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EN
Gte Base En V1.5
G
Gte Base En V1.5
Developed by Alibaba-NLP
GTE-base-en-v1.5 is an English sentence transformer model focused on sentence similarity tasks, excelling in multiple text embedding benchmarks.
Downloads 1.5M
Release Time : 4/20/2024
Model Overview
This model is a general-purpose English text embedding model capable of converting sentences into high-dimensional vector representations for tasks such as sentence similarity, information retrieval, and text classification.
Model Features
High-performance Sentence Embedding
Outstanding performance in multiple MTEB benchmarks, particularly in sentence similarity and classification tasks
Multi-task Adaptability
Capable of handling various text-related tasks including classification, clustering, retrieval, and reranking
Open-source License
Licensed under Apache-2.0, allowing for commercial and research use
Model Capabilities
Sentence similarity calculation
Text classification
Information retrieval
Text clustering
Semantic search
Sentence embedding generation
Use Cases
E-commerce
Product Review Classification
Sentiment analysis of Amazon product reviews
Achieved 93.02% accuracy on MTEB AmazonPolarityClassification
Counterfactual Analysis
Identifying counterfactual statements in Amazon product reviews
Achieved 74.79% accuracy on MTEB AmazonCounterfactualClassification
Finance
Bank Customer Service Classification
Classifying bank customer inquiries
Achieved 86.73% accuracy on MTEB Banking77Classification
Academic Research
Paper Clustering
Topic clustering of arXiv and biorxiv papers
Achieved 47.51 V-measure on MTEB ArxivClusteringP2P
library_name: transformers tags:
- sentence-transformers
- gte
- mteb
- transformers.js
- sentence-similarity license: apache-2.0 language:
- en model-index:
- name: gte-base-en-v1.5
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 74.7910447761194
- type: ap value: 37.053785713650626
- type: f1 value: 68.51101510998551
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy value: 93.016875
- type: ap value: 89.17750268426342
- type: f1 value: 92.9970977240524
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 53.312000000000005
- type: f1 value: 52.98175784163017
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1 value: 38.193
- type: map_at_10 value: 54.848
- type: map_at_100 value: 55.388000000000005
- type: map_at_1000 value: 55.388999999999996
- type: map_at_3 value: 50.427
- type: map_at_5 value: 53.105000000000004
- type: mrr_at_1 value: 39.047
- type: mrr_at_10 value: 55.153
- type: mrr_at_100 value: 55.686
- type: mrr_at_1000 value: 55.688
- type: mrr_at_3 value: 50.676
- type: mrr_at_5 value: 53.417
- type: ndcg_at_1 value: 38.193
- type: ndcg_at_10 value: 63.486
- type: ndcg_at_100 value: 65.58
- type: ndcg_at_1000 value: 65.61
- type: ndcg_at_3 value: 54.494
- type: ndcg_at_5 value: 59.339
- type: precision_at_1 value: 38.193
- type: precision_at_10 value: 9.075
- type: precision_at_100 value: 0.9939999999999999
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 22.096
- type: precision_at_5 value: 15.619
- type: recall_at_1 value: 38.193
- type: recall_at_10 value: 90.754
- type: recall_at_100 value: 99.431
- type: recall_at_1000 value: 99.644
- type: recall_at_3 value: 66.28699999999999
- type: recall_at_5 value: 78.094
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure value: 47.508221208908964
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure value: 42.04668382560096
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map value: 61.828759903716815
- type: mrr value: 74.37343358395991
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson value: 85.03673698773017
- type: cos_sim_spearman value: 83.6470866785058
- type: euclidean_pearson value: 82.64048673096565
- type: euclidean_spearman value: 83.63142367101115
- type: manhattan_pearson value: 82.71493099760228
- type: manhattan_spearman value: 83.60491704294326
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy value: 86.73376623376623
- type: f1 value: 86.70294049278262
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure value: 40.31923804167062
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure value: 37.552547125348454
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-android
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1 value: 30.567
- type: map_at_10 value: 41.269
- type: map_at_100 value: 42.689
- type: map_at_1000 value: 42.84
- type: map_at_3 value: 37.567
- type: map_at_5 value: 39.706
- type: mrr_at_1 value: 37.053000000000004
- type: mrr_at_10 value: 46.900999999999996
- type: mrr_at_100 value: 47.662
- type: mrr_at_1000 value: 47.713
- type: mrr_at_3 value: 43.801
- type: mrr_at_5 value: 45.689
- type: ndcg_at_1 value: 37.053000000000004
- type: ndcg_at_10 value: 47.73
- type: ndcg_at_100 value: 53.128
- type: ndcg_at_1000 value: 55.300000000000004
- type: ndcg_at_3 value: 42.046
- type: ndcg_at_5 value: 44.782
- type: precision_at_1 value: 37.053000000000004
- type: precision_at_10 value: 9.142
- type: precision_at_100 value: 1.485
- type: precision_at_1000 value: 0.197
- type: precision_at_3 value: 20.076
- type: precision_at_5 value: 14.535
- type: recall_at_1 value: 30.567
- type: recall_at_10 value: 60.602999999999994
- type: recall_at_100 value: 83.22800000000001
- type: recall_at_1000 value: 96.696
- type: recall_at_3 value: 44.336999999999996
- type: recall_at_5 value: 51.949
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-english
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1 value: 28.538000000000004
- type: map_at_10 value: 38.757999999999996
- type: map_at_100 value: 40.129
- type: map_at_1000 value: 40.262
- type: map_at_3 value: 35.866
- type: map_at_5 value: 37.417
- type: mrr_at_1 value: 36.051
- type: mrr_at_10 value: 44.868
- type: mrr_at_100 value: 45.568999999999996
- type: mrr_at_1000 value: 45.615
- type: mrr_at_3 value: 42.558
- type: mrr_at_5 value: 43.883
- type: ndcg_at_1 value: 36.051
- type: ndcg_at_10 value: 44.584
- type: ndcg_at_100 value: 49.356
- type: ndcg_at_1000 value: 51.39
- type: ndcg_at_3 value: 40.389
- type: ndcg_at_5 value: 42.14
- type: precision_at_1 value: 36.051
- type: precision_at_10 value: 8.446
- type: precision_at_100 value: 1.411
- type: precision_at_1000 value: 0.19
- type: precision_at_3 value: 19.639
- type: precision_at_5 value: 13.796
- type: recall_at_1 value: 28.538000000000004
- type: recall_at_10 value: 54.99000000000001
- type: recall_at_100 value: 75.098
- type: recall_at_1000 value: 87.848
- type: recall_at_3 value: 42.236000000000004
- type: recall_at_5 value: 47.377
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gaming
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1 value: 37.188
- type: map_at_10 value: 50.861000000000004
- type: map_at_100 value: 51.917
- type: map_at_1000 value: 51.964999999999996
- type: map_at_3 value: 47.144000000000005
- type: map_at_5 value: 49.417
- type: mrr_at_1 value: 42.571
- type: mrr_at_10 value: 54.086999999999996
- type: mrr_at_100 value: 54.739000000000004
- type: mrr_at_1000 value: 54.762
- type: mrr_at_3 value: 51.285000000000004
- type: mrr_at_5 value: 53.0
- type: ndcg_at_1 value: 42.571
- type: ndcg_at_10 value: 57.282
- type: ndcg_at_100 value: 61.477000000000004
- type: ndcg_at_1000 value: 62.426
- type: ndcg_at_3 value: 51.0
- type: ndcg_at_5 value: 54.346000000000004
- type: precision_at_1 value: 42.571
- type: precision_at_10 value: 9.467
- type: precision_at_100 value: 1.2550000000000001
- type: precision_at_1000 value: 0.13799999999999998
- type: precision_at_3 value: 23.114
- type: precision_at_5 value: 16.250999999999998
- type: recall_at_1 value: 37.188
- type: recall_at_10 value: 73.068
- type: recall_at_100 value: 91.203
- type: recall_at_1000 value: 97.916
- type: recall_at_3 value: 56.552
- type: recall_at_5 value: 64.567
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gis
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1 value: 25.041000000000004
- type: map_at_10 value: 33.86
- type: map_at_100 value: 34.988
- type: map_at_1000 value: 35.064
- type: map_at_3 value: 31.049
- type: map_at_5 value: 32.845
- type: mrr_at_1 value: 26.893
- type: mrr_at_10 value: 35.594
- type: mrr_at_100 value: 36.617
- type: mrr_at_1000 value: 36.671
- type: mrr_at_3 value: 33.051
- type: mrr_at_5 value: 34.61
- type: ndcg_at_1 value: 26.893
- type: ndcg_at_10 value: 38.674
- type: ndcg_at_100 value: 44.178
- type: ndcg_at_1000 value: 46.089999999999996
- type: ndcg_at_3 value: 33.485
- type: ndcg_at_5 value: 36.402
- type: precision_at_1 value: 26.893
- type: precision_at_10 value: 5.989
- type: precision_at_100 value: 0.918
- type: precision_at_1000 value: 0.11100000000000002
- type: precision_at_3 value: 14.2
- type: precision_at_5 value: 10.26
- type: recall_at_1 value: 25.041000000000004
- type: recall_at_10 value: 51.666000000000004
- type: recall_at_100 value: 76.896
- type: recall_at_1000 value: 91.243
- type: recall_at_3 value: 38.035999999999994
- type: recall_at_5 value: 44.999
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-mathematica
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1 value: 15.909999999999998
- type: map_at_10 value: 23.901
- type: map_at_100 value: 25.165
- type: map_at_1000 value: 25.291000000000004
- type: map_at_3 value: 21.356
- type: map_at_5 value: 22.816
- type: mrr_at_1 value: 20.025000000000002
- type: mrr_at_10 value: 28.382
- type: mrr_at_100 value: 29.465000000000003
- type: mrr_at_1000 value: 29.535
- type: mrr_at_3 value: 25.933
- type: mrr_at_5 value: 27.332
- type: ndcg_at_1 value: 20.025000000000002
- type: ndcg_at_10 value: 29.099000000000004
- type: ndcg_at_100 value: 35.127
- type: ndcg_at_1000 value: 38.096000000000004
- type: ndcg_at_3 value: 24.464
- type: ndcg_at_5 value: 26.709
- type: precision_at_1 value: 20.025000000000002
- type: precision_at_10 value: 5.398
- type: precision_at_100 value: 0.9690000000000001
- type: precision_at_1000 value: 0.13699999999999998
- type: precision_at_3 value: 11.774
- type: precision_at_5 value: 8.632
- type: recall_at_1 value: 15.909999999999998
- type: recall_at_10 value: 40.672000000000004
- type: recall_at_100 value: 66.855
- type: recall_at_1000 value: 87.922
- type: recall_at_3 value: 28.069
- type: recall_at_5 value: 33.812
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-physics
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1 value: 30.175
- type: map_at_10 value: 41.36
- type: map_at_100 value: 42.701
- type: map_at_1000 value: 42.817
- type: map_at_3 value: 37.931
- type: map_at_5 value: 39.943
- type: mrr_at_1 value: 35.611
- type: mrr_at_10 value: 46.346
- type: mrr_at_100 value: 47.160000000000004
- type: mrr_at_1000 value: 47.203
- type: mrr_at_3 value: 43.712
- type: mrr_at_5 value: 45.367000000000004
- type: ndcg_at_1 value: 35.611
- type: ndcg_at_10 value: 47.532000000000004
- type: ndcg_at_100 value: 53.003
- type: ndcg_at_1000 value: 55.007
- type: ndcg_at_3 value: 42.043
- type: ndcg_at_5 value: 44.86
- type: precision_at_1 value: 35.611
- type: precision_at_10 value: 8.624
- type: precision_at_100 value: 1.332
- type: precision_at_1000 value: 0.169
- type: precision_at_3 value: 20.083000000000002
- type: precision_at_5 value: 14.437
- type: recall_at_1 value: 30.175
- type: recall_at_10 value: 60.5
- type: recall_at_100 value: 83.399
- type: recall_at_1000 value: 96.255
- type: recall_at_3 value: 45.448
- type: recall_at_5 value: 52.432
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-programmers
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1 value: 22.467000000000002
- type: map_at_10 value: 33.812999999999995
- type: map_at_100 value: 35.248000000000005
- type: map_at_1000 value: 35.359
- type: map_at_3 value: 30.316
- type: map_at_5 value: 32.233000000000004
- type: mrr_at_1 value: 28.310999999999996
- type: mrr_at_10 value: 38.979
- type: mrr_at_100 value: 39.937
- type: mrr_at_1000 value: 39.989999999999995
- type: mrr_at_3 value: 36.244
- type: mrr_at_5 value: 37.871
- type: ndcg_at_1 value: 28.310999999999996
- type: ndcg_at_10 value: 40.282000000000004
- type: ndcg_at_100 value: 46.22
- type: ndcg_at_1000 value: 48.507
- type: ndcg_at_3 value: 34.596
- type: ndcg_at_5 value: 37.267
- type: precision_at_1 value: 28.310999999999996
- type: precision_at_10 value: 7.831
- type: precision_at_100 value: 1.257
- type: precision_at_1000 value: 0.164
- type: precision_at_3 value: 17.275
- type: precision_at_5 value: 12.556999999999999
- type: recall_at_1 value: 22.467000000000002
- type: recall_at_10 value: 54.14099999999999
- type: recall_at_100 value: 79.593
- type: recall_at_1000 value: 95.063
- type: recall_at_3 value: 38.539
- type: recall_at_5 value: 45.403
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1 value: 24.18591666666667
- type: map_at_10 value: 33.84258333333333
- type: map_at_100 value: 35.11391666666666
- type: map_at_1000 value: 35.23258333333333
- type: map_at_3 value: 30.764249999999997
- type: map_at_5 value: 32.52333333333334
- type: mrr_at_1 value: 28.54733333333333
- type: mrr_at_10 value: 37.81725
- type: mrr_at_100 value: 38.716499999999996
- type: mrr_at_1000 value: 38.77458333333333
- type: mrr_at_3 value: 35.157833333333336
- type: mrr_at_5 value: 36.69816666666667
- type: ndcg_at_1 value: 28.54733333333333
- type: ndcg_at_10 value: 39.51508333333334
- type: ndcg_at_100 value: 44.95316666666666
- type: ndcg_at_1000 value: 47.257083333333334
- type: ndcg_at_3 value: 34.205833333333324
- type: ndcg_at_5 value: 36.78266666666667
- type: precision_at_1 value: 28.54733333333333
- type: precision_at_10 value: 7.082583333333334
- type: precision_at_100 value: 1.1590833333333332
- type: precision_at_1000 value: 0.15516666666666662
- type: precision_at_3 value: 15.908750000000001
- type: precision_at_5 value: 11.505416666666669
- type: recall_at_1 value: 24.18591666666667
- type: recall_at_10 value: 52.38758333333333
- type: recall_at_100 value: 76.13666666666667
- type: recall_at_1000 value: 91.99066666666667
- type: recall_at_3 value: 37.78333333333334
- type: recall_at_5 value: 44.30141666666666
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-stats
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1 value: 21.975
- type: map_at_10 value: 29.781000000000002
- type: map_at_100 value: 30.847
- type: map_at_1000 value: 30.94
- type: map_at_3 value: 27.167
- type: map_at_5 value: 28.633999999999997
- type: mrr_at_1 value: 24.387
- type: mrr_at_10 value: 32.476
- type: mrr_at_100 value: 33.337
- type: mrr_at_1000 value: 33.403
- type: mrr_at_3 value: 29.881999999999998
- type: mrr_at_5 value: 31.339
- type: ndcg_at_1 value: 24.387
- type: ndcg_at_10 value: 34.596
- type: ndcg_at_100 value: 39.635
- type: ndcg_at_1000 value: 42.079
- type: ndcg_at_3 value: 29.516
- type: ndcg_at_5 value: 31.959
- type: precision_at_1 value: 24.387
- type: precision_at_10 value: 5.6129999999999995
- type: precision_at_100 value: 0.8909999999999999
- type: precision_at_1000 value: 0.117
- type: precision_at_3 value: 12.73
- type: precision_at_5 value: 9.171999999999999
- type: recall_at_1 value: 21.975
- type: recall_at_10 value: 46.826
- type: recall_at_100 value: 69.554
- type: recall_at_1000 value: 87.749
- type: recall_at_3 value: 33.016
- type: recall_at_5 value: 38.97
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-tex
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1 value: 15.614
- type: map_at_10 value: 22.927
- type: map_at_100 value: 24.185000000000002
- type: map_at_1000 value: 24.319
- type: map_at_3 value: 20.596
- type: map_at_5 value: 21.854000000000003
- type: mrr_at_1 value: 18.858
- type: mrr_at_10 value: 26.535999999999998
- type: mrr_at_100 value: 27.582
- type: mrr_at_1000 value: 27.665
- type: mrr_at_3 value: 24.295
- type: mrr_at_5 value: 25.532
- type: ndcg_at_1 value: 18.858
- type: ndcg_at_10 value: 27.583000000000002
- type: ndcg_at_100 value: 33.635
- type: ndcg_at_1000 value: 36.647
- type: ndcg_at_3 value: 23.348
- type: ndcg_at_5 value: 25.257
- type: precision_at_1 value: 18.858
- type: precision_at_10 value: 5.158
- type: precision_at_100 value: 0.964
- type: precision_at_1000 value: 0.13999999999999999
- type: precision_at_3 value: 11.092
- type: precision_at_5 value: 8.1
- type: recall_at_1 value: 15.614
- type: recall_at_10 value: 37.916
- type: recall_at_100 value: 65.205
- type: recall_at_1000 value: 86.453
- type: recall_at_3 value: 26.137
- type: recall_at_5 value: 31.087999999999997
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-unix
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1 value: 23.078000000000003
- type: map_at_10 value: 31.941999999999997
- type: map_at_100 value: 33.196999999999996
- type: map_at_1000 value: 33.303
- type: map_at_3 value: 28.927000000000003
- type: map_at_5 value: 30.707
- type: mrr_at_1 value: 26.866
- type: mrr_at_10 value: 35.557
- type: mrr_at_100 value: 36.569
- type: mrr_at_1000 value: 36.632
- type: mrr_at_3 value: 32.897999999999996
- type: mrr_at_5 value: 34.437
- type: ndcg_at_1 value: 26.866
- type: ndcg_at_10 value: 37.372
- type: ndcg_at_100 value: 43.248
- type: ndcg_at_1000 value: 45.632
- type: ndcg_at_3 value: 31.852999999999998
- type: ndcg_at_5 value: 34.582
- type: precision_at_1 value: 26.866
- type: precision_at_10 value: 6.511
- type: precision_at_100 value: 1.078
- type: precision_at_1000 value: 0.13899999999999998
- type: precision_at_3 value: 14.582999999999998
- type: precision_at_5 value: 10.634
- type: recall_at_1 value: 23.078000000000003
- type: recall_at_10 value: 50.334
- type: recall_at_100 value: 75.787
- type: recall_at_1000 value: 92.485
- type: recall_at_3 value: 35.386
- type: recall_at_5 value: 42.225
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-webmasters
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1 value: 22.203999999999997
- type: map_at_10 value: 31.276
- type: map_at_100 value: 32.844
- type: map_at_1000 value: 33.062999999999995
- type: map_at_3 value: 27.733999999999998
- type: map_at_5 value: 29.64
- type: mrr_at_1 value: 27.272999999999996
- type: mrr_at_10 value: 36.083
- type: mrr_at_100 value: 37.008
- type: mrr_at_1000 value: 37.076
- type: mrr_at_3 value: 33.004
- type: mrr_at_5 value: 34.664
- type: ndcg_at_1 value: 27.272999999999996
- type: ndcg_at_10 value: 37.763000000000005
- type: ndcg_at_100 value: 43.566
- type: ndcg_at_1000 value: 46.356
- type: ndcg_at_3 value: 31.673000000000002
- type: ndcg_at_5 value: 34.501
- type: precision_at_1 value: 27.272999999999996
- type: precision_at_10 value: 7.470000000000001
- type: precision_at_100 value: 1.502
- type: precision_at_1000 value: 0.24
- type: precision_at_3 value: 14.756
- type: precision_at_5 value: 11.225
- type: recall_at_1 value: 22.203999999999997
- type: recall_at_10 value: 51.437999999999995
- type: recall_at_100 value: 76.845
- type: recall_at_1000 value: 94.38600000000001
- type: recall_at_3 value: 34.258
- type: recall_at_5 value: 41.512
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-wordpress
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1 value: 17.474
- type: map_at_10 value: 26.362999999999996
- type: map_at_100 value: 27.456999999999997
- type: map_at_1000 value: 27.567999999999998
- type: map_at_3 value: 23.518
- type: map_at_5 value: 25.068
- type: mrr_at_1 value: 18.669
- type: mrr_at_10 value: 27.998
- type: mrr_at_100 value: 28.953
- type: mrr_at_1000 value: 29.03
- type: mrr_at_3 value: 25.230999999999998
- type: mrr_at_5 value: 26.654
- type: ndcg_at_1 value: 18.669
- type: ndcg_at_10 value: 31.684
- type: ndcg_at_100 value: 36.864999999999995
- type: ndcg_at_1000 value: 39.555
- type: ndcg_at_3 value: 26.057000000000002
- type: ndcg_at_5 value: 28.587
- type: precision_at_1 value: 18.669
- type: precision_at_10 value: 5.3420000000000005
- type: precision_at_100 value: 0.847
- type: precision_at_1000 value: 0.12
- type: precision_at_3 value: 11.583
- type: precision_at_5 value: 8.466
- type: recall_at_1 value: 17.474
- type: recall_at_10 value: 46.497
- type: recall_at_100 value: 69.977
- type: recall_at_1000 value: 89.872
- type: recall_at_3 value: 31.385999999999996
- type: recall_at_5 value: 37.283
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1 value: 17.173
- type: map_at_10 value: 30.407
- type: map_at_100 value: 32.528
- type: map_at_1000 value: 32.698
- type: map_at_3 value: 25.523
- type: map_at_5 value: 28.038
- type: mrr_at_1 value: 38.958
- type: mrr_at_10 value: 51.515
- type: mrr_at_100 value: 52.214000000000006
- type: mrr_at_1000 value: 52.237
- type: mrr_at_3 value: 48.502
- type: mrr_at_5 value: 50.251000000000005
- type: ndcg_at_1 value: 38.958
- type: ndcg_at_10 value: 40.355000000000004
- type: ndcg_at_100 value: 47.68
- type: ndcg_at_1000 value: 50.370000000000005
- type: ndcg_at_3 value: 33.946
- type: ndcg_at_5 value: 36.057
- type: precision_at_1 value: 38.958
- type: precision_at_10 value: 12.508
- type: precision_at_100 value: 2.054
- type: precision_at_1000 value: 0.256
- type: precision_at_3 value: 25.581
- type: precision_at_5 value: 19.256999999999998
- type: recall_at_1 value: 17.173
- type: recall_at_10 value: 46.967
- type: recall_at_100 value: 71.47200000000001
- type: recall_at_1000 value: 86.238
- type: recall_at_3 value: 30.961
- type: recall_at_5 value: 37.539
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1 value: 8.999
- type: map_at_10 value: 18.989
- type: map_at_100 value: 26.133
- type: map_at_1000 value: 27.666
- type: map_at_3 value: 13.918
- type: map_at_5 value: 16.473
- type: mrr_at_1 value: 66.25
- type: mrr_at_10 value: 74.161
- type: mrr_at_100 value: 74.516
- type: mrr_at_1000 value: 74.524
- type: mrr_at_3 value: 72.875
- type: mrr_at_5 value: 73.613
- type: ndcg_at_1 value: 54.37499999999999
- type: ndcg_at_10 value: 39.902
- type: ndcg_at_100 value: 44.212
- type: ndcg_at_1000 value: 51.62
- type: ndcg_at_3 value: 45.193
- type: ndcg_at_5 value: 42.541000000000004
- type: precision_at_1 value: 66.25
- type: precision_at_10 value: 30.425
- type: precision_at_100 value: 9.754999999999999
- type: precision_at_1000 value: 2.043
- type: precision_at_3 value: 48.25
- type: precision_at_5 value: 40.65
- type: recall_at_1 value: 8.999
- type: recall_at_10 value: 24.133
- type: recall_at_100 value: 49.138999999999996
- type: recall_at_1000 value: 72.639
- type: recall_at_3 value: 15.287999999999998
- type: recall_at_5 value: 19.415
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy value: 46.38999999999999
- type: f1 value: 41.444205512055234
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1 value: 87.35000000000001
- type: map_at_10 value: 92.837
- type: map_at_100 value: 92.996
- type: map_at_1000 value: 93.006
- type: map_at_3 value: 92.187
- type: map_at_5 value: 92.595
- type: mrr_at_1 value: 93.864
- type: mrr_at_10 value: 96.723
- type: mrr_at_100 value: 96.72500000000001
- type: mrr_at_1000 value: 96.72500000000001
- type: mrr_at_3 value: 96.64
- type: mrr_at_5 value: 96.71499999999999
- type: ndcg_at_1 value: 93.864
- type: ndcg_at_10 value: 94.813
- type: ndcg_at_100 value: 95.243
- type: ndcg_at_1000 value: 95.38600000000001
- type: ndcg_at_3 value: 94.196
- type: ndcg_at_5 value: 94.521
- type: precision_at_1 value: 93.864
- type: precision_at_10 value: 10.951
- type: precision_at_100 value: 1.1400000000000001
- type: precision_at_1000 value: 0.117
- type: precision_at_3 value: 35.114000000000004
- type: precision_at_5 value: 21.476
- type: recall_at_1 value: 87.35000000000001
- type: recall_at_10 value: 96.941
- type: recall_at_100 value: 98.397
- type: recall_at_1000 value: 99.21600000000001
- type: recall_at_3 value: 95.149
- type: recall_at_5 value: 96.131
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1 value: 24.476
- type: map_at_10 value: 40.11
- type: map_at_100 value: 42.229
- type: map_at_1000 value: 42.378
- type: map_at_3 value: 34.512
- type: map_at_5 value: 38.037
- type: mrr_at_1 value: 47.839999999999996
- type: mrr_at_10 value: 57.053
- type: mrr_at_100 value: 57.772
- type: mrr_at_1000 value: 57.799
- type: mrr_at_3 value: 54.552
- type: mrr_at_5 value: 56.011
- type: ndcg_at_1 value: 47.839999999999996
- type: ndcg_at_10 value: 48.650999999999996
- type: ndcg_at_100 value: 55.681000000000004
- type: ndcg_at_1000 value: 57.979
- type: ndcg_at_3 value: 43.923
- type: ndcg_at_5 value: 46.037
- type: precision_at_1 value: 47.839999999999996
- type: precision_at_10 value: 13.395000000000001
- type: precision_at_100 value: 2.0660000000000003
- type: precision_at_1000 value: 0.248
- type: precision_at_3 value: 29.064
- type: precision_at_5 value: 22.006
- type: recall_at_1 value: 24.476
- type: recall_at_10 value: 56.216
- type: recall_at_100 value: 81.798
- type: recall_at_1000 value: 95.48299999999999
- type: recall_at_3 value: 39.357
- type: recall_at_5 value: 47.802
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1 value: 42.728
- type: map_at_10 value: 57.737
- type: map_at_100 value: 58.531
- type: map_at_1000 value: 58.594
- type: map_at_3 value: 54.869
- type: map_at_5 value: 56.55
- type: mrr_at_1 value: 85.456
- type: mrr_at_10 value: 90.062
- type: mrr_at_100 value: 90.159
- type: mrr_at_1000 value: 90.16
- type: mrr_at_3 value: 89.37899999999999
- type: mrr_at_5 value: 89.81
- type: ndcg_at_1 value: 85.456
- type: ndcg_at_10 value: 67.755
- type: ndcg_at_100 value: 70.341
- type: ndcg_at_1000 value: 71.538
- type: ndcg_at_3 value: 63.735
- type: ndcg_at_5 value: 65.823
- type: precision_at_1 value: 85.456
- type: precision_at_10 value: 13.450000000000001
- type: precision_at_100 value: 1.545
- type: precision_at_1000 value: 0.16999999999999998
- type: precision_at_3 value: 38.861000000000004
- type: precision_at_5 value: 24.964
- type: recall_at_1 value: 42.728
- type: recall_at_10 value: 67.252
- type: recall_at_100 value: 77.265
- type: recall_at_1000 value: 85.246
- type: recall_at_3 value: 58.292
- type: recall_at_5 value: 62.41100000000001
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy value: 87.4836
- type: ap value: 82.29552224030336
- type: f1 value: 87.42791432227448
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1 value: 23.015
- type: map_at_10 value: 35.621
- type: map_at_100 value: 36.809
- type: map_at_1000 value: 36.853
- type: map_at_3 value: 31.832
- type: map_at_5 value: 34.006
- type: mrr_at_1 value: 23.738999999999997
- type: mrr_at_10 value: 36.309999999999995
- type: mrr_at_100 value: 37.422
- type: mrr_at_1000 value: 37.461
- type: mrr_at_3 value: 32.592999999999996
- type: mrr_at_5 value: 34.736
- type: ndcg_at_1 value: 23.724999999999998
- type: ndcg_at_10 value: 42.617
- type: ndcg_at_100 value: 48.217999999999996
- type: ndcg_at_1000 value: 49.309
- type: ndcg_at_3 value: 34.905
- type: ndcg_at_5 value: 38.769
- type: precision_at_1 value: 23.724999999999998
- type: precision_at_10 value: 6.689
- type: precision_at_100 value: 0.9480000000000001
- type: precision_at_1000 value: 0.104
- type: precision_at_3 value: 14.89
- type: precision_at_5 value: 10.897
- type: recall_at_1 value: 23.015
- type: recall_at_10 value: 64.041
- type: recall_at_100 value: 89.724
- type: recall_at_1000 value: 98.00999999999999
- type: recall_at_3 value: 43.064
- type: recall_at_5 value: 52.31099999999999
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 96.49794801641588
- type: f1 value: 96.28931114498003
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy value: 82.81121751025992
- type: f1 value: 63.18740125901853
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy value: 77.66644250168123
- type: f1 value: 74.93211186867839
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy value: 81.77202420981843
- type: f1 value: 81.63681969283554
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure value: 34.596687684870645
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure value: 32.26965660101405
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map value: 31.33619694846802
- type: mrr value: 32.53719657720334
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1 value: 6.0729999999999995
- type: map_at_10 value: 13.245999999999999
- type: map_at_100 value: 16.747999999999998
- type: map_at_1000 value: 18.163
- type: map_at_3 value: 10.064
- type: map_at_5 value: 11.513
- type: mrr_at_1 value: 49.536
- type: mrr_at_10 value: 58.092
- type: mrr_at_100 value: 58.752
- type: mrr_at_1000 value: 58.78
- type: mrr_at_3 value: 56.398
- type: mrr_at_5 value: 57.389
- type: ndcg_at_1 value: 47.059
- type: ndcg_at_10 value: 35.881
- type: ndcg_at_100 value: 32.751999999999995
- type: ndcg_at_1000 value: 41.498000000000005
- type: ndcg_at_3 value: 42.518
- type: ndcg_at_5 value: 39.550999999999995
- type: precision_at_1 value: 49.536
- type: precision_at_10 value: 26.316
- type: precision_at_100 value: 8.084
- type: precision_at_1000 value: 2.081
- type: precision_at_3 value: 39.938
- type: precision_at_5 value: 34.056
- type: recall_at_1 value: 6.0729999999999995
- type: recall_at_10 value: 16.593
- type: recall_at_100 value: 32.883
- type: recall_at_1000 value: 64.654
- type: recall_at_3 value: 11.174000000000001
- type: recall_at_5 value: 13.528
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1 value: 30.043
- type: map_at_10 value: 45.318999999999996
- type: map_at_100 value: 46.381
- type: map_at_1000 value: 46.412
- type: map_at_3 value: 40.941
- type: map_at_5 value: 43.662
- type: mrr_at_1 value: 33.98
- type: mrr_at_10 value: 47.870000000000005
- type: mrr_at_100 value: 48.681999999999995
- type: mrr_at_1000 value: 48.703
- type: mrr_at_3 value: 44.341
- type: mrr_at_5 value: 46.547
- type: ndcg_at_1 value: 33.98
- type: ndcg_at_10 value: 52.957
- type: ndcg_at_100 value: 57.434
- type: ndcg_at_1000 value: 58.103
- type: ndcg_at_3 value: 44.896
- type: ndcg_at_5 value: 49.353
- type: precision_at_1 value: 33.98
- type: precision_at_10 value: 8.786
- type: precision_at_100 value: 1.1280000000000001
- type: precision_at_1000 value: 0.11900000000000001
- type: precision_at_3 value: 20.577
- type: precision_at_5 value: 14.942
- type: recall_at_1 value: 30.043
- type: recall_at_10 value: 73.593
- type: recall_at_100 value: 93.026
- type: recall_at_1000 value: 97.943
- type: recall_at_3 value: 52.955
- type: recall_at_5 value: 63.132
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 70.808
- type: map_at_10 value: 84.675
- type: map_at_100 value: 85.322
- type: map_at_1000 value: 85.33800000000001
- type: map_at_3 value: 81.68900000000001
- type: map_at_5 value: 83.543
- type: mrr_at_1 value: 81.5
- type: mrr_at_10 value: 87.59700000000001
- type: mrr_at_100 value: 87.705
- type: mrr_at_1000 value: 87.70599999999999
- type: mrr_at_3 value: 86.607
- type: mrr_at_5 value: 87.289
- type: ndcg_at_1 value: 81.51
- type: ndcg_at_10 value: 88.41799999999999
- type: ndcg_at_100 value: 89.644
- type: ndcg_at_1000 value: 89.725
- type: ndcg_at_3 value: 85.49900000000001
- type: ndcg_at_5 value: 87.078
- type: precision_at_1 value: 81.51
- type: precision_at_10 value: 13.438
- type: precision_at_100 value: 1.532
- type: precision_at_1000 value: 0.157
- type: precision_at_3 value: 37.363
- type: precision_at_5 value: 24.57
- type: recall_at_1 value: 70.808
- type: recall_at_10 value: 95.575
- type: recall_at_100 value: 99.667
- type: recall_at_1000 value: 99.98899999999999
- type: recall_at_3 value: 87.223
- type: recall_at_5 value: 91.682
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure value: 58.614831329137715
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure value: 66.86580408560826
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 5.093
- type: map_at_10 value: 13.014000000000001
- type: map_at_100 value: 15.412999999999998
- type: map_at_1000 value: 15.756999999999998
- type: map_at_3 value: 9.216000000000001
- type: map_at_5 value: 11.036999999999999
- type: mrr_at_1 value: 25.1
- type: mrr_at_10 value: 37.133
- type: mrr_at_100 value: 38.165
- type: mrr_at_1000 value: 38.198
- type: mrr_at_3 value: 33.217
- type: mrr_at_5 value: 35.732
- type: ndcg_at_1 value: 25.1
- type: ndcg_at_10 value: 21.918000000000003
- type: ndcg_at_100 value: 30.983
- type: ndcg_at_1000 value: 36.629
- type: ndcg_at_3 value: 20.544999999999998
- type: ndcg_at_5 value: 18.192
- type: precision_at_1 value: 25.1
- type: precision_at_10 value: 11.44
- type: precision_at_100 value: 2.459
- type: precision_at_1000 value: 0.381
- type: precision_at_3 value: 19.267
- type: precision_at_5 value: 16.16
- type: recall_at_1 value: 5.093
- type: recall_at_10 value: 23.215
- type: recall_at_100 value: 49.902
- type: recall_at_1000 value: 77.403
- type: recall_at_3 value: 11.733
- type: recall_at_5 value: 16.372999999999998
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson value: 82.9365442977452
- type: cos_sim_spearman value: 79.36960687383745
- type: euclidean_pearson value: 79.6045204840714
- type: euclidean_spearman value: 79.26382712751337
- type: manhattan_pearson value: 79.4805084789529
- type: manhattan_spearman value: 79.21847863209523
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson value: 83.27906192961453
- type: cos_sim_spearman value: 74.38364712099211
- type: euclidean_pearson value: 78.54358927241223
- type: euclidean_spearman value: 74.22185560806376
- type: manhattan_pearson value: 78.50904327377751
- type: manhattan_spearman value: 74.2627500781748
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson value: 84.66863742649639
- type: cos_sim_spearman value: 84.70630905216271
- type: euclidean_pearson value: 84.64498334705334
- type: euclidean_spearman value: 84.87204770690148
- type: manhattan_pearson value: 84.65774227976077
- type: manhattan_spearman value: 84.91251851797985
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson value: 83.1577763924467
- type: cos_sim_spearman value: 80.10314039230198
- type: euclidean_pearson value: 81.51346991046043
- type: euclidean_spearman value: 80.08678485109435
- type: manhattan_pearson value: 81.57058914661894
- type: manhattan_spearman value: 80.1516230725106
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson value: 86.40310839662533
- type: cos_sim_spearman value: 87.16293477217867
- type: euclidean_pearson value: 86.50688711184775
- type: euclidean_spearman value: 87.08651444923031
- type: manhattan_pearson value: 86.54674677557857
- type: manhattan_spearman value: 87.15079017870971
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson value: 84.32886275207817
- type: cos_sim_spearman value: 85.0190460590732
- type: euclidean_pearson value: 84.42553652784679
- type: euclidean_spearman value: 85.20027364279328
- type: manhattan_pearson value: 84.42926246281078
- type: manhattan_spearman value: 85.20187419804306
- 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: 90.76732216967812
- type: cos_sim_spearman value: 90.63701653633909
- type: euclidean_pearson value: 90.26678186114682
- type: euclidean_spearman value: 90.67288073455427
- type: manhattan_pearson value: 90.20772020584582
- type: manhattan_spearman value: 90.60764863983702
- 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: 69.09280387698125
- type: cos_sim_spearman value: 68.62743151172162
- type: euclidean_pearson value: 69.89386398104689
- type: euclidean_spearman value: 68.71191066733556
- type: manhattan_pearson value: 69.92516500604872
- type: manhattan_spearman value: 68.80452846992576
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson value: 86.13178592019887
- type: cos_sim_spearman value: 86.03947178806887
- type: euclidean_pearson value: 85.87029414285313
- type: euclidean_spearman value: 86.04960843306998
- type: manhattan_pearson value: 85.92946858580146
- type: manhattan_spearman value: 86.12575341860442
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map value: 85.16657063002837
- type: mrr value: 95.73671063867141
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1 value: 63.510999999999996
- type: map_at_10 value: 72.76899999999999
- type: map_at_100 value: 73.303
- type: map_at_1000 value: 73.32499999999999
- type: map_at_3 value: 70.514
- type: map_at_5 value: 71.929
- type: mrr_at_1 value: 66.333
- type: mrr_at_10 value: 73.75
- type: mrr_at_100 value: 74.119
- type: mrr_at_1000 value: 74.138
- type: mrr_at_3 value: 72.222
- type: mrr_at_5 value: 73.122
- type: ndcg_at_1 value: 66.333
- type: ndcg_at_10 value: 76.774
- type: ndcg_at_100 value: 78.78500000000001
- type: ndcg_at_1000 value: 79.254
- type: ndcg_at_3 value: 73.088
- type: ndcg_at_5 value: 75.002
- type: precision_at_1 value: 66.333
- type: precision_at_10 value: 9.833
- type: precision_at_100 value: 1.093
- type: precision_at_1000 value: 0.11299999999999999
- type: precision_at_3 value: 28.222
- type: precision_at_5 value: 18.333
- type: recall_at_1 value: 63.510999999999996
- type: recall_at_10 value: 87.98899999999999
- type: recall_at_100 value: 96.5
- type: recall_at_1000 value: 100.0
- type: recall_at_3 value: 77.86699999999999
- type: recall_at_5 value: 82.73899999999999
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy value: 99.78514851485149
- type: cos_sim_ap value: 94.94214383862038
- type: cos_sim_f1 value: 89.02255639097744
- type: cos_sim_precision value: 89.2462311557789
- type: cos_sim_recall value: 88.8
- type: dot_accuracy value: 99.78217821782178
- type: dot_ap value: 94.69965247836805
- type: dot_f1 value: 88.78695208970439
- type: dot_precision value: 90.54054054054053
- type: dot_recall value: 87.1
- type: euclidean_accuracy value: 99.78118811881188
- type: euclidean_ap value: 94.9865187695411
- type: euclidean_f1 value: 88.99950223992036
- type: euclidean_precision value: 88.60257680872151
- type: euclidean_recall value: 89.4
- type: manhattan_accuracy value: 99.78811881188119
- type: manhattan_ap value: 95.0021236766459
- type: manhattan_f1 value: 89.12071535022356
- type: manhattan_precision value: 88.54886475814413
- type: manhattan_recall value: 89.7
- type: max_accuracy value: 99.78811881188119
- type: max_ap value: 95.0021236766459
- type: max_f1 value: 89.12071535022356
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure value: 68.93190546593995
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure value: 37.602808534760655
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map value: 52.29214480978073
- type: mrr value: 53.123169722434426
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson value: 30.967800769650022
- type: cos_sim_spearman value: 31.168490040206926
- type: dot_pearson value: 30.888603021128553
- type: dot_spearman value: 31.028241262520385
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 0.22300000000000003
- type: map_at_10 value: 1.781
- type: map_at_100 value: 9.905999999999999
- type: map_at_1000 value: 23.455000000000002
- type: map_at_3 value: 0.569
- type: map_at_5 value: 0.918
- type: mrr_at_1 value: 84.0
- type: mrr_at_10 value: 91.067
- type: mrr_at_100 value: 91.067
- type: mrr_at_1000 value: 91.067
- type: mrr_at_3 value: 90.667
- type: mrr_at_5 value: 91.067
- type: ndcg_at_1 value: 78.0
- type: ndcg_at_10 value: 73.13499999999999
- type: ndcg_at_100 value: 55.32
- type: ndcg_at_1000 value: 49.532
- type: ndcg_at_3 value: 73.715
- type: ndcg_at_5 value: 72.74199999999999
- type: precision_at_1 value: 84.0
- type: precision_at_10 value: 78.8
- type: precision_at_100 value: 56.32
- type: precision_at_1000 value: 21.504
- type: precision_at_3 value: 77.333
- type: precision_at_5 value: 78.0
- type: recall_at_1 value: 0.22300000000000003
- type: recall_at_10 value: 2.049
- type: recall_at_100 value: 13.553
- type: recall_at_1000 value: 46.367999999999995
- type: recall_at_3 value: 0.604
- type: recall_at_5 value: 1.015
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1 value: 3.0380000000000003
- type: map_at_10 value: 10.188
- type: map_at_100 value: 16.395
- type: map_at_1000 value: 18.024
- type: map_at_3 value: 6.236
- type: map_at_5 value: 7.276000000000001
- type: mrr_at_1 value: 34.694
- type: mrr_at_10 value: 46.292
- type: mrr_at_100 value: 47.446
- type: mrr_at_1000 value: 47.446
- type: mrr_at_3 value: 41.156
- type: mrr_at_5 value: 44.32
- type: ndcg_at_1 value: 32.653
- type: ndcg_at_10 value: 25.219
- type: ndcg_at_100 value: 37.802
- type: ndcg_at_1000 value: 49.274
- type: ndcg_at_3 value: 28.605999999999998
- type: ndcg_at_5 value: 26.21
- type: precision_at_1 value: 34.694
- type: precision_at_10 value: 21.837
- type: precision_at_100 value: 7.776
- type: precision_at_1000 value: 1.522
- type: precision_at_3 value: 28.571
- type: precision_at_5 value: 25.306
- type: recall_at_1 value: 3.0380000000000003
- type: recall_at_10 value: 16.298000000000002
- type: recall_at_100 value: 48.712
- type: recall_at_1000 value: 83.16799999999999
- type: recall_at_3 value: 7.265000000000001
- type: recall_at_5 value: 9.551
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy value: 83.978
- type: ap value: 24.751887949330015
- type: f1 value: 66.8685134049279
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy value: 61.573288058856825
- type: f1 value: 61.973261751726604
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure value: 48.75483298792469
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy value: 86.36824223639506
- type: cos_sim_ap value: 75.53126388573047
- type: cos_sim_f1 value: 67.9912831688245
- type: cos_sim_precision value: 66.11817501869858
- type: cos_sim_recall value: 69.9736147757256
- type: dot_accuracy value: 86.39804494248078
- type: dot_ap value: 75.27598891718046
- type: dot_f1 value: 67.91146284159763
- type: dot_precision value: 63.90505003490807
- type: dot_recall value: 72.45382585751979
- type: euclidean_accuracy value: 86.36228169517793
- type: euclidean_ap value: 75.51438087434647
- type: euclidean_f1 value: 68.02370523061066
- type: euclidean_precision value: 66.46525679758308
- type: euclidean_recall value: 69.65699208443272
- type: manhattan_accuracy value: 86.46361089586935
- type: manhattan_ap value: 75.50800785730111
- type: manhattan_f1 value: 67.9220437187253
- type: manhattan_precision value: 67.79705573080967
- type: manhattan_recall value: 68.04749340369392
- type: max_accuracy value: 86.46361089586935
- type: max_ap value: 75.53126388573047
- type: max_f1 value: 68.02370523061066
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy value: 88.80350836341057
- type: cos_sim_ap value: 85.51101933260743
- type: cos_sim_f1 value: 77.9152271629704
- type: cos_sim_precision value: 75.27815662910056
- type: cos_sim_recall value: 80.74376347397599
- type: dot_accuracy value: 88.84425815966158
- type: dot_ap value: 85.49726945962519
- type: dot_f1 value: 77.94445269567801
- type: dot_precision value: 75.27251864601261
- type: dot_recall value: 80.81305820757623
- type: euclidean_accuracy value: 88.80350836341057
- type: euclidean_ap value: 85.4882880790211
- type: euclidean_f1 value: 77.87063284615103
- type: euclidean_precision value: 74.61022927689595
- type: euclidean_recall value: 81.42901139513397
- type: manhattan_accuracy value: 88.7161873714441
- type: manhattan_ap value: 85.45753871906821
- type: manhattan_f1 value: 77.8686401480111
- type: manhattan_precision value: 74.95903683123174
- type: manhattan_recall value: 81.01324299353249
- type: max_accuracy value: 88.84425815966158
- type: max_ap value: 85.51101933260743
- type: max_f1 value: 77.94445269567801
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
gte-base-en-v1.5
We introduce gte-v1.5 series, upgraded gte embeddings that support the context length of up to 8192, while further enhancing model performance.
The models are built upon the transformer++ encoder backbone (BERT + RoPE + GLU).
The gte-v1.5 series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation).
We also present the gte-Qwen1.5-7B-instruct,
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
- Developed by: Institute for Intelligent Computing, Alibaba Group
- Model type: Text Embeddings
- Paper: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
Model list
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|---|---|---|---|---|---|---|
gte-Qwen1.5-7B-instruct |
Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
gte-large-en-v1.5 |
English | 434 | 8192 | 1024 | 65.39 | 86.71 |
gte-base-en-v1.5 |
English | 137 | 8192 | 768 | 64.11 | 87.44 |
How to Get Started with the Model
Use the code below to get started with the model.
# Requires transformers>=4.36.0
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
model_path = 'Alibaba-NLP/gte-base-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.
Use with sentence-transformers:
# Requires sentence_transformers>=2.7.0
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
Use with transformers.js:
// npm i @xenova/transformers
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-base-en-v1.5', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [34.504930869007296, 64.03973265120138, 19.520042686034362]
Use with infinity: Infinity is a MIT licensed server for OpenAI-compatible deployment.
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.68 \
v2 --model-id Alibaba-NLP/gte-base-en-v1.5 --revision "4c742dc2b781e4ab062a4a77f4f7cbad4bdee970" --dtype bfloat16 --batch-size 32 --device cuda --engine torch --port 7997
Training Details
Training Data
- Masked language modeling (MLM):
c4-en - Weak-supervised contrastive pre-training (CPT): GTE pre-training data
- Supervised contrastive fine-tuning: GTE fine-tuning data
Training Procedure
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
The entire training process is as follows:
- MLM-2048: lr 5e-4, mlm_probability 0.3, batch_size 4096, num_steps 70000, rope_base 10000
- MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 20000, rope_base 500000
- CPT: max_len 512, lr 2e-4, batch_size 32768, num_steps 100000
- Fine-tuning: TODO
Evaluation
MTEB
The results of other models are retrieved from MTEB leaderboard.
The gte evaluation setting: mteb==1.2.0, fp16 auto mix precision, max_length=8192, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| gte-large-en-v1.5 | 434 | 1024 | 8192 | 65.39 | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
| mxbai-embed-large-v1 | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
| multilingual-e5-large-instruct | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
| bge-large-en-v1.5 | 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
| gte-base-en-v1.5 | 137 | 768 | 8192 | 64.11 | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
| bge-base-en-v1.5 | 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
LoCo
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|---|---|---|---|---|---|---|---|---|
| gte-qwen1.5-7b | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
| gte-large-v1.5 | 1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
| gte-base-v1.5 | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
Citation
If you find our paper or models helpful, please consider citing them as follows:
@misc{zhang2024mgte,
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
author={Xin Zhang and Yanzhao Zhang and Dingkun Long and Wen Xie and Ziqi Dai and Jialong Tang and Huan Lin and Baosong Yang and Pengjun Xie and Fei Huang and Meishan Zhang and Wenjie Li and Min Zhang},
year={2024},
eprint={2407.19669},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2407.19669},
}
@misc{li2023gte,
title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
year={2023},
eprint={2308.03281},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2308.03281},
}
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