README.md

metadata

model-index:
  - name: jasper_en_vision_language_v1
    results:
      - dataset:
          config: en-ext
          name: MTEB AmazonCounterfactualClassification (en-ext)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 95.7271
          - type: f1
            value: 89.25450000000001
          - type: f1_weighted
            value: 95.8563
          - type: ap
            value: 67.1563
          - type: ap_weighted
            value: 67.1563
          - type: main_score
            value: 95.7271
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 93.7761
          - type: f1
            value: 90.7582
          - type: f1_weighted
            value: 93.974
          - type: ap
            value: 74.88759999999999
          - type: ap_weighted
            value: 74.88759999999999
          - type: main_score
            value: 93.7761
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification (default)
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
        metrics:
          - type: accuracy
            value: 97.5809
          - type: f1
            value: 97.5808
          - type: f1_weighted
            value: 97.5808
          - type: ap
            value: 96.3911
          - type: ap_weighted
            value: 96.3911
          - type: main_score
            value: 97.5809
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 62.918
          - type: f1
            value: 60.696099999999994
          - type: f1_weighted
            value: 60.696099999999994
          - type: main_score
            value: 62.918
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ArguAna (default)
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
          split: test
          type: mteb/arguana
        metrics:
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            value: 41.323
          - type: ndcg_at_3
            value: 56.737
          - type: ndcg_at_5
            value: 61.254
          - type: ndcg_at_10
            value: 65.204
          - type: ndcg_at_20
            value: 66.75
          - type: ndcg_at_100
            value: 67.254
          - type: ndcg_at_1000
            value: 67.254
          - type: map_at_1
            value: 41.323
          - type: map_at_3
            value: 52.881
          - type: map_at_5
            value: 55.373000000000005
          - type: map_at_10
            value: 57.013999999999996
          - type: map_at_20
            value: 57.457
          - type: map_at_100
            value: 57.538
          - type: map_at_1000
            value: 57.538
          - type: recall_at_1
            value: 41.323
          - type: recall_at_3
            value: 67.923
          - type: recall_at_5
            value: 78.947
          - type: recall_at_10
            value: 91.11
          - type: recall_at_20
            value: 97.084
          - type: recall_at_100
            value: 99.644
          - type: recall_at_1000
            value: 99.644
          - type: precision_at_1
            value: 41.323
          - type: precision_at_3
            value: 22.641
          - type: precision_at_5
            value: 15.789
          - type: precision_at_10
            value: 9.110999999999999
          - type: precision_at_20
            value: 4.854
          - type: precision_at_100
            value: 0.996
          - type: precision_at_1000
            value: 0.1
          - type: mrr_at_1
            value: 41.6785
          - type: mrr_at_3
            value: 52.963499999999996
          - type: mrr_at_5
            value: 55.456399999999995
          - type: mrr_at_10
            value: 57.104299999999995
          - type: mrr_at_20
            value: 57.5402
          - type: mrr_at_100
            value: 57.6213
          - type: mrr_at_1000
            value: 57.6213
          - type: nauc_ndcg_at_1_max
            value: -12.633600000000001
          - type: nauc_ndcg_at_1_std
            value: -19.747700000000002
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            value: 25.820300000000003
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            value: -8.480799999999999
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            value: -18.928900000000002
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            value: 19.409499999999998
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            value: -7.9172
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            value: -19.2306
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            value: 18.5809
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            value: -9.7812
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            value: -19.9964
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            value: 18.0753
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            value: -8.6546
          - type: nauc_ndcg_at_20_std
            value: -19.3205
          - type: nauc_ndcg_at_20_diff1
            value: 20.1741
          - type: nauc_ndcg_at_100_max
            value: -9.376900000000001
          - type: nauc_ndcg_at_100_std
            value: -19.5093
          - type: nauc_ndcg_at_100_diff1
            value: 20.4997
          - type: nauc_ndcg_at_1000_max
            value: -9.376900000000001
          - type: nauc_ndcg_at_1000_std
            value: -19.5093
          - type: nauc_ndcg_at_1000_diff1
            value: 20.4997
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            value: -12.633600000000001
          - type: nauc_map_at_1_std
            value: -19.747700000000002
          - type: nauc_map_at_1_diff1
            value: 25.820300000000003
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            value: -19.3235
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            value: 20.9083
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            value: -19.4593
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            value: -19.6759
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            value: -9.8837
          - type: nauc_map_at_100_std
            value: -19.741400000000002
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            value: 21.067700000000002
          - type: nauc_map_at_1000_max
            value: -9.8837
          - type: nauc_map_at_1000_std
            value: -19.741400000000002
          - type: nauc_map_at_1000_diff1
            value: 21.067700000000002
          - type: nauc_recall_at_1_max
            value: -12.633600000000001
          - type: nauc_recall_at_1_std
            value: -19.747700000000002
          - type: nauc_recall_at_1_diff1
            value: 25.820300000000003
          - type: nauc_recall_at_3_max
            value: -4.7886999999999995
          - type: nauc_recall_at_3_std
            value: -17.4764
          - type: nauc_recall_at_3_diff1
            value: 14.3308
          - type: nauc_recall_at_5_max
            value: -1.4803
          - type: nauc_recall_at_5_std
            value: -18.2137
          - type: nauc_recall_at_5_diff1
            value: 9.4604
          - type: nauc_recall_at_10_max
            value: -8.8102
          - type: nauc_recall_at_10_std
            value: -21.8279
          - type: nauc_recall_at_10_diff1
            value: -5.5388
          - type: nauc_recall_at_20_max
            value: 25.202400000000004
          - type: nauc_recall_at_20_std
            value: -4.8613
          - type: nauc_recall_at_20_diff1
            value: 9.3917
          - type: nauc_recall_at_100_max
            value: 37.0551
          - type: nauc_recall_at_100_std
            value: 84.5472
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            value: 19.5116
          - type: nauc_recall_at_1000_max
            value: 37.0551
          - type: nauc_recall_at_1000_std
            value: 84.5472
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            value: 19.5116
          - type: nauc_precision_at_1_max
            value: -12.633600000000001
          - type: nauc_precision_at_1_std
            value: -19.747700000000002
          - type: nauc_precision_at_1_diff1
            value: 25.820300000000003
          - type: nauc_precision_at_3_max
            value: -4.7886999999999995
          - type: nauc_precision_at_3_std
            value: -17.4764
          - type: nauc_precision_at_3_diff1
            value: 14.3308
          - type: nauc_precision_at_5_max
            value: -1.4803
          - type: nauc_precision_at_5_std
            value: -18.2137
          - type: nauc_precision_at_5_diff1
            value: 9.4604
          - type: nauc_precision_at_10_max
            value: -8.8102
          - type: nauc_precision_at_10_std
            value: -21.8279
          - type: nauc_precision_at_10_diff1
            value: -5.5388
          - type: nauc_precision_at_20_max
            value: 25.202400000000004
          - type: nauc_precision_at_20_std
            value: -4.8613
          - type: nauc_precision_at_20_diff1
            value: 9.3917
          - type: nauc_precision_at_100_max
            value: 37.0551
          - type: nauc_precision_at_100_std
            value: 84.5472
          - type: nauc_precision_at_100_diff1
            value: 19.5116
          - type: nauc_precision_at_1000_max
            value: 37.0551
          - type: nauc_precision_at_1000_std
            value: 84.5472
          - type: nauc_precision_at_1000_diff1
            value: 19.5116
          - type: nauc_mrr_at_1_max
            value: -11.9728
          - type: nauc_mrr_at_1_std
            value: -19.4014
          - type: nauc_mrr_at_1_diff1
            value: 24.8653
          - type: nauc_mrr_at_3_max
            value: -9.6607
          - type: nauc_mrr_at_3_std
            value: -19.1819
          - type: nauc_mrr_at_3_diff1
            value: 20.0205
          - type: nauc_mrr_at_5_max
            value: -9.4261
          - type: nauc_mrr_at_5_std
            value: -19.3098
          - type: nauc_mrr_at_5_diff1
            value: 19.6347
          - type: nauc_mrr_at_10_max
            value: -10.1698
          - type: nauc_mrr_at_10_std
            value: -19.683
          - type: nauc_mrr_at_10_diff1
            value: 19.4823
          - type: nauc_mrr_at_20_max
            value: -9.913
          - type: nauc_mrr_at_20_std
            value: -19.517300000000002
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            value: 20.0066
          - type: nauc_mrr_at_100_max
            value: -10.0053
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            value: -19.5824
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            value: 20.061899999999998
          - type: nauc_mrr_at_1000_max
            value: -10.0053
          - type: nauc_mrr_at_1000_std
            value: -19.5824
          - type: nauc_mrr_at_1000_diff1
            value: 20.061899999999998
          - type: main_score
            value: 65.204
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P (default)
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 55.710300000000004
          - type: v_measure_std
            value: 14.551900000000002
          - type: main_score
            value: 55.710300000000004
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S (default)
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
          split: test
          type: mteb/arxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 51.004599999999996
          - type: v_measure_std
            value: 14.868899999999998
          - type: main_score
            value: 51.004599999999996
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions (default)
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
          split: test
          type: mteb/askubuntudupquestions-reranking
        metrics:
          - type: map
            value: 67.38119999999999
          - type: mrr
            value: 79.0692
          - type: nAUC_map_max
            value: 21.9757
          - type: nAUC_map_std
            value: 14.3545
          - type: nAUC_map_diff1
            value: 11.82
          - type: nAUC_mrr_max
            value: 40.3449
          - type: nAUC_mrr_std
            value: 18.2678
          - type: nAUC_mrr_diff1
            value: 21.1596
          - type: main_score
            value: 67.38119999999999
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB BIOSSES (default)
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: pearson
            value: 87.4332
          - type: spearman
            value: 84.65979999999999
          - type: cosine_pearson
            value: 87.4332
          - type: cosine_spearman
            value: 84.65979999999999
          - type: manhattan_pearson
            value: 86.37270000000001
          - type: manhattan_spearman
            value: 85.20309999999999
          - type: euclidean_pearson
            value: 86.1672
          - type: euclidean_spearman
            value: 84.7453
          - type: main_score
            value: 84.65979999999999
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB Banking77Classification (default)
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
          split: test
          type: mteb/banking77
        metrics:
          - type: accuracy
            value: 87.224
          - type: f1
            value: 86.7235
          - type: f1_weighted
            value: 86.7235
          - type: main_score
            value: 87.224
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BiorxivClusteringP2P (default)
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
          split: test
          type: mteb/biorxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 52.04990000000001
          - type: v_measure_std
            value: 0.7255
          - type: main_score
            value: 52.04990000000001
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB BiorxivClusteringS2S (default)
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
          split: test
          type: mteb/biorxiv-clustering-s2s
        metrics:
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            value: 47.5811
          - type: v_measure_std
            value: 0.8049000000000001
          - type: main_score
            value: 47.5811
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CQADupstackAndroidRetrieval (default)
          revision: f46a197baaae43b4f621051089b82a364682dfeb
          split: test
          type: mteb/cqadupstack-android
        metrics:
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            value: 54.371
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          - type: ndcg_at_1000
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            value: 46.005
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            value: 48.32
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            value: 50.181
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            value: 51.062
          - type: map_at_100
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            value: 53.94
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          - type: recall_at_100
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          - type: recall_at_1000
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          - type: precision_at_1000
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      - dataset:
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          name: MTEB MTOPDomainClassification (en)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
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        task:
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          name: MTEB MTOPIntentClassification (en)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
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        task:
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      - dataset:
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          name: MTEB MassiveIntentClassification (en)
          revision: 4672e20407010da34463acc759c162ca9734bca6
          split: test
          type: mteb/amazon_massive_intent
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        task:
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      - dataset:
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          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
          split: test
          type: mteb/amazon_massive_scenario
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      - dataset:
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          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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          type: mteb/medrxiv-clustering-p2p
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        task:
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      - dataset:
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          name: MTEB MedrxivClusteringS2S (default)
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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          type: mteb/medrxiv-clustering-s2s
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        task:
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      - dataset:
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          name: MTEB MindSmallReranking (default)
          revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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          type: mteb/mind_small
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          name: MTEB NFCorpus (default)
          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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          type: mteb/nfcorpus
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      - dataset:
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          name: MTEB RedditClustering (default)
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
          split: test
          type: mteb/reddit-clustering
        metrics:
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          - type: v_measure_std
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          - type: main_score
            value: 73.0223
        task:
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      - dataset:
          config: default
          name: MTEB RedditClusteringP2P (default)
          revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
          split: test
          type: mteb/reddit-clustering-p2p
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          - type: main_score
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        task:
          type: Clustering
      - dataset:
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          name: MTEB SCIDOCS (default)
          revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
          split: test
          type: mteb/scidocs
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            value: 18.5224
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        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SICK-R (default)
          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
          split: test
          type: mteb/sickr-sts
        metrics:
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          - type: spearman
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          - type: cosine_pearson
            value: 86.8981
          - type: cosine_spearman
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          - type: manhattan_pearson
            value: 84.1165
          - type: manhattan_spearman
            value: 82.063
          - type: euclidean_pearson
            value: 84.0688
          - type: euclidean_spearman
            value: 82.2329
          - type: main_score
            value: 82.234
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS12 (default)
          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
        metrics:
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          - type: spearman
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          - type: cosine_pearson
            value: 87.8269
          - type: cosine_spearman
            value: 80.2578
          - type: manhattan_pearson
            value: 85.09020000000001
          - type: manhattan_spearman
            value: 80.5777
          - type: euclidean_pearson
            value: 84.71990000000001
          - type: euclidean_spearman
            value: 80.2547
          - type: main_score
            value: 80.2578
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS13 (default)
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
        metrics:
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            value: 89.45769999999999
          - type: spearman
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          - type: cosine_pearson
            value: 89.45769999999999
          - type: cosine_spearman
            value: 89.69200000000001
          - type: manhattan_pearson
            value: 89.1685
          - type: manhattan_spearman
            value: 89.8889
          - type: euclidean_pearson
            value: 89.07969999999999
          - type: euclidean_spearman
            value: 89.7003
          - type: main_score
            value: 89.69200000000001
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS14 (default)
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
        metrics:
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            value: 87.01989999999999
          - type: spearman
            value: 85.3155
          - type: cosine_pearson
            value: 87.01989999999999
          - type: cosine_spearman
            value: 85.3155
          - type: manhattan_pearson
            value: 86.24159999999999
          - type: manhattan_spearman
            value: 85.544
          - type: euclidean_pearson
            value: 86.1032
          - type: euclidean_spearman
            value: 85.31490000000001
          - type: main_score
            value: 85.3155
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS15 (default)
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
        metrics:
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            value: 89.593
          - type: spearman
            value: 90.1574
          - type: cosine_pearson
            value: 89.593
          - type: cosine_spearman
            value: 90.1574
          - type: manhattan_pearson
            value: 89.93910000000001
          - type: manhattan_spearman
            value: 90.4357
          - type: euclidean_pearson
            value: 89.76650000000001
          - type: euclidean_spearman
            value: 90.1554
          - type: main_score
            value: 90.1574
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS16 (default)
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
        metrics:
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            value: 85.03540000000001
          - type: spearman
            value: 86.4191
          - type: cosine_pearson
            value: 85.03540000000001
          - type: cosine_spearman
            value: 86.4191
          - type: manhattan_pearson
            value: 85.8992
          - type: manhattan_spearman
            value: 86.7274
          - type: euclidean_pearson
            value: 85.638
          - type: euclidean_spearman
            value: 86.42
          - type: main_score
            value: 86.4191
        task:
          type: STS
      - dataset:
          config: en-ar
          name: MTEB STS17 (en-ar)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
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          - type: spearman
            value: 52.721
          - type: cosine_pearson
            value: 54.1098
          - type: cosine_spearman
            value: 52.721
          - type: manhattan_pearson
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          - type: manhattan_spearman
            value: 52.727900000000005
          - type: euclidean_pearson
            value: 54.65410000000001
          - type: euclidean_spearman
            value: 52.8582
          - type: main_score
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        task:
          type: STS
      - dataset:
          config: fr-en
          name: MTEB STS17 (fr-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
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          - type: spearman
            value: 84.1421
          - type: cosine_pearson
            value: 84.3262
          - type: cosine_spearman
            value: 84.1421
          - type: manhattan_pearson
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          - type: manhattan_spearman
            value: 83.9987
          - type: euclidean_pearson
            value: 84.0174
          - type: euclidean_spearman
            value: 84.1711
          - type: main_score
            value: 84.1421
        task:
          type: STS
      - dataset:
          config: en-en
          name: MTEB STS17 (en-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
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            value: 91.38969999999999
          - type: spearman
            value: 91.00789999999999
          - type: cosine_pearson
            value: 91.38969999999999
          - type: cosine_spearman
            value: 91.00789999999999
          - type: manhattan_pearson
            value: 90.8917
          - type: manhattan_spearman
            value: 90.7834
          - type: euclidean_pearson
            value: 91.0361
          - type: euclidean_spearman
            value: 91.011
          - type: main_score
            value: 91.00789999999999
        task:
          type: STS
      - dataset:
          config: nl-en
          name: MTEB STS17 (nl-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 84.636
          - type: spearman
            value: 84.1977
          - type: cosine_pearson
            value: 84.636
          - type: cosine_spearman
            value: 84.1977
          - type: manhattan_pearson
            value: 84.6895
          - type: manhattan_spearman
            value: 84.1849
          - type: euclidean_pearson
            value: 84.753
          - type: euclidean_spearman
            value: 84.2251
          - type: main_score
            value: 84.1977
        task:
          type: STS
      - dataset:
          config: es-en
          name: MTEB STS17 (es-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 85.8595
          - type: spearman
            value: 86.9965
          - type: cosine_pearson
            value: 85.8595
          - type: cosine_spearman
            value: 86.9965
          - type: manhattan_pearson
            value: 86.92450000000001
          - type: manhattan_spearman
            value: 87.39489999999999
          - type: euclidean_pearson
            value: 86.5575
          - type: euclidean_spearman
            value: 86.997
          - type: main_score
            value: 86.9965
        task:
          type: STS
      - dataset:
          config: it-en
          name: MTEB STS17 (it-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 85.959
          - type: spearman
            value: 86.3103
          - type: cosine_pearson
            value: 85.959
          - type: cosine_spearman
            value: 86.3103
          - type: manhattan_pearson
            value: 85.7486
          - type: manhattan_spearman
            value: 86.0977
          - type: euclidean_pearson
            value: 85.8361
          - type: euclidean_spearman
            value: 86.2741
          - type: main_score
            value: 86.3103
        task:
          type: STS
      - dataset:
          config: en-de
          name: MTEB STS17 (en-de)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 86.2629
          - type: spearman
            value: 85.8372
          - type: cosine_pearson
            value: 86.2629
          - type: cosine_spearman
            value: 85.8372
          - type: manhattan_pearson
            value: 86.2491
          - type: manhattan_spearman
            value: 85.4973
          - type: euclidean_pearson
            value: 86.5074
          - type: euclidean_spearman
            value: 85.83370000000001
          - type: main_score
            value: 85.8372
        task:
          type: STS
      - dataset:
          config: en-tr
          name: MTEB STS17 (en-tr)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 57.5675
          - type: spearman
            value: 55.564899999999994
          - type: cosine_pearson
            value: 57.5675
          - type: cosine_spearman
            value: 55.564899999999994
          - type: manhattan_pearson
            value: 57.4686
          - type: manhattan_spearman
            value: 55.3816
          - type: euclidean_pearson
            value: 57.635099999999994
          - type: euclidean_spearman
            value: 55.501900000000006
          - type: main_score
            value: 55.564899999999994
        task:
          type: STS
      - dataset:
          config: pl-en
          name: MTEB STS22 (pl-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 76.1237
          - type: spearman
            value: 76.4
          - type: cosine_pearson
            value: 76.1237
          - type: cosine_spearman
            value: 76.4
          - type: manhattan_pearson
            value: 78.6794
          - type: manhattan_spearman
            value: 77.2282
          - type: euclidean_pearson
            value: 77.1688
          - type: euclidean_spearman
            value: 76.4
          - type: main_score
            value: 76.4
        task:
          type: STS
      - dataset:
          config: es-en
          name: MTEB STS22 (es-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 73.89099999999999
          - type: spearman
            value: 77.8292
          - type: cosine_pearson
            value: 73.89099999999999
          - type: cosine_spearman
            value: 77.8292
          - type: manhattan_pearson
            value: 77.53229999999999
          - type: manhattan_spearman
            value: 78.3926
          - type: euclidean_pearson
            value: 76.6734
          - type: euclidean_spearman
            value: 77.8721
          - type: main_score
            value: 77.8292
        task:
          type: STS
      - dataset:
          config: en
          name: MTEB STS22 (en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 68.31099999999999
          - type: spearman
            value: 68.0764
          - type: cosine_pearson
            value: 68.31099999999999
          - type: cosine_spearman
            value: 68.0764
          - type: manhattan_pearson
            value: 69.7423
          - type: manhattan_spearman
            value: 68.3212
          - type: euclidean_pearson
            value: 69.5701
          - type: euclidean_spearman
            value: 68.2299
          - type: main_score
            value: 68.0764
        task:
          type: STS
      - dataset:
          config: de-en
          name: MTEB STS22 (de-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 65.0636
          - type: spearman
            value: 59.4669
          - type: cosine_pearson
            value: 65.0636
          - type: cosine_spearman
            value: 59.4669
          - type: manhattan_pearson
            value: 69.723
          - type: manhattan_spearman
            value: 60.3823
          - type: euclidean_pearson
            value: 68.6454
          - type: euclidean_spearman
            value: 59.384899999999995
          - type: main_score
            value: 59.4669
        task:
          type: STS
      - dataset:
          config: zh-en
          name: MTEB STS22 (zh-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 73.7603
          - type: spearman
            value: 73.0799
          - type: cosine_pearson
            value: 73.7603
          - type: cosine_spearman
            value: 73.0799
          - type: manhattan_pearson
            value: 76.2351
          - type: manhattan_spearman
            value: 73.60719999999999
          - type: euclidean_pearson
            value: 75.61529999999999
          - type: euclidean_spearman
            value: 72.9809
          - type: main_score
            value: 73.0799
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSBenchmark (default)
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
          split: test
          type: mteb/stsbenchmark-sts
        metrics:
          - type: pearson
            value: 88.1242
          - type: spearman
            value: 88.8456
          - type: cosine_pearson
            value: 88.1242
          - type: cosine_spearman
            value: 88.8456
          - type: manhattan_pearson
            value: 88.6122
          - type: manhattan_spearman
            value: 89.0198
          - type: euclidean_pearson
            value: 88.5314
          - type: euclidean_spearman
            value: 88.8432
          - type: main_score
            value: 88.8456
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SciDocsRR (default)
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
          split: test
          type: mteb/scidocs-reranking
        metrics:
          - type: map
            value: 89.06920000000001
          - type: mrr
            value: 96.977
          - type: nAUC_map_max
            value: 49.1778
          - type: nAUC_map_std
            value: 62.091499999999996
          - type: nAUC_map_diff1
            value: -13.373899999999999
          - type: nAUC_mrr_max
            value: 86.7325
          - type: nAUC_mrr_std
            value: 74.958
          - type: nAUC_mrr_diff1
            value: 25.6955
          - type: main_score
            value: 89.06920000000001
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SciFact (default)
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
          split: test
          type: mteb/scifact
        metrics:
          - type: ndcg_at_1
            value: 67.333
          - type: ndcg_at_3
            value: 75.142
          - type: ndcg_at_5
            value: 78.873
          - type: ndcg_at_10
            value: 80.372
          - type: ndcg_at_20
            value: 80.914
          - type: ndcg_at_100
            value: 81.563
          - type: ndcg_at_1000
            value: 81.601
          - type: map_at_1
            value: 63.99399999999999
          - type: map_at_3
            value: 72.181
          - type: map_at_5
            value: 74.665
          - type: map_at_10
            value: 75.51299999999999
          - type: map_at_20
            value: 75.693
          - type: map_at_100
            value: 75.78
          - type: map_at_1000
            value: 75.78099999999999
          - type: recall_at_1
            value: 63.99399999999999
          - type: recall_at_3
            value: 80.63900000000001
          - type: recall_at_5
            value: 90.017
          - type: recall_at_10
            value: 94.167
          - type: recall_at_20
            value: 96.167
          - type: recall_at_100
            value: 99.667
          - type: recall_at_1000
            value: 100
          - type: precision_at_1
            value: 67.333
          - type: precision_at_3
            value: 29.444
          - type: precision_at_5
            value: 20.067
          - type: precision_at_10
            value: 10.667
          - type: precision_at_20
            value: 5.45
          - type: precision_at_100
            value: 1.127
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: mrr_at_1
            value: 67.3333
          - type: mrr_at_3
            value: 73.9444
          - type: mrr_at_5
            value: 76.0444
          - type: mrr_at_10
            value: 76.4517
          - type: mrr_at_20
            value: 76.5739
          - type: mrr_at_100
            value: 76.6556
          - type: mrr_at_1000
            value: 76.6563
          - type: nauc_ndcg_at_1_max
            value: 55.373099999999994
          - type: nauc_ndcg_at_1_std
            value: -11.362
          - type: nauc_ndcg_at_1_diff1
            value: 81.5232
          - type: nauc_ndcg_at_3_max
            value: 58.0376
          - type: nauc_ndcg_at_3_std
            value: -14.4854
          - type: nauc_ndcg_at_3_diff1
            value: 77.848
          - type: nauc_ndcg_at_5_max
            value: 55.789699999999996
          - type: nauc_ndcg_at_5_std
            value: -15.0412
          - type: nauc_ndcg_at_5_diff1
            value: 77.7873
          - type: nauc_ndcg_at_10_max
            value: 58.6243
          - type: nauc_ndcg_at_10_std
            value: -13.867799999999999
          - type: nauc_ndcg_at_10_diff1
            value: 78.071
          - type: nauc_ndcg_at_20_max
            value: 58.9032
          - type: nauc_ndcg_at_20_std
            value: -12.7998
          - type: nauc_ndcg_at_20_diff1
            value: 78.12010000000001
          - type: nauc_ndcg_at_100_max
            value: 58.0772
          - type: nauc_ndcg_at_100_std
            value: -12.4604
          - type: nauc_ndcg_at_100_diff1
            value: 78.7002
          - type: nauc_ndcg_at_1000_max
            value: 58.0835
          - type: nauc_ndcg_at_1000_std
            value: -12.662799999999999
          - type: nauc_ndcg_at_1000_diff1
            value: 78.7157
          - type: nauc_map_at_1_max
            value: 45.2711
          - type: nauc_map_at_1_std
            value: -21.685
          - type: nauc_map_at_1_diff1
            value: 80.8536
          - type: nauc_map_at_3_max
            value: 53.970600000000005
          - type: nauc_map_at_3_std
            value: -18.0178
          - type: nauc_map_at_3_diff1
            value: 78.95309999999999
          - type: nauc_map_at_5_max
            value: 54.7643
          - type: nauc_map_at_5_std
            value: -15.943999999999999
          - type: nauc_map_at_5_diff1
            value: 78.9665
          - type: nauc_map_at_10_max
            value: 56.737899999999996
          - type: nauc_map_at_10_std
            value: -14.355599999999999
          - type: nauc_map_at_10_diff1
            value: 79.095
          - type: nauc_map_at_20_max
            value: 56.804
          - type: nauc_map_at_20_std
            value: -14.033599999999998
          - type: nauc_map_at_20_diff1
            value: 79.0729
          - type: nauc_map_at_100_max
            value: 56.7168
          - type: nauc_map_at_100_std
            value: -14.000000000000002
          - type: nauc_map_at_100_diff1
            value: 79.1456
          - type: nauc_map_at_1000_max
            value: 56.7168
          - type: nauc_map_at_1000_std
            value: -14.003099999999998
          - type: nauc_map_at_1000_diff1
            value: 79.1458
          - type: nauc_recall_at_1_max
            value: 45.2711
          - type: nauc_recall_at_1_std
            value: -21.685
          - type: nauc_recall_at_1_diff1
            value: 80.8536
          - type: nauc_recall_at_3_max
            value: 55.911100000000005
          - type: nauc_recall_at_3_std
            value: -19.1603
          - type: nauc_recall_at_3_diff1
            value: 72.51689999999999
          - type: nauc_recall_at_5_max
            value: 50.6317
          - type: nauc_recall_at_5_std
            value: -21.4004
          - type: nauc_recall_at_5_diff1
            value: 68.3824
          - type: nauc_recall_at_10_max
            value: 67.3176
          - type: nauc_recall_at_10_std
            value: -23.6601
          - type: nauc_recall_at_10_diff1
            value: 67.20150000000001
          - type: nauc_recall_at_20_max
            value: 76.566
          - type: nauc_recall_at_20_std
            value: -12.4873
          - type: nauc_recall_at_20_diff1
            value: 63.6849
          - type: nauc_recall_at_100_max
            value: 55.415499999999994
          - type: nauc_recall_at_100_std
            value: 72.2222
          - type: nauc_recall_at_100_diff1
            value: 72.2222
          - type: nauc_recall_at_1000_max
            value: .nan
          - type: nauc_recall_at_1000_std
            value: .nan
          - type: nauc_recall_at_1000_diff1
            value: .nan
          - type: nauc_precision_at_1_max
            value: 55.373099999999994
          - type: nauc_precision_at_1_std
            value: -11.362
          - type: nauc_precision_at_1_diff1
            value: 81.5232
          - type: nauc_precision_at_3_max
            value: 59.23969999999999
          - type: nauc_precision_at_3_std
            value: 17.3868
          - type: nauc_precision_at_3_diff1
            value: 37.0438
          - type: nauc_precision_at_5_max
            value: 51.358599999999996
          - type: nauc_precision_at_5_std
            value: 39.9722
          - type: nauc_precision_at_5_diff1
            value: 9.2927
          - type: nauc_precision_at_10_max
            value: 44.6166
          - type: nauc_precision_at_10_std
            value: 46.4231
          - type: nauc_precision_at_10_diff1
            value: -7.435700000000001
          - type: nauc_precision_at_20_max
            value: 41.1852
          - type: nauc_precision_at_20_std
            value: 51.89359999999999
          - type: nauc_precision_at_20_diff1
            value: -14.6318
          - type: nauc_precision_at_100_max
            value: 33.6188
          - type: nauc_precision_at_100_std
            value: 60.621700000000004
          - type: nauc_precision_at_100_diff1
            value: -26.376500000000004
          - type: nauc_precision_at_1000_max
            value: 33.2976
          - type: nauc_precision_at_1000_std
            value: 60.4508
          - type: nauc_precision_at_1000_diff1
            value: -27.829500000000003
          - type: nauc_mrr_at_1_max
            value: 55.373099999999994
          - type: nauc_mrr_at_1_std
            value: -11.362
          - type: nauc_mrr_at_1_diff1
            value: 81.5232
          - type: nauc_mrr_at_3_max
            value: 58.7918
          - type: nauc_mrr_at_3_std
            value: -11.580300000000001
          - type: nauc_mrr_at_3_diff1
            value: 78.79310000000001
          - type: nauc_mrr_at_5_max
            value: 58.13270000000001
          - type: nauc_mrr_at_5_std
            value: -11.2346
          - type: nauc_mrr_at_5_diff1
            value: 78.8896
          - type: nauc_mrr_at_10_max
            value: 58.4249
          - type: nauc_mrr_at_10_std
            value: -11.842600000000001
          - type: nauc_mrr_at_10_diff1
            value: 79.0629
          - type: nauc_mrr_at_20_max
            value: 58.4409
          - type: nauc_mrr_at_20_std
            value: -11.7012
          - type: nauc_mrr_at_20_diff1
            value: 79.1097
          - type: nauc_mrr_at_100_max
            value: 58.3685
          - type: nauc_mrr_at_100_std
            value: -11.6333
          - type: nauc_mrr_at_100_diff1
            value: 79.1871
          - type: nauc_mrr_at_1000_max
            value: 58.3686
          - type: nauc_mrr_at_1000_std
            value: -11.6365
          - type: nauc_mrr_at_1000_diff1
            value: 79.1874
          - type: main_score
            value: 80.372
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SprintDuplicateQuestions (default)
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
          split: test
          type: mteb/sprintduplicatequestions-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 99.8317
          - type: similarity_accuracy_threshold
            value: 87.7015
          - type: similarity_f1
            value: 91.2821
          - type: similarity_f1_threshold
            value: 87.68599999999999
          - type: similarity_precision
            value: 93.68419999999999
          - type: similarity_recall
            value: 89
          - type: similarity_ap
            value: 96.249
          - type: cosine_accuracy
            value: 99.8317
          - type: cosine_accuracy_threshold
            value: 87.7015
          - type: cosine_f1
            value: 91.2821
          - type: cosine_f1_threshold
            value: 87.68599999999999
          - type: cosine_precision
            value: 93.68419999999999
          - type: cosine_recall
            value: 89
          - type: cosine_ap
            value: 96.249
          - type: manhattan_accuracy
            value: 99.83370000000001
          - type: manhattan_accuracy_threshold
            value: 4133.4675
          - type: manhattan_f1
            value: 91.52369999999999
          - type: manhattan_f1_threshold
            value: 4244.392400000001
          - type: manhattan_precision
            value: 92.3625
          - type: manhattan_recall
            value: 90.7
          - type: manhattan_ap
            value: 96.4021
          - type: euclidean_accuracy
            value: 99.83070000000001
          - type: euclidean_accuracy_threshold
            value: 49.684200000000004
          - type: euclidean_f1
            value: 91.24419999999999
          - type: euclidean_f1_threshold
            value: 49.7175
          - type: euclidean_precision
            value: 93.4942
          - type: euclidean_recall
            value: 89.1
          - type: euclidean_ap
            value: 96.24589999999999
          - type: dot_accuracy
            value: 99.8277
          - type: dot_accuracy_threshold
            value: 87.3743
          - type: dot_f1
            value: 91.1495
          - type: dot_f1_threshold
            value: 87.3743
          - type: dot_precision
            value: 92.7536
          - type: dot_recall
            value: 89.60000000000001
          - type: dot_ap
            value: 96.23509999999999
          - type: max_accuracy
            value: 99.83370000000001
          - type: max_f1
            value: 91.52369999999999
          - type: max_precision
            value: 93.68419999999999
          - type: max_recall
            value: 90.7
          - type: max_ap
            value: 96.4021
          - type: main_score
            value: 96.4021
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB StackExchangeClustering (default)
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
          split: test
          type: mteb/stackexchange-clustering
        metrics:
          - type: v_measure
            value: 80.19420000000001
          - type: v_measure_std
            value: 2.8956
          - type: main_score
            value: 80.19420000000001
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackExchangeClusteringP2P (default)
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
          split: test
          type: mteb/stackexchange-clustering-p2p
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            value: 1.5938
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            value: 49.416900000000005
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          config: default
          name: MTEB StackOverflowDupQuestions (default)
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
          split: test
          type: mteb/stackoverflowdupquestions-reranking
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            value: 39.2785
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            value: 54.579
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          name: MTEB SummEval (default)
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
          split: test
          type: mteb/summeval
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          revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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      - dataset:
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          name: MTEB Touche2020 (default)
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
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      - dataset:
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          name: MTEB ToxicConversationsClassification (default)
          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
          split: test
          type: mteb/toxic_conversations_50k
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        task:
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      - dataset:
          config: default
          name: MTEB TweetSentimentExtractionClassification (default)
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
          split: test
          type: mteb/tweet_sentiment_extraction
        metrics:
          - type: accuracy
            value: 77.24109999999999
          - type: f1
            value: 77.4964
          - type: f1_weighted
            value: 77.0248
          - type: main_score
            value: 77.24109999999999
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TwentyNewsgroupsClustering (default)
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
          split: test
          type: mteb/twentynewsgroups-clustering
        metrics:
          - type: v_measure
            value: 63.132200000000005
          - type: v_measure_std
            value: 0.7163
          - type: main_score
            value: 63.132200000000005
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB TwitterSemEval2015 (default)
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
          split: test
          type: mteb/twittersemeval2015-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 88.061
          - type: similarity_accuracy_threshold
            value: 85.9546
          - type: similarity_f1
            value: 73.36240000000001
          - type: similarity_f1_threshold
            value: 84.0127
          - type: similarity_precision
            value: 71.47149999999999
          - type: similarity_recall
            value: 75.3562
          - type: similarity_ap
            value: 80.2787
          - type: cosine_accuracy
            value: 88.061
          - type: cosine_accuracy_threshold
            value: 85.9546
          - type: cosine_f1
            value: 73.36240000000001
          - type: cosine_f1_threshold
            value: 84.0127
          - type: cosine_precision
            value: 71.47149999999999
          - type: cosine_recall
            value: 75.3562
          - type: cosine_ap
            value: 80.2787
          - type: manhattan_accuracy
            value: 87.8941
          - type: manhattan_accuracy_threshold
            value: 4374.3263
          - type: manhattan_f1
            value: 72.929
          - type: manhattan_f1_threshold
            value: 4637.7289
          - type: manhattan_precision
            value: 71.3636
          - type: manhattan_recall
            value: 74.5646
          - type: manhattan_ap
            value: 79.98140000000001
          - type: euclidean_accuracy
            value: 88.0789
          - type: euclidean_accuracy_threshold
            value: 52.9742
          - type: euclidean_f1
            value: 73.3591
          - type: euclidean_f1_threshold
            value: 56.4815
          - type: euclidean_precision
            value: 71.608
          - type: euclidean_recall
            value: 75.19789999999999
          - type: euclidean_ap
            value: 80.2656
          - type: dot_accuracy
            value: 88.0789
          - type: dot_accuracy_threshold
            value: 85.8034
          - type: dot_f1
            value: 73.40379999999999
          - type: dot_f1_threshold
            value: 84.3213
          - type: dot_precision
            value: 72.6544
          - type: dot_recall
            value: 74.16890000000001
          - type: dot_ap
            value: 80.32010000000001
          - type: max_accuracy
            value: 88.0789
          - type: max_f1
            value: 73.40379999999999
          - type: max_precision
            value: 72.6544
          - type: max_recall
            value: 75.3562
          - type: max_ap
            value: 80.32010000000001
          - type: main_score
            value: 80.32010000000001
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB TwitterURLCorpus (default)
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
          split: test
          type: mteb/twitterurlcorpus-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 89.93090000000001
          - type: similarity_accuracy_threshold
            value: 82.1349
          - type: similarity_f1
            value: 80.1377
          - type: similarity_f1_threshold
            value: 80.5047
          - type: similarity_precision
            value: 77.1539
          - type: similarity_recall
            value: 83.3616
          - type: similarity_ap
            value: 87.6917
          - type: cosine_accuracy
            value: 89.93090000000001
          - type: cosine_accuracy_threshold
            value: 82.1349
          - type: cosine_f1
            value: 80.1377
          - type: cosine_f1_threshold
            value: 80.5047
          - type: cosine_precision
            value: 77.1539
          - type: cosine_recall
            value: 83.3616
          - type: cosine_ap
            value: 87.6917
          - type: manhattan_accuracy
            value: 89.86880000000001
          - type: manhattan_accuracy_threshold
            value: 4882.7347
          - type: manhattan_f1
            value: 80.2323
          - type: manhattan_f1_threshold
            value: 5185.1944
          - type: manhattan_precision
            value: 76.74889999999999
          - type: manhattan_recall
            value: 84.0468
          - type: manhattan_ap
            value: 87.70750000000001
          - type: euclidean_accuracy
            value: 89.94640000000001
          - type: euclidean_accuracy_threshold
            value: 59.9149
          - type: euclidean_f1
            value: 80.1527
          - type: euclidean_f1_threshold
            value: 62.3611
          - type: euclidean_precision
            value: 77.2744
          - type: euclidean_recall
            value: 83.2538
          - type: euclidean_ap
            value: 87.6922
          - type: dot_accuracy
            value: 89.9038
          - type: dot_accuracy_threshold
            value: 82.53049999999999
          - type: dot_f1
            value: 80.0969
          - type: dot_f1_threshold
            value: 80.7285
          - type: dot_precision
            value: 77.5853
          - type: dot_recall
            value: 82.77640000000001
          - type: dot_ap
            value: 87.668
          - type: max_accuracy
            value: 89.94640000000001
          - type: max_f1
            value: 80.2323
          - type: max_precision
            value: 77.5853
          - type: max_recall
            value: 84.0468
          - type: max_ap
            value: 87.70750000000001
          - type: main_score
            value: 87.70750000000001
        task:
          type: PairClassification
datasets:
  - BAAI/Infinity-MM
  - HuggingFaceFW/fineweb-edu
language:
  - en
base_model:
  - dunzhang/stella_en_1.5B_v5
  - google/siglip-so400m-patch14-384
tags:
  - mteb

Introduction

Based on dunzhang/stella_en_1.5B_v5 and google/siglip-so400m-patch14-384.

It can encode both text and image.

The training code, data and report will be released soon.

The core training code will be integrated into the rag-retrieval library(https://github.com/NLPJCL/RAG-Retrieval) in the near future. (Welcome to star)

This work was accomplished during my free time; please grant time a little time.

Usage

import functools
import PIL
import numpy as np
import torch
from typing import Dict
from io import BytesIO
from transformers import SiglipImageProcessor
from sentence_transformers import SentenceTransformer


def jasper_vl_forward(self, features: dict[str, torch.Tensor], **kwargs) -> dict[str, torch.Tensor]:
    trans_features = {"input_ids": features["input_ids"], "attention_mask": features["attention_mask"]}
    if "pixel_values" in features:
        trans_features["pixel_values"] = features["pixel_values"]
    sentence_embedding = self.auto_model(**trans_features, **kwargs)["sentence_embedding"]
    features.update({"sentence_embedding": sentence_embedding})
    return features


def jasper_vl_tokenize(self, texts: list[Dict] | list[str]) -> dict[str, torch.Tensor]:
    img_start_token = "<|jasper_img_start|>"
    img_token = "<|jasper_img_token|>"
    img_end_token = "<|jasper_img_end|>"
    num_img_tokens = 300

    def process_text_item(item):
        if isinstance(item, str):
            return item, []
        text, images = "", []
        for sub_item in item:
            if sub_item["type"] == "text":
                text += sub_item["content"]
            elif sub_item["type"] == "image_bytes":
                text += img_start_token + img_token * num_img_tokens + img_end_token
                images.append(PIL.Image.open(BytesIO(sub_item["content"])).convert("RGB"))
            elif sub_item["type"] == "image_path":
                text += img_start_token + img_token * num_img_tokens + img_end_token
                images.append(PIL.Image.open(sub_item["content"]).convert("RGB"))
            else:
                raise ValueError(f"unknown data type {sub_item['type']}")
        return text, images

    all_texts, all_images = [], []
    for item in texts:
        text, images = process_text_item(item)
        all_texts.append(text)
        all_images.extend(images)
    ipt = self.tokenizer(all_texts, padding="longest", truncation=True, max_length=1024, return_tensors="pt")
    if all_images:
        ipt["pixel_values"] = self.processor(
            images=all_images,
            return_tensors="pt"
        )["pixel_values"]
        # For the sake of demonstration, external variables are used here, please modify the code according to your own environment.
        if use_gpu:
            ipt["pixel_values"] = ipt["pixel_values"].bfloat16()
    return ipt


DOC1 = """
Blue light is scattered in all directions by the tiny molecules of air in Earth's atmosphere. 
Blue is scattered more than other colors because it travels as shorter, smaller waves. This is why we see a blue sky most of the time. 
Closer to the horizon, the sky fades to a lighter blue or white.
"""
DOC2 = """
When choosing colors, you can consider the following factors:
Color theory: Understand how colors work together and how they can evoke different reactions. 
Color psychology: Consider how colors affect emotions, behaviors, and responses. 
Brand identity: Colors can convey meaning and information about a brand. 
Mood: Consider the mood you want to create. For example, brighter colors can feel cheerful, while cooler colors can be calming.
Space: Consider the size of the space and the amount of natural light it receives. Dark colors can make a room feel smaller, while light colors can make it feel larger.
Color wheel: Use the color wheel to identify primary, secondary, and tertiary colors. 
Color combinations: Decide how to best complement your preferred color with others. 
Color palette: Limit your color palette to a main color and one or two additional colors. 
60-30-10 rule: Use a primary color 60% of the time, a secondary color 30% of the time, and an accent color 10% of the time
"""
prompt_dict = {
    "s2p_query": "Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: ",
    "s2s_query": "Instruct: Retrieve semantically similar text.\nQuery: "
}
if __name__ == "__main__":
    # load model
    use_gpu = False
    model_name = "infgrad/jasper_en_vision_language_v1"
    model = SentenceTransformer(
        model_name,
        trust_remote_code=True,
        device="cpu",
        model_kwargs={
            "torch_dtype": torch.bfloat16 if use_gpu else torch.float32,
            "attn_implementation": "sdpa"
        },
        # vector_dim must be 12288, 1024, 512, 256
        ## 1024 is recommended
        # set is_text_encoder 'True', if you do not encode image
        config_kwargs={"is_text_encoder": False, "vector_dim": 1024},
        tokenizer_kwargs={"padding_side": "right"}
    )
    # jasper model cannot directly be used in SentenceTransformer, do some modifications
    model.processor = SiglipImageProcessor.from_pretrained(model_name)
    model.tokenize = functools.partial(jasper_vl_tokenize, model)
    model._first_module().forward = functools.partial(jasper_vl_forward, model._first_module())
    model.max_seq_length = 1024
    # data
    q_list = [
        "Why the sky is blue?",
        "how to choose suitable color",
    ]
    doc_list = [
        DOC1,
        [{"type": "image_path", "content": "./assets/img1.png"}, {"type": "text", "content": "Hope this image helps!"}],
        DOC2,
        [{"type": "image_path", "content": "./assets/img2.png"}],

    ]
    q_vecs = model.encode([prompt_dict["s2p_query"] + text for text in q_list], normalize_embeddings=True)
    doc_vecs = model.encode(doc_list, normalize_embeddings=True)
    print(np.matmul(q_vecs, doc_vecs.T))
    # the output is:
    # [[0.777521   0.75944513 0.24291277 0.2187205]
    #  [0.32261407 0.30536035 0.74208796 0.5484469]]

License

This model should not be used for any commercial purpose!