transformers safetensors minicpm feature-extraction mteb sentence-transformers custom_code arxiv:2202.08904 model-index region:us
---
library_name: transformers
pipeline_tag: feature-extraction
tags:
- mteb
- sentence-transformers
model-index:
- name: no_model_name_available
  results:
  - task:
      type: STS
    dataset:
      name: MTEB AFQMC (default)
      type: C-MTEB/AFQMC
      config: default
      split: validation
      revision: b44c3b011063adb25877c13823db83bb193913c4
    metrics:
    - type: cosine_pearson
      value: 31.60219205269865
    - type: cosine_spearman
      value: 32.26566089398552
    - type: euclidean_pearson
      value: 31.38659295608159
    - type: euclidean_spearman
      value: 32.265680997074284
    - type: main_score
      value: 32.26566089398552
    - type: manhattan_pearson
      value: 31.012318343485934
    - type: manhattan_spearman
      value: 31.881347232593882
    - type: pearson
      value: 31.60219205269865
    - type: spearman
      value: 32.26566089398552
  - task:
      type: STS
    dataset:
      name: MTEB ATEC (default)
      type: C-MTEB/ATEC
      config: default
      split: test
      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
    metrics:
    - type: cosine_pearson
      value: 40.89963324512739
    - type: cosine_spearman
      value: 40.342262626966686
    - type: euclidean_pearson
      value: 43.26579075620696
    - type: euclidean_spearman
      value: 40.34226375259283
    - type: main_score
      value: 40.342262626966686
    - type: manhattan_pearson
      value: 43.09428997760782
    - type: manhattan_spearman
      value: 40.132604575720485
    - type: pearson
      value: 40.89963324512739
    - type: spearman
      value: 40.342262626966686
  - task:
      type: STS
    dataset:
      name: MTEB ATEC (default)
      type: C-MTEB/ATEC
      config: default
      split: validation
      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
    metrics:
    - type: cosine_pearson
      value: 40.97674579633659
    - type: cosine_spearman
      value: 41.15073385665892
    - type: euclidean_pearson
      value: 43.12674145119401
    - type: euclidean_spearman
      value: 41.15073497290901
    - type: main_score
      value: 41.15073385665892
    - type: manhattan_pearson
      value: 43.016332350517416
    - type: manhattan_spearman
      value: 40.99128368771293
    - type: pearson
      value: 40.97674579633659
    - type: spearman
      value: 41.15073385665892
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 74.1492537313433
    - type: ap
      value: 36.58820102143676
    - type: ap_weighted
      value: 36.58820102143676
    - type: f1
      value: 67.93641050300623
    - type: f1_weighted
      value: 76.47946936836382
    - type: main_score
      value: 74.1492537313433
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification (default)
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 92.57937499999998
    - type: ap
      value: 89.09881932276382
    - type: ap_weighted
      value: 89.09881932276382
    - type: f1
      value: 92.57389464257594
    - type: f1_weighted
      value: 92.57389464257594
    - type: main_score
      value: 92.57937499999998
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 47.95399999999999
    - type: f1
      value: 45.23480325168402
    - type: f1_weighted
      value: 45.23480325168403
    - type: main_score
      value: 47.95399999999999
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (zh)
      type: mteb/amazon_reviews_multi
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 43.916000000000004
    - type: f1
      value: 40.79038102586015
    - type: f1_weighted
      value: 40.79038102586015
    - type: main_score
      value: 43.916000000000004
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: validation
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 47.716
    - type: f1
      value: 44.97469896514136
    - type: f1_weighted
      value: 44.97469896514136
    - type: main_score
      value: 47.716
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (zh)
      type: mteb/amazon_reviews_multi
      config: zh
      split: validation
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 43.016000000000005
    - type: f1
      value: 39.88062282479835
    - type: f1_weighted
      value: 39.88062282479835
    - type: main_score
      value: 43.016000000000005
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna (default)
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
    metrics:
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      value: 55.97299999999999
    - type: map_at_1
      value: 31.009999999999998
    - type: map_at_10
      value: 46.951
    - type: map_at_100
      value: 47.788000000000004
    - type: map_at_1000
      value: 47.794
    - type: map_at_20
      value: 47.656
    - type: map_at_3
      value: 41.69
    - type: map_at_5
      value: 44.795
    - type: mrr_at_1
      value: 31.57894736842105
    - type: mrr_at_10
      value: 47.150336426652245
    - type: mrr_at_100
      value: 48.00003421265431
    - type: mrr_at_1000
      value: 48.006517491673485
    - type: mrr_at_20
      value: 47.86823495425013
    - type: mrr_at_3
      value: 41.90374585111427
    - type: mrr_at_5
      value: 45.00474158368897
    - type: nauc_map_at_1000_diff1
      value: 14.400156277962079
    - type: nauc_map_at_1000_max
      value: -6.074701279893042
    - type: nauc_map_at_1000_std
      value: -12.047730490841793
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    - type: nauc_mrr_at_5_std
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      value: 18.2188090059407
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      value: -6.90680836409332
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      value: -11.42044016086847
    - type: nauc_ndcg_at_20_diff1
      value: 13.19308743032763
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      value: -5.925869069550241
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      value: 14.098445595476438
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      value: -5.438990657735945
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      value: -13.026198448199588
    - type: nauc_ndcg_at_5_diff1
      value: 12.887695825204021
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      value: -5.527892954283733
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    - type: nauc_precision_at_1000_diff1
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      value: 34.44303415297543
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      value: -14.18952698929006
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      value: 8.856000600248196
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      value: -3.5855091847389
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    - type: nauc_recall_at_1000_diff1
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      value: 16.970591395954745
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      value: 34.443034152975024
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      value: 10.924572747165762
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      value: 0.724533690511315
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      value: 18.2188090059407
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      value: -6.90680836409332
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      value: 8.856000600248263
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      value: -3.5855091847388603
    - type: nauc_recall_at_5_std
      value: -13.869699312071909
    - type: ndcg_at_1
      value: 31.009999999999998
    - type: ndcg_at_10
      value: 55.97299999999999
    - type: ndcg_at_100
      value: 59.272000000000006
    - type: ndcg_at_1000
      value: 59.407
    - type: ndcg_at_20
      value: 58.449
    - type: ndcg_at_3
      value: 45.227000000000004
    - type: ndcg_at_5
      value: 50.792
    - type: precision_at_1
      value: 31.009999999999998
    - type: precision_at_10
      value: 8.485
    - type: precision_at_100
      value: 0.9860000000000001
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_20
      value: 4.723
    - type: precision_at_3
      value: 18.492
    - type: precision_at_5
      value: 13.783999999999999
    - type: recall_at_1
      value: 31.009999999999998
    - type: recall_at_10
      value: 84.851
    - type: recall_at_100
      value: 98.649
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_20
      value: 94.452
    - type: recall_at_3
      value: 55.477
    - type: recall_at_5
      value: 68.919
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P (default)
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: main_score
      value: 48.31683216128774
    - type: v_measure
      value: 48.31683216128774
    - type: v_measure_std
      value: 13.795207109799703
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S (default)
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: main_score
      value: 40.2951016935384
    - type: v_measure
      value: 40.2951016935384
    - type: v_measure_std
      value: 14.193710444297869
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions (default)
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: main_score
      value: 60.45095169935259
    - type: map
      value: 60.45095169935259
    - type: mrr
      value: 73.43567251461988
    - type: nAUC_map_diff1
      value: 15.357222913791704
    - type: nAUC_map_max
      value: 24.301239659848346
    - type: nAUC_map_std
      value: 18.26732583044278
    - type: nAUC_mrr_diff1
      value: 24.108010981589057
    - type: nAUC_mrr_max
      value: 34.90261214387396
    - type: nAUC_mrr_std
      value: 20.350034497982126
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES (default)
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cosine_pearson
      value: 90.16604991710759
    - type: cosine_spearman
      value: 88.4670760233051
    - type: euclidean_pearson
      value: 89.02378164860428
    - type: euclidean_spearman
      value: 88.4670760233051
    - type: main_score
      value: 88.4670760233051
    - type: manhattan_pearson
      value: 88.8866912507422
    - type: manhattan_spearman
      value: 88.2755053931781
    - type: pearson
      value: 90.16604991710759
    - type: spearman
      value: 88.4670760233051
  - task:
      type: STS
    dataset:
      name: MTEB BQ (default)
      type: C-MTEB/BQ
      config: default
      split: test
      revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
    metrics:
    - type: cosine_pearson
      value: 49.45233904713201
    - type: cosine_spearman
      value: 49.77342815602789
    - type: euclidean_pearson
      value: 49.13579036236359
    - type: euclidean_spearman
      value: 49.77342122767529
    - type: main_score
      value: 49.77342815602789
    - type: manhattan_pearson
      value: 49.01322677955527
    - type: manhattan_spearman
      value: 49.702538779772226
    - type: pearson
      value: 49.45233904713201
    - type: spearman
      value: 49.77342815602789
  - task:
      type: STS
    dataset:
      name: MTEB BQ (default)
      type: C-MTEB/BQ
      config: default
      split: validation
      revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
    metrics:
    - type: cosine_pearson
      value: 53.43473222697715
    - type: cosine_spearman
      value: 54.24325202324013
    - type: euclidean_pearson
      value: 53.4053341221681
    - type: euclidean_spearman
      value: 54.2432485591385
    - type: main_score
      value: 54.24325202324013
    - type: manhattan_pearson
      value: 53.31602762068146
    - type: manhattan_spearman
      value: 54.180811590825925
    - type: pearson
      value: 53.43473222697715
    - type: spearman
      value: 54.24325202324013
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification (default)
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 82.11038961038962
    - type: f1
      value: 81.50275371635729
    - type: f1_weighted
      value: 81.50275371635732
    - type: main_score
      value: 82.11038961038962
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P (default)
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: main_score
      value: 39.85718105201471
    - type: v_measure
      value: 39.85718105201471
    - type: v_measure_std
      value: 0.9098592525717781
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S (default)
      type: mteb/biorxiv-clustering-s2s
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      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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      type: C-MTEB/CLSClusteringP2P
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      revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
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      type: C-MTEB/CLSClusteringS2S
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      revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
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  - task:
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      type: C-MTEB/CMedQAv1-reranking
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      revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
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      type: C-MTEB/CMedQAv2-reranking
      config: default
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  - task:
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      type: mteb/cqadupstack-android
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      revision: f46a197baaae43b4f621051089b82a364682dfeb
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      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
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  - task:
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      type: C-MTEB/CMNLI
      config: default
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      revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
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      type: C-MTEB/CovidRetrieval
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  - task:
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      type: mteb/dbpedia
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  - task:
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  - task:
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  - task:
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    dataset:
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      type: mteb/hotpotqa
      config: default
      split: test
      revision: ab518f4d6fcca38d87c25209f94beba119d02014
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  - task:
      type: Classification
    dataset:
      name: MTEB IFlyTek (default)
      type: C-MTEB/IFlyTek-classification
      config: default
      split: test
      revision: 421605374b29664c5fc098418fe20ada9bd55f8a
    metrics:
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    - type: f1
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      value: 0.0
  - task:
      type: Classification
    dataset:
      name: MTEB IFlyTek (default)
      type: C-MTEB/IFlyTek-classification
      config: default
      split: validation
      revision: 421605374b29664c5fc098418fe20ada9bd55f8a
    metrics:
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    - type: main_score
      value: 47.97999230473259
  - task:
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    dataset:
      name: MTEB ImdbClassification (default)
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
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    - type: ap_weighted
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    - type: f1
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  - task:
      type: Classification
    dataset:
      name: MTEB JDReview (default)
      type: C-MTEB/JDReview-classification
      config: default
      split: test
      revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
    metrics:
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  - task:
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      name: MTEB LCQMC (default)
      type: C-MTEB/LCQMC
      config: default
      split: test
      revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
    metrics:
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  - task:
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      type: C-MTEB/Mmarco-reranking
      config: default
      split: dev
      revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
    metrics:
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  - task:
      type: Retrieval
    dataset:
      name: MTEB MMarcoRetrieval (default)
      type: C-MTEB/MMarcoRetrieval
      config: default
      split: test
      revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
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  - task:
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  - task:
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  - task:
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  - task:
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  - task:
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      type: mteb/amazon_massive_intent
      config: zh-CN
      split: validation
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 71.17560255779637
    - type: f1
      value: 67.53436094771642
    - type: f1_weighted
      value: 69.85911870240461
    - type: main_score
      value: 71.17560255779637
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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    - type: accuracy
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    - type: f1
      value: 74.81652741027294
    - type: f1_weighted
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    - type: main_score
      value: 75.83725622057834
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (zh-CN)
      type: mteb/amazon_massive_scenario
      config: zh-CN
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 74.77471418964359
    - type: f1
      value: 74.50834305419674
    - type: f1_weighted
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    - type: main_score
      value: 74.77471418964359
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: validation
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 75.2680767338908
    - type: f1
      value: 73.84891408751763
    - type: f1_weighted
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    - type: main_score
      value: 75.2680767338908
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (zh-CN)
      type: mteb/amazon_massive_scenario
      config: zh-CN
      split: validation
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
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    - type: f1
      value: 73.56517145836091
    - type: f1_weighted
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    - type: main_score
      value: 74.20068863748155
  - task:
      type: Retrieval
    dataset:
      name: MTEB MedicalRetrieval (default)
      type: C-MTEB/MedicalRetrieval
      config: default
      split: test
      revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
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    - type: map_at_100
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    - type: map_at_1000
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    - type: ndcg_at_1
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    - type: ndcg_at_10
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    - type: ndcg_at_100
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    - type: ndcg_at_1000
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    - type: ndcg_at_20
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    - type: ndcg_at_3
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    - type: ndcg_at_5
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    - type: precision_at_1
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    - type: precision_at_10
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    - type: precision_at_100
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    - type: precision_at_1000
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    - type: precision_at_3
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    - type: precision_at_5
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    - type: recall_at_1
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    - type: recall_at_10
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    - type: recall_at_100
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    - type: recall_at_1000
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      value: 71.5
    - type: recall_at_3
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    - type: recall_at_5
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  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P (default)
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: main_score
      value: 34.731017731166105
    - type: v_measure
      value: 34.731017731166105
    - type: v_measure_std
      value: 1.5618103916501433
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S (default)
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
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      value: 31.545874099031675
    - type: v_measure
      value: 31.545874099031675
    - type: v_measure_std
      value: 1.4489482273302008
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking (default)
      type: mteb/mind_small
      config: default
      split: test
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
    metrics:
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      value: 32.68299163296308
    - type: map
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    - type: mrr
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    - type: nAUC_map_diff1
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  - task:
      type: Classification
    dataset:
      name: MTEB MultilingualSentiment (default)
      type: C-MTEB/MultilingualSentiment-classification
      config: default
      split: test
      revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
    metrics:
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    - type: f1
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    - type: f1_weighted
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    - type: main_score
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  - task:
      type: Classification
    dataset:
      name: MTEB MultilingualSentiment (default)
      type: C-MTEB/MultilingualSentiment-classification
      config: default
      split: validation
      revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
    metrics:
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      value: 71.30311389484187
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      value: 71.95333333333333
  - task:
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    dataset:
      name: MTEB NFCorpus (default)
      type: mteb/nfcorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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    - type: recall_at_1
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      value: 22.902
    - type: recall_at_3
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    - type: recall_at_5
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  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ (default)
      type: mteb/nq
      config: default
      split: test
      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
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    - type: map_at_100
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    - type: mrr_at_100
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    - type: mrr_at_1000
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    - type: ndcg_at_100
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    - type: ndcg_at_1000
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    - type: ndcg_at_20
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      value: 61.693
    - type: precision_at_1
      value: 45.452
    - type: precision_at_10
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    - type: precision_at_100
      value: 1.176
    - type: precision_at_1000
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    - type: precision_at_3
      value: 25.628
    - type: precision_at_5
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    - type: recall_at_1
      value: 40.624
    - type: recall_at_10
      value: 84.096
    - type: recall_at_100
      value: 96.734
    - type: recall_at_1000
      value: 99.401
    - type: recall_at_20
      value: 90.276
    - type: recall_at_3
      value: 65.892
    - type: recall_at_5
      value: 75.847
  - task:
      type: PairClassification
    dataset:
      name: MTEB Ocnli (default)
      type: C-MTEB/OCNLI
      config: default
      split: validation
      revision: 66e76a618a34d6d565d5538088562851e6daa7ec
    metrics:
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      value: 57.715213860314016
    - type: cosine_accuracy_threshold
      value: 70.3215479850769
    - type: cosine_ap
      value: 58.8326699963807
    - type: cosine_f1
      value: 67.816091954023
    - type: cosine_f1_threshold
      value: 42.0912504196167
    - type: cosine_precision
      value: 51.38813282525857
    - type: cosine_recall
      value: 99.68321013727561
    - type: dot_accuracy
      value: 57.715213860314016
    - type: dot_accuracy_threshold
      value: 70.3215479850769
    - type: dot_ap
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    - type: dot_f1
      value: 67.816091954023
    - type: dot_f1_threshold
      value: 42.0912504196167
    - type: dot_precision
      value: 51.38813282525857
    - type: dot_recall
      value: 99.68321013727561
    - type: euclidean_accuracy
      value: 57.715213860314016
    - type: euclidean_accuracy_threshold
      value: 77.04342603683472
    - type: euclidean_ap
      value: 58.8326699963807
    - type: euclidean_f1
      value: 67.816091954023
    - type: euclidean_f1_threshold
      value: 107.61815309524536
    - type: euclidean_precision
      value: 51.38813282525857
    - type: euclidean_recall
      value: 99.68321013727561
    - type: main_score
      value: 57.877639415268
    - type: manhattan_accuracy
      value: 57.877639415268
    - type: manhattan_accuracy_threshold
      value: 1952.0273208618164
    - type: manhattan_ap
      value: 58.70263102269272
    - type: manhattan_f1
      value: 67.84172661870504
    - type: manhattan_f1_threshold
      value: 2661.929702758789
    - type: manhattan_precision
      value: 51.44571740316422
    - type: manhattan_recall
      value: 99.57761351636748
    - type: max_accuracy
      value: 57.877639415268
    - type: max_ap
      value: 58.8326699963807
    - type: max_f1
      value: 67.84172661870504
    - type: max_precision
      value: 51.44571740316422
    - type: max_recall
      value: 99.68321013727561
    - type: similarity_accuracy
      value: 57.715213860314016
    - type: similarity_accuracy_threshold
      value: 70.3215479850769
    - type: similarity_ap
      value: 58.8326699963807
    - type: similarity_f1
      value: 67.816091954023
    - type: similarity_f1_threshold
      value: 42.0912504196167
    - type: similarity_precision
      value: 51.38813282525857
    - type: similarity_recall
      value: 99.68321013727561
  - task:
      type: Classification
    dataset:
      name: MTEB OnlineShopping (default)
      type: C-MTEB/OnlineShopping-classification
      config: default
      split: test
      revision: e610f2ebd179a8fda30ae534c3878750a96db120
    metrics:
    - type: accuracy
      value: 92.24
    - type: ap
      value: 91.00427236651306
    - type: ap_weighted
      value: 91.00427236651306
    - type: f1
      value: 92.23673939008314
    - type: f1_weighted
      value: 92.24091264330853
    - type: main_score
      value: 92.24
  - task:
      type: STS
    dataset:
      name: MTEB PAWSX (default)
      type: C-MTEB/PAWSX
      config: default
      split: test
      revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
    metrics:
    - type: cosine_pearson
      value: 13.62339858857132
    - type: cosine_spearman
      value: 14.886062653753804
    - type: euclidean_pearson
      value: 16.711204002219453
    - type: euclidean_spearman
      value: 14.8864092068256
    - type: main_score
      value: 14.886062653753804
    - type: manhattan_pearson
      value: 16.658236019215405
    - type: manhattan_spearman
      value: 14.868816375702131
    - type: pearson
      value: 13.62339858857132
    - type: spearman
      value: 14.886062653753804
  - task:
      type: STS
    dataset:
      name: MTEB QBQTC (default)
      type: C-MTEB/QBQTC
      config: default
      split: test
      revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
    metrics:
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      value: 37.88649935702812
    - type: cosine_spearman
      value: 38.59331260288544
    - type: euclidean_pearson
      value: 37.492219553708004
    - type: euclidean_spearman
      value: 38.59333707659388
    - type: main_score
      value: 38.59331260288544
    - type: manhattan_pearson
      value: 37.59659518440478
    - type: manhattan_spearman
      value: 38.70529977801903
    - type: pearson
      value: 37.88649935702812
    - type: spearman
      value: 38.59331260288544
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval (default)
      type: mteb/quora
      config: default
      split: test
      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
    metrics:
    - type: main_score
      value: 88.828
    - type: map_at_1
      value: 70.86200000000001
    - type: map_at_10
      value: 85.146
    - type: map_at_100
      value: 85.76599999999999
    - type: map_at_1000
      value: 85.78
    - type: map_at_20
      value: 85.56200000000001
    - type: map_at_3
      value: 82.15299999999999
    - type: map_at_5
      value: 84.06400000000001
    - type: mrr_at_1
      value: 81.55
    - type: mrr_at_10
      value: 87.83961904761877
    - type: mrr_at_100
      value: 87.9277183426686
    - type: mrr_at_1000
      value: 87.92847303870963
    - type: mrr_at_20
      value: 87.90789174563797
    - type: mrr_at_3
      value: 86.88999999999969
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      type: mteb/reddit-clustering
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  - task:
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      type: mteb/sickr-sts
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    - type: manhattan_pearson
      value: 78.48511276416917
    - type: manhattan_spearman
      value: 74.39838905100096
    - type: pearson
      value: 80.05738628240671
    - type: spearman
      value: 74.67900925815495
  - task:
      type: STS
    dataset:
      name: MTEB STS12 (default)
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cosine_pearson
      value: 59.8887701896054
    - type: cosine_spearman
      value: 61.76924747645064
    - type: euclidean_pearson
      value: 59.33178599145238
    - type: euclidean_spearman
      value: 61.76932878335521
    - type: main_score
      value: 61.76924747645064
    - type: manhattan_pearson
      value: 59.07929980423876
    - type: manhattan_spearman
      value: 61.729703658805704
    - type: pearson
      value: 59.8887701896054
    - type: spearman
      value: 61.76924747645064
  - task:
      type: STS
    dataset:
      name: MTEB STS13 (default)
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cosine_pearson
      value: 73.46830128106028
    - type: cosine_spearman
      value: 75.65482017135808
    - type: euclidean_pearson
      value: 75.32146398293357
    - type: euclidean_spearman
      value: 75.65482017135808
    - type: main_score
      value: 75.65482017135808
    - type: manhattan_pearson
      value: 75.11839624254772
    - type: manhattan_spearman
      value: 75.52002809163668
    - type: pearson
      value: 73.46830128106028
    - type: spearman
      value: 75.65482017135808
  - task:
      type: STS
    dataset:
      name: MTEB STS14 (default)
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cosine_pearson
      value: 71.49707687668383
    - type: cosine_spearman
      value: 68.16858223090989
    - type: euclidean_pearson
      value: 71.22162204420484
    - type: euclidean_spearman
      value: 68.16860152985392
    - type: main_score
      value: 68.16858223090989
    - type: manhattan_pearson
      value: 70.91495914707767
    - type: manhattan_spearman
      value: 67.98861350196948
    - type: pearson
      value: 71.49707687668383
    - type: spearman
      value: 68.16858223090989
  - task:
      type: STS
    dataset:
      name: MTEB STS15 (default)
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cosine_pearson
      value: 76.4178409313179
    - type: cosine_spearman
      value: 78.88504631843803
    - type: euclidean_pearson
      value: 78.71618727706142
    - type: euclidean_spearman
      value: 78.88501053846501
    - type: main_score
      value: 78.88504631843803
    - type: manhattan_pearson
      value: 78.89331900480339
    - type: manhattan_spearman
      value: 79.04826734191282
    - type: pearson
      value: 76.4178409313179
    - type: spearman
      value: 78.88504631843803
  - task:
      type: STS
    dataset:
      name: MTEB STS16 (default)
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cosine_pearson
      value: 77.55495044464278
    - type: cosine_spearman
      value: 80.3363642808641
    - type: euclidean_pearson
      value: 79.96424489786347
    - type: euclidean_spearman
      value: 80.3363642808641
    - type: main_score
      value: 80.3363642808641
    - type: manhattan_pearson
      value: 80.05244658476923
    - type: manhattan_spearman
      value: 80.42606943747235
    - type: pearson
      value: 77.55495044464278
    - type: spearman
      value: 80.3363642808641
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: cosine_pearson
      value: 79.38935754316142
    - type: cosine_spearman
      value: 85.46849738990943
    - type: euclidean_pearson
      value: 83.64718180060812
    - type: euclidean_spearman
      value: 85.46849738990943
    - type: main_score
      value: 85.46849738990943
    - type: manhattan_pearson
      value: 83.67702948761875
    - type: manhattan_spearman
      value: 85.34710495908027
    - type: pearson
      value: 79.38935754316142
    - type: spearman
      value: 85.46849738990943
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (zh-en)
      type: mteb/sts22-crosslingual-sts
      config: zh-en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: cosine_pearson
      value: 73.89485634957391
    - type: cosine_spearman
      value: 73.90825698961848
    - type: euclidean_pearson
      value: 75.25124910546262
    - type: euclidean_spearman
      value: 73.90825698961848
    - type: main_score
      value: 73.90825698961848
    - type: manhattan_pearson
      value: 75.11860084263171
    - type: manhattan_spearman
      value: 73.69593141677598
    - type: pearson
      value: 73.89485634957391
    - type: spearman
      value: 73.90825698961848
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: cosine_pearson
      value: 67.81059898950983
    - type: cosine_spearman
      value: 68.30115315444206
    - type: euclidean_pearson
      value: 69.27103790429173
    - type: euclidean_spearman
      value: 68.30115315444206
    - type: main_score
      value: 68.30115315444206
    - type: manhattan_pearson
      value: 69.46849620900602
    - type: manhattan_spearman
      value: 68.45651992521948
    - type: pearson
      value: 67.81059898950983
    - type: spearman
      value: 68.30115315444206
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (zh)
      type: mteb/sts22-crosslingual-sts
      config: zh
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: cosine_pearson
      value: 74.44033998959976
    - type: cosine_spearman
      value: 73.58772060484971
    - type: euclidean_pearson
      value: 73.06488074477468
    - type: euclidean_spearman
      value: 73.58772060484971
    - type: main_score
      value: 73.58772060484971
    - type: manhattan_pearson
      value: 73.00608049548906
    - type: manhattan_spearman
      value: 73.55105762622729
    - type: pearson
      value: 74.44033998959976
    - type: spearman
      value: 73.58772060484971
  - task:
      type: STS
    dataset:
      name: MTEB STSB (default)
      type: C-MTEB/STSB
      config: default
      split: test
      revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
    metrics:
    - type: cosine_pearson
      value: 68.09021885283748
    - type: cosine_spearman
      value: 69.45189378034146
    - type: euclidean_pearson
      value: 69.51961611366887
    - type: euclidean_spearman
      value: 69.45189378034146
    - type: main_score
      value: 69.45189378034146
    - type: manhattan_pearson
      value: 69.30192429794056
    - type: manhattan_spearman
      value: 69.22518486689475
    - type: pearson
      value: 68.09021885283748
    - type: spearman
      value: 69.45189378034146
  - task:
      type: STS
    dataset:
      name: MTEB STSB (default)
      type: C-MTEB/STSB
      config: default
      split: validation
      revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
    metrics:
    - type: cosine_pearson
      value: 69.85149506856348
    - type: cosine_spearman
      value: 72.05319943168429
    - type: euclidean_pearson
      value: 72.41468551288946
    - type: euclidean_spearman
      value: 72.05319943168429
    - type: main_score
      value: 72.05319943168429
    - type: manhattan_pearson
      value: 72.08871687183135
    - type: manhattan_spearman
      value: 71.63960073768047
    - type: pearson
      value: 69.85149506856348
    - type: spearman
      value: 72.05319943168429
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark (default)
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cosine_pearson
      value: 71.64554885631986
    - type: cosine_spearman
      value: 74.23136916844818
    - type: euclidean_pearson
      value: 74.06782419242319
    - type: euclidean_spearman
      value: 74.23136916844818
    - type: main_score
      value: 74.23136916844818
    - type: manhattan_pearson
      value: 74.0008422515175
    - type: manhattan_spearman
      value: 74.10730250032161
    - type: pearson
      value: 71.64554885631986
    - type: spearman
      value: 74.23136916844818
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR (default)
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: main_score
      value: 84.06925933025143
    - type: map
      value: 84.06925933025143
    - type: mrr
      value: 95.48704382037715
    - type: nAUC_map_diff1
      value: -1.2531960147273353
    - type: nAUC_map_max
      value: 53.63794852890932
    - type: nAUC_map_std
      value: 66.72236818008908
    - type: nAUC_mrr_diff1
      value: 44.74963455842425
    - type: nAUC_mrr_max
      value: 85.95161801239465
    - type: nAUC_mrr_std
      value: 81.36111675398224
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact (default)
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: main_score
      value: 76.10900000000001
    - type: map_at_1
      value: 60.760999999999996
    - type: map_at_10
      value: 71.654
    - type: map_at_100
      value: 72.137
    - type: map_at_1000
      value: 72.149
    - type: map_at_20
      value: 72.021
    - type: map_at_3
      value: 68.772
    - type: map_at_5
      value: 70.36200000000001
    - type: mrr_at_1
      value: 63.66666666666667
    - type: mrr_at_10
      value: 72.70595238095237
    - type: mrr_at_100
      value: 73.04863520816613
    - type: mrr_at_1000
      value: 73.06075018033764
    - type: mrr_at_20
      value: 72.93638474099001
    - type: mrr_at_3
      value: 70.66666666666667
    - type: mrr_at_5
      value: 71.78333333333333
    - type: nauc_map_at_1000_diff1
      value: 71.35087668762627
    - type: nauc_map_at_1000_max
      value: 54.011748108009186
    - type: nauc_map_at_1000_std
      value: 7.678845781955053
    - type: nauc_map_at_100_diff1
      value: 71.34472898717338
    - type: nauc_map_at_100_max
      value: 54.0145296319552
    - type: nauc_map_at_100_std
      value: 7.676258294947637
    - type: nauc_map_at_10_diff1
      value: 71.37574967668972
    - type: nauc_map_at_10_max
      value: 53.92702576795545
    - type: nauc_map_at_10_std
      value: 7.758848033974705
    - type: nauc_map_at_1_diff1
      value: 73.89906137509777
    - type: nauc_map_at_1_max
      value: 44.9910089561678
    - type: nauc_map_at_1_std
      value: -3.560024114726528
    - type: nauc_map_at_20_diff1
      value: 71.3016202130441
    - type: nauc_map_at_20_max
      value: 54.0858492905278
    - type: nauc_map_at_20_std
      value: 7.758638712425257
    - type: nauc_map_at_3_diff1
      value: 71.0655101710049
    - type: nauc_map_at_3_max
      value: 51.54952135715274
    - type: nauc_map_at_3_std
      value: 3.191945160660174
    - type: nauc_map_at_5_diff1
      value: 71.71790408466262
    - type: nauc_map_at_5_max
      value: 53.98746745769737
    - type: nauc_map_at_5_std
      value: 6.607248876321941
    - type: nauc_mrr_at_1000_diff1
      value: 71.35164281601696
    - type: nauc_mrr_at_1000_max
      value: 55.476178340437
    - type: nauc_mrr_at_1000_std
      value: 10.970185730462788
    - type: nauc_mrr_at_100_diff1
      value: 71.34535141418533
    - type: nauc_mrr_at_100_max
      value: 55.47824801045241
    - type: nauc_mrr_at_100_std
      value: 10.965792309322826
    - type: nauc_mrr_at_10_diff1
      value: 71.25095755664336
    - type: nauc_mrr_at_10_max
      value: 55.697524040234235
    - type: nauc_mrr_at_10_std
      value: 11.470516804375386
    - type: nauc_mrr_at_1_diff1
      value: 73.2821036264498
    - type: nauc_mrr_at_1_max
      value: 50.30171935076129
    - type: nauc_mrr_at_1_std
      value: 5.150119259795942
    - type: nauc_mrr_at_20_diff1
      value: 71.29800338727547
    - type: nauc_mrr_at_20_max
      value: 55.54151075144868
    - type: nauc_mrr_at_20_std
      value: 11.02928131756253
    - type: nauc_mrr_at_3_diff1
      value: 71.00310802253107
    - type: nauc_mrr_at_3_max
      value: 55.197709395727045
    - type: nauc_mrr_at_3_std
      value: 10.04566210045661
    - type: nauc_mrr_at_5_diff1
      value: 71.20451267727627
    - type: nauc_mrr_at_5_max
      value: 56.42653941908357
    - type: nauc_mrr_at_5_std
      value: 12.134736985103611
    - type: nauc_ndcg_at_1000_diff1
      value: 70.82229943036683
    - type: nauc_ndcg_at_1000_max
      value: 55.754118035528776
    - type: nauc_ndcg_at_1000_std
      value: 10.535060943270949
    - type: nauc_ndcg_at_100_diff1
      value: 70.56950271178324
    - type: nauc_ndcg_at_100_max
      value: 55.8594687697972
    - type: nauc_ndcg_at_100_std
      value: 10.666914593212478
    - type: nauc_ndcg_at_10_diff1
      value: 70.38141205486814
    - type: nauc_ndcg_at_10_max
      value: 56.37560065613112
    - type: nauc_ndcg_at_10_std
      value: 12.026555946404496
    - type: nauc_ndcg_at_1_diff1
      value: 73.2821036264498
    - type: nauc_ndcg_at_1_max
      value: 50.30171935076129
    - type: nauc_ndcg_at_1_std
      value: 5.150119259795942
    - type: nauc_ndcg_at_20_diff1
      value: 70.3390226823462
    - type: nauc_ndcg_at_20_max
      value: 56.500553855618605
    - type: nauc_ndcg_at_20_std
      value: 11.29004765829262
    - type: nauc_ndcg_at_3_diff1
      value: 69.49806863319228
    - type: nauc_ndcg_at_3_max
      value: 54.71563247265625
    - type: nauc_ndcg_at_3_std
      value: 7.436156809946794
    - type: nauc_ndcg_at_5_diff1
      value: 70.92542004817086
    - type: nauc_ndcg_at_5_max
      value: 57.28530843114872
    - type: nauc_ndcg_at_5_std
      value: 11.33887216009956
    - type: nauc_precision_at_1000_diff1
      value: -35.28994929585216
    - type: nauc_precision_at_1000_max
      value: 14.893397453096902
    - type: nauc_precision_at_1000_std
      value: 51.396256011227734
    - type: nauc_precision_at_100_diff1
      value: -23.15302836790124
    - type: nauc_precision_at_100_max
      value: 20.885275194425965
    - type: nauc_precision_at_100_std
      value: 47.38237004790941
    - type: nauc_precision_at_10_diff1
      value: 0.6508705327922056
    - type: nauc_precision_at_10_max
      value: 35.86490378321761
    - type: nauc_precision_at_10_std
      value: 47.767508836235066
    - type: nauc_precision_at_1_diff1
      value: 73.2821036264498
    - type: nauc_precision_at_1_max
      value: 50.30171935076129
    - type: nauc_precision_at_1_std
      value: 5.150119259795942
    - type: nauc_precision_at_20_diff1
      value: -10.7729801324503
    - type: nauc_precision_at_20_max
      value: 29.079735099337757
    - type: nauc_precision_at_20_std
      value: 45.845298013245014
    - type: nauc_precision_at_3_diff1
      value: 34.75492967022111
    - type: nauc_precision_at_3_max
      value: 50.28675231734285
    - type: nauc_precision_at_3_std
      value: 28.248258905786056
    - type: nauc_precision_at_5_diff1
      value: 20.985818128495684
    - type: nauc_precision_at_5_max
      value: 49.058382386827766
    - type: nauc_precision_at_5_std
      value: 42.45972725558781
    - type: nauc_recall_at_1000_diff1
      value: .nan
    - type: nauc_recall_at_1000_max
      value: .nan
    - type: nauc_recall_at_1000_std
      value: .nan
    - type: nauc_recall_at_100_diff1
      value: 59.20634920634924
    - type: nauc_recall_at_100_max
      value: 61.545284780578854
    - type: nauc_recall_at_100_std
      value: 20.144724556489304
    - type: nauc_recall_at_10_diff1
      value: 64.66373744374164
    - type: nauc_recall_at_10_max
      value: 63.97713233823764
    - type: nauc_recall_at_10_std
      value: 26.664192167250576
    - type: nauc_recall_at_1_diff1
      value: 73.89906137509777
    - type: nauc_recall_at_1_max
      value: 44.9910089561678
    - type: nauc_recall_at_1_std
      value: -3.560024114726528
    - type: nauc_recall_at_20_diff1
      value: 62.63616557734212
    - type: nauc_recall_at_20_max
      value: 67.73835460109973
    - type: nauc_recall_at_20_std
      value: 23.744164332399645
    - type: nauc_recall_at_3_diff1
      value: 66.22059143579988
    - type: nauc_recall_at_3_max
      value: 56.70083839895786
    - type: nauc_recall_at_3_std
      value: 8.353413350691936
    - type: nauc_recall_at_5_diff1
      value: 67.74494960835959
    - type: nauc_recall_at_5_max
      value: 65.60091243576522
    - type: nauc_recall_at_5_std
      value: 21.75399137900112
    - type: ndcg_at_1
      value: 63.666999999999994
    - type: ndcg_at_10
      value: 76.10900000000001
    - type: ndcg_at_100
      value: 77.989
    - type: ndcg_at_1000
      value: 78.391
    - type: ndcg_at_20
      value: 77.199
    - type: ndcg_at_3
      value: 71.53699999999999
    - type: ndcg_at_5
      value: 73.662
    - type: precision_at_1
      value: 63.666999999999994
    - type: precision_at_10
      value: 10.033
    - type: precision_at_100
      value: 1.097
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_20
      value: 5.267
    - type: precision_at_3
      value: 27.889000000000003
    - type: precision_at_5
      value: 18.267
    - type: recall_at_1
      value: 60.760999999999996
    - type: recall_at_10
      value: 88.43299999999999
    - type: recall_at_100
      value: 96.667
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_20
      value: 92.5
    - type: recall_at_3
      value: 76.461
    - type: recall_at_5
      value: 81.678
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions (default)
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cosine_accuracy
      value: 99.76831683168317
    - type: cosine_accuracy_threshold
      value: 75.98594427108765
    - type: cosine_ap
      value: 94.10650199330435
    - type: cosine_f1
      value: 87.77429467084639
    - type: cosine_f1_threshold
      value: 75.98594427108765
    - type: cosine_precision
      value: 91.90371991247265
    - type: cosine_recall
      value: 84.0
    - type: dot_accuracy
      value: 99.76831683168317
    - type: dot_accuracy_threshold
      value: 75.98594427108765
    - type: dot_ap
      value: 94.10650199330435
    - type: dot_f1
      value: 87.77429467084639
    - type: dot_f1_threshold
      value: 75.98594427108765
    - type: dot_precision
      value: 91.90371991247265
    - type: dot_recall
      value: 84.0
    - type: euclidean_accuracy
      value: 99.76831683168317
    - type: euclidean_accuracy_threshold
      value: 69.30232048034668
    - type: euclidean_ap
      value: 94.10650199330436
    - type: euclidean_f1
      value: 87.77429467084639
    - type: euclidean_f1_threshold
      value: 69.30232048034668
    - type: euclidean_precision
      value: 91.90371991247265
    - type: euclidean_recall
      value: 84.0
    - type: main_score
      value: 94.31118902382526
    - type: manhattan_accuracy
      value: 99.77227722772277
    - type: manhattan_accuracy_threshold
      value: 1752.2960662841797
    - type: manhattan_ap
      value: 94.31118902382526
    - type: manhattan_f1
      value: 87.98328108672936
    - type: manhattan_f1_threshold
      value: 1752.2960662841797
    - type: manhattan_precision
      value: 92.12253829321662
    - type: manhattan_recall
      value: 84.2
    - type: max_accuracy
      value: 99.77227722772277
    - type: max_ap
      value: 94.31118902382526
    - type: max_f1
      value: 87.98328108672936
    - type: max_precision
      value: 92.12253829321662
    - type: max_recall
      value: 84.2
    - type: similarity_accuracy
      value: 99.76831683168317
    - type: similarity_accuracy_threshold
      value: 75.98594427108765
    - type: similarity_ap
      value: 94.10650199330435
    - type: similarity_f1
      value: 87.77429467084639
    - type: similarity_f1_threshold
      value: 75.98594427108765
    - type: similarity_precision
      value: 91.90371991247265
    - type: similarity_recall
      value: 84.0
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering (default)
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: main_score
      value: 65.28699161008417
    - type: v_measure
      value: 65.28699161008417
    - type: v_measure_std
      value: 5.01676559317753
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P (default)
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: main_score
      value: 32.18986400821423
    - type: v_measure
      value: 32.18986400821423
    - type: v_measure_std
      value: 1.7607695643068701
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions (default)
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: main_score
      value: 51.373735094128726
    - type: map
      value: 51.373735094128726
    - type: mrr
      value: 52.03188661828367
    - type: nAUC_map_diff1
      value: 37.47429390492891
    - type: nAUC_map_max
      value: 11.050764243820572
    - type: nAUC_map_std
      value: 8.32183046644254
    - type: nAUC_mrr_diff1
      value: 38.215874831509836
    - type: nAUC_mrr_max
      value: 12.326444149252634
    - type: nAUC_mrr_std
      value: 9.234015034873362
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval (default)
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
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      value: 30.04547230414472
    - type: cosine_spearman
      value: 30.62051882468504
    - type: dot_pearson
      value: 30.04547442370404
    - type: dot_spearman
      value: 30.62051882468504
    - type: main_score
      value: 30.62051882468504
    - type: pearson
      value: 30.04547230414472
    - type: spearman
      value: 30.62051882468504
  - task:
      type: Reranking
    dataset:
      name: MTEB T2Reranking (default)
      type: C-MTEB/T2Reranking
      config: default
      split: dev
      revision: 76631901a18387f85eaa53e5450019b87ad58ef9
    metrics:
    - type: main_score
      value: 67.491940360769
    - type: map
      value: 67.491940360769
    - type: mrr
      value: 77.88394939334343
    - type: nAUC_map_diff1
      value: -9.62446631785462
    - type: nAUC_map_max
      value: 36.249702987605744
    - type: nAUC_map_std
      value: -2.805167498766831
    - type: nAUC_mrr_diff1
      value: -6.20324917287488
    - type: nAUC_mrr_max
      value: 31.812094369246875
    - type: nAUC_mrr_std
      value: -4.075688771938606
  - task:
      type: Retrieval
    dataset:
      name: MTEB T2Retrieval (default)
      type: C-MTEB/T2Retrieval
      config: default
      split: test
      revision: 8731a845f1bf500a4f111cf1070785c793d10e64
    metrics:
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      value: 84.787
    - type: map_at_1
      value: 27.672
    - type: map_at_10
      value: 77.376
    - type: map_at_100
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    - type: map_at_1000
      value: 81.003
    - type: map_at_20
      value: 80.10300000000001
    - type: map_at_3
      value: 54.591
    - type: map_at_5
      value: 66.989
    - type: mrr_at_1
      value: 90.43485884622129
    - type: mrr_at_10
      value: 92.71924029124197
    - type: mrr_at_100
      value: 92.80671884902306
    - type: mrr_at_1000
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    - type: mrr_at_20
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    - type: mrr_at_3
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    - type: ndcg_at_1
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    - type: ndcg_at_10
      value: 84.787
    - type: ndcg_at_100
      value: 88.276
    - type: ndcg_at_1000
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    - type: ndcg_at_20
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    - type: ndcg_at_3
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    - type: ndcg_at_5
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    - type: precision_at_1
      value: 90.435
    - type: precision_at_10
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    - type: precision_at_100
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    - type: precision_at_1000
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    - type: precision_at_3
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    - type: precision_at_5
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    - type: recall_at_1
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    - type: recall_at_10
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    - type: recall_at_100
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    - type: recall_at_1000
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    - type: recall_at_20
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    - type: recall_at_3
      value: 56.16
    - type: recall_at_5
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  - task:
      type: Classification
    dataset:
      name: MTEB TNews (default)
      type: C-MTEB/TNews-classification
      config: default
      split: test
      revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
    metrics:
    - type: accuracy
      value: 0.0
    - type: f1
      value: 0.0
    - type: f1_weighted
      value: 0.0
    - type: main_score
      value: 0.0
  - task:
      type: Classification
    dataset:
      name: MTEB TNews (default)
      type: C-MTEB/TNews-classification
      config: default
      split: validation
      revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
    metrics:
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      value: 50.82700000000001
    - type: f1
      value: 48.96846676189542
    - type: f1_weighted
      value: 50.893856756125246
    - type: main_score
      value: 50.82700000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID (default)
      type: mteb/trec-covid
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
    metrics:
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    - type: map_at_10
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    - type: map_at_100
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    - type: map_at_1000
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    - type: map_at_20
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    - type: map_at_3
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    - type: map_at_5
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    - type: mrr_at_1
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    - type: mrr_at_10
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    - type: mrr_at_100
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    - type: mrr_at_1000
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    - type: mrr_at_20
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    - type: mrr_at_3
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    - type: mrr_at_5
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  - task:
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    dataset:
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      type: C-MTEB/ThuNewsClusteringP2P
      config: default
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      revision: 5798586b105c0434e4f0fe5e767abe619442cf93
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  - task:
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    dataset:
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      type: C-MTEB/ThuNewsClusteringS2S
      config: default
      split: test
      revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
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    - type: v_measure
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  - task:
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      type: mteb/touche2020
      config: default
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  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification (default)
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
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    - type: ap_weighted
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    - type: f1
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    - type: main_score
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  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification (default)
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
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    - type: f1
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    - type: f1_weighted
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    - type: main_score
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  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering (default)
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
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      value: 46.12488681435932
    - type: v_measure
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      value: 1.5095413626412524
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015 (default)
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
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    - type: cosine_ap
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    - type: cosine_recall
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    - type: euclidean_recall
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    - type: manhattan_precision
      value: 64.43156037307789
    - type: manhattan_recall
      value: 67.44063324538259
    - type: max_accuracy
      value: 85.17017345174942
    - type: max_ap
      value: 71.56900157150807
    - type: max_f1
      value: 66.13515565679575
    - type: max_precision
      value: 64.43156037307789
    - type: max_recall
      value: 68.94459102902375
    - type: similarity_accuracy
      value: 85.17017345174942
    - type: similarity_accuracy_threshold
      value: 73.24392199516296
    - type: similarity_ap
      value: 71.5689998301689
    - type: similarity_f1
      value: 66.13515565679575
    - type: similarity_f1_threshold
      value: 69.43247318267822
    - type: similarity_precision
      value: 63.545719844357976
    - type: similarity_recall
      value: 68.94459102902375
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus (default)
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cosine_accuracy
      value: 89.17995886211045
    - type: cosine_accuracy_threshold
      value: 70.09445428848267
    - type: cosine_ap
      value: 86.05864466686238
    - type: cosine_f1
      value: 78.36959806588094
    - type: cosine_f1_threshold
      value: 67.36087203025818
    - type: cosine_precision
      value: 76.92821121328983
    - type: cosine_recall
      value: 79.8660301817062
    - type: dot_accuracy
      value: 89.17995886211045
    - type: dot_accuracy_threshold
      value: 70.09446620941162
    - type: dot_ap
      value: 86.05863925325839
    - type: dot_f1
      value: 78.36959806588094
    - type: dot_f1_threshold
      value: 67.36087799072266
    - type: dot_precision
      value: 76.92821121328983
    - type: dot_recall
      value: 79.8660301817062
    - type: euclidean_accuracy
      value: 89.17995886211045
    - type: euclidean_accuracy_threshold
      value: 77.33762264251709
    - type: euclidean_ap
      value: 86.05864473129277
    - type: euclidean_f1
      value: 78.36959806588094
    - type: euclidean_f1_threshold
      value: 80.79496026039124
    - type: euclidean_precision
      value: 76.92821121328983
    - type: euclidean_recall
      value: 79.8660301817062
    - type: main_score
      value: 86.05864473129277
    - type: manhattan_accuracy
      value: 89.15279233127644
    - type: manhattan_accuracy_threshold
      value: 1960.135269165039
    - type: manhattan_ap
      value: 86.00803071652211
    - type: manhattan_f1
      value: 78.28386279840602
    - type: manhattan_f1_threshold
      value: 2062.195587158203
    - type: manhattan_precision
      value: 75.81331602106326
    - type: manhattan_recall
      value: 80.92085001539883
    - type: max_accuracy
      value: 89.17995886211045
    - type: max_ap
      value: 86.05864473129277
    - type: max_f1
      value: 78.36959806588094
    - type: max_precision
      value: 76.92821121328983
    - type: max_recall
      value: 80.92085001539883
    - type: similarity_accuracy
      value: 89.17995886211045
    - type: similarity_accuracy_threshold
      value: 70.09445428848267
    - type: similarity_ap
      value: 86.05864466686238
    - type: similarity_f1
      value: 78.36959806588094
    - type: similarity_f1_threshold
      value: 67.36087203025818
    - type: similarity_precision
      value: 76.92821121328983
    - type: similarity_recall
      value: 79.8660301817062
  - task:
      type: Retrieval
    dataset:
      name: MTEB VideoRetrieval (default)
      type: C-MTEB/VideoRetrieval
      config: default
      split: test
      revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
    metrics:
    - type: main_score
      value: 76.259
    - type: map_at_1
      value: 63.1
    - type: map_at_10
      value: 72.214
    - type: map_at_100
      value: 72.595
    - type: map_at_1000
      value: 72.604
    - type: map_at_20
      value: 72.482
    - type: map_at_3
      value: 70.45
    - type: map_at_5
      value: 71.565
    - type: mrr_at_1
      value: 63.1
    - type: mrr_at_10
      value: 72.2140873015873
    - type: mrr_at_100
      value: 72.59450024817937
    - type: mrr_at_1000
      value: 72.60392898169485
    - type: mrr_at_20
      value: 72.48216831972637
    - type: mrr_at_3
      value: 70.45000000000005
    - type: mrr_at_5
      value: 71.56500000000004
    - type: nauc_map_at_1000_diff1
      value: 74.28035434647866
    - type: nauc_map_at_1000_max
      value: 24.9986630946608
    - type: nauc_map_at_1000_std
      value: -39.39693868573278
    - type: nauc_map_at_100_diff1
      value: 74.26844900002635
    - type: nauc_map_at_100_max
      value: 25.018503837474537
    - type: nauc_map_at_100_std
      value: -39.37583943184342
    - type: nauc_map_at_10_diff1
      value: 74.26680601603067
    - type: nauc_map_at_10_max
      value: 25.09926459399667
    - type: nauc_map_at_10_std
      value: -39.89610049620442
    - type: nauc_map_at_1_diff1
      value: 77.10780012025063
    - type: nauc_map_at_1_max
      value: 19.806657111504965
    - type: nauc_map_at_1_std
      value: -37.2015516410441
    - type: nauc_map_at_20_diff1
      value: 74.21838781014907
    - type: nauc_map_at_20_max
      value: 25.063007078847527
    - type: nauc_map_at_20_std
      value: -39.43384966895805
    - type: nauc_map_at_3_diff1
      value: 74.18933637722046
    - type: nauc_map_at_3_max
      value: 24.353710639698495
    - type: nauc_map_at_3_std
      value: -40.981597611432065
    - type: nauc_map_at_5_diff1
      value: 74.10850958842639
    - type: nauc_map_at_5_max
      value: 24.97711616082726
    - type: nauc_map_at_5_std
      value: -40.679823827809464
    - type: nauc_mrr_at_1000_diff1
      value: 74.28035434647866
    - type: nauc_mrr_at_1000_max
      value: 24.9986630946608
    - type: nauc_mrr_at_1000_std
      value: -39.39693868573278
    - type: nauc_mrr_at_100_diff1
      value: 74.26844900002635
    - type: nauc_mrr_at_100_max
      value: 25.018503837474537
    - type: nauc_mrr_at_100_std
      value: -39.37583943184342
    - type: nauc_mrr_at_10_diff1
      value: 74.26680601603067
    - type: nauc_mrr_at_10_max
      value: 25.09926459399667
    - type: nauc_mrr_at_10_std
      value: -39.89610049620442
    - type: nauc_mrr_at_1_diff1
      value: 77.10780012025063
    - type: nauc_mrr_at_1_max
      value: 19.806657111504965
    - type: nauc_mrr_at_1_std
      value: -37.2015516410441
    - type: nauc_mrr_at_20_diff1
      value: 74.21838781014907
    - type: nauc_mrr_at_20_max
      value: 25.063007078847527
    - type: nauc_mrr_at_20_std
      value: -39.43384966895805
    - type: nauc_mrr_at_3_diff1
      value: 74.18933637722046
    - type: nauc_mrr_at_3_max
      value: 24.353710639698495
    - type: nauc_mrr_at_3_std
      value: -40.981597611432065
    - type: nauc_mrr_at_5_diff1
      value: 74.10850958842639
    - type: nauc_mrr_at_5_max
      value: 24.97711616082726
    - type: nauc_mrr_at_5_std
      value: -40.679823827809464
    - type: nauc_ndcg_at_1000_diff1
      value: 73.61936333916759
    - type: nauc_ndcg_at_1000_max
      value: 27.116854943289013
    - type: nauc_ndcg_at_1000_std
      value: -37.54463044764084
    - type: nauc_ndcg_at_100_diff1
      value: 73.34360970001946
    - type: nauc_ndcg_at_100_max
      value: 27.730908397954856
    - type: nauc_ndcg_at_100_std
      value: -36.847816086959746
    - type: nauc_ndcg_at_10_diff1
      value: 73.26740567929617
    - type: nauc_ndcg_at_10_max
      value: 28.07969523618901
    - type: nauc_ndcg_at_10_std
      value: -39.5613267347007
    - type: nauc_ndcg_at_1_diff1
      value: 77.10780012025063
    - type: nauc_ndcg_at_1_max
      value: 19.806657111504965
    - type: nauc_ndcg_at_1_std
      value: -37.2015516410441
    - type: nauc_ndcg_at_20_diff1
      value: 73.03206507305698
    - type: nauc_ndcg_at_20_max
      value: 27.998040243323953
    - type: nauc_ndcg_at_20_std
      value: -37.708810040181056
    - type: nauc_ndcg_at_3_diff1
      value: 73.14698395698991
    - type: nauc_ndcg_at_3_max
      value: 26.27353390787337
    - type: nauc_ndcg_at_3_std
      value: -42.26161377498877
    - type: nauc_ndcg_at_5_diff1
      value: 72.95385082418426
    - type: nauc_ndcg_at_5_max
      value: 27.616141938964926
    - type: nauc_ndcg_at_5_std
      value: -41.73620432852272
    - type: nauc_precision_at_1000_diff1
      value: 62.222222222221426
    - type: nauc_precision_at_1000_max
      value: 84.86150015561716
    - type: nauc_precision_at_1000_std
      value: 70.37659508247653
    - type: nauc_precision_at_100_diff1
      value: 57.83489866534799
    - type: nauc_precision_at_100_max
      value: 79.34448289119527
    - type: nauc_precision_at_100_std
      value: 41.03229527104971
    - type: nauc_precision_at_10_diff1
      value: 67.38577178030296
    - type: nauc_precision_at_10_max
      value: 47.06185741341976
    - type: nauc_precision_at_10_std
      value: -35.28392180735959
    - type: nauc_precision_at_1_diff1
      value: 77.10780012025063
    - type: nauc_precision_at_1_max
      value: 19.806657111504965
    - type: nauc_precision_at_1_std
      value: -37.2015516410441
    - type: nauc_precision_at_20_diff1
      value: 62.29131652661053
    - type: nauc_precision_at_20_max
      value: 54.85838779956423
    - type: nauc_precision_at_20_std
      value: -14.987239340181135
    - type: nauc_precision_at_3_diff1
      value: 69.30752094380478
    - type: nauc_precision_at_3_max
      value: 33.47195304146769
    - type: nauc_precision_at_3_std
      value: -46.94646447336974
    - type: nauc_precision_at_5_diff1
      value: 67.75036818851238
    - type: nauc_precision_at_5_max
      value: 39.96410162002936
    - type: nauc_precision_at_5_std
      value: -46.30185321551299
    - type: nauc_recall_at_1000_diff1
      value: 62.22222222222214
    - type: nauc_recall_at_1000_max
      value: 84.86150015561832
    - type: nauc_recall_at_1000_std
      value: 70.37659508247839
    - type: nauc_recall_at_100_diff1
      value: 57.83489866534869
    - type: nauc_recall_at_100_max
      value: 79.34448289119563
    - type: nauc_recall_at_100_std
      value: 41.03229527105021
    - type: nauc_recall_at_10_diff1
      value: 67.38577178030309
    - type: nauc_recall_at_10_max
      value: 47.061857413419816
    - type: nauc_recall_at_10_std
      value: -35.283921807359164
    - type: nauc_recall_at_1_diff1
      value: 77.10780012025063
    - type: nauc_recall_at_1_max
      value: 19.806657111504965
    - type: nauc_recall_at_1_std
      value: -37.2015516410441
    - type: nauc_recall_at_20_diff1
      value: 62.29131652661064
    - type: nauc_recall_at_20_max
      value: 54.858387799564234
    - type: nauc_recall_at_20_std
      value: -14.987239340180611
    - type: nauc_recall_at_3_diff1
      value: 69.30752094380468
    - type: nauc_recall_at_3_max
      value: 33.47195304146769
    - type: nauc_recall_at_3_std
      value: -46.94646447336982
    - type: nauc_recall_at_5_diff1
      value: 67.75036818851241
    - type: nauc_recall_at_5_max
      value: 39.96410162002941
    - type: nauc_recall_at_5_std
      value: -46.30185321551307
    - type: ndcg_at_1
      value: 63.1
    - type: ndcg_at_10
      value: 76.259
    - type: ndcg_at_100
      value: 77.985
    - type: ndcg_at_1000
      value: 78.227
    - type: ndcg_at_20
      value: 77.208
    - type: ndcg_at_3
      value: 72.684
    - type: ndcg_at_5
      value: 74.698
    - type: precision_at_1
      value: 63.1
    - type: precision_at_10
      value: 8.88
    - type: precision_at_100
      value: 0.966
    - type: precision_at_1000
      value: 0.099
    - type: precision_at_20
      value: 4.625
    - type: precision_at_3
      value: 26.367
    - type: precision_at_5
      value: 16.8
    - type: recall_at_1
      value: 63.1
    - type: recall_at_10
      value: 88.8
    - type: recall_at_100
      value: 96.6
    - type: recall_at_1000
      value: 98.5
    - type: recall_at_20
      value: 92.5
    - type: recall_at_3
      value: 79.10000000000001
    - type: recall_at_5
      value: 84.0
  - task:
      type: Classification
    dataset:
      name: MTEB Waimai (default)
      type: C-MTEB/waimai-classification
      config: default
      split: test
      revision: 339287def212450dcaa9df8c22bf93e9980c7023
    metrics:
    - type: accuracy
      value: 85.77999999999999
    - type: ap
      value: 68.2216414015039
    - type: ap_weighted
      value: 68.2216414015039
    - type: f1
      value: 83.82399258910729
    - type: f1_weighted
      value: 85.78923735869199
    - type: main_score
      value: 85.77999999999999
---
## MiniCPM-Embedding-Light

**MiniCPM-Embedding-Light** 是面壁智能与清华大学自然语言处理实验室（THUNLP）、东北大学信息检索小组（NEUIR）共同开发的中英双语言文本嵌入模型，有如下特点：
- 出色的中文、英文检索能力。
- 出色的中英跨语言检索能力。
- 支持长文本（最长8192token）。
- 提供稠密向量与token级别的稀疏向量。
- 可变的稠密向量维度（套娃表征）。

MiniCPM-Embedding-Light结构上采取双向注意力和 Weighted Mean Pooling [1]。采取多阶段训练方式，共使用包括开源数据、机造数据、闭源数据在内的约 260M 条训练数据。

欢迎关注 UltraRAG 系列：

- 检索模型：[MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)
- 重排模型：[MiniCPM-Reranker-Light](https://huggingface.co/openbmb/MiniCPM-Reranker-Light)
- 领域自适应RAG框架：[UltraRAG](https://github.com/openbmb/UltraRAG)

**MiniCPM-Embedding-Light** is a bilingual & cross-lingual text embedding model developed by ModelBest Inc. , THUNLP and NEUIR , featuring:

- Exceptional Chinese and English retrieval capabilities.
- Outstanding cross-lingual retrieval capabilities between Chinese and English.
- Long-text support (up to 8192 tokens).
- Dense vectors and token-level sparse vectors.
- Variable dense vector dimensions (Matryoshka representation [2]).

MiniCPM-Embedding-Light incorporates bidirectional attention and Weighted Mean Pooling [1] in its architecture. The model underwent multi-stage training using approximately 260 million training examples, including open-source, synthetic, and proprietary data.

We also invite you to explore the UltraRAG series:

- Retrieval Model: [MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)
- Re-ranking Model: [MiniCPM-Reranker-Light](https://huggingface.co/openbmb/MiniCPM-Reranker-Light)
- Domain Adaptive RAG Framework: [UltraRAG](https://github.com/openbmb/UltraRAG)

[1] Muennighoff, N. (2022). Sgpt: Gpt sentence embeddings for semantic search. arXiv preprint arXiv:2202.08904.
[2] Kusupati, Aditya, et al. "Matryoshka representation learning." Advances in Neural Information Processing Systems 35 (2022): 30233-30249.

## 模型信息 Model Information

- 模型大小：440M
- 嵌入维度：1024
- 最大输入token数：8192

- Model Size: 440M
- Embedding Dimension: 1024
- Max Input Tokens: 8192

## 使用方法 Usage

### 输入格式 Input Format

本模型支持 query 侧指令，格式如下：

MiniCPM-Embedding-Light supports query-side instructions in the following format:

```
Instruction: {{ instruction }} Query: {{ query }}
```

例如：

For example:

```
Instruction: 为这个医学问题检索相关回答。Query: 咽喉癌的成因是什么？
```

```
Instruction: Given a claim about climate change, retrieve documents that support or refute the claim. Query: However the warming trend is slower than most climate models have forecast.
```

也可以不提供指令，即采取如下格式：

MiniCPM-Embedding-Light also works in instruction-free mode in the following format:

```
Query: {{ query }}
```

### 环境要求 Requirements

```
transformers==4.37.2
```

### 示例脚本 Demo

#### Huggingface Transformers
```python
from transformers import AutoModel
import torch

model_name = "openbmb/MiniCPM-Embedding-Light"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16).to("cuda")

# you can use flash_attention_2 for faster inference
# model = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda") 

model.eval()

queries = ["MiniCPM-o 2.6 A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone"]
passages = ["MiniCPM-o 2.6 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for real-time speech conversation and multimodal live streaming."]

embeddings_query_dense, embeddings_query_sparse = model.encode_query(queries, return_sparse_vectors=True)
embeddings_doc_dense, embeddings_doc_sparse = model.encode_corpus(passages, return_sparse_vectors=True)

dense_scores = (embeddings_query_dense @ embeddings_doc_dense.T)
print(dense_scores.tolist())  # [[0.6512398719787598]]
print(model.compute_sparse_score_dicts(embeddings_query_sparse,  embeddings_doc_sparse)) # [[0.27202296]]

dense_scores, sparse_scores, mixed_scores = model.compute_score(queries, passages)
print(dense_scores) # [[0.65123993]]
print(sparse_scores) # [[0.27202296]]
print(mixed_scores) # [[0.73284686]]
```

#### Sentence Transformers

```python
import torch
from sentence_transformers import SentenceTransformer


model_name = "openbmb/MiniCPM-Embedding-Light"
model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"torch_dtype": torch.float16})

# you can use flash_attention_2 for faster inference
# model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"attn_implementation": "flash_attention_2", "torch_dtype": torch.float16})

queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"

INSTRUCTION = "Query: "

embeddings_query = model.encode(queries, prompt=INSTRUCTION)
embeddings_doc = model.encode(passages)

scores = (embeddings_query @ embeddings_doc.T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183440685272217]]
```

#### Infinity

```python
import asyncio
from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine
import numpy as np

array = AsyncEngineArray.from_args([
  EngineArgs(model_name_or_path = "openbmb/MiniCPM-Embedding-Light", engine="torch", dtype="float16", bettertransformer=False, pooling_method="mean", trust_remote_code=True),
])
queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"

INSTRUCTION = "Query:"
queries = [f"{INSTRUCTION} {query}" for query in queries]


async def embed_text(engine: AsyncEmbeddingEngine,sentences): 
    async with engine: 
        embeddings, usage = await engine.embed(sentences=sentences)
    return embeddings

queries_embedding = asyncio.run(embed_text(array[0],queries))
passages_embedding = asyncio.run(embed_text(array[0],passages))

scores = (np.array(queries_embedding) @ np.array(passages_embedding).T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183443665504456]]
```

#### FlagEmbedding
```python
from FlagEmbedding import FlagModel


model = FlagModel("openbmb/MiniCPM-Embedding-Light", 
                          query_instruction_for_retrieval="Query: ",
                          pooling_method="mean",
                          trust_remote_code=True,
                          normalize_embeddings=True,
                          use_fp16=True)
# You can hack the __init__() method of the FlagEmbedding BaseEmbedder class to use flash_attention_2 for faster inference
#  self.model = AutoModel.from_pretrained(
#             model_name_or_path,
#             trust_remote_code=trust_remote_code,
#             cache_dir=cache_dir,
#             # torch_dtype=torch.float16, # we need to add this line to use fp16
#             # attn_implementation="flash_attention_2", # we need to add this line to use flash_attention_2
#         )

queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"


embeddings_query = model.encode_queries(queries)
embeddings_doc = model.encode_corpus(passages)

scores = (embeddings_query @ embeddings_doc.T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183440685272217]]
```


## 实验结果 Evaluation Results

### 中文与英文检索结果 CN/EN Retrieval Results

| 模型 Model                                         | C-MTEB/Retrieval(NDCG@10) | BEIR(NDCG@10) |
|----------------------------------------------------|-------------------|---------------|
| bge-large-zh-v1.5                                  | 70.46             | -             |
| gte-large-zh                                       | 72.49             | -             |
| Conan-embedding-v1                                 | 76.67             |               |
| bge-large-en-v1.5                                  | -                 | 54.29         |
| modernbert-embed-large                             | -                 | 54.36         |
| snowflake-arctic-embed-l                           | -                 | 55.98         |
| gte-en-large-v1.5                                  | -                 | 57.91         |
| me5-large                                          | 63.66             | 51.43         |
| bge-m3(Dense)                                      | 65.43             | 48.82         |
| gte-multilingual-base(Dense)                       | 71.95             | 51.08         |
| jina-embeddings-v3                                 | 68.60              | 53.88         |
| gte-Qwen2-1.5B-instruct                            | 71.86             | 58.29         |
| MiniCPM-Embedding                                  | 76.76             | 58.56         |
| MiniCPM-Embedding-Light(Dense)                          | 72.71             | 55.27         |
| MiniCPM-Embedding-Light(Dense+Sparse)                   | 73.13             | 56.31         |
| MiniCPM-Embedding-Light(Dense+Sparse)+MiniCPM-Reranker-Light | 76.34             | 61.49         |


### 中英跨语言检索结果 CN-EN Cross-lingual Retrieval Results

| 模型  Model                | MKQA En-Zh_CN (Recall@20) | NeuCLIR22 (NDCG@10) | NeuCLIR23 (NDCG@10) |
|------------------------------|--------------------|--------------------|--------------------|
| me5-large                                   | 44.3               | 9.01               | 25.33              |
| bge-m3(Dense)                               | 66.4               | 30.49              | 41.09              |
| gte-multilingual-base(Dense)                | 68.2               | 39.46              | 45.86              |
| MiniCPM-Embedding                           | 72.95              | 52.65              | 49.95              |
| MiniCPM-Embedding-Light(Dense)                   | 68.29              | 41.17              | 45.83              |
| MiniCPM-Embedding-Light(Dense)+MiniCPM-Reranker-Light | 71.86              | 54.32              | 56.50               |

## 许可证 License

- 本仓库中代码依照 [Apache-2.0 协议](https://github.com/openbmb/MiniCPM/blob/main/LICENSE)开源。
- MiniCPM-Embedding-Light 模型权重的使用则需要遵循 [MiniCPM 模型协议](https://github.com/openbmb/MiniCPM/blob/main/MiniCPM%20Model%20License.md)。
- MiniCPM-Embedding-Light 模型权重对学术研究完全开放。如需将模型用于商业用途，请填写[此问卷](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g)。

* The code in this repo is released under the [Apache-2.0](https://github.com/openbmb/MiniCPM/blob/main/LICENSE) License. 
* The usage of MiniCPM-Embedding-Light model weights must strictly follow [MiniCPM Model License.md](https://github.com/openbmb/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
* The models and weights of MiniCPM-Embedding-Light are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-Embedding-Light weights are also available for free commercial use.
