README.md

metadata

base_model: baai/bge-base-en-v1.5
language:
  - en
library_name: model2vec
license: mit
model_name: brown-fairy-base-v0
tags:
  - embeddings
  - static-embeddings
  - sentence-transformers
  - mteb
model-index:
  - name: bhavnicksm/brown-fairy-base-v0
    results:
      - dataset:
          config: en
          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 69.52239999999999
          - type: f1
            value: 63.4127
          - type: f1_weighted
            value: 72.48599999999999
          - type: ap
            value: 31.8446
          - type: ap_weighted
            value: 31.8446
          - type: main_score
            value: 69.52239999999999
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification (default)
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
        metrics:
          - type: accuracy
            value: 68.709
          - type: f1
            value: 68.2583
          - type: f1_weighted
            value: 68.2583
          - type: ap
            value: 63.728899999999996
          - type: ap_weighted
            value: 63.728899999999996
          - type: main_score
            value: 68.709
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 34.014
          - type: f1
            value: 33.4588
          - type: f1_weighted
            value: 33.4588
          - type: main_score
            value: 34.014
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ArguAna (default)
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
          split: test
          type: mteb/arguana
        metrics:
          - type: ndcg_at_1
            value: 20.341
          - type: ndcg_at_3
            value: 30.547
          - type: ndcg_at_5
            value: 34.963
          - type: ndcg_at_10
            value: 39.805
          - type: ndcg_at_20
            value: 42.397
          - type: ndcg_at_100
            value: 45.216
          - type: ndcg_at_1000
            value: 46.339999999999996
          - type: map_at_1
            value: 20.341
          - type: map_at_3
            value: 27.962999999999997
          - type: map_at_5
            value: 30.409999999999997
          - type: map_at_10
            value: 32.4
          - type: map_at_20
            value: 33.113
          - type: map_at_100
            value: 33.512
          - type: map_at_1000
            value: 33.556000000000004
          - type: recall_at_1
            value: 20.341
          - type: recall_at_3
            value: 38.051
          - type: recall_at_5
            value: 48.791000000000004
          - type: recall_at_10
            value: 63.798
          - type: recall_at_20
            value: 74.03999999999999
          - type: recall_at_100
            value: 89.118
          - type: recall_at_1000
            value: 97.866
          - type: precision_at_1
            value: 20.341
          - type: precision_at_3
            value: 12.684000000000001
          - type: precision_at_5
            value: 9.758
          - type: precision_at_10
            value: 6.38
          - type: precision_at_20
            value: 3.702
          - type: precision_at_100
            value: 0.8909999999999999
          - type: precision_at_1000
            value: 0.098
          - type: mrr_at_1
            value: 20.6259
          - type: mrr_at_3
            value: 28.058300000000003
          - type: mrr_at_5
            value: 30.4979
          - type: mrr_at_10
            value: 32.5131
          - type: mrr_at_20
            value: 33.222699999999996
          - type: mrr_at_100
            value: 33.6243
          - type: mrr_at_1000
            value: 33.6687
          - type: nauc_ndcg_at_1_max
            value: -6.208
          - type: nauc_ndcg_at_1_std
            value: 0.6887
          - type: nauc_ndcg_at_1_diff1
            value: 5.5123
          - type: nauc_ndcg_at_3_max
            value: -1.8608
          - type: nauc_ndcg_at_3_std
            value: 3.7832999999999997
          - type: nauc_ndcg_at_3_diff1
            value: 7.5778
          - type: nauc_ndcg_at_5_max
            value: 0.0929
          - type: nauc_ndcg_at_5_std
            value: 5.8453
          - type: nauc_ndcg_at_5_diff1
            value: 9.316
          - type: nauc_ndcg_at_10_max
            value: 0.557
          - type: nauc_ndcg_at_10_std
            value: 5.8692
          - type: nauc_ndcg_at_10_diff1
            value: 8.3828
          - type: nauc_ndcg_at_20_max
            value: 1.567
          - type: nauc_ndcg_at_20_std
            value: 8.2355
          - type: nauc_ndcg_at_20_diff1
            value: 9.1907
          - type: nauc_ndcg_at_100_max
            value: 1.0833000000000002
          - type: nauc_ndcg_at_100_std
            value: 8.6248
          - type: nauc_ndcg_at_100_diff1
            value: 9.0073
          - type: nauc_ndcg_at_1000_max
            value: -0.166
          - type: nauc_ndcg_at_1000_std
            value: 7.394100000000001
          - type: nauc_ndcg_at_1000_diff1
            value: 8.1955
          - type: nauc_map_at_1_max
            value: -6.208
          - type: nauc_map_at_1_std
            value: 0.6887
          - type: nauc_map_at_1_diff1
            value: 5.5123
          - type: nauc_map_at_3_max
            value: -3.0332999999999997
          - type: nauc_map_at_3_std
            value: 2.9010000000000002
          - type: nauc_map_at_3_diff1
            value: 6.8088
          - type: nauc_map_at_5_max
            value: -1.9215
          - type: nauc_map_at_5_std
            value: 4.023000000000001
          - type: nauc_map_at_5_diff1
            value: 7.8248999999999995
          - type: nauc_map_at_10_max
            value: -1.8037
          - type: nauc_map_at_10_std
            value: 3.9838
          - type: nauc_map_at_10_diff1
            value: 7.3617
          - type: nauc_map_at_20_max
            value: -1.5614
          - type: nauc_map_at_20_std
            value: 4.6065000000000005
          - type: nauc_map_at_20_diff1
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          - type: nauc_map_at_100_max
            value: -1.6330999999999998
          - type: nauc_map_at_100_std
            value: 4.693
          - type: nauc_map_at_100_diff1
            value: 7.5309
          - type: nauc_map_at_1000_max
            value: -1.6847999999999999
          - type: nauc_map_at_1000_std
            value: 4.6508
          - type: nauc_map_at_1000_diff1
            value: 7.5036000000000005
          - type: nauc_recall_at_1_max
            value: -6.208
          - type: nauc_recall_at_1_std
            value: 0.6887
          - type: nauc_recall_at_1_diff1
            value: 5.5123
          - type: nauc_recall_at_3_max
            value: 1.2662
          - type: nauc_recall_at_3_std
            value: 6.1506
          - type: nauc_recall_at_3_diff1
            value: 9.6919
          - type: nauc_recall_at_5_max
            value: 5.7511
          - type: nauc_recall_at_5_std
            value: 11.0652
          - type: nauc_recall_at_5_diff1
            value: 13.5713
          - type: nauc_recall_at_10_max
            value: 8.5342
          - type: nauc_recall_at_10_std
            value: 12.2161
          - type: nauc_recall_at_10_diff1
            value: 11.6188
          - type: nauc_recall_at_20_max
            value: 15.7488
          - type: nauc_recall_at_20_std
            value: 25.6755
          - type: nauc_recall_at_20_diff1
            value: 16.3568
          - type: nauc_recall_at_100_max
            value: 24.424799999999998
          - type: nauc_recall_at_100_std
            value: 47.6945
          - type: nauc_recall_at_100_diff1
            value: 22.4622
          - type: nauc_recall_at_1000_max
            value: 3.0951
          - type: nauc_recall_at_1000_std
            value: 84.10419999999999
          - type: nauc_recall_at_1000_diff1
            value: -2.6364
          - type: nauc_precision_at_1_max
            value: -6.208
          - type: nauc_precision_at_1_std
            value: 0.6887
          - type: nauc_precision_at_1_diff1
            value: 5.5123
          - type: nauc_precision_at_3_max
            value: 1.2662
          - type: nauc_precision_at_3_std
            value: 6.1506
          - type: nauc_precision_at_3_diff1
            value: 9.6919
          - type: nauc_precision_at_5_max
            value: 5.7511
          - type: nauc_precision_at_5_std
            value: 11.0652
          - type: nauc_precision_at_5_diff1
            value: 13.5713
          - type: nauc_precision_at_10_max
            value: 8.5342
          - type: nauc_precision_at_10_std
            value: 12.2161
          - type: nauc_precision_at_10_diff1
            value: 11.6188
          - type: nauc_precision_at_20_max
            value: 15.7488
          - type: nauc_precision_at_20_std
            value: 25.6755
          - type: nauc_precision_at_20_diff1
            value: 16.3568
          - type: nauc_precision_at_100_max
            value: 24.424799999999998
          - type: nauc_precision_at_100_std
            value: 47.6945
          - type: nauc_precision_at_100_diff1
            value: 22.4622
          - type: nauc_precision_at_1000_max
            value: 3.0951
          - type: nauc_precision_at_1000_std
            value: 84.10419999999999
          - type: nauc_precision_at_1000_diff1
            value: -2.6364
          - type: nauc_mrr_at_1_max
            value: -5.611800000000001
          - type: nauc_mrr_at_1_std
            value: 0.2596
          - type: nauc_mrr_at_1_diff1
            value: 4.5101
          - type: nauc_mrr_at_3_max
            value: -3.1917
          - type: nauc_mrr_at_3_std
            value: 2.7559
          - type: nauc_mrr_at_3_diff1
            value: 5.756
          - type: nauc_mrr_at_5_max
            value: -2.1292999999999997
          - type: nauc_mrr_at_5_std
            value: 3.7653
          - type: nauc_mrr_at_5_diff1
            value: 6.7995
          - type: nauc_mrr_at_10_max
            value: -1.8915000000000002
          - type: nauc_mrr_at_10_std
            value: 3.778
          - type: nauc_mrr_at_10_diff1
            value: 6.4253
          - type: nauc_mrr_at_20_max
            value: -1.6753
          - type: nauc_mrr_at_20_std
            value: 4.389
          - type: nauc_mrr_at_20_diff1
            value: 6.6081
          - type: nauc_mrr_at_100_max
            value: -1.7302000000000002
          - type: nauc_mrr_at_100_std
            value: 4.4796000000000005
          - type: nauc_mrr_at_100_diff1
            value: 6.563199999999999
          - type: nauc_mrr_at_1000_max
            value: -1.7819000000000003
          - type: nauc_mrr_at_1000_std
            value: 4.4372
          - type: nauc_mrr_at_1000_diff1
            value: 6.5346
          - type: main_score
            value: 39.805
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P (default)
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 30.9023
          - type: v_measure_std
            value: 14.6095
          - type: main_score
            value: 30.9023
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S (default)
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
          split: test
          type: mteb/arxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 19.1012
          - type: v_measure_std
            value: 15.511800000000001
          - type: main_score
            value: 19.1012
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions (default)
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
          split: test
          type: mteb/askubuntudupquestions-reranking
        metrics:
          - type: map
            value: 54.0474
          - type: mrr
            value: 67.00150000000001
          - type: nAUC_map_max
            value: 14.266100000000002
          - type: nAUC_map_std
            value: 11.7906
          - type: nAUC_map_diff1
            value: 7.5044
          - type: nAUC_mrr_max
            value: 20.1721
          - type: nAUC_mrr_std
            value: 13.1225
          - type: nAUC_mrr_diff1
            value: 14.3512
          - type: main_score
            value: 54.0474
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB BIOSSES (default)
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: pearson
            value: 73.3465
          - type: spearman
            value: 69.6932
          - type: cosine_pearson
            value: 73.3465
          - type: cosine_spearman
            value: 69.6932
          - type: manhattan_pearson
            value: 54.115899999999996
          - type: manhattan_spearman
            value: 54.1759
          - type: euclidean_pearson
            value: 54.2153
          - type: euclidean_spearman
            value: 54.0488
          - type: main_score
            value: 69.6932
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB Banking77Classification (default)
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
          split: test
          type: mteb/banking77
        metrics:
          - type: accuracy
            value: 74.2987
          - type: f1
            value: 73.85119999999999
          - type: f1_weighted
            value: 73.85119999999999
          - type: main_score
            value: 74.2987
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BiorxivClusteringP2P (default)
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
          split: test
          type: mteb/biorxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 29.8415
          - type: v_measure_std
            value: 0.7605
          - type: main_score
            value: 29.8415
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB BiorxivClusteringS2S (default)
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
          split: test
          type: mteb/biorxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 16.4917
          - type: v_measure_std
            value: 1.2364
          - type: main_score
            value: 16.4917
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CQADupstackRetrieval (default)
          revision: '1'
          split: test
          type: CQADupstackRetrieval_is_a_combined_dataset
        metrics:
          - type: ndcg_at_10
            value: 21.9561
          - type: main_score
            value: 21.9561
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ClimateFEVER (default)
          revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
          split: test
          type: mteb/climate-fever
        metrics:
          - type: ndcg_at_1
            value: 18.826999999999998
          - type: ndcg_at_3
            value: 16.482
          - type: ndcg_at_5
            value: 17.9
          - type: ndcg_at_10
            value: 20.948
          - type: ndcg_at_20
            value: 23.665
          - type: ndcg_at_100
            value: 28.192
          - type: ndcg_at_1000
            value: 31.846999999999998
          - type: map_at_1
            value: 8.221
          - type: map_at_3
            value: 11.72
          - type: map_at_5
            value: 12.844
          - type: map_at_10
            value: 14.17
          - type: map_at_20
            value: 15.043000000000001
          - type: map_at_100
            value: 15.842
          - type: map_at_1000
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          - type: recall_at_1
            value: 8.221
          - type: recall_at_3
            value: 15.214
          - type: recall_at_5
            value: 19.185
          - type: recall_at_10
            value: 26.14
          - type: recall_at_20
            value: 33.931
          - type: recall_at_100
            value: 51.429
          - type: recall_at_1000
            value: 72.269
          - type: precision_at_1
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          - type: precision_at_3
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          - type: precision_at_5
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          - type: precision_at_10
            value: 6.84
          - type: precision_at_20
            value: 4.557
          - type: precision_at_100
            value: 1.461
          - type: precision_at_1000
            value: 0.212
          - type: mrr_at_1
            value: 18.8274
          - type: mrr_at_3
            value: 25.2226
          - type: mrr_at_5
            value: 27.163999999999998
          - type: mrr_at_10
            value: 28.6116
          - type: mrr_at_20
            value: 29.3082
          - type: mrr_at_100
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          - type: mrr_at_1000
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          - type: nauc_ndcg_at_1_max
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          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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          type: mteb/mtop_intent
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        task:
          type: Retrieval
      - dataset:
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          name: MTEB SICK-R (default)
          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
          split: test
          type: mteb/sickr-sts
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          - type: cosine_pearson
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          - type: cosine_spearman
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          - type: euclidean_spearman
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          - type: main_score
            value: 63.871500000000005
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS12 (default)
          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
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          - type: cosine_pearson
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          - type: cosine_spearman
            value: 63.610299999999995
          - type: manhattan_pearson
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          - type: manhattan_spearman
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          - type: euclidean_pearson
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          - type: euclidean_spearman
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          - type: main_score
            value: 63.610299999999995
        task:
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      - dataset:
          config: default
          name: MTEB STS13 (default)
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
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          - type: spearman
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          - type: cosine_pearson
            value: 76.4727
          - type: cosine_spearman
            value: 76.983
          - type: manhattan_pearson
            value: 49.4803
          - type: manhattan_spearman
            value: 51.1301
          - type: euclidean_pearson
            value: 49.4542
          - type: euclidean_spearman
            value: 51.19669999999999
          - type: main_score
            value: 76.983
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS14 (default)
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
        metrics:
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            value: 75.777
          - type: spearman
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          - type: cosine_pearson
            value: 75.777
          - type: cosine_spearman
            value: 71.2099
          - type: manhattan_pearson
            value: 52.475899999999996
          - type: manhattan_spearman
            value: 53.8072
          - type: euclidean_pearson
            value: 52.416799999999995
          - type: euclidean_spearman
            value: 53.725500000000004
          - type: main_score
            value: 71.2099
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS15 (default)
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
        metrics:
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            value: 80.1072
          - type: spearman
            value: 80.735
          - type: cosine_pearson
            value: 80.1072
          - type: cosine_spearman
            value: 80.7349
          - type: manhattan_pearson
            value: 50.711600000000004
          - type: manhattan_spearman
            value: 53.491299999999995
          - type: euclidean_pearson
            value: 50.6255
          - type: euclidean_spearman
            value: 53.47539999999999
          - type: main_score
            value: 80.7349
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS16 (default)
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
        metrics:
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            value: 73.1658
          - type: spearman
            value: 74.2121
          - type: cosine_pearson
            value: 73.1658
          - type: cosine_spearman
            value: 74.2121
          - type: manhattan_pearson
            value: 43.4074
          - type: manhattan_spearman
            value: 47.193200000000004
          - type: euclidean_pearson
            value: 43.438300000000005
          - type: euclidean_spearman
            value: 47.2757
          - type: main_score
            value: 74.2121
        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: 81.8156
          - type: spearman
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          - type: cosine_pearson
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          - type: cosine_spearman
            value: 81.9457
          - type: manhattan_pearson
            value: 59.4332
          - type: manhattan_spearman
            value: 60.5687
          - type: euclidean_pearson
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          - type: euclidean_spearman
            value: 60.39679999999999
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            value: 81.9457
        task:
          type: STS
      - dataset:
          config: en
          name: MTEB STS22 (en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
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          - type: spearman
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          - type: cosine_pearson
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          - type: cosine_spearman
            value: 55.862500000000004
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          - type: manhattan_spearman
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          - type: euclidean_spearman
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        task:
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      - dataset:
          config: default
          name: MTEB STSBenchmark (default)
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
          split: test
          type: mteb/stsbenchmark-sts
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          - type: cosine_pearson
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          - type: cosine_spearman
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        task:
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      - dataset:
          config: default
          name: MTEB SciDocsRR (default)
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
          split: test
          type: mteb/scidocs-reranking
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            value: 69.6523
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          - type: main_score
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        task:
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          name: MTEB SciFact (default)
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
          split: test
          type: mteb/scifact
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          - type: nauc_recall_at_1000_std
            value: 81.7519
          - type: nauc_recall_at_1000_diff1
            value: 46.0741
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            value: 36.6444
          - type: nauc_precision_at_1_std
            value: -7.4722
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            value: 63.631099999999996
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            value: 37.9141
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            value: -2.6281
          - type: nauc_precision_at_3_diff1
            value: 45.406600000000005
          - type: nauc_precision_at_5_max
            value: 35.0402
          - type: nauc_precision_at_5_std
            value: 0.7128
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          - type: nauc_precision_at_10_max
            value: 37.4825
          - type: nauc_precision_at_10_std
            value: 15.613199999999999
          - type: nauc_precision_at_10_diff1
            value: 33.1716
          - type: nauc_precision_at_20_max
            value: 36.1575
          - type: nauc_precision_at_20_std
            value: 30.4446
          - type: nauc_precision_at_20_diff1
            value: 23.3224
          - type: nauc_precision_at_100_max
            value: 29.5019
          - type: nauc_precision_at_100_std
            value: 52.942
          - type: nauc_precision_at_100_diff1
            value: 9.0284
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            value: 20.350099999999998
          - type: nauc_precision_at_1000_std
            value: 52.2915
          - type: nauc_precision_at_1000_diff1
            value: -8.6009
          - type: nauc_mrr_at_1_max
            value: 36.6444
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            value: -7.4722
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            value: 63.631099999999996
          - type: nauc_mrr_at_3_max
            value: 38.016299999999994
          - type: nauc_mrr_at_3_std
            value: -8.0229
          - type: nauc_mrr_at_3_diff1
            value: 58.757400000000004
          - type: nauc_mrr_at_5_max
            value: 37.433899999999994
          - type: nauc_mrr_at_5_std
            value: -8.1996
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            value: 58.235899999999994
          - type: nauc_mrr_at_10_max
            value: 37.7997
          - type: nauc_mrr_at_10_std
            value: -7.542699999999999
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            value: 58.8486
          - type: nauc_mrr_at_20_max
            value: 37.8879
          - type: nauc_mrr_at_20_std
            value: -7.133000000000001
          - type: nauc_mrr_at_20_diff1
            value: 58.834900000000005
          - type: nauc_mrr_at_100_max
            value: 37.8627
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            value: -6.9667
          - type: nauc_mrr_at_100_diff1
            value: 58.880900000000004
          - type: nauc_mrr_at_1000_max
            value: 37.8675
          - type: nauc_mrr_at_1000_std
            value: -6.9817
          - type: nauc_mrr_at_1000_diff1
            value: 58.904500000000006
          - type: main_score
            value: 57.422
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SprintDuplicateQuestions (default)
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
          split: test
          type: mteb/sprintduplicatequestions-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 99.6703
          - type: similarity_accuracy_threshold
            value: 81.69669999999999
          - type: similarity_f1
            value: 82.5479
          - type: similarity_f1_threshold
            value: 80.97919999999999
          - type: similarity_precision
            value: 85.6069
          - type: similarity_recall
            value: 79.7
          - type: similarity_ap
            value: 87.6918
          - type: cosine_accuracy
            value: 99.6703
          - type: cosine_accuracy_threshold
            value: 81.69669999999999
          - type: cosine_f1
            value: 82.5479
          - type: cosine_f1_threshold
            value: 80.97919999999999
          - type: cosine_precision
            value: 85.6069
          - type: cosine_recall
            value: 79.7
          - type: cosine_ap
            value: 87.6918
          - type: manhattan_accuracy
            value: 99.4327
          - type: manhattan_accuracy_threshold
            value: 2292.4838999999997
          - type: manhattan_f1
            value: 66.0851
          - type: manhattan_f1_threshold
            value: 2517.333
          - type: manhattan_precision
            value: 72.6619
          - type: manhattan_recall
            value: 60.6
          - type: manhattan_ap
            value: 68.1683
          - type: euclidean_accuracy
            value: 99.4327
          - type: euclidean_accuracy_threshold
            value: 105.6427
          - type: euclidean_f1
            value: 66.1605
          - type: euclidean_f1_threshold
            value: 114.9346
          - type: euclidean_precision
            value: 72.2749
          - type: euclidean_recall
            value: 61
          - type: euclidean_ap
            value: 68.2419
          - type: dot_accuracy
            value: 99.0168
          - type: dot_accuracy_threshold
            value: 1011.5417000000001
          - type: dot_f1
            value: 18.6459
          - type: dot_f1_threshold
            value: 554.0581999999999
          - type: dot_precision
            value: 20.9476
          - type: dot_recall
            value: 16.8
          - type: dot_ap
            value: 11.5838
          - type: max_accuracy
            value: 99.6703
          - type: max_f1
            value: 82.5479
          - type: max_precision
            value: 85.6069
          - type: max_recall
            value: 79.7
          - type: max_ap
            value: 87.6918
          - type: main_score
            value: 87.6918
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB StackExchangeClustering (default)
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
          split: test
          type: mteb/stackexchange-clustering
        metrics:
          - type: v_measure
            value: 27.147700000000004
          - type: v_measure_std
            value: 4.3151
          - type: main_score
            value: 27.147700000000004
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackExchangeClusteringP2P (default)
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
          split: test
          type: mteb/stackexchange-clustering-p2p
        metrics:
          - type: v_measure
            value: 28.9253
          - type: v_measure_std
            value: 1.6500000000000001
          - type: main_score
            value: 28.9253
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackOverflowDupQuestions (default)
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
          split: test
          type: mteb/stackoverflowdupquestions-reranking
        metrics:
          - type: map
            value: 42.7933
          - type: mrr
            value: 43.2531
          - type: nAUC_map_max
            value: 15.137400000000001
          - type: nAUC_map_std
            value: 4.6048
          - type: nAUC_map_diff1
            value: 31.665100000000002
          - type: nAUC_mrr_max
            value: 16.429299999999998
          - type: nAUC_mrr_std
            value: 4.943899999999999
          - type: nAUC_mrr_diff1
            value: 30.8849
          - type: main_score
            value: 42.7933
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SummEval (default)
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
          split: test
          type: mteb/summeval
        metrics:
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            value: 31.8891
          - type: spearman
            value: 30.635299999999997
          - type: cosine_spearman
            value: 30.635299999999997
          - type: cosine_pearson
            value: 31.8891
          - type: dot_spearman
            value: 23.1495
          - type: dot_pearson
            value: 20.2811
          - type: main_score
            value: 30.635299999999997
        task:
          type: Summarization
      - dataset:
          config: default
          name: MTEB TRECCOVID (default)
          revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
          split: test
          type: mteb/trec-covid
        metrics:
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            value: 60
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            value: 56.592
          - type: ndcg_at_5
            value: 52.15
          - type: ndcg_at_10
            value: 48.264
          - type: ndcg_at_20
            value: 43.568
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            value: 31.196
          - type: ndcg_at_1000
            value: 26.101000000000003
          - type: map_at_1
            value: 0.153
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            value: 0.4
          - type: map_at_5
            value: 0.601
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            value: 1.016
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            value: 1.6099999999999999
          - type: map_at_100
            value: 4.169
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            value: 9.733
          - type: recall_at_1
            value: 0.153
          - type: recall_at_3
            value: 0.42300000000000004
          - type: recall_at_5
            value: 0.6629999999999999
          - type: recall_at_10
            value: 1.201
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            value: 2.022
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            value: 6.5409999999999995
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            value: 24.422
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            value: 64
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            value: 58.667
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            value: 54
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            value: 49.8
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            value: 44.3
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            value: 31.180000000000003
          - type: precision_at_1000
            value: 12.21
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            value: 64
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            value: 68.6667
          - type: mrr_at_5
            value: 69.9667
          - type: mrr_at_10
            value: 71.2222
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            value: 71.3651
          - type: mrr_at_100
            value: 71.4965
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            value: 71.51429999999999
          - type: nauc_ndcg_at_1_max
            value: 37.0018
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            value: 3.0042
          - type: nauc_ndcg_at_1_diff1
            value: 1.0129000000000001
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            value: 42.3179
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            value: 1.1211
          - type: nauc_ndcg_at_3_diff1
            value: -1.3197999999999999
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            value: 38.2867
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            value: 1.436
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            value: -0.635
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            value: 36.545100000000005
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            value: 9.4313
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            value: 0.7185
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            value: 28.841499999999996
          - type: nauc_ndcg_at_20_std
            value: 14.584
          - type: nauc_ndcg_at_20_diff1
            value: 0.2278
          - type: nauc_ndcg_at_100_max
            value: 22.2284
          - type: nauc_ndcg_at_100_std
            value: 30.9548
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            value: 1.7124000000000001
          - type: nauc_ndcg_at_1000_max
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            value: 43.918
          - type: nauc_ndcg_at_1000_diff1
            value: 1.1608
          - type: nauc_map_at_1_max
            value: 16.718700000000002
          - type: nauc_map_at_1_std
            value: -14.5026
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            value: 6.9494
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            value: -14.2379
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            value: 2.6883
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            value: 26.8639
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            value: -11.9289
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            value: 28.7924
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            value: -6.2317
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            value: 0.153
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            value: 24.3923
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            value: 1.5524
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            value: 14.5538
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            value: 29.851499999999998
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            value: -1.5013
          - type: nauc_map_at_1000_max
            value: 6.609800000000001
          - type: nauc_map_at_1000_std
            value: 50.8853
          - type: nauc_map_at_1000_diff1
            value: 2.2463
          - type: nauc_recall_at_1_max
            value: 16.718700000000002
          - type: nauc_recall_at_1_std
            value: -14.5026
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            value: 6.9494
          - type: nauc_recall_at_3_max
            value: 26.313
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            value: -16.5391
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          - type: nauc_recall_at_5_max
            value: 27.136
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            value: -13.486999999999998
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          - type: nauc_recall_at_10_max
            value: 27.9019
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            value: -7.2991
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            value: 0.35729999999999995
          - type: nauc_recall_at_20_max
            value: 24.1923
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            value: 0.3075
          - type: nauc_recall_at_20_diff1
            value: -2.6993
          - type: nauc_recall_at_100_max
            value: 15.928400000000002
          - type: nauc_recall_at_100_std
            value: 24.5423
          - type: nauc_recall_at_100_diff1
            value: -4.0408
          - type: nauc_recall_at_1000_max
            value: -0.2523
          - type: nauc_recall_at_1000_std
            value: 49.0728
          - type: nauc_recall_at_1000_diff1
            value: -0.1562
          - type: nauc_precision_at_1_max
            value: 42.5437
          - type: nauc_precision_at_1_std
            value: 0.859
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          - type: nauc_precision_at_3_max
            value: 46.4231
          - type: nauc_precision_at_3_std
            value: -2.6254
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            value: -5.129700000000001
          - type: nauc_precision_at_5_max
            value: 40.022600000000004
          - type: nauc_precision_at_5_std
            value: 1.4931
          - type: nauc_precision_at_5_diff1
            value: -5.634399999999999
          - type: nauc_precision_at_10_max
            value: 37.8846
          - type: nauc_precision_at_10_std
            value: 11.4085
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            value: -2.3909
          - type: nauc_precision_at_20_max
            value: 26.971400000000003
          - type: nauc_precision_at_20_std
            value: 17.3784
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            value: -1.5310000000000001
          - type: nauc_precision_at_100_max
            value: 19.9237
          - type: nauc_precision_at_100_std
            value: 35.952400000000004
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          - type: nauc_precision_at_1000_max
            value: 6.1676
          - type: nauc_precision_at_1000_std
            value: 50.53959999999999
          - type: nauc_precision_at_1000_diff1
            value: 3.8484
          - type: nauc_mrr_at_1_max
            value: 42.5437
          - type: nauc_mrr_at_1_std
            value: 0.859
          - type: nauc_mrr_at_1_diff1
            value: -7.6319
          - type: nauc_mrr_at_3_max
            value: 44.3255
          - type: nauc_mrr_at_3_std
            value: -4.5994
          - type: nauc_mrr_at_3_diff1
            value: -12.2252
          - type: nauc_mrr_at_5_max
            value: 45.7817
          - type: nauc_mrr_at_5_std
            value: -3.1611000000000002
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            value: -10.706100000000001
          - type: nauc_mrr_at_10_max
            value: 45.5444
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            value: -1.1735
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          - type: nauc_mrr_at_20_max
            value: 45.3001
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            value: -0.8477999999999999
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          - type: nauc_mrr_at_100_max
            value: 45.3697
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            value: -1.2326
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          - type: nauc_mrr_at_1000_max
            value: 45.356
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            value: -1.2729000000000001
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            value: -9.2226
          - type: main_score
            value: 48.264
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB Touche2020 (default)
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
          split: test
          type: mteb/touche2020
        metrics:
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            value: 13.264999999999999
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            value: 16.817
          - type: ndcg_at_5
            value: 17.718999999999998
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            value: 17.318
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          - type: ndcg_at_100
            value: 28.137
          - type: ndcg_at_1000
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            value: 1.335
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            value: 2.94
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            value: 4.37
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            value: 6.447
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            value: 8.141
          - type: map_at_100
            value: 10.428999999999998
          - type: map_at_1000
            value: 12.23
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            value: 1.335
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            value: 4.05
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            value: 7.507999999999999
          - type: recall_at_10
            value: 12.862000000000002
          - type: recall_at_20
            value: 18.953999999999997
          - type: recall_at_100
            value: 40.384
          - type: recall_at_1000
            value: 82.421
          - type: precision_at_1
            value: 16.326999999999998
          - type: precision_at_3
            value: 21.088
          - type: precision_at_5
            value: 21.224
          - type: precision_at_10
            value: 17.755000000000003
          - type: precision_at_20
            value: 13.264999999999999
          - type: precision_at_100
            value: 6.5920000000000005
          - type: precision_at_1000
            value: 1.516
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            value: 16.3265
          - type: mrr_at_3
            value: 29.251700000000003
          - type: mrr_at_5
            value: 32.9252
          - type: mrr_at_10
            value: 34.613699999999994
          - type: mrr_at_20
            value: 35.3587
          - type: mrr_at_100
            value: 35.6307
          - type: mrr_at_1000
            value: 35.6307
          - type: nauc_ndcg_at_1_max
            value: -32.3322
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            value: -13.9866
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            value: -9.2265
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            value: -38.3363
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            value: -19.017999999999997
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            value: 0.7867000000000001
          - type: nauc_ndcg_at_10_max
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            value: -36.0452
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            value: -31.8569
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            value: 14.1121
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            value: -21.2172
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          - type: nauc_map_at_1_max
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          - type: nauc_map_at_3_max
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            value: -21.5959
          - type: nauc_map_at_5_max
            value: -45.9079
          - type: nauc_map_at_5_std
            value: -28.181099999999997
          - type: nauc_map_at_5_diff1
            value: -14.3231
          - type: nauc_map_at_10_max
            value: -45.8169
          - type: nauc_map_at_10_std
            value: -41.293400000000005
          - type: nauc_map_at_10_diff1
            value: -0.7166
          - type: nauc_map_at_20_max
            value: -42.233900000000006
          - type: nauc_map_at_20_std
            value: -42.2579
          - type: nauc_map_at_20_diff1
            value: 9.9162
          - type: nauc_map_at_100_max
            value: -42.6044
          - type: nauc_map_at_100_std
            value: -39.921
          - type: nauc_map_at_100_diff1
            value: 10.408900000000001
          - type: nauc_map_at_1000_max
            value: -41.4171
          - type: nauc_map_at_1000_std
            value: -37.167899999999996
          - type: nauc_map_at_1000_diff1
            value: 11.7185
          - type: nauc_recall_at_1_max
            value: -39.5068
          - type: nauc_recall_at_1_std
            value: -16.150000000000002
          - type: nauc_recall_at_1_diff1
            value: -31.249900000000004
          - type: nauc_recall_at_3_max
            value: -38.8655
          - type: nauc_recall_at_3_std
            value: -21.6066
          - type: nauc_recall_at_3_diff1
            value: -11.395900000000001
          - type: nauc_recall_at_5_max
            value: -47.9991
          - type: nauc_recall_at_5_std
            value: -32.9137
          - type: nauc_recall_at_5_diff1
            value: -1.0116
          - type: nauc_recall_at_10_max
            value: -49.586999999999996
          - type: nauc_recall_at_10_std
            value: -48.6293
          - type: nauc_recall_at_10_diff1
            value: 13.092699999999999
          - type: nauc_recall_at_20_max
            value: -45.1018
          - type: nauc_recall_at_20_std
            value: -46.1638
          - type: nauc_recall_at_20_diff1
            value: 20.9848
          - type: nauc_recall_at_100_max
            value: -48.106700000000004
          - type: nauc_recall_at_100_std
            value: -30.618699999999997
          - type: nauc_recall_at_100_diff1
            value: 8.3225
          - type: nauc_recall_at_1000_max
            value: -35.183
          - type: nauc_recall_at_1000_std
            value: 9.1089
          - type: nauc_recall_at_1000_diff1
            value: 14.8164
          - type: nauc_precision_at_1_max
            value: -36.7404
          - type: nauc_precision_at_1_std
            value: -20.7164
          - type: nauc_precision_at_1_diff1
            value: -24.9514
          - type: nauc_precision_at_3_max
            value: -32.1394
          - type: nauc_precision_at_3_std
            value: -14.9321
          - type: nauc_precision_at_3_diff1
            value: -5.2914
          - type: nauc_precision_at_5_max
            value: -39.6017
          - type: nauc_precision_at_5_std
            value: -27.8755
          - type: nauc_precision_at_5_diff1
            value: 6.2789
          - type: nauc_precision_at_10_max
            value: -42.565799999999996
          - type: nauc_precision_at_10_std
            value: -45.101200000000006
          - type: nauc_precision_at_10_diff1
            value: 18.4024
          - type: nauc_precision_at_20_max
            value: -36.074
          - type: nauc_precision_at_20_std
            value: -41.6858
          - type: nauc_precision_at_20_diff1
            value: 29.625899999999998
          - type: nauc_precision_at_100_max
            value: -20.7563
          - type: nauc_precision_at_100_std
            value: -6.5164
          - type: nauc_precision_at_100_diff1
            value: 13.5108
          - type: nauc_precision_at_1000_max
            value: 41.492200000000004
          - type: nauc_precision_at_1000_std
            value: 45.918
          - type: nauc_precision_at_1000_diff1
            value: 9.314400000000001
          - type: nauc_mrr_at_1_max
            value: -36.7404
          - type: nauc_mrr_at_1_std
            value: -20.7164
          - type: nauc_mrr_at_1_diff1
            value: -24.9514
          - type: nauc_mrr_at_3_max
            value: -34.8748
          - type: nauc_mrr_at_3_std
            value: -11.2167
          - type: nauc_mrr_at_3_diff1
            value: -14.4811
          - type: nauc_mrr_at_5_max
            value: -39.5232
          - type: nauc_mrr_at_5_std
            value: -18.9591
          - type: nauc_mrr_at_5_diff1
            value: -13.2719
          - type: nauc_mrr_at_10_max
            value: -41.7821
          - type: nauc_mrr_at_10_std
            value: -18.368399999999998
          - type: nauc_mrr_at_10_diff1
            value: -13.4359
          - type: nauc_mrr_at_20_max
            value: -42.8581
          - type: nauc_mrr_at_20_std
            value: -18.6052
          - type: nauc_mrr_at_20_diff1
            value: -13.6098
          - type: nauc_mrr_at_100_max
            value: -42.0696
          - type: nauc_mrr_at_100_std
            value: -18.1447
          - type: nauc_mrr_at_100_diff1
            value: -14.102500000000001
          - type: nauc_mrr_at_1000_max
            value: -42.0696
          - type: nauc_mrr_at_1000_std
            value: -18.1447
          - type: nauc_mrr_at_1000_diff1
            value: -14.102500000000001
          - type: main_score
            value: 17.318
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ToxicConversationsClassification (default)
          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
          split: test
          type: mteb/toxic_conversations_50k
        metrics:
          - type: accuracy
            value: 74.0283
          - type: f1
            value: 54.813100000000006
          - type: f1_weighted
            value: 79.4125
          - type: ap
            value: 12.750800000000002
          - type: ap_weighted
            value: 12.750800000000002
          - type: main_score
            value: 74.0283
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TweetSentimentExtractionClassification (default)
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
          split: test
          type: mteb/tweet_sentiment_extraction
        metrics:
          - type: accuracy
            value: 52.818299999999994
          - type: f1
            value: 52.8999
          - type: f1_weighted
            value: 52.223299999999995
          - type: main_score
            value: 52.818299999999994
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TwentyNewsgroupsClustering (default)
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
          split: test
          type: mteb/twentynewsgroups-clustering
        metrics:
          - type: v_measure
            value: 14.5905
          - type: v_measure_std
            value: 1.0532
          - type: main_score
            value: 14.5905
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB TwitterSemEval2015 (default)
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
          split: test
          type: mteb/twittersemeval2015-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 80.3481
          - type: similarity_accuracy_threshold
            value: 85.3551
          - type: similarity_f1
            value: 51.27850000000001
          - type: similarity_f1_threshold
            value: 75.8966
          - type: similarity_precision
            value: 45.8247
          - type: similarity_recall
            value: 58.205799999999996
          - type: similarity_ap
            value: 52.295100000000005
          - type: cosine_accuracy
            value: 80.3481
          - type: cosine_accuracy_threshold
            value: 85.3551
          - type: cosine_f1
            value: 51.27850000000001
          - type: cosine_f1_threshold
            value: 75.8966
          - type: cosine_precision
            value: 45.8247
          - type: cosine_recall
            value: 58.205799999999996
          - type: cosine_ap
            value: 52.295199999999994
          - type: manhattan_accuracy
            value: 78.9712
          - type: manhattan_accuracy_threshold
            value: 3046.9002
          - type: manhattan_f1
            value: 44.784600000000005
          - type: manhattan_f1_threshold
            value: 4624.7635
          - type: manhattan_precision
            value: 35.5133
          - type: manhattan_recall
            value: 60.606899999999996
          - type: manhattan_ap
            value: 44.4155
          - type: euclidean_accuracy
            value: 78.9772
          - type: euclidean_accuracy_threshold
            value: 141.3014
          - type: euclidean_f1
            value: 44.8638
          - type: euclidean_f1_threshold
            value: 210.8781
          - type: euclidean_precision
            value: 35.3191
          - type: euclidean_recall
            value: 61.477599999999995
          - type: euclidean_ap
            value: 44.3973
          - type: dot_accuracy
            value: 77.4095
          - type: dot_accuracy_threshold
            value: 3833.3893000000003
          - type: dot_f1
            value: 41.7116
          - type: dot_f1_threshold
            value: 336.5812
          - type: dot_precision
            value: 28.259600000000002
          - type: dot_recall
            value: 79.6042
          - type: dot_ap
            value: 30.7809
          - type: max_accuracy
            value: 80.3481
          - type: max_f1
            value: 51.27850000000001
          - type: max_precision
            value: 45.8247
          - type: max_recall
            value: 79.6042
          - type: max_ap
            value: 52.295199999999994
          - type: main_score
            value: 52.295199999999994
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB TwitterURLCorpus (default)
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
          split: test
          type: mteb/twitterurlcorpus-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 85.9025
          - type: similarity_accuracy_threshold
            value: 71.6078
          - type: similarity_f1
            value: 70.9832
          - type: similarity_f1_threshold
            value: 66.4079
          - type: similarity_precision
            value: 68.9871
          - type: similarity_recall
            value: 73.0982
          - type: similarity_ap
            value: 79.2622
          - type: cosine_accuracy
            value: 85.9025
          - type: cosine_accuracy_threshold
            value: 71.6078
          - type: cosine_f1
            value: 70.9832
          - type: cosine_f1_threshold
            value: 66.4079
          - type: cosine_precision
            value: 68.9871
          - type: cosine_recall
            value: 73.0982
          - type: cosine_ap
            value: 79.2622
          - type: manhattan_accuracy
            value: 81.8954
          - type: manhattan_accuracy_threshold
            value: 2754.9084000000003
          - type: manhattan_f1
            value: 58.4303
          - type: manhattan_f1_threshold
            value: 3301.9608
          - type: manhattan_precision
            value: 56.1511
          - type: manhattan_recall
            value: 60.9024
          - type: manhattan_ap
            value: 66.2046
          - type: euclidean_accuracy
            value: 81.8974
          - type: euclidean_accuracy_threshold
            value: 122.74810000000001
          - type: euclidean_f1
            value: 58.455
          - type: euclidean_f1_threshold
            value: 151.3654
          - type: euclidean_precision
            value: 55.0722
          - type: euclidean_recall
            value: 62.2806
          - type: euclidean_ap
            value: 66.22019999999999
          - type: dot_accuracy
            value: 78.7402
          - type: dot_accuracy_threshold
            value: 317.0264
          - type: dot_f1
            value: 58.2905
          - type: dot_f1_threshold
            value: 187.0591
          - type: dot_precision
            value: 48.1454
          - type: dot_recall
            value: 73.8528
          - type: dot_ap
            value: 58.116
          - type: max_accuracy
            value: 85.9025
          - type: max_f1
            value: 70.9832
          - type: max_precision
            value: 68.9871
          - type: max_recall
            value: 73.8528
          - type: max_ap
            value: 79.2622
          - type: main_score
            value: 79.2622
        task:
          type: PairClassification

🧚🏻‍♀️ brown-fairy-base-v0 Model Card

Fairies are among the most enchanting and magical beings in folklore and mythology. They appear across countless cultures and stories, from ancient forests to modern gardens. They are celebrated for their ability to bridge the mundane and magical realms, known for their ethereal grace and transformative powers. Fairies are tiny, higher-dimensional beings that can interact with the world in ways that are beyond our understanding.

The fairy series of models are an attempt to tune the beetle series of models to be more suitable for downstream tasks. These models are meant to fully open experiments at making state-of-the-art static embeddings.

The brown-fairy-base-v0 model is a distillation of the baai/bge-base-en-v1.5 model into the brown-beetle-base-v0 model. There was no PCA or Zipf applied to this model.

Installation

Install model2vec using pip:

pip install model2vec

Usage

Load this model using the from_pretrained method:

from model2vec import StaticModel

# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("bhavnicksm/brown-fairy-base-v0")

# Compute text embeddings
embeddings = model.encode(["Example sentence"])

Read more about the Model2Vec library here.

Reproduce this model

This model was trained on a subset of the 2 Million texts from the FineWeb-Edu dataset, which was labeled by the baai/bge-base-en-v1.5 model.

Training Code

Note: The datasets need to me made seperately and loaded with the datasets library.

static_embedding = StaticEmbedding.from_model2vec("bhavnicksm/brown-beetle-base-v0")
model = SentenceTransformer(
    modules=[static_embedding]
)

loss = MSELoss(model)

run_name = "brown-fairy-base-v0"
args = SentenceTransformerTrainingArguments(
    # Required parameter:
    output_dir=f"output/{run_name}",
    # Optional training parameters:
    num_train_epochs=1,
    per_device_train_batch_size=2048,
    per_device_eval_batch_size=2048,
    learning_rate=1e-1,
    warmup_ratio=0.1,
    fp16=False,  # Set to False if you get an error that your GPU can't run on FP16
    bf16=True,  # Set to True if you have a GPU that supports BF16
    batch_sampler=BatchSamplers.NO_DUPLICATES,  
    # Optional tracking/debugging parameters:
    eval_strategy="steps",
    eval_steps=50,
    save_strategy="steps",
    save_steps=50,
    save_total_limit=5,
    logging_steps=50,
    logging_first_step=True,
    run_name=run_name, 
)

evaluator = NanoBEIREvaluator()
evaluator(model)

trainer = SentenceTransformerTrainer(
    model=model,
    args=args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    loss=loss,
    evaluator=evaluator,
)
trainer.train()

evaluator(model)

model.save_pretrained(f"output/{run_name}")

Comparison with other models

Coming soon...

Acknowledgements

This model is based on the Model2Vec library. Credit goes to the Minish Lab team for developing this library.

Citation

This model builds on work done by Minish Lab. Please cite the Model2Vec repository if you use this model in your work.

@software{minishlab2024model2vec,
  authors = {Stephan Tulkens, Thomas van Dongen},
  title = {Model2Vec: Turn any Sentence Transformer into a Small Fast Model},
  year = {2024},
  url = {https://github.com/MinishLab/model2vec},
}