094355
          - type: max_ap
            value: 78.57015336777854
          - type: max_f1
            value: 72.38883143743536
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 89.95032405790352
          - type: cos_sim_ap
            value: 88.03104739249996
          - type: cos_sim_f1
            value: 80.34377190070451
          - type: cos_sim_precision
            value: 77.11534376548892
          - type: cos_sim_recall
            value: 83.85432707114259
          - type: dot_accuracy
            value: 89.95032405790352
          - type: dot_ap
            value: 88.03105328515932
          - type: dot_f1
            value: 80.34377190070451
          - type: dot_precision
            value: 77.11534376548892
          - type: dot_recall
            value: 83.85432707114259
          - type: euclidean_accuracy
            value: 89.95032405790352
          - type: euclidean_ap
            value: 88.03105084564575
          - type: euclidean_f1
            value: 80.34377190070451
          - type: euclidean_precision
            value: 77.11534376548892
          - type: euclidean_recall
            value: 83.85432707114259
          - type: manhattan_accuracy
            value: 89.88046726433035
          - type: manhattan_ap
            value: 88.01484191858279
          - type: manhattan_f1
            value: 80.34005593993817
          - type: manhattan_precision
            value: 76.95290468133108
          - type: manhattan_recall
            value: 84.03911302740991
          - type: max_accuracy
            value: 89.95032405790352
          - type: max_ap
            value: 88.03105328515932
          - type: max_f1
            value: 80.34377190070451
language:
  - en
license: cc-by-nc-4.0

Salesforce/SFR-Embedding-2_R

SFR-Embedding by Salesforce Research.

The model is for research purposes only.

More technical details will be updated later. Meanwhile, please refer to our previous work SFR-Embedding for details.

SFR-Embedding Team (∗indicates equal contributors, † indicates co-leaders).

  • Rui Meng*
  • Ye Liu*
  • Tong Niu
  • Shafiq Rayhan Joty
  • Caiming Xiong †
  • Yingbo Zhou †
  • Semih Yavuz †

Citation

@misc{SFR-embedding-2,
  title={SFR-Embedding-2: Advanced Text Embedding with Multi-stage Training},
  author={Rui Meng*, Ye Liu*, Shafiq Rayhan Joty, Caiming Xiong, Yingbo Zhou, Semih Yavuz},
  year={2024},
  url={https://huggingface.co/Salesforce/SFR-Embedding-2_R}
}

How to run

Transformers

The models can be used as follows:

import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel

def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]

def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'

# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
    get_detailed_instruct(task, 'How to bake a chocolate cake'),
    get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
    "To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
    "The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]

# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Salesforce/SFR-Embedding-2_R')
model = AutoModel.from_pretrained('Salesforce/SFR-Embedding-2_R')

# get the embeddings
max_length = 4096
input_texts = queries + passages
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[40.132083892822266, 25.032529830932617], [15.006855010986328, 39.93733215332031]]

Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Salesforce/SFR-Embedding-2_R")

def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'

# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
    get_detailed_instruct(task, 'How to bake a chocolate cake'),
    get_detailed_instruct(task, 'Symptoms of the flu')
]
# No need to add instruction for retrieval documents
passages = [
    "To bake a delicious chocolate cake, you'll need the following ingredients: all-purpose flour, sugar, cocoa powder, baking powder, baking soda, salt, eggs, milk, vegetable oil, and vanilla extract. Start by preheating your oven to 350°F (175°C). In a mixing bowl, combine the dry ingredients (flour, sugar, cocoa powder, baking powder, baking soda, and salt). In a separate bowl, whisk together the wet ingredients (eggs, milk, vegetable oil, and vanilla extract). Gradually add the wet mixture to the dry ingredients, stirring until well combined. Pour the batter into a greased cake pan and bake for 30-35 minutes. Let it cool before frosting with your favorite chocolate frosting. Enjoy your homemade chocolate cake!",
    "The flu, or influenza, is an illness caused by influenza viruses. Common symptoms of the flu include a high fever, chills, cough, sore throat, runny or stuffy nose, body aches, headache, fatigue, and sometimes nausea and vomiting. These symptoms can come on suddenly and are usually more severe than the common cold. It's important to get plenty of rest, stay hydrated, and consult a healthcare professional if you suspect you have the flu. In some cases, antiviral medications can help alleviate symptoms and reduce the duration of the illness."
]

embeddings = model.encode(queries + passages)
scores = model.similarity(embeddings[:2], embeddings[2:]) * 100
print(scores.tolist())
# [[40.13203811645508, 25.032546997070312], [15.00684642791748, 39.937339782714844]]