---
license: apache-2.0
base_model:
- Qwen/Qwen2-VL-7B-Instruct
language:
- en
- zh
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2-VL
- sentence-similarity
- vidore
model-index:
- name: gme-Qwen2-VL-7B-Instruct
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: 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:
type: C-MTEB/ATEC
name: 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:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
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:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
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:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 64.794
- type: f1
value: 63.7329780206882
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.099999999999994
- type: f1
value: 53.115528412999666
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: 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:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 54.96111428218386
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 50.637711388838945
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.0741897266483
- type: mrr
value: 76.11440882909028
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
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:
type: C-MTEB/BQ
name: 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:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.76298701298703
- type: f1
value: 84.24881789367576
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 46.86757924102047
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 43.86043680479362
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
metrics:
- type: v_measure
value: 45.684222588040605
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
metrics:
- type: v_measure
value: 45.45639765303432
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
metrics:
- type: map
value: 88.7058672660788
- type: mrr
value: 90.5795634920635
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
metrics:
- type: map
value: 90.50750030424048
- type: mrr
value: 92.3970634920635
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
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
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
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:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- 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:
type: mteb/climate-fever
name: MTEB ClimateFEVER
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:
type: C-MTEB/CmedqaRetrieval
name: 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:
type: C-MTEB/CMNLI
name: 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:
type: C-MTEB/CovidRetrieval
name: 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:
type: mteb/dbpedia
name: 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:
type: C-MTEB/DuRetrieval
name: 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:
type: C-MTEB/EcomRetrieval
name: 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:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 67.39500000000001
- type: f1
value: 62.01837785021389
- task:
type: Retrieval
dataset:
type: mteb/fever
name: 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:
type: mteb/fiqa
name: MTEB FiQA2018
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:
type: mteb/hotpotqa
name: 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:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: 421605374b29664c5fc098418fe20ada9bd55f8a
metrics:
- type: accuracy
value: 54.520969603693736
- type: f1
value: 42.359043311419626
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
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:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
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:
type: C-MTEB/LCQMC
name: 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:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
metrics:
- type: map
value: 32.68592539803807
- type: mrr
value: 31.58968253968254
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: 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:
type: mteb/msmarco
name: 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:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 97.55129958960327
- type: f1
value: 97.43464802675416
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 90.4719562243502
- type: f1
value: 70.76460034443902
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 83.49024882313383
- type: f1
value: 81.44067057564666
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (zh-CN)
config: zh-CN
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 79.88231338264963
- type: f1
value: 77.13536609019927
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 87.23268325487558
- type: f1
value: 86.36737921996752
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (zh-CN)
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 84.50571620712844
- type: f1
value: 83.4128768262944
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: 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:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 40.32149153753394
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 39.40319973495386
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 33.9769104898534
- type: mrr
value: 35.32831430710564
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
metrics:
- type: accuracy
value: 81.80666666666667
- type: f1
value: 81.83278699395508
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: 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:
type: mteb/nq
name: 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:
type: C-MTEB/OCNLI
name: 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:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
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:
type: C-MTEB/PAWSX
name: 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:
type: C-MTEB/QBQTC
name: 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:
type: mteb/quora
name: MTEB QuoraRetrieval
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:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 70.12802769698337
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 71.19047621740276
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: 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:
type: mteb/sickr-sts
name: MTEB SICK-R
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:
type: mteb/sts12-sts
name: MTEB STS12
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:
type: mteb/sts13-sts
name: MTEB STS13
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:
type: mteb/sts14-sts
name: MTEB STS14
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:
type: mteb/sts15-sts
name: MTEB STS15
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:
type: mteb/sts16-sts
name: MTEB STS16
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:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
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:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
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:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (zh)
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:
type: C-MTEB/STSB
name: 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:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
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:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 88.98026856845443
- type: mrr
value: 96.80987494712984
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: 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:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
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:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 78.89230351770412
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 47.52328347080355
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 57.74702024461137
- type: mrr
value: 58.88074548001018
- task:
type: Summarization
dataset:
type: mteb/summeval
name: 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:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
metrics:
- type: map
value: 66.54177017978034
- type: mrr
value: 76.76094292377299
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: 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:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
metrics:
- type: accuracy
value: 56.691999999999986
- type: f1
value: 54.692084702788065
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
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:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
metrics:
- type: v_measure
value: 78.68783858143624
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
metrics:
- type: v_measure
value: 77.04148998956299
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: 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:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
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:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 77.80418788907753
- type: f1
value: 78.1047638421972
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 59.20888659980063
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
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:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
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:
type: C-MTEB/VideoRetrieval
name: 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:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: 339287def212450dcaa9df8c22bf93e9980c7023
metrics:
- type: accuracy
value: 91.13
- type: ap
value: 79.55231335947015
- type: f1
value: 89.63091922203914
---
GME: General Multimodal Embedding
## 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-Qwen2VL-2B/blob/main/scripts/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)
```
## 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)**.
## 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 dingkun.ldk@alibaba-inc.com.
## 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},
}
```