sentence-transformers safetensors qwen2_vl image-text-to-text mteb transformers Qwen2-VL sentence-similarity vidore en zh arxiv:2412.16855 base_model:Qwen/Qwen2-VL-7B-Instruct base_model:finetune:Qwen/Qwen2-VL-7B-Instruct license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- Qwen/Qwen2-VL-7B-Instruct
language:
- en
- zh
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2-VL
- sentence-similarity
- vidore
model-index:
- name: gme-Qwen2-VL-7B-Instruct
  results:
  - task:
      type: STS
    dataset:
      name: MTEB AFQMC
      type: C-MTEB/AFQMC
      config: default
      split: validation
      revision: b44c3b011063adb25877c13823db83bb193913c4
    metrics:
    - type: cos_sim_pearson
      value: 64.72351048394194
    - type: cos_sim_spearman
      value: 71.66842612591344
    - type: euclidean_pearson
      value: 70.0342809043895
    - type: euclidean_spearman
      value: 71.66842612323917
    - type: manhattan_pearson
      value: 69.94743870947117
    - type: manhattan_spearman
      value: 71.53159630946965
  - task:
      type: STS
    dataset:
      name: MTEB ATEC
      type: C-MTEB/ATEC
      config: default
      split: test
      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
    metrics:
    - type: cos_sim_pearson
      value: 52.38188106868689
    - type: cos_sim_spearman
      value: 55.468235529709766
    - type: euclidean_pearson
      value: 56.974786979175086
    - type: euclidean_spearman
      value: 55.468231026153745
    - type: manhattan_pearson
      value: 56.94467132566259
    - type: manhattan_spearman
      value: 55.39037386224014
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 77.61194029850746
    - type: ap
      value: 41.29789064067677
    - type: f1
      value: 71.69633278678522
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 97.3258
    - type: ap
      value: 95.91845683387056
    - type: f1
      value: 97.32526074864263
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 64.794
    - type: f1
      value: 63.7329780206882
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (zh)
      type: mteb/amazon_reviews_multi
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 55.099999999999994
    - type: f1
      value: 53.115528412999666
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
    metrics:
    - type: map_at_1
      value: 40.541
    - type: map_at_10
      value: 56.315000000000005
    - type: map_at_100
      value: 56.824
    - type: map_at_1000
      value: 56.825
    - type: map_at_3
      value: 51.778
    - type: map_at_5
      value: 54.623
    - type: mrr_at_1
      value: 41.038000000000004
    - type: mrr_at_10
      value: 56.532000000000004
    - type: mrr_at_100
      value: 57.034
    - type: mrr_at_1000
      value: 57.034
    - type: mrr_at_3
      value: 52.015
    - type: mrr_at_5
      value: 54.835
    - type: ndcg_at_1
      value: 40.541
    - type: ndcg_at_10
      value: 64.596
    - type: ndcg_at_100
      value: 66.656
    - type: ndcg_at_1000
      value: 66.666
    - type: ndcg_at_3
      value: 55.415000000000006
    - type: ndcg_at_5
      value: 60.527
    - type: precision_at_1
      value: 40.541
    - type: precision_at_10
      value: 9.083
    - type: precision_at_100
      value: 0.996
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 21.977
    - type: precision_at_5
      value: 15.661
    - type: recall_at_1
      value: 40.541
    - type: recall_at_10
      value: 90.825
    - type: recall_at_100
      value: 99.57300000000001
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 65.932
    - type: recall_at_5
      value: 78.307
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 54.96111428218386
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 50.637711388838945
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 64.0741897266483
    - type: mrr
      value: 76.11440882909028
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 86.2557839280406
    - type: cos_sim_spearman
      value: 82.58200216886888
    - type: euclidean_pearson
      value: 84.80588838508498
    - type: euclidean_spearman
      value: 82.58200216886888
    - type: manhattan_pearson
      value: 84.53082035185592
    - type: manhattan_spearman
      value: 82.4964580510134
  - task:
      type: STS
    dataset:
      name: MTEB BQ
      type: C-MTEB/BQ
      config: default
      split: test
      revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
    metrics:
    - type: cos_sim_pearson
      value: 76.98420285210636
    - type: cos_sim_spearman
      value: 78.95549489000658
    - type: euclidean_pearson
      value: 79.14591532018991
    - type: euclidean_spearman
      value: 78.95549488953284
    - type: manhattan_pearson
      value: 79.26212116856509
    - type: manhattan_spearman
      value: 79.02104262086006
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 84.76298701298703
    - type: f1
      value: 84.24881789367576
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 46.86757924102047
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 43.86043680479362
  - task:
      type: Clustering
    dataset:
      name: MTEB CLSClusteringP2P
      type: C-MTEB/CLSClusteringP2P
      config: default
      split: test
      revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
    metrics:
    - type: v_measure
      value: 45.684222588040605
  - task:
      type: Clustering
    dataset:
      name: MTEB CLSClusteringS2S
      type: C-MTEB/CLSClusteringS2S
      config: default
      split: test
      revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
    metrics:
    - type: v_measure
      value: 45.45639765303432
  - task:
      type: Reranking
    dataset:
      name: MTEB CMedQAv1
      type: C-MTEB/CMedQAv1-reranking
      config: default
      split: test
      revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
    metrics:
    - type: map
      value: 88.7058672660788
    - type: mrr
      value: 90.5795634920635
  - task:
      type: Reranking
    dataset:
      name: MTEB CMedQAv2
      type: C-MTEB/CMedQAv2-reranking
      config: default
      split: test
      revision: 23d186750531a14a0357ca22cd92d712fd512ea0
    metrics:
    - type: map
      value: 90.50750030424048
    - type: mrr
      value: 92.3970634920635
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: f46a197baaae43b4f621051089b82a364682dfeb
    metrics:
    - type: map_at_1
      value: 28.848000000000003
    - type: map_at_10
      value: 40.453
    - type: map_at_100
      value: 42.065000000000005
    - type: map_at_1000
      value: 42.176
    - type: map_at_3
      value: 36.697
    - type: map_at_5
      value: 38.855000000000004
    - type: mrr_at_1
      value: 34.764
    - type: mrr_at_10
      value: 45.662000000000006
    - type: mrr_at_100
      value: 46.56
    - type: mrr_at_1000
      value: 46.597
    - type: mrr_at_3
      value: 42.632
    - type: mrr_at_5
      value: 44.249
    - type: ndcg_at_1
      value: 34.764
    - type: ndcg_at_10
      value: 47.033
    - type: ndcg_at_100
      value: 53.089
    - type: ndcg_at_1000
      value: 54.818
    - type: ndcg_at_3
      value: 41.142
    - type: ndcg_at_5
      value: 43.928
    - type: precision_at_1
      value: 34.764
    - type: precision_at_10
      value: 9.027000000000001
    - type: precision_at_100
      value: 1.465
    - type: precision_at_1000
      value: 0.192
    - type: precision_at_3
      value: 19.695
    - type: precision_at_5
      value: 14.535
    - type: recall_at_1
      value: 28.848000000000003
    - type: recall_at_10
      value: 60.849
    - type: recall_at_100
      value: 85.764
    - type: recall_at_1000
      value: 96.098
    - type: recall_at_3
      value: 44.579
    - type: recall_at_5
      value: 51.678999999999995
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackEnglishRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
    metrics:
    - type: map_at_1
      value: 30.731
    - type: map_at_10
      value: 41.859
    - type: map_at_100
      value: 43.13
    - type: map_at_1000
      value: 43.257
    - type: map_at_3
      value: 38.384
    - type: map_at_5
      value: 40.284
    - type: mrr_at_1
      value: 38.471
    - type: mrr_at_10
      value: 47.531
    - type: mrr_at_100
      value: 48.199
    - type: mrr_at_1000
      value: 48.24
    - type: mrr_at_3
      value: 44.989000000000004
    - type: mrr_at_5
      value: 46.403
    - type: ndcg_at_1
      value: 38.471
    - type: ndcg_at_10
      value: 48.022999999999996
    - type: ndcg_at_100
      value: 52.32599999999999
    - type: ndcg_at_1000
      value: 54.26
    - type: ndcg_at_3
      value: 42.986999999999995
    - type: ndcg_at_5
      value: 45.23
    - type: precision_at_1
      value: 38.471
    - type: precision_at_10
      value: 9.248000000000001
    - type: precision_at_100
      value: 1.469
    - type: precision_at_1000
      value: 0.193
    - type: precision_at_3
      value: 20.892
    - type: precision_at_5
      value: 14.892
    - type: recall_at_1
      value: 30.731
    - type: recall_at_10
      value: 59.561
    - type: recall_at_100
      value: 77.637
    - type: recall_at_1000
      value: 89.64999999999999
    - type: recall_at_3
      value: 44.897999999999996
    - type: recall_at_5
      value: 51.181
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackGamingRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
    metrics:
    - type: map_at_1
      value: 34.949000000000005
    - type: map_at_10
      value: 48.117
    - type: map_at_100
      value: 49.355
    - type: map_at_1000
      value: 49.409
    - type: map_at_3
      value: 44.732
    - type: map_at_5
      value: 46.555
    - type: mrr_at_1
      value: 40.188
    - type: mrr_at_10
      value: 51.452
    - type: mrr_at_100
      value: 52.219
    - type: mrr_at_1000
      value: 52.24100000000001
    - type: mrr_at_3
      value: 48.642
    - type: mrr_at_5
      value: 50.134
    - type: ndcg_at_1
      value: 40.188
    - type: ndcg_at_10
      value: 54.664
    - type: ndcg_at_100
      value: 59.38099999999999
    - type: ndcg_at_1000
      value: 60.363
    - type: ndcg_at_3
      value: 48.684
    - type: ndcg_at_5
      value: 51.406
    - type: precision_at_1
      value: 40.188
    - type: precision_at_10
      value: 9.116
    - type: precision_at_100
      value: 1.248
    - type: precision_at_1000
      value: 0.13699999999999998
    - type: precision_at_3
      value: 22.236
    - type: precision_at_5
      value: 15.310000000000002
    - type: recall_at_1
      value: 34.949000000000005
    - type: recall_at_10
      value: 70.767
    - type: recall_at_100
      value: 90.79
    - type: recall_at_1000
      value: 97.57900000000001
    - type: recall_at_3
      value: 54.723
    - type: recall_at_5
      value: 61.404
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackGisRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 5003b3064772da1887988e05400cf3806fe491f2
    metrics:
    - type: map_at_1
      value: 25.312
    - type: map_at_10
      value: 34.799
    - type: map_at_100
      value: 35.906
    - type: map_at_1000
      value: 35.983
    - type: map_at_3
      value: 31.582
    - type: map_at_5
      value: 33.507999999999996
    - type: mrr_at_1
      value: 27.232
    - type: mrr_at_10
      value: 36.82
    - type: mrr_at_100
      value: 37.733
    - type: mrr_at_1000
      value: 37.791000000000004
    - type: mrr_at_3
      value: 33.804
    - type: mrr_at_5
      value: 35.606
    - type: ndcg_at_1
      value: 27.232
    - type: ndcg_at_10
      value: 40.524
    - type: ndcg_at_100
      value: 45.654
    - type: ndcg_at_1000
      value: 47.557
    - type: ndcg_at_3
      value: 34.312
    - type: ndcg_at_5
      value: 37.553
    - type: precision_at_1
      value: 27.232
    - type: precision_at_10
      value: 6.52
    - type: precision_at_100
      value: 0.9530000000000001
    - type: precision_at_1000
      value: 0.11399999999999999
    - type: precision_at_3
      value: 14.915000000000001
    - type: precision_at_5
      value: 10.847
    - type: recall_at_1
      value: 25.312
    - type: recall_at_10
      value: 56.169000000000004
    - type: recall_at_100
      value: 79.16499999999999
    - type: recall_at_1000
      value: 93.49300000000001
    - type: recall_at_3
      value: 39.5
    - type: recall_at_5
      value: 47.288999999999994
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackMathematicaRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
    metrics:
    - type: map_at_1
      value: 17.153
    - type: map_at_10
      value: 27.671
    - type: map_at_100
      value: 29.186
    - type: map_at_1000
      value: 29.299999999999997
    - type: map_at_3
      value: 24.490000000000002
    - type: map_at_5
      value: 26.178
    - type: mrr_at_1
      value: 21.144
    - type: mrr_at_10
      value: 32.177
    - type: mrr_at_100
      value: 33.247
    - type: mrr_at_1000
      value: 33.306000000000004
    - type: mrr_at_3
      value: 29.187
    - type: mrr_at_5
      value: 30.817
    - type: ndcg_at_1
      value: 21.144
    - type: ndcg_at_10
      value: 33.981
    - type: ndcg_at_100
      value: 40.549
    - type: ndcg_at_1000
      value: 43.03
    - type: ndcg_at_3
      value: 28.132
    - type: ndcg_at_5
      value: 30.721999999999998
    - type: precision_at_1
      value: 21.144
    - type: precision_at_10
      value: 6.666999999999999
    - type: precision_at_100
      value: 1.147
    - type: precision_at_1000
      value: 0.149
    - type: precision_at_3
      value: 14.302999999999999
    - type: precision_at_5
      value: 10.423
    - type: recall_at_1
      value: 17.153
    - type: recall_at_10
      value: 48.591
    - type: recall_at_100
      value: 76.413
    - type: recall_at_1000
      value: 93.8
    - type: recall_at_3
      value: 32.329
    - type: recall_at_5
      value: 38.958999999999996
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackPhysicsRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
    metrics:
    - type: map_at_1
      value: 27.909
    - type: map_at_10
      value: 40.168
    - type: map_at_100
      value: 41.524
    - type: map_at_1000
      value: 41.626000000000005
    - type: map_at_3
      value: 36.274
    - type: map_at_5
      value: 38.411
    - type: mrr_at_1
      value: 34.649
    - type: mrr_at_10
      value: 45.613
    - type: mrr_at_100
      value: 46.408
    - type: mrr_at_1000
      value: 46.444
    - type: mrr_at_3
      value: 42.620999999999995
    - type: mrr_at_5
      value: 44.277
    - type: ndcg_at_1
      value: 34.649
    - type: ndcg_at_10
      value: 47.071000000000005
    - type: ndcg_at_100
      value: 52.559999999999995
    - type: ndcg_at_1000
      value: 54.285000000000004
    - type: ndcg_at_3
      value: 40.63
    - type: ndcg_at_5
      value: 43.584
    - type: precision_at_1
      value: 34.649
    - type: precision_at_10
      value: 8.855
    - type: precision_at_100
      value: 1.361
    - type: precision_at_1000
      value: 0.167
    - type: precision_at_3
      value: 19.538
    - type: precision_at_5
      value: 14.187
    - type: recall_at_1
      value: 27.909
    - type: recall_at_10
      value: 62.275000000000006
    - type: recall_at_100
      value: 84.95
    - type: recall_at_1000
      value: 96.02000000000001
    - type: recall_at_3
      value: 44.767
    - type: recall_at_5
      value: 52.03
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackProgrammersRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
    metrics:
    - type: map_at_1
      value: 25.846000000000004
    - type: map_at_10
      value: 36.870999999999995
    - type: map_at_100
      value: 38.294
    - type: map_at_1000
      value: 38.401
    - type: map_at_3
      value: 33.163
    - type: map_at_5
      value: 35.177
    - type: mrr_at_1
      value: 31.849
    - type: mrr_at_10
      value: 41.681000000000004
    - type: mrr_at_100
      value: 42.658
    - type: mrr_at_1000
      value: 42.71
    - type: mrr_at_3
      value: 39.003
    - type: mrr_at_5
      value: 40.436
    - type: ndcg_at_1
      value: 31.849
    - type: ndcg_at_10
      value: 43.291000000000004
    - type: ndcg_at_100
      value: 49.136
    - type: ndcg_at_1000
      value: 51.168
    - type: ndcg_at_3
      value: 37.297999999999995
    - type: ndcg_at_5
      value: 39.934
    - type: precision_at_1
      value: 31.849
    - type: precision_at_10
      value: 8.219
    - type: precision_at_100
      value: 1.318
    - type: precision_at_1000
      value: 0.167
    - type: precision_at_3
      value: 18.151
    - type: precision_at_5
      value: 13.242
    - type: recall_at_1
      value: 25.846000000000004
    - type: recall_at_10
      value: 57.642
    - type: recall_at_100
      value: 82.069
    - type: recall_at_1000
      value: 95.684
    - type: recall_at_3
      value: 40.778999999999996
    - type: recall_at_5
      value: 47.647
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
    metrics:
    - type: map_at_1
      value: 25.34866666666667
    - type: map_at_10
      value: 35.65541666666667
    - type: map_at_100
      value: 36.982416666666666
    - type: map_at_1000
      value: 37.09416666666667
    - type: map_at_3
      value: 32.421499999999995
    - type: map_at_5
      value: 34.20266666666667
    - type: mrr_at_1
      value: 30.02116666666667
    - type: mrr_at_10
      value: 39.781666666666666
    - type: mrr_at_100
      value: 40.69733333333333
    - type: mrr_at_1000
      value: 40.74875
    - type: mrr_at_3
      value: 37.043083333333335
    - type: mrr_at_5
      value: 38.56391666666666
    - type: ndcg_at_1
      value: 30.02116666666667
    - type: ndcg_at_10
      value: 41.66133333333333
    - type: ndcg_at_100
      value: 47.21474999999999
    - type: ndcg_at_1000
      value: 49.29600000000001
    - type: ndcg_at_3
      value: 36.06958333333334
    - type: ndcg_at_5
      value: 38.66858333333333
    - type: precision_at_1
      value: 30.02116666666667
    - type: precision_at_10
      value: 7.497249999999999
    - type: precision_at_100
      value: 1.2044166666666667
    - type: precision_at_1000
      value: 0.15766666666666665
    - type: precision_at_3
      value: 16.83458333333333
    - type: precision_at_5
      value: 12.134
    - type: recall_at_1
      value: 25.34866666666667
    - type: recall_at_10
      value: 55.40541666666666
    - type: recall_at_100
      value: 79.38683333333333
    - type: recall_at_1000
      value: 93.50958333333334
    - type: recall_at_3
      value: 39.99858333333334
    - type: recall_at_5
      value: 46.55741666666666
    - type: map_at_1
      value: 18.336
    - type: map_at_10
      value: 26.811
    - type: map_at_100
      value: 27.892
    - type: map_at_1000
      value: 27.986
    - type: map_at_3
      value: 23.976
    - type: map_at_5
      value: 25.605
    - type: mrr_at_1
      value: 20.148
    - type: mrr_at_10
      value: 28.898000000000003
    - type: mrr_at_100
      value: 29.866
    - type: mrr_at_1000
      value: 29.929
    - type: mrr_at_3
      value: 26.247999999999998
    - type: mrr_at_5
      value: 27.744999999999997
    - type: ndcg_at_1
      value: 20.148
    - type: ndcg_at_10
      value: 32.059
    - type: ndcg_at_100
      value: 37.495
    - type: ndcg_at_1000
      value: 39.855000000000004
    - type: ndcg_at_3
      value: 26.423000000000002
    - type: ndcg_at_5
      value: 29.212
    - type: precision_at_1
      value: 20.148
    - type: precision_at_10
      value: 5.268
    - type: precision_at_100
      value: 0.872
    - type: precision_at_1000
      value: 0.11900000000000001
    - type: precision_at_3
      value: 11.459999999999999
    - type: precision_at_5
      value: 8.503
    - type: recall_at_1
      value: 18.336
    - type: recall_at_10
      value: 46.411
    - type: recall_at_100
      value: 71.33500000000001
    - type: recall_at_1000
      value: 88.895
    - type: recall_at_3
      value: 31.134
    - type: recall_at_5
      value: 37.862
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackStatsRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
    metrics:
    - type: map_at_1
      value: 25.102000000000004
    - type: map_at_10
      value: 33.31
    - type: map_at_100
      value: 34.443
    - type: map_at_1000
      value: 34.547
    - type: map_at_3
      value: 30.932
    - type: map_at_5
      value: 32.126
    - type: mrr_at_1
      value: 28.221
    - type: mrr_at_10
      value: 36.519
    - type: mrr_at_100
      value: 37.425000000000004
    - type: mrr_at_1000
      value: 37.498
    - type: mrr_at_3
      value: 34.254
    - type: mrr_at_5
      value: 35.388999999999996
    - type: ndcg_at_1
      value: 28.221
    - type: ndcg_at_10
      value: 38.340999999999994
    - type: ndcg_at_100
      value: 43.572
    - type: ndcg_at_1000
      value: 45.979
    - type: ndcg_at_3
      value: 33.793
    - type: ndcg_at_5
      value: 35.681000000000004
    - type: precision_at_1
      value: 28.221
    - type: precision_at_10
      value: 6.135
    - type: precision_at_100
      value: 0.946
    - type: precision_at_1000
      value: 0.123
    - type: precision_at_3
      value: 14.519000000000002
    - type: precision_at_5
      value: 9.969
    - type: recall_at_1
      value: 25.102000000000004
    - type: recall_at_10
      value: 50.639
    - type: recall_at_100
      value: 74.075
    - type: recall_at_1000
      value: 91.393
    - type: recall_at_3
      value: 37.952000000000005
    - type: recall_at_5
      value: 42.71
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackTexRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 46989137a86843e03a6195de44b09deda022eec7
    metrics:
    - type: map_at_1
      value: 18.618000000000002
    - type: map_at_10
      value: 26.714
    - type: map_at_100
      value: 27.929
    - type: map_at_1000
      value: 28.057
    - type: map_at_3
      value: 24.134
    - type: map_at_5
      value: 25.575
    - type: mrr_at_1
      value: 22.573999999999998
    - type: mrr_at_10
      value: 30.786
    - type: mrr_at_100
      value: 31.746000000000002
    - type: mrr_at_1000
      value: 31.822
    - type: mrr_at_3
      value: 28.412
    - type: mrr_at_5
      value: 29.818
    - type: ndcg_at_1
      value: 22.573999999999998
    - type: ndcg_at_10
      value: 31.852000000000004
    - type: ndcg_at_100
      value: 37.477
    - type: ndcg_at_1000
      value: 40.331
    - type: ndcg_at_3
      value: 27.314
    - type: ndcg_at_5
      value: 29.485
    - type: precision_at_1
      value: 22.573999999999998
    - type: precision_at_10
      value: 5.86
    - type: precision_at_100
      value: 1.012
    - type: precision_at_1000
      value: 0.146
    - type: precision_at_3
      value: 13.099
    - type: precision_at_5
      value: 9.56
    - type: recall_at_1
      value: 18.618000000000002
    - type: recall_at_10
      value: 43.134
    - type: recall_at_100
      value: 68.294
    - type: recall_at_1000
      value: 88.283
    - type: recall_at_3
      value: 30.397999999999996
    - type: recall_at_5
      value: 35.998000000000005
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackUnixRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
    metrics:
    - type: map_at_1
      value: 27.76
    - type: map_at_10
      value: 37.569
    - type: map_at_100
      value: 38.784
    - type: map_at_1000
      value: 38.884
    - type: map_at_3
      value: 34.379
    - type: map_at_5
      value: 36.092999999999996
    - type: mrr_at_1
      value: 32.556000000000004
    - type: mrr_at_10
      value: 41.870000000000005
    - type: mrr_at_100
      value: 42.759
    - type: mrr_at_1000
      value: 42.806
    - type: mrr_at_3
      value: 39.086
    - type: mrr_at_5
      value: 40.574
    - type: ndcg_at_1
      value: 32.556000000000004
    - type: ndcg_at_10
      value: 43.382
    - type: ndcg_at_100
      value: 48.943
    - type: ndcg_at_1000
      value: 50.961999999999996
    - type: ndcg_at_3
      value: 37.758
    - type: ndcg_at_5
      value: 40.282000000000004
    - type: precision_at_1
      value: 32.556000000000004
    - type: precision_at_10
      value: 7.463
    - type: precision_at_100
      value: 1.1480000000000001
    - type: precision_at_1000
      value: 0.14300000000000002
    - type: precision_at_3
      value: 17.133000000000003
    - type: precision_at_5
      value: 12.164
    - type: recall_at_1
      value: 27.76
    - type: recall_at_10
      value: 56.71000000000001
    - type: recall_at_100
      value: 81.053
    - type: recall_at_1000
      value: 94.75
    - type: recall_at_3
      value: 41.387
    - type: recall_at_5
      value: 47.818
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackWebmastersRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
    metrics:
    - type: map_at_1
      value: 23.62
    - type: map_at_10
      value: 33.522999999999996
    - type: map_at_100
      value: 35.281
    - type: map_at_1000
      value: 35.504000000000005
    - type: map_at_3
      value: 30.314999999999998
    - type: map_at_5
      value: 32.065
    - type: mrr_at_1
      value: 28.458
    - type: mrr_at_10
      value: 38.371
    - type: mrr_at_100
      value: 39.548
    - type: mrr_at_1000
      value: 39.601
    - type: mrr_at_3
      value: 35.638999999999996
    - type: mrr_at_5
      value: 37.319
    - type: ndcg_at_1
      value: 28.458
    - type: ndcg_at_10
      value: 39.715
    - type: ndcg_at_100
      value: 46.394999999999996
    - type: ndcg_at_1000
      value: 48.943999999999996
    - type: ndcg_at_3
      value: 34.361999999999995
    - type: ndcg_at_5
      value: 37.006
    - type: precision_at_1
      value: 28.458
    - type: precision_at_10
      value: 7.5889999999999995
    - type: precision_at_100
      value: 1.514
    - type: precision_at_1000
      value: 0.242
    - type: precision_at_3
      value: 16.073999999999998
    - type: precision_at_5
      value: 11.976
    - type: recall_at_1
      value: 23.62
    - type: recall_at_10
      value: 52.117000000000004
    - type: recall_at_100
      value: 81.097
    - type: recall_at_1000
      value: 96.47
    - type: recall_at_3
      value: 37.537
    - type: recall_at_5
      value: 44.112
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: mteb/climate-fever
      config: default
      split: test
      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
    metrics:
    - type: map_at_1
      value: 21.149
    - type: map_at_10
      value: 35.251
    - type: map_at_100
      value: 37.342
    - type: map_at_1000
      value: 37.516
    - type: map_at_3
      value: 30.543
    - type: map_at_5
      value: 33.19
    - type: mrr_at_1
      value: 47.687000000000005
    - type: mrr_at_10
      value: 59.391000000000005
    - type: mrr_at_100
      value: 59.946999999999996
    - type: mrr_at_1000
      value: 59.965999999999994
    - type: mrr_at_3
      value: 56.938
    - type: mrr_at_5
      value: 58.498000000000005
    - type: ndcg_at_1
      value: 47.687000000000005
    - type: ndcg_at_10
      value: 45.381
    - type: ndcg_at_100
      value: 52.405
    - type: ndcg_at_1000
      value: 55.041
    - type: ndcg_at_3
      value: 40.024
    - type: ndcg_at_5
      value: 41.821999999999996
    - type: precision_at_1
      value: 47.687000000000005
    - type: precision_at_10
      value: 13.355
    - type: precision_at_100
      value: 2.113
    - type: precision_at_1000
      value: 0.261
    - type: precision_at_3
      value: 29.793999999999997
    - type: precision_at_5
      value: 21.811
    - type: recall_at_1
      value: 21.149
    - type: recall_at_10
      value: 49.937
    - type: recall_at_100
      value: 73.382
    - type: recall_at_1000
      value: 87.606
    - type: recall_at_3
      value: 35.704
    - type: recall_at_5
      value: 42.309000000000005
  - task:
      type: Retrieval
    dataset:
      name: MTEB CmedqaRetrieval
      type: C-MTEB/CmedqaRetrieval
      config: default
      split: dev
      revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
    metrics:
    - type: map_at_1
      value: 28.74
    - type: map_at_10
      value: 41.981
    - type: map_at_100
      value: 43.753
    - type: map_at_1000
      value: 43.858999999999995
    - type: map_at_3
      value: 37.634
    - type: map_at_5
      value: 40.158
    - type: mrr_at_1
      value: 43.086
    - type: mrr_at_10
      value: 51.249
    - type: mrr_at_100
      value: 52.154
    - type: mrr_at_1000
      value: 52.190999999999995
    - type: mrr_at_3
      value: 48.787000000000006
    - type: mrr_at_5
      value: 50.193
    - type: ndcg_at_1
      value: 43.086
    - type: ndcg_at_10
      value: 48.703
    - type: ndcg_at_100
      value: 55.531
    - type: ndcg_at_1000
      value: 57.267999999999994
    - type: ndcg_at_3
      value: 43.464000000000006
    - type: ndcg_at_5
      value: 45.719
    - type: precision_at_1
      value: 43.086
    - type: precision_at_10
      value: 10.568
    - type: precision_at_100
      value: 1.616
    - type: precision_at_1000
      value: 0.184
    - type: precision_at_3
      value: 24.256
    - type: precision_at_5
      value: 17.509
    - type: recall_at_1
      value: 28.74
    - type: recall_at_10
      value: 59.349
    - type: recall_at_100
      value: 87.466
    - type: recall_at_1000
      value: 98.914
    - type: recall_at_3
      value: 43.322
    - type: recall_at_5
      value: 50.409000000000006
  - task:
      type: PairClassification
    dataset:
      name: MTEB Cmnli
      type: C-MTEB/CMNLI
      config: default
      split: validation
      revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
    metrics:
    - type: cos_sim_accuracy
      value: 79.03788334335539
    - type: cos_sim_ap
      value: 87.21703260472833
    - type: cos_sim_f1
      value: 79.87784187309127
    - type: cos_sim_precision
      value: 77.36634531113059
    - type: cos_sim_recall
      value: 82.55786766425064
    - type: dot_accuracy
      value: 79.03788334335539
    - type: dot_ap
      value: 87.22906528217948
    - type: dot_f1
      value: 79.87784187309127
    - type: dot_precision
      value: 77.36634531113059
    - type: dot_recall
      value: 82.55786766425064
    - type: euclidean_accuracy
      value: 79.03788334335539
    - type: euclidean_ap
      value: 87.21703670465753
    - type: euclidean_f1
      value: 79.87784187309127
    - type: euclidean_precision
      value: 77.36634531113059
    - type: euclidean_recall
      value: 82.55786766425064
    - type: manhattan_accuracy
      value: 78.28021647624774
    - type: manhattan_ap
      value: 86.66244127855394
    - type: manhattan_f1
      value: 79.24485643228577
    - type: manhattan_precision
      value: 76.71262858393521
    - type: manhattan_recall
      value: 81.94996492868833
    - type: max_accuracy
      value: 79.03788334335539
    - type: max_ap
      value: 87.22906528217948
    - type: max_f1
      value: 79.87784187309127
  - task:
      type: Retrieval
    dataset:
      name: MTEB CovidRetrieval
      type: C-MTEB/CovidRetrieval
      config: default
      split: dev
      revision: 1271c7809071a13532e05f25fb53511ffce77117
    metrics:
    - type: map_at_1
      value: 67.597
    - type: map_at_10
      value: 75.81599999999999
    - type: map_at_100
      value: 76.226
    - type: map_at_1000
      value: 76.23100000000001
    - type: map_at_3
      value: 73.907
    - type: map_at_5
      value: 75.08200000000001
    - type: mrr_at_1
      value: 67.756
    - type: mrr_at_10
      value: 75.8
    - type: mrr_at_100
      value: 76.205
    - type: mrr_at_1000
      value: 76.21
    - type: mrr_at_3
      value: 73.955
    - type: mrr_at_5
      value: 75.093
    - type: ndcg_at_1
      value: 67.756
    - type: ndcg_at_10
      value: 79.598
    - type: ndcg_at_100
      value: 81.34400000000001
    - type: ndcg_at_1000
      value: 81.477
    - type: ndcg_at_3
      value: 75.876
    - type: ndcg_at_5
      value: 77.94200000000001
    - type: precision_at_1
      value: 67.756
    - type: precision_at_10
      value: 9.231
    - type: precision_at_100
      value: 1.0
    - type: precision_at_1000
      value: 0.101
    - type: precision_at_3
      value: 27.362
    - type: precision_at_5
      value: 17.45
    - type: recall_at_1
      value: 67.597
    - type: recall_at_10
      value: 91.307
    - type: recall_at_100
      value: 98.946
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 81.428
    - type: recall_at_5
      value: 86.407
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: mteb/dbpedia
      config: default
      split: test
      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
    metrics:
    - type: map_at_1
      value: 9.33
    - type: map_at_10
      value: 23.118
    - type: map_at_100
      value: 34.28
    - type: map_at_1000
      value: 36.574
    - type: map_at_3
      value: 15.576
    - type: map_at_5
      value: 18.778
    - type: mrr_at_1
      value: 75.25
    - type: mrr_at_10
      value: 81.958
    - type: mrr_at_100
      value: 82.282
    - type: mrr_at_1000
      value: 82.285
    - type: mrr_at_3
      value: 81.042
    - type: mrr_at_5
      value: 81.62899999999999
    - type: ndcg_at_1
      value: 63.625
    - type: ndcg_at_10
      value: 50.781
    - type: ndcg_at_100
      value: 55.537000000000006
    - type: ndcg_at_1000
      value: 62.651
    - type: ndcg_at_3
      value: 55.297
    - type: ndcg_at_5
      value: 53.103
    - type: precision_at_1
      value: 75.25
    - type: precision_at_10
      value: 41.475
    - type: precision_at_100
      value: 13.5
    - type: precision_at_1000
      value: 2.686
    - type: precision_at_3
      value: 59.333000000000006
    - type: precision_at_5
      value: 51.9
    - type: recall_at_1
      value: 9.33
    - type: recall_at_10
      value: 29.398000000000003
    - type: recall_at_100
      value: 61.951
    - type: recall_at_1000
      value: 85.463
    - type: recall_at_3
      value: 17.267
    - type: recall_at_5
      value: 21.89
  - task:
      type: Retrieval
    dataset:
      name: MTEB DuRetrieval
      type: C-MTEB/DuRetrieval
      config: default
      split: dev
      revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
    metrics:
    - type: map_at_1
      value: 25.608999999999998
    - type: map_at_10
      value: 78.649
    - type: map_at_100
      value: 81.67699999999999
    - type: map_at_1000
      value: 81.71000000000001
    - type: map_at_3
      value: 54.112
    - type: map_at_5
      value: 68.34700000000001
    - type: mrr_at_1
      value: 87.75
    - type: mrr_at_10
      value: 92.175
    - type: mrr_at_100
      value: 92.225
    - type: mrr_at_1000
      value: 92.227
    - type: mrr_at_3
      value: 91.833
    - type: mrr_at_5
      value: 92.06800000000001
    - type: ndcg_at_1
      value: 87.75
    - type: ndcg_at_10
      value: 86.56700000000001
    - type: ndcg_at_100
      value: 89.519
    - type: ndcg_at_1000
      value: 89.822
    - type: ndcg_at_3
      value: 84.414
    - type: ndcg_at_5
      value: 83.721
    - type: precision_at_1
      value: 87.75
    - type: precision_at_10
      value: 41.665
    - type: precision_at_100
      value: 4.827
    - type: precision_at_1000
      value: 0.49
    - type: precision_at_3
      value: 75.533
    - type: precision_at_5
      value: 64.01
    - type: recall_at_1
      value: 25.608999999999998
    - type: recall_at_10
      value: 88.708
    - type: recall_at_100
      value: 98.007
    - type: recall_at_1000
      value: 99.555
    - type: recall_at_3
      value: 57.157000000000004
    - type: recall_at_5
      value: 74.118
  - task:
      type: Retrieval
    dataset:
      name: MTEB EcomRetrieval
      type: C-MTEB/EcomRetrieval
      config: default
      split: dev
      revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
    metrics:
    - type: map_at_1
      value: 55.800000000000004
    - type: map_at_10
      value: 65.952
    - type: map_at_100
      value: 66.413
    - type: map_at_1000
      value: 66.426
    - type: map_at_3
      value: 63.3
    - type: map_at_5
      value: 64.945
    - type: mrr_at_1
      value: 55.800000000000004
    - type: mrr_at_10
      value: 65.952
    - type: mrr_at_100
      value: 66.413
    - type: mrr_at_1000
      value: 66.426
    - type: mrr_at_3
      value: 63.3
    - type: mrr_at_5
      value: 64.945
    - type: ndcg_at_1
      value: 55.800000000000004
    - type: ndcg_at_10
      value: 71.00800000000001
    - type: ndcg_at_100
      value: 72.974
    - type: ndcg_at_1000
      value: 73.302
    - type: ndcg_at_3
      value: 65.669
    - type: ndcg_at_5
      value: 68.634
    - type: precision_at_1
      value: 55.800000000000004
    - type: precision_at_10
      value: 8.690000000000001
    - type: precision_at_100
      value: 0.955
    - type: precision_at_1000
      value: 0.098
    - type: precision_at_3
      value: 24.166999999999998
    - type: precision_at_5
      value: 15.939999999999998
    - type: recall_at_1
      value: 55.800000000000004
    - type: recall_at_10
      value: 86.9
    - type: recall_at_100
      value: 95.5
    - type: recall_at_1000
      value: 98.0
    - type: recall_at_3
      value: 72.5
    - type: recall_at_5
      value: 79.7
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 67.39500000000001
    - type: f1
      value: 62.01837785021389
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: mteb/fever
      config: default
      split: test
      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
    metrics:
    - type: map_at_1
      value: 86.27
    - type: map_at_10
      value: 92.163
    - type: map_at_100
      value: 92.351
    - type: map_at_1000
      value: 92.36
    - type: map_at_3
      value: 91.36
    - type: map_at_5
      value: 91.888
    - type: mrr_at_1
      value: 92.72399999999999
    - type: mrr_at_10
      value: 95.789
    - type: mrr_at_100
      value: 95.80300000000001
    - type: mrr_at_1000
      value: 95.804
    - type: mrr_at_3
      value: 95.64200000000001
    - type: mrr_at_5
      value: 95.75
    - type: ndcg_at_1
      value: 92.72399999999999
    - type: ndcg_at_10
      value: 94.269
    - type: ndcg_at_100
      value: 94.794
    - type: ndcg_at_1000
      value: 94.94
    - type: ndcg_at_3
      value: 93.427
    - type: ndcg_at_5
      value: 93.914
    - type: precision_at_1
      value: 92.72399999999999
    - type: precision_at_10
      value: 11.007
    - type: precision_at_100
      value: 1.153
    - type: precision_at_1000
      value: 0.11800000000000001
    - type: precision_at_3
      value: 34.993
    - type: precision_at_5
      value: 21.542
    - type: recall_at_1
      value: 86.27
    - type: recall_at_10
      value: 97.031
    - type: recall_at_100
      value: 98.839
    - type: recall_at_1000
      value: 99.682
    - type: recall_at_3
      value: 94.741
    - type: recall_at_5
      value: 96.03
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: mteb/fiqa
      config: default
      split: test
      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
    metrics:
    - type: map_at_1
      value: 29.561999999999998
    - type: map_at_10
      value: 48.52
    - type: map_at_100
      value: 50.753
    - type: map_at_1000
      value: 50.878
    - type: map_at_3
      value: 42.406
    - type: map_at_5
      value: 45.994
    - type: mrr_at_1
      value: 54.784
    - type: mrr_at_10
      value: 64.51400000000001
    - type: mrr_at_100
      value: 65.031
    - type: mrr_at_1000
      value: 65.05199999999999
    - type: mrr_at_3
      value: 62.474
    - type: mrr_at_5
      value: 63.562
    - type: ndcg_at_1
      value: 54.784
    - type: ndcg_at_10
      value: 57.138
    - type: ndcg_at_100
      value: 63.666999999999994
    - type: ndcg_at_1000
      value: 65.379
    - type: ndcg_at_3
      value: 52.589
    - type: ndcg_at_5
      value: 54.32599999999999
    - type: precision_at_1
      value: 54.784
    - type: precision_at_10
      value: 15.693999999999999
    - type: precision_at_100
      value: 2.259
    - type: precision_at_1000
      value: 0.256
    - type: precision_at_3
      value: 34.774
    - type: precision_at_5
      value: 25.772000000000002
    - type: recall_at_1
      value: 29.561999999999998
    - type: recall_at_10
      value: 64.708
    - type: recall_at_100
      value: 87.958
    - type: recall_at_1000
      value: 97.882
    - type: recall_at_3
      value: 48.394
    - type: recall_at_5
      value: 56.101
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: mteb/hotpotqa
      config: default
      split: test
      revision: ab518f4d6fcca38d87c25209f94beba119d02014
    metrics:
    - type: map_at_1
      value: 43.72
    - type: map_at_10
      value: 71.905
    - type: map_at_100
      value: 72.685
    - type: map_at_1000
      value: 72.72800000000001
    - type: map_at_3
      value: 68.538
    - type: map_at_5
      value: 70.675
    - type: mrr_at_1
      value: 87.441
    - type: mrr_at_10
      value: 91.432
    - type: mrr_at_100
      value: 91.512
    - type: mrr_at_1000
      value: 91.513
    - type: mrr_at_3
      value: 90.923
    - type: mrr_at_5
      value: 91.252
    - type: ndcg_at_1
      value: 87.441
    - type: ndcg_at_10
      value: 79.212
    - type: ndcg_at_100
      value: 81.694
    - type: ndcg_at_1000
      value: 82.447
    - type: ndcg_at_3
      value: 74.746
    - type: ndcg_at_5
      value: 77.27199999999999
    - type: precision_at_1
      value: 87.441
    - type: precision_at_10
      value: 16.42
    - type: precision_at_100
      value: 1.833
    - type: precision_at_1000
      value: 0.193
    - type: precision_at_3
      value: 48.184
    - type: precision_at_5
      value: 30.897999999999996
    - type: recall_at_1
      value: 43.72
    - type: recall_at_10
      value: 82.1
    - type: recall_at_100
      value: 91.62700000000001
    - type: recall_at_1000
      value: 96.556
    - type: recall_at_3
      value: 72.275
    - type: recall_at_5
      value: 77.24499999999999
  - task:
      type: Classification
    dataset:
      name: MTEB IFlyTek
      type: C-MTEB/IFlyTek-classification
      config: default
      split: validation
      revision: 421605374b29664c5fc098418fe20ada9bd55f8a
    metrics:
    - type: accuracy
      value: 54.520969603693736
    - type: f1
      value: 42.359043311419626
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 96.72559999999999
    - type: ap
      value: 95.01759461773742
    - type: f1
      value: 96.72429945397575
  - task:
      type: Classification
    dataset:
      name: MTEB JDReview
      type: C-MTEB/JDReview-classification
      config: default
      split: test
      revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
    metrics:
    - type: accuracy
      value: 90.1688555347092
    - type: ap
      value: 63.36583667477521
    - type: f1
      value: 85.6845016521436
  - task:
      type: STS
    dataset:
      name: MTEB LCQMC
      type: C-MTEB/LCQMC
      config: default
      split: test
      revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
    metrics:
    - type: cos_sim_pearson
      value: 67.35114066823127
    - type: cos_sim_spearman
      value: 72.98875207056305
    - type: euclidean_pearson
      value: 71.45620183630378
    - type: euclidean_spearman
      value: 72.98875207022671
    - type: manhattan_pearson
      value: 71.3845159780333
    - type: manhattan_spearman
      value: 72.92710990543166
  - task:
      type: Reranking
    dataset:
      name: MTEB MMarcoReranking
      type: C-MTEB/Mmarco-reranking
      config: default
      split: dev
      revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
    metrics:
    - type: map
      value: 32.68592539803807
    - type: mrr
      value: 31.58968253968254
  - task:
      type: Retrieval
    dataset:
      name: MTEB MMarcoRetrieval
      type: C-MTEB/MMarcoRetrieval
      config: default
      split: dev
      revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
    metrics:
    - type: map_at_1
      value: 71.242
    - type: map_at_10
      value: 80.01
    - type: map_at_100
      value: 80.269
    - type: map_at_1000
      value: 80.276
    - type: map_at_3
      value: 78.335
    - type: map_at_5
      value: 79.471
    - type: mrr_at_1
      value: 73.668
    - type: mrr_at_10
      value: 80.515
    - type: mrr_at_100
      value: 80.738
    - type: mrr_at_1000
      value: 80.744
    - type: mrr_at_3
      value: 79.097
    - type: mrr_at_5
      value: 80.045
    - type: ndcg_at_1
      value: 73.668
    - type: ndcg_at_10
      value: 83.357
    - type: ndcg_at_100
      value: 84.442
    - type: ndcg_at_1000
      value: 84.619
    - type: ndcg_at_3
      value: 80.286
    - type: ndcg_at_5
      value: 82.155
    - type: precision_at_1
      value: 73.668
    - type: precision_at_10
      value: 9.905
    - type: precision_at_100
      value: 1.043
    - type: precision_at_1000
      value: 0.106
    - type: precision_at_3
      value: 30.024
    - type: precision_at_5
      value: 19.017
    - type: recall_at_1
      value: 71.242
    - type: recall_at_10
      value: 93.11
    - type: recall_at_100
      value: 97.85000000000001
    - type: recall_at_1000
      value: 99.21900000000001
    - type: recall_at_3
      value: 85.137
    - type: recall_at_5
      value: 89.548
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: mteb/msmarco
      config: default
      split: dev
      revision: c5a29a104738b98a9e76336939199e264163d4a0
    metrics:
    - type: map_at_1
      value: 22.006999999999998
    - type: map_at_10
      value: 34.994
    - type: map_at_100
      value: 36.183
    - type: map_at_1000
      value: 36.227
    - type: map_at_3
      value: 30.75
    - type: map_at_5
      value: 33.155
    - type: mrr_at_1
      value: 22.679
    - type: mrr_at_10
      value: 35.619
    - type: mrr_at_100
      value: 36.732
    - type: mrr_at_1000
      value: 36.77
    - type: mrr_at_3
      value: 31.44
    - type: mrr_at_5
      value: 33.811
    - type: ndcg_at_1
      value: 22.679
    - type: ndcg_at_10
      value: 42.376000000000005
    - type: ndcg_at_100
      value: 48.001
    - type: ndcg_at_1000
      value: 49.059999999999995
    - type: ndcg_at_3
      value: 33.727000000000004
    - type: ndcg_at_5
      value: 38.013000000000005
    - type: precision_at_1
      value: 22.679
    - type: precision_at_10
      value: 6.815
    - type: precision_at_100
      value: 0.962
    - type: precision_at_1000
      value: 0.105
    - type: precision_at_3
      value: 14.441
    - type: precision_at_5
      value: 10.817
    - type: recall_at_1
      value: 22.006999999999998
    - type: recall_at_10
      value: 65.158
    - type: recall_at_100
      value: 90.997
    - type: recall_at_1000
      value: 98.996
    - type: recall_at_3
      value: 41.646
    - type: recall_at_5
      value: 51.941
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 97.55129958960327
    - type: f1
      value: 97.43464802675416
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 90.4719562243502
    - type: f1
      value: 70.76460034443902
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 83.49024882313383
    - type: f1
      value: 81.44067057564666
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (zh-CN)
      type: mteb/amazon_massive_intent
      config: zh-CN
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 79.88231338264963
    - type: f1
      value: 77.13536609019927
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 87.23268325487558
    - type: f1
      value: 86.36737921996752
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (zh-CN)
      type: mteb/amazon_massive_scenario
      config: zh-CN
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 84.50571620712844
    - type: f1
      value: 83.4128768262944
  - task:
      type: Retrieval
    dataset:
      name: MTEB MedicalRetrieval
      type: C-MTEB/MedicalRetrieval
      config: default
      split: dev
      revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
    metrics:
    - type: map_at_1
      value: 56.89999999999999
    - type: map_at_10
      value: 63.438
    - type: map_at_100
      value: 63.956
    - type: map_at_1000
      value: 63.991
    - type: map_at_3
      value: 61.983
    - type: map_at_5
      value: 62.778
    - type: mrr_at_1
      value: 56.99999999999999
    - type: mrr_at_10
      value: 63.483000000000004
    - type: mrr_at_100
      value: 63.993
    - type: mrr_at_1000
      value: 64.02799999999999
    - type: mrr_at_3
      value: 62.017
    - type: mrr_at_5
      value: 62.812
    - type: ndcg_at_1
      value: 56.89999999999999
    - type: ndcg_at_10
      value: 66.61
    - type: ndcg_at_100
      value: 69.387
    - type: ndcg_at_1000
      value: 70.327
    - type: ndcg_at_3
      value: 63.583999999999996
    - type: ndcg_at_5
      value: 65.0
    - type: precision_at_1
      value: 56.89999999999999
    - type: precision_at_10
      value: 7.66
    - type: precision_at_100
      value: 0.902
    - type: precision_at_1000
      value: 0.098
    - type: precision_at_3
      value: 22.733
    - type: precision_at_5
      value: 14.32
    - type: recall_at_1
      value: 56.89999999999999
    - type: recall_at_10
      value: 76.6
    - type: recall_at_100
      value: 90.2
    - type: recall_at_1000
      value: 97.6
    - type: recall_at_3
      value: 68.2
    - type: recall_at_5
      value: 71.6
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 40.32149153753394
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 39.40319973495386
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 33.9769104898534
    - type: mrr
      value: 35.32831430710564
  - task:
      type: Classification
    dataset:
      name: MTEB MultilingualSentiment
      type: C-MTEB/MultilingualSentiment-classification
      config: default
      split: validation
      revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
    metrics:
    - type: accuracy
      value: 81.80666666666667
    - type: f1
      value: 81.83278699395508
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: mteb/nfcorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
    metrics:
    - type: map_at_1
      value: 6.3
    - type: map_at_10
      value: 14.151
    - type: map_at_100
      value: 18.455
    - type: map_at_1000
      value: 20.186999999999998
    - type: map_at_3
      value: 10.023
    - type: map_at_5
      value: 11.736
    - type: mrr_at_1
      value: 49.536
    - type: mrr_at_10
      value: 58.516
    - type: mrr_at_100
      value: 59.084
    - type: mrr_at_1000
      value: 59.114
    - type: mrr_at_3
      value: 56.45
    - type: mrr_at_5
      value: 57.642
    - type: ndcg_at_1
      value: 47.522999999999996
    - type: ndcg_at_10
      value: 38.4
    - type: ndcg_at_100
      value: 35.839999999999996
    - type: ndcg_at_1000
      value: 44.998
    - type: ndcg_at_3
      value: 43.221
    - type: ndcg_at_5
      value: 40.784
    - type: precision_at_1
      value: 49.536
    - type: precision_at_10
      value: 28.977999999999998
    - type: precision_at_100
      value: 9.378
    - type: precision_at_1000
      value: 2.2769999999999997
    - type: precision_at_3
      value: 40.454
    - type: precision_at_5
      value: 35.418
    - type: recall_at_1
      value: 6.3
    - type: recall_at_10
      value: 19.085
    - type: recall_at_100
      value: 38.18
    - type: recall_at_1000
      value: 71.219
    - type: recall_at_3
      value: 11.17
    - type: recall_at_5
      value: 13.975999999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: mteb/nq
      config: default
      split: test
      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
    metrics:
    - type: map_at_1
      value: 43.262
    - type: map_at_10
      value: 60.387
    - type: map_at_100
      value: 61.102000000000004
    - type: map_at_1000
      value: 61.111000000000004
    - type: map_at_3
      value: 56.391999999999996
    - type: map_at_5
      value: 58.916000000000004
    - type: mrr_at_1
      value: 48.725
    - type: mrr_at_10
      value: 62.812999999999995
    - type: mrr_at_100
      value: 63.297000000000004
    - type: mrr_at_1000
      value: 63.304
    - type: mrr_at_3
      value: 59.955999999999996
    - type: mrr_at_5
      value: 61.785999999999994
    - type: ndcg_at_1
      value: 48.696
    - type: ndcg_at_10
      value: 67.743
    - type: ndcg_at_100
      value: 70.404
    - type: ndcg_at_1000
      value: 70.60600000000001
    - type: ndcg_at_3
      value: 60.712999999999994
    - type: ndcg_at_5
      value: 64.693
    - type: precision_at_1
      value: 48.696
    - type: precision_at_10
      value: 10.513
    - type: precision_at_100
      value: 1.196
    - type: precision_at_1000
      value: 0.121
    - type: precision_at_3
      value: 27.221
    - type: precision_at_5
      value: 18.701999999999998
    - type: recall_at_1
      value: 43.262
    - type: recall_at_10
      value: 87.35300000000001
    - type: recall_at_100
      value: 98.31299999999999
    - type: recall_at_1000
      value: 99.797
    - type: recall_at_3
      value: 69.643
    - type: recall_at_5
      value: 78.645
  - task:
      type: PairClassification
    dataset:
      name: MTEB Ocnli
      type: C-MTEB/OCNLI
      config: default
      split: validation
      revision: 66e76a618a34d6d565d5538088562851e6daa7ec
    metrics:
    - type: cos_sim_accuracy
      value: 72.65836491608013
    - type: cos_sim_ap
      value: 78.75807247519593
    - type: cos_sim_f1
      value: 74.84662576687117
    - type: cos_sim_precision
      value: 63.97003745318352
    - type: cos_sim_recall
      value: 90.17951425554382
    - type: dot_accuracy
      value: 72.65836491608013
    - type: dot_ap
      value: 78.75807247519593
    - type: dot_f1
      value: 74.84662576687117
    - type: dot_precision
      value: 63.97003745318352
    - type: dot_recall
      value: 90.17951425554382
    - type: euclidean_accuracy
      value: 72.65836491608013
    - type: euclidean_ap
      value: 78.75807247519593
    - type: euclidean_f1
      value: 74.84662576687117
    - type: euclidean_precision
      value: 63.97003745318352
    - type: euclidean_recall
      value: 90.17951425554382
    - type: manhattan_accuracy
      value: 72.00866269626421
    - type: manhattan_ap
      value: 78.34663376353235
    - type: manhattan_f1
      value: 74.13234613604813
    - type: manhattan_precision
      value: 65.98023064250413
    - type: manhattan_recall
      value: 84.58289334741288
    - type: max_accuracy
      value: 72.65836491608013
    - type: max_ap
      value: 78.75807247519593
    - type: max_f1
      value: 74.84662576687117
  - task:
      type: Classification
    dataset:
      name: MTEB OnlineShopping
      type: C-MTEB/OnlineShopping-classification
      config: default
      split: test
      revision: e610f2ebd179a8fda30ae534c3878750a96db120
    metrics:
    - type: accuracy
      value: 94.46999999999998
    - type: ap
      value: 93.56401511160975
    - type: f1
      value: 94.46692790889986
  - task:
      type: STS
    dataset:
      name: MTEB PAWSX
      type: C-MTEB/PAWSX
      config: default
      split: test
      revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
    metrics:
    - type: cos_sim_pearson
      value: 46.851404503762474
    - type: cos_sim_spearman
      value: 52.74603680597415
    - type: euclidean_pearson
      value: 51.596358967977295
    - type: euclidean_spearman
      value: 52.74603680597415
    - type: manhattan_pearson
      value: 51.81838023379299
    - type: manhattan_spearman
      value: 52.79611669731429
  - task:
      type: STS
    dataset:
      name: MTEB QBQTC
      type: C-MTEB/QBQTC
      config: default
      split: test
      revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
    metrics:
    - type: cos_sim_pearson
      value: 31.928376136347016
    - type: cos_sim_spearman
      value: 34.38497204533162
    - type: euclidean_pearson
      value: 32.658432953090674
    - type: euclidean_spearman
      value: 34.38497204533162
    - type: manhattan_pearson
      value: 32.887190283203054
    - type: manhattan_spearman
      value: 34.69496960849327
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: mteb/quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 69.952
    - type: map_at_10
      value: 84.134
    - type: map_at_100
      value: 84.795
    - type: map_at_1000
      value: 84.809
    - type: map_at_3
      value: 81.085
    - type: map_at_5
      value: 82.976
    - type: mrr_at_1
      value: 80.56
    - type: mrr_at_10
      value: 87.105
    - type: mrr_at_100
      value: 87.20700000000001
    - type: mrr_at_1000
      value: 87.208
    - type: mrr_at_3
      value: 86.118
    - type: mrr_at_5
      value: 86.79299999999999
    - type: ndcg_at_1
      value: 80.57
    - type: ndcg_at_10
      value: 88.047
    - type: ndcg_at_100
      value: 89.266
    - type: ndcg_at_1000
      value: 89.34299999999999
    - type: ndcg_at_3
      value: 85.052
    - type: ndcg_at_5
      value: 86.68299999999999
    - type: precision_at_1
      value: 80.57
    - type: precision_at_10
      value: 13.439
    - type: precision_at_100
      value: 1.536
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 37.283
    - type: precision_at_5
      value: 24.558
    - type: recall_at_1
      value: 69.952
    - type: recall_at_10
      value: 95.599
    - type: recall_at_100
      value: 99.67099999999999
    - type: recall_at_1000
      value: 99.983
    - type: recall_at_3
      value: 87.095
    - type: recall_at_5
      value: 91.668
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 70.12802769698337
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 71.19047621740276
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: mteb/scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.208
    - type: map_at_10
      value: 17.036
    - type: map_at_100
      value: 20.162
    - type: map_at_1000
      value: 20.552
    - type: map_at_3
      value: 11.591999999999999
    - type: map_at_5
      value: 14.349
    - type: mrr_at_1
      value: 30.599999999999998
    - type: mrr_at_10
      value: 43.325
    - type: mrr_at_100
      value: 44.281
    - type: mrr_at_1000
      value: 44.31
    - type: mrr_at_3
      value: 39.300000000000004
    - type: mrr_at_5
      value: 41.730000000000004
    - type: ndcg_at_1
      value: 30.599999999999998
    - type: ndcg_at_10
      value: 27.378000000000004
    - type: ndcg_at_100
      value: 37.768
    - type: ndcg_at_1000
      value: 43.275000000000006
    - type: ndcg_at_3
      value: 25.167
    - type: ndcg_at_5
      value: 22.537
    - type: precision_at_1
      value: 30.599999999999998
    - type: precision_at_10
      value: 14.46
    - type: precision_at_100
      value: 2.937
    - type: precision_at_1000
      value: 0.424
    - type: precision_at_3
      value: 23.666999999999998
    - type: precision_at_5
      value: 20.14
    - type: recall_at_1
      value: 6.208
    - type: recall_at_10
      value: 29.29
    - type: recall_at_100
      value: 59.565
    - type: recall_at_1000
      value: 85.963
    - type: recall_at_3
      value: 14.407
    - type: recall_at_5
      value: 20.412
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 82.65489797062479
    - type: cos_sim_spearman
      value: 75.34808277034776
    - type: euclidean_pearson
      value: 79.28097508609059
    - type: euclidean_spearman
      value: 75.3480824481771
    - type: manhattan_pearson
      value: 78.83529262858895
    - type: manhattan_spearman
      value: 74.96318170787025
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 85.06920163624117
    - type: cos_sim_spearman
      value: 77.24549887905519
    - type: euclidean_pearson
      value: 85.58740280635266
    - type: euclidean_spearman
      value: 77.24652170306867
    - type: manhattan_pearson
      value: 85.77917470895854
    - type: manhattan_spearman
      value: 77.54426264008778
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 80.9762185094084
    - type: cos_sim_spearman
      value: 80.98090253728394
    - type: euclidean_pearson
      value: 80.88451512135202
    - type: euclidean_spearman
      value: 80.98090253728394
    - type: manhattan_pearson
      value: 80.7606664599805
    - type: manhattan_spearman
      value: 80.87197716950068
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 81.91239166620251
    - type: cos_sim_spearman
      value: 76.36798509005328
    - type: euclidean_pearson
      value: 80.6393872615655
    - type: euclidean_spearman
      value: 76.36798836339655
    - type: manhattan_pearson
      value: 80.50765898709096
    - type: manhattan_spearman
      value: 76.31958999372227
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 83.68800355225011
    - type: cos_sim_spearman
      value: 84.47549220803403
    - type: euclidean_pearson
      value: 83.86859896384159
    - type: euclidean_spearman
      value: 84.47551564954756
    - type: manhattan_pearson
      value: 83.74201103044383
    - type: manhattan_spearman
      value: 84.39903759718152
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 78.24197302553398
    - type: cos_sim_spearman
      value: 79.44526946553684
    - type: euclidean_pearson
      value: 79.12747636563053
    - type: euclidean_spearman
      value: 79.44526946553684
    - type: manhattan_pearson
      value: 78.94407504115144
    - type: manhattan_spearman
      value: 79.24858249553934
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 89.15329071763895
    - type: cos_sim_spearman
      value: 88.67251952242073
    - type: euclidean_pearson
      value: 89.16908249259637
    - type: euclidean_spearman
      value: 88.67251952242073
    - type: manhattan_pearson
      value: 89.1279735094785
    - type: manhattan_spearman
      value: 88.81731953658254
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 69.44962535524695
    - type: cos_sim_spearman
      value: 71.75861316291065
    - type: euclidean_pearson
      value: 72.42347748883483
    - type: euclidean_spearman
      value: 71.75861316291065
    - type: manhattan_pearson
      value: 72.57545073534365
    - type: manhattan_spearman
      value: 71.90087671205625
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (zh)
      type: mteb/sts22-crosslingual-sts
      config: zh
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 68.9945443484093
    - type: cos_sim_spearman
      value: 71.46807157842791
    - type: euclidean_pearson
      value: 69.24911748374225
    - type: euclidean_spearman
      value: 69.46807157842791
    - type: manhattan_pearson
      value: 69.65580071876552
    - type: manhattan_spearman
      value: 69.68775795734852
  - task:
      type: STS
    dataset:
      name: MTEB STSB
      type: C-MTEB/STSB
      config: default
      split: test
      revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
    metrics:
    - type: cos_sim_pearson
      value: 77.39283860361535
    - type: cos_sim_spearman
      value: 77.14577975930179
    - type: euclidean_pearson
      value: 76.64560889817044
    - type: euclidean_spearman
      value: 77.14577975930179
    - type: manhattan_pearson
      value: 76.82848456242104
    - type: manhattan_spearman
      value: 77.37708521460667
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 84.14036697885552
    - type: cos_sim_spearman
      value: 83.10901632378086
    - type: euclidean_pearson
      value: 83.59991244380554
    - type: euclidean_spearman
      value: 83.10901632378086
    - type: manhattan_pearson
      value: 83.56632266895113
    - type: manhattan_spearman
      value: 83.17610542379353
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 88.98026856845443
    - type: mrr
      value: 96.80987494712984
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: map_at_1
      value: 41.661
    - type: map_at_10
      value: 55.492
    - type: map_at_100
      value: 56.237
    - type: map_at_1000
      value: 56.255
    - type: map_at_3
      value: 51.05
    - type: map_at_5
      value: 54.01200000000001
    - type: mrr_at_1
      value: 44.0
    - type: mrr_at_10
      value: 56.443
    - type: mrr_at_100
      value: 57.13700000000001
    - type: mrr_at_1000
      value: 57.152
    - type: mrr_at_3
      value: 52.944
    - type: mrr_at_5
      value: 55.37800000000001
    - type: ndcg_at_1
      value: 44.0
    - type: ndcg_at_10
      value: 62.312999999999995
    - type: ndcg_at_100
      value: 65.63900000000001
    - type: ndcg_at_1000
      value: 66.019
    - type: ndcg_at_3
      value: 54.67999999999999
    - type: ndcg_at_5
      value: 59.284000000000006
    - type: precision_at_1
      value: 44.0
    - type: precision_at_10
      value: 9.367
    - type: precision_at_100
      value: 1.0999999999999999
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 22.778000000000002
    - type: precision_at_5
      value: 16.467000000000002
    - type: recall_at_1
      value: 41.661
    - type: recall_at_10
      value: 82.306
    - type: recall_at_100
      value: 97.167
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 62.461
    - type: recall_at_5
      value: 73.411
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.90693069306931
    - type: cos_sim_ap
      value: 97.86562522779887
    - type: cos_sim_f1
      value: 95.27162977867204
    - type: cos_sim_precision
      value: 95.8502024291498
    - type: cos_sim_recall
      value: 94.69999999999999
    - type: dot_accuracy
      value: 99.90693069306931
    - type: dot_ap
      value: 97.86562522779887
    - type: dot_f1
      value: 95.27162977867204
    - type: dot_precision
      value: 95.8502024291498
    - type: dot_recall
      value: 94.69999999999999
    - type: euclidean_accuracy
      value: 99.90693069306931
    - type: euclidean_ap
      value: 97.86562522779887
    - type: euclidean_f1
      value: 95.27162977867204
    - type: euclidean_precision
      value: 95.8502024291498
    - type: euclidean_recall
      value: 94.69999999999999
    - type: manhattan_accuracy
      value: 99.90693069306931
    - type: manhattan_ap
      value: 97.85527044211135
    - type: manhattan_f1
      value: 95.27638190954774
    - type: manhattan_precision
      value: 95.75757575757575
    - type: manhattan_recall
      value: 94.8
    - type: max_accuracy
      value: 99.90693069306931
    - type: max_ap
      value: 97.86562522779887
    - type: max_f1
      value: 95.27638190954774
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 78.89230351770412
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 47.52328347080355
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 57.74702024461137
    - type: mrr
      value: 58.88074548001018
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.047929797503592
    - type: cos_sim_spearman
      value: 29.465371781983567
    - type: dot_pearson
      value: 30.047927690552335
    - type: dot_spearman
      value: 29.465371781983567
  - task:
      type: Reranking
    dataset:
      name: MTEB T2Reranking
      type: C-MTEB/T2Reranking
      config: default
      split: dev
      revision: 76631901a18387f85eaa53e5450019b87ad58ef9
    metrics:
    - type: map
      value: 66.54177017978034
    - type: mrr
      value: 76.76094292377299
  - task:
      type: Retrieval
    dataset:
      name: MTEB T2Retrieval
      type: C-MTEB/T2Retrieval
      config: default
      split: dev
      revision: 8731a845f1bf500a4f111cf1070785c793d10e64
    metrics:
    - type: map_at_1
      value: 28.608
    - type: map_at_10
      value: 81.266
    - type: map_at_100
      value: 84.714
    - type: map_at_1000
      value: 84.758
    - type: map_at_3
      value: 56.967
    - type: map_at_5
      value: 70.14
    - type: mrr_at_1
      value: 91.881
    - type: mrr_at_10
      value: 94.11699999999999
    - type: mrr_at_100
      value: 94.178
    - type: mrr_at_1000
      value: 94.181
    - type: mrr_at_3
      value: 93.772
    - type: mrr_at_5
      value: 93.997
    - type: ndcg_at_1
      value: 91.881
    - type: ndcg_at_10
      value: 87.954
    - type: ndcg_at_100
      value: 90.904
    - type: ndcg_at_1000
      value: 91.326
    - type: ndcg_at_3
      value: 88.838
    - type: ndcg_at_5
      value: 87.764
    - type: precision_at_1
      value: 91.881
    - type: precision_at_10
      value: 43.628
    - type: precision_at_100
      value: 5.082
    - type: precision_at_1000
      value: 0.518
    - type: precision_at_3
      value: 77.62400000000001
    - type: precision_at_5
      value: 65.269
    - type: recall_at_1
      value: 28.608
    - type: recall_at_10
      value: 87.06
    - type: recall_at_100
      value: 96.815
    - type: recall_at_1000
      value: 98.969
    - type: recall_at_3
      value: 58.506
    - type: recall_at_5
      value: 73.21600000000001
  - task:
      type: Classification
    dataset:
      name: MTEB TNews
      type: C-MTEB/TNews-classification
      config: default
      split: validation
      revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
    metrics:
    - type: accuracy
      value: 56.691999999999986
    - type: f1
      value: 54.692084702788065
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: mteb/trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.181
    - type: map_at_10
      value: 1.2
    - type: map_at_100
      value: 6.078
    - type: map_at_1000
      value: 14.940000000000001
    - type: map_at_3
      value: 0.45599999999999996
    - type: map_at_5
      value: 0.692
    - type: mrr_at_1
      value: 66.0
    - type: mrr_at_10
      value: 75.819
    - type: mrr_at_100
      value: 76.168
    - type: mrr_at_1000
      value: 76.168
    - type: mrr_at_3
      value: 72.667
    - type: mrr_at_5
      value: 74.86699999999999
    - type: ndcg_at_1
      value: 59.0
    - type: ndcg_at_10
      value: 52.60399999999999
    - type: ndcg_at_100
      value: 38.049
    - type: ndcg_at_1000
      value: 38.576
    - type: ndcg_at_3
      value: 57.235
    - type: ndcg_at_5
      value: 56.147000000000006
    - type: precision_at_1
      value: 66.0
    - type: precision_at_10
      value: 55.2
    - type: precision_at_100
      value: 38.78
    - type: precision_at_1000
      value: 16.986
    - type: precision_at_3
      value: 62.666999999999994
    - type: precision_at_5
      value: 60.8
    - type: recall_at_1
      value: 0.181
    - type: recall_at_10
      value: 1.471
    - type: recall_at_100
      value: 9.748999999999999
    - type: recall_at_1000
      value: 37.667
    - type: recall_at_3
      value: 0.49300000000000005
    - type: recall_at_5
      value: 0.7979999999999999
  - task:
      type: Clustering
    dataset:
      name: MTEB ThuNewsClusteringP2P
      type: C-MTEB/ThuNewsClusteringP2P
      config: default
      split: test
      revision: 5798586b105c0434e4f0fe5e767abe619442cf93
    metrics:
    - type: v_measure
      value: 78.68783858143624
  - task:
      type: Clustering
    dataset:
      name: MTEB ThuNewsClusteringS2S
      type: C-MTEB/ThuNewsClusteringS2S
      config: default
      split: test
      revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
    metrics:
    - type: v_measure
      value: 77.04148998956299
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: mteb/touche2020
      config: default
      split: test
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
    metrics:
    - type: map_at_1
      value: 1.936
    - type: map_at_10
      value: 8.942
    - type: map_at_100
      value: 14.475999999999999
    - type: map_at_1000
      value: 16.156000000000002
    - type: map_at_3
      value: 4.865
    - type: map_at_5
      value: 6.367000000000001
    - type: mrr_at_1
      value: 26.531
    - type: mrr_at_10
      value: 42.846000000000004
    - type: mrr_at_100
      value: 43.441
    - type: mrr_at_1000
      value: 43.441
    - type: mrr_at_3
      value: 36.735
    - type: mrr_at_5
      value: 40.510000000000005
    - type: ndcg_at_1
      value: 24.490000000000002
    - type: ndcg_at_10
      value: 23.262
    - type: ndcg_at_100
      value: 34.959
    - type: ndcg_at_1000
      value: 47.258
    - type: ndcg_at_3
      value: 25.27
    - type: ndcg_at_5
      value: 24.246000000000002
    - type: precision_at_1
      value: 26.531
    - type: precision_at_10
      value: 20.408
    - type: precision_at_100
      value: 7.306
    - type: precision_at_1000
      value: 1.541
    - type: precision_at_3
      value: 26.531
    - type: precision_at_5
      value: 24.082
    - type: recall_at_1
      value: 1.936
    - type: recall_at_10
      value: 15.712000000000002
    - type: recall_at_100
      value: 45.451
    - type: recall_at_1000
      value: 83.269
    - type: recall_at_3
      value: 6.442
    - type: recall_at_5
      value: 9.151
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 86.564
    - type: ap
      value: 34.58766846081731
    - type: f1
      value: 72.32759831978161
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 77.80418788907753
    - type: f1
      value: 78.1047638421972
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 59.20888659980063
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 85.45627943017226
    - type: cos_sim_ap
      value: 72.25550061847534
    - type: cos_sim_f1
      value: 66.0611487783037
    - type: cos_sim_precision
      value: 64.11720884032779
    - type: cos_sim_recall
      value: 68.12664907651715
    - type: dot_accuracy
      value: 85.45627943017226
    - type: dot_ap
      value: 72.25574305366213
    - type: dot_f1
      value: 66.0611487783037
    - type: dot_precision
      value: 64.11720884032779
    - type: dot_recall
      value: 68.12664907651715
    - type: euclidean_accuracy
      value: 85.45627943017226
    - type: euclidean_ap
      value: 72.2557084446673
    - type: euclidean_f1
      value: 66.0611487783037
    - type: euclidean_precision
      value: 64.11720884032779
    - type: euclidean_recall
      value: 68.12664907651715
    - type: manhattan_accuracy
      value: 85.32514752339513
    - type: manhattan_ap
      value: 71.52919143472248
    - type: manhattan_f1
      value: 65.60288251190322
    - type: manhattan_precision
      value: 64.02913840743531
    - type: manhattan_recall
      value: 67.25593667546174
    - type: max_accuracy
      value: 85.45627943017226
    - type: max_ap
      value: 72.25574305366213
    - type: max_f1
      value: 66.0611487783037
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.34167733923235
    - type: cos_sim_ap
      value: 84.58587730660244
    - type: cos_sim_f1
      value: 77.14170010676287
    - type: cos_sim_precision
      value: 73.91181657848324
    - type: cos_sim_recall
      value: 80.66676932553126
    - type: dot_accuracy
      value: 88.34167733923235
    - type: dot_ap
      value: 84.58585083616217
    - type: dot_f1
      value: 77.14170010676287
    - type: dot_precision
      value: 73.91181657848324
    - type: dot_recall
      value: 80.66676932553126
    - type: euclidean_accuracy
      value: 88.34167733923235
    - type: euclidean_ap
      value: 84.5858781355044
    - type: euclidean_f1
      value: 77.14170010676287
    - type: euclidean_precision
      value: 73.91181657848324
    - type: euclidean_recall
      value: 80.66676932553126
    - type: manhattan_accuracy
      value: 88.28152287809989
    - type: manhattan_ap
      value: 84.53184837110165
    - type: manhattan_f1
      value: 77.13582823915313
    - type: manhattan_precision
      value: 74.76156069364161
    - type: manhattan_recall
      value: 79.66584539574993
    - type: max_accuracy
      value: 88.34167733923235
    - type: max_ap
      value: 84.5858781355044
    - type: max_f1
      value: 77.14170010676287
  - task:
      type: Retrieval
    dataset:
      name: MTEB VideoRetrieval
      type: C-MTEB/VideoRetrieval
      config: default
      split: dev
      revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
    metrics:
    - type: map_at_1
      value: 66.10000000000001
    - type: map_at_10
      value: 75.238
    - type: map_at_100
      value: 75.559
    - type: map_at_1000
      value: 75.565
    - type: map_at_3
      value: 73.68299999999999
    - type: map_at_5
      value: 74.63300000000001
    - type: mrr_at_1
      value: 66.10000000000001
    - type: mrr_at_10
      value: 75.238
    - type: mrr_at_100
      value: 75.559
    - type: mrr_at_1000
      value: 75.565
    - type: mrr_at_3
      value: 73.68299999999999
    - type: mrr_at_5
      value: 74.63300000000001
    - type: ndcg_at_1
      value: 66.10000000000001
    - type: ndcg_at_10
      value: 79.25999999999999
    - type: ndcg_at_100
      value: 80.719
    - type: ndcg_at_1000
      value: 80.862
    - type: ndcg_at_3
      value: 76.08200000000001
    - type: ndcg_at_5
      value: 77.782
    - type: precision_at_1
      value: 66.10000000000001
    - type: precision_at_10
      value: 9.17
    - type: precision_at_100
      value: 0.983
    - type: precision_at_1000
      value: 0.099
    - type: precision_at_3
      value: 27.667
    - type: precision_at_5
      value: 17.419999999999998
    - type: recall_at_1
      value: 66.10000000000001
    - type: recall_at_10
      value: 91.7
    - type: recall_at_100
      value: 98.3
    - type: recall_at_1000
      value: 99.4
    - type: recall_at_3
      value: 83.0
    - type: recall_at_5
      value: 87.1
  - task:
      type: Classification
    dataset:
      name: MTEB Waimai
      type: C-MTEB/waimai-classification
      config: default
      split: test
      revision: 339287def212450dcaa9df8c22bf93e9980c7023
    metrics:
    - type: accuracy
      value: 91.13
    - type: ap
      value: 79.55231335947015
    - type: f1
      value: 89.63091922203914
---

<p align="center">
  <img src="https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/raw/main/images/gme_logo.png" alt="GME Logo" style="width: 100%; max-width: 450px;">
</p>

<p align="center"><b>GME: General Multimodal Embedding</b></p>

## gme-Qwen2-VL-7B

We are excited to present `GME-Qwen2VL` series of unified **multimodal embedding models**,
which are based on the advanced [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d) multimodal large language models (MLLMs).

The `GME` models support three types of input: **text**, **image**, and **image-text pair**, all of which can produce universal vector representations and have powerful retrieval performance.

**Key Enhancements of GME Models**:

- **Unified Multimodal Representation**: GME models can process both single-modal and combined-modal inputs, resulting in a unified vector representation. This enables versatile retrieval scenarios (Any2Any Search), supporting tasks such as text retrieval, image retrieval from text, and image-to-image searches.
- **High Performance**: Achieves state-of-the-art (SOTA) results in our universal multimodal retrieval benchmark (**UMRB**) and demonstrate strong evaluation scores in the Multimodal Textual Evaluation Benchmark (**MTEB**).
- **Dynamic Image Resolution**: Benefiting from `Qwen2-VL` and our training data, GME models support dynamic resolution image input.
- **Strong Visual Retrieval Performance**: Enhanced by the Qwen2-VL model series, our models excel in visual document retrieval tasks that require a nuanced understanding of document screenshots.
  This capability is particularly beneficial for complex document understanding scenarios,
  such as multimodal retrieval-augmented generation (RAG) applications focused on academic papers.

**Developed by**: Tongyi Lab, Alibaba Group

**Paper**: [GME: Improving Universal Multimodal Retrieval by Multimodal LLMs](http://arxiv.org/abs/2412.16855)


## Model List
| Models | Model Size | Max Seq. Length | Dimension | MTEB-en| MTEB-zh | UMRB |
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|[`gme-Qwen2-VL-2B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct) | 2.21B | 32768 | 1536 | 65.27 | 68.41 | 64.45 |
|[`gme-Qwen2-VL-7B`](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct) | 8.29B | 32768 | 3584 | 67.48 | 71.36 | 67.44 |

## Usage 
**Use with custom code**

```python
# You can find the script gme_inference.py in https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct/blob/main/gme_inference.py
from gme_inference import GmeQwen2VL

model = GmeQwen2VL('Alibaba-NLP/gme-Qwen2-VL-7B-Instruct')

texts = [
    "What kind of car is this?",
    "The Tesla Cybertruck is a battery electric pickup truck built by Tesla, Inc. since 2023."
]
images = [
    'https://en.wikipedia.org/wiki/File:Tesla_Cybertruck_damaged_window.jpg',
    'https://en.wikipedia.org/wiki/File:2024_Tesla_Cybertruck_Foundation_Series,_front_left_(Greenwich).jpg',
]

# Single-modal embedding
e_text = gme.get_text_embeddings(texts=texts)
e_image = gme.get_image_embeddings(images=images)
print((e_text * e_image).sum(-1))
## tensor([0.1702, 0.5278], dtype=torch.float16)

# How to set embedding instruction
e_query = gme.get_text_embeddings(texts=texts, instruction='Find an image that matches the given text.')
# If is_query=False, we always use the default instruction.
e_corpus = gme.get_image_embeddings(images=images, is_query=False)
print((e_query * e_corpus).sum(-1))
## tensor([0.2000, 0.5752], dtype=torch.float16)

# Fused-modal embedding
e_fused = gme.get_fused_embeddings(texts=texts, images=images)
print((e_fused[0] * e_fused[1]).sum())
## tensor(0.6826, dtype=torch.float16)

```

<!-- <details>
<summary>With transformers</summary>

```python
# Requires transformers>=4.46.2

TODO

# [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]]
```

</details>
 -->

## Evaluation

We validated the performance on our universal multimodal retrieval benchmark (**UMRB**) among others.

|                    |      | Single-modal |           | Cross-modal |             |           | Fused-modal |            |            |             |  Avg.      |
|--------------------|------|:------------:|:---------:|:-----------:|:-----------:|:---------:|:-----------:|:----------:|:----------:|:-----------:|:----------:|
|                    |      | T→T (16)     |  I→I (1)  |  T→I (4)    |  T→VD (10)  |  I→T (4)  |  T→IT (2)   |  IT→T (5)  |  IT→I (2)  |  IT→IT (3)  |  (47)      |
| VISTA              | 0.2B | 55.15        | **31.98** | 32.88       | 10.12       | 31.23     | 45.81       | 53.32      | 8.97       | 26.26       | 37.32      |
| CLIP-SF            | 0.4B | 39.75        | 31.42     | 59.05       | 24.09       | 62.95     | 66.41       | 53.32      | 34.9       | 55.65       | 43.66      |
| One-Peace          | 4B   | 43.54        | 31.27     | 61.38       | 42.9        | 65.59     | 42.72       | 28.29      | 6.73       | 23.41       | 42.01      |
| DSE                | 4.2B | 48.94        | 27.92     | 40.75       | 78.21       | 52.54     | 49.62       | 35.44      | 8.36       | 40.18       | 50.04      |
| E5-V               | 8.4B | 52.41        | 27.36     | 46.56       | 41.22       | 47.95     | 54.13       | 32.9       | 23.17      | 7.23        | 42.52      |
| **[GME-Qwen2-VL-2B](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-2B-Instruct)** | 2.2B | 55.93 | 29.86	| 57.36	| 87.84	| 61.93 |	76.47	| 64.58	 | 37.02	| 66.47 | 64.45 |
| **[GME-Qwen2-VL-7B](https://huggingface.co/Alibaba-NLP/gme-Qwen2-VL-7B-Instruct)** | 8.3B | **58.19** | 31.89 | **61.35** | **89.92** | **65.83** | **80.94** | **66.18** | **42.56** | **73.62** | **67.44** |

The [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) English tab shows the text embeddings performence of our model.

**More detailed experimental results can be found in the [paper](http://arxiv.org/abs/2412.16855)**.

## Community support

### Fine-tuning

GME models can be fine-tuned by SWIFT：

```shell
pip install ms-swift -U
```

```shell
# MAX_PIXELS settings to reduce memory usage
# check: https://swift.readthedocs.io/en/latest/BestPractices/Embedding.html
nproc_per_node=8
MAX_PIXELS=1003520 \
USE_HF=1 \
NPROC_PER_NODE=$nproc_per_node \
swift sft \
    --model Alibaba-NLP/gme-Qwen2-VL-7B-Instruct \
    --train_type lora \
    --dataset 'HuggingFaceM4/TextCaps:emb' \
    --torch_dtype bfloat16 \
    --num_train_epochs 1 \
    --per_device_train_batch_size 2 \
    --per_device_eval_batch_size 2 \
    --gradient_accumulation_steps $(expr 64 / $nproc_per_node) \
    --eval_steps 100 \
    --save_steps 100 \
    --eval_strategy steps \
    --save_total_limit 5 \
    --logging_steps 5 \
    --output_dir output \
    --lazy_tokenize true \
    --warmup_ratio 0.05 \
    --learning_rate 5e-6 \
    --deepspeed zero3 \
    --dataloader_num_workers 4 \
    --task_type embedding \
    --loss_type infonce \
    --dataloader_drop_last true
```

## Limitations

- **Single Image Input**: In `Qwen2-VL`, an image could be converted into a very large number of visual tokens. We limit the number of visual tokens to 1024 to obtain a good training efficiency.
Due to the lack of relevant data, our models and evaluations retain one single image.
- **English-only Training**: Our models are trained on english data only. Although the `Qwen2-VL` models are multilingual, the multilingual-multimodal embedding performance are not guaranteed.

We will extend to multi-image input, image-text interleaved data as well as multilingual data in the future version.


## Redistribution and Use

We encourage and value diverse applications of GME models and continuous enhancements to the models themselves.

- If you distribute or make GME models (or any derivative works) available, or if you create a product or service (including another AI model) that incorporates them, you must prominently display `Built with GME` on your website, user interface, blog post, About page, or product documentation.

- If you utilize GME models or their outputs to develop, train, fine-tune, or improve an AI model that is distributed or made available, you must prefix the name of any such AI model with `GME`.

## Cloud API Services

In addition to the open-source [GME](https://huggingface.co/collections/Alibaba-NLP/gme-models-67667e092da3491f630964d6) series models, GME series models are also available as commercial API services on Alibaba Cloud.

- [MultiModal Embedding Models](https://help.aliyun.com/zh/model-studio/developer-reference/multimodal-embedding-api-reference?spm=a2c4g.11186623.0.0.321c1d1cqmoJ5C): The `multimodal-embedding-v1` model service is available.

Note that the models behind the commercial APIs are not entirely identical to the open-source models.

## Hiring

We have open positions for Research Interns and Full-Time Researchers to join our team at Tongyi Lab. 
We are seeking passionate individuals with expertise in representation learning, LLM-driven information retrieval, Retrieval-Augmented Generation (RAG), and agent-based systems. 
Our team is located in the vibrant cities of Beijing and Hangzhou, offering a collaborative and dynamic work environment where you can contribute to cutting-edge advancements in artificial intelligence and machine learning.
If you are driven by curiosity and eager to make a meaningful impact through your work, we would love to hear from you. Please submit your resume along with a brief introduction to <a href="mailto:dingkun.ldk@alibaba-inc.com">dingkun.ldk@alibaba-inc.com</a>.


## Citation
If you find our paper or models helpful, please consider cite:

```
@misc{zhang2024gme,
      title={GME: Improving Universal Multimodal Retrieval by Multimodal LLMs}, 
      author={Zhang, Xin and Zhang, Yanzhao and Xie, Wen and Li, Mingxin and Dai, Ziqi and Long, Dingkun and Xie, Pengjun and Zhang, Meishan and Li, Wenjie and Zhang, Min},
      year={2024},
      eprint={2412.16855},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={http://arxiv.org/abs/2412.16855}, 
}
```
