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EN
Gme Qwen2 VL 7B Instruct
G
Gme Qwen2 VL 7B Instruct
Developed by Alibaba-NLP
Qwen2-VL-7B-Instruct は Qwen2 アーキテクチャに基づくマルチモーダル視覚言語モデルで、中国語と英語をサポートし、さまざまな自然言語処理タスクに適しています。
テキスト生成画像
Transformers Supports Multiple LanguagesOpen Source License:Apache-2.0#マルチモーダル理解#中英バイリンガル処理#高精度類似度計算
Transformers Downloads 3,844
Release Time : 12/21/2024
Model Overview
このモデルは7Bパラメータ規模の視覚言語モデルで、テキストと画像入力を処理し、テキスト生成、画像理解、マルチモーダルタスクを実行できます。
Model Features
マルチモーダル能力
テキストと画像入力をサポートし、マルチモーダル情報を理解して処理できます。
多言語サポート
英語と中国語をサポートし、クロスランゲージアプリケーションシナリオに適しています。
高性能
複数のベンチマークテストで優れたパフォーマンスを示し、特にテキスト類似度と分類タスクで顕著です。
Model Capabilities
テキスト類似度計算
テキスト分類
テキストクラスタリング
情報検索
再ランキング
マルチモーダル理解
Use Cases
電子商取引
商品レビュー分類
商品レビューに対して感情分析と分類を行います
Amazonレビュー分類タスクで97.33%の精度を達成
金融
銀行カスタマーサポート分類
銀行の顧客質問を自動分類します
Banking77データセットで84.76%の精度を達成
学術研究
論文クラスタリング
学術論文をテーマ別にクラスタリングします
Arxiv論文クラスタリングタスクでV-measure54.96%を達成
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
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: b44c3b011063adb25877c13823db83bb193913c4
metrics:
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 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-VLand 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
Model List
| Models | Model Size | Max Seq. Length | Dimension | MTEB-en | MTEB-zh | UMRB |
|---|---|---|---|---|---|---|
gme-Qwen2-VL-2B |
2.21B | 32768 | 1536 | 65.27 | 68.41 | 64.45 |
gme-Qwen2-VL-7B |
8.29B | 32768 | 3584 | 67.48 | 71.36 | 67.44 |
Usage
Use with custom code
# 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)
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 | 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 | 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 English tab shows the text embeddings performence of our model.
More detailed experimental results can be found in the paper.
Community support
Fine-tuning
GME models can be fine-tuned by SWIFT：
pip install ms-swift -U
# 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-VLmodels 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 GMEon 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 series models, GME series models are also available as commercial API services on Alibaba Cloud.
- MultiModal Embedding Models: The
multimodal-embedding-v1model 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},
}
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