---
tags:
- mteb
model-index:
- name: Salesforce/SFR-Embedding-2_R
  results:
  - task:
      type: Classification
    dataset:
      type: mteb/amazon_counterfactual
      name: MTEB AmazonCounterfactualClassification
      config: default
      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
      config: default
      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
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    - type: map_at_1000
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    - type: map_at_3
      value: 49.514
    - type: map_at_5
      value: 52.059999999999995
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      value: 38.26458036984353
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      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
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    - type: ndcg_at_1
      value: 37.767
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      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
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      value: 57.93899999999999
    - type: precision_at_1
      value: 37.767
    - type: precision_at_10
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    - type: precision_at_100
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    - type: precision_at_1000
      value: 0.1
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      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
    - type: nAUC_map_diff1
      value: 12.107502181210737
    - type: nAUC_map_max
      value: 26.88330114153595
    - type: nAUC_mrr_diff1
      value: 21.07548754737264
    - type: nAUC_mrr_max
      value: 43.7325419146008
  - 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-android
      name: MTEB CQADupstackAndroidRetrieval
      config: default
      split: test
      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
    metrics:
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      value: 28.056666666666665
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    - type: map_at_100
      value: 41.00666666666666
    - type: map_at_1000
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  - task:
      type: Retrieval
    dataset:
      type: mteb/climate-fever
      name: MTEB ClimateFEVER
      config: default
      split: test
      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
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  - task:
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  - task:
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  - task:
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  - task:
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  - task:
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  - task:
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  - task:
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  - task:
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      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
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  - task:
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      config: default
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      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
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  - task:
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      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
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  - task:
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  - task:
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      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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  - task:
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  - task:
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  - task:
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  - task:
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      config: default
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      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
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  - task:
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  - task:
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      config: default
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  - task:
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      name: MTEB STS22
      config: default
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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  - task:
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      name: MTEB STSBenchmark
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
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  - task:
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      type: mteb/scidocs-reranking
      name: MTEB SciDocsRR
      config: default
      split: test
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  - task:
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      name: MTEB SciFact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
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    - type: recall_at_1
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    - type: recall_at_1000
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    - type: recall_at_3
      value: 88.64999999999999
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  - task:
      type: PairClassification
    dataset:
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      name: MTEB SprintDuplicateQuestions
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
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    - type: cos_sim_f1
      value: 94.51097804391219
    - type: cos_sim_precision
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    - type: cos_sim_recall
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    - type: dot_accuracy
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    - type: dot_ap
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    - type: dot_f1
      value: 94.51097804391219
    - type: dot_precision
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    - type: dot_recall
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    - type: euclidean_accuracy
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    - type: euclidean_ap
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    - type: euclidean_f1
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    - type: euclidean_precision
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    - type: euclidean_recall
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    - type: manhattan_accuracy
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    - type: manhattan_f1
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    - type: manhattan_precision
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    - type: max_accuracy
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  - task:
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      name: MTEB StackExchangeClustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
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  - task:
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    dataset:
      type: mteb/stackexchange-clustering-p2p
      name: MTEB StackExchangeClusteringP2P
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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  - task:
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    dataset:
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      name: MTEB StackOverflowDupQuestions
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
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  - task:
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      name: MTEB SummEval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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      value: 30.712965330802174
  - task:
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    dataset:
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      name: MTEB TRECCOVID
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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    - type: map_at_100
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      value: 0.715
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  - task:
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    dataset:
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      name: MTEB Touche2020
      config: default
      split: test
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      value: 15.425473480157507
    - type: nauc_mrr_at_1000_max
      value: -2.1811857816236877
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      value: 15.425473480157507
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      value: -2.1811857816236877
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      value: 15.903075429674777
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      value: -2.2906761318729316
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      value: 15.745291718170254
    - type: nauc_mrr_at_1_max
      value: 1.3133671227650265
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      value: 15.55498078589214
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      value: 12.884821567496274
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      value: 7.729725342433009
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      value: 2.2605086242588257
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      value: 3.4608738145262774
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      value: 3.3909557257855236
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      value: 12.077987725517026
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      value: 28.18
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      value: 31.114000000000004
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      value: 18.878
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      value: 31.293
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      value: 29.387999999999998
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      value: 2.806
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      value: 17.776
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      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
---

<h1 align="center">Salesforce/SFR-Embedding-2_R</h1>

**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](https://blog.salesforceairesearch.com/sfr-embedded-mistral/) for details.


SFR-Embedding Team (∗indicates equal contributors, † indicates co-leaders).
* Rui Meng*
* Ye Liu*
* Shafiq Rayhan Joty
* Caiming Xiong †
* Yingbo Zhou †
* Semih Yavuz †

### Citation
```bibtex
@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:
```python
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
```python

from sentence_transformers import SentenceTransformer, util

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 = util.cos_sim(embeddings[:2], embeddings[2:]) * 100
print(scores.tolist())
# [[40.132083892822266, 25.032529830932617], [15.006855010986328, 39.93733215332031]]
```