%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
EN
Ember V1
E
Ember V1
Developed by llmrails
Ember v1 is an embedding model based on sentence-transformers, primarily used for feature extraction and sentence similarity calculation.
Downloads 51.52k
Release Time : 10/10/2023
Model Overview
This model focuses on text embedding generation, capable of converting sentences into high-dimensional vector representations, suitable for various natural language processing tasks such as classification, clustering, retrieval, and sentence similarity calculation.
Model Features
Multi-task support
Supports various natural language processing tasks including classification, clustering, retrieval, and sentence similarity calculation.
High performance
Excellent performance on multiple benchmarks such as Amazon classification tasks and BIOSSES sentence similarity tasks.
Flexible embedding generation
Capable of converting sentences into high-dimensional vectors for subsequent similarity calculations or other machine learning tasks.
Model Capabilities
Text embedding generation
Sentence similarity calculation
Text classification
Text clustering
Information retrieval
Use Cases
E-commerce
Product review classification
Used for polarity classification (positive/negative) of Amazon product reviews.
Achieved 91.977% accuracy on the AmazonPolarityClassification task.
Counterfactual review detection
Detecting counterfactual reviews on Amazon.
Achieved 76.06% accuracy on the AmazonCounterfactualClassification task.
Academic research
Paper clustering
Cluster analysis of papers from arXiv and bioRxiv.
Achieved 48.58% v_measure on the ArxivClusteringP2P task.
Q&A systems
Duplicate question detection
Detecting duplicate questions in the AskUbuntu community.
Achieved 77.86% mrr on the AskUbuntuDupQuestions task.
tags:
- mteb
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers language: en license: mit model-index:
- name: ember_v1
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 76.05970149253731
- type: ap value: 38.76045348512767
- type: f1 value: 69.8824007294685
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy value: 91.977
- type: ap value: 88.63507587170176
- type: f1 value: 91.9524133311038
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 47.938
- type: f1 value: 47.58273047536129
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 41.252
- type: map_at_10 value: 56.567
- type: map_at_100 value: 57.07600000000001
- type: map_at_1000 value: 57.08
- type: map_at_3 value: 52.394
- type: map_at_5 value: 55.055
- type: mrr_at_1 value: 42.39
- type: mrr_at_10 value: 57.001999999999995
- type: mrr_at_100 value: 57.531
- type: mrr_at_1000 value: 57.535000000000004
- type: mrr_at_3 value: 52.845
- type: mrr_at_5 value: 55.47299999999999
- type: ndcg_at_1 value: 41.252
- type: ndcg_at_10 value: 64.563
- type: ndcg_at_100 value: 66.667
- type: ndcg_at_1000 value: 66.77
- type: ndcg_at_3 value: 56.120000000000005
- type: ndcg_at_5 value: 60.889
- type: precision_at_1 value: 41.252
- type: precision_at_10 value: 8.982999999999999
- type: precision_at_100 value: 0.989
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 22.309
- type: precision_at_5 value: 15.690000000000001
- type: recall_at_1 value: 41.252
- type: recall_at_10 value: 89.82900000000001
- type: recall_at_100 value: 98.86200000000001
- type: recall_at_1000 value: 99.644
- type: recall_at_3 value: 66.927
- type: recall_at_5 value: 78.45
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure value: 48.5799968717232
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure value: 43.142844164856136
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map value: 64.45997990276463
- type: mrr value: 77.85560392208592
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson value: 86.38299310075898
- type: cos_sim_spearman value: 85.81038898286454
- type: euclidean_pearson value: 84.28002556389774
- type: euclidean_spearman value: 85.80315990248238
- type: manhattan_pearson value: 83.9755390675032
- type: manhattan_spearman value: 85.30435335611396
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy value: 87.89935064935065
- type: f1 value: 87.87886687103833
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure value: 38.84335510371379
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure value: 36.377963093857005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 32.557
- type: map_at_10 value: 44.501000000000005
- type: map_at_100 value: 46.11
- type: map_at_1000 value: 46.232
- type: map_at_3 value: 40.711000000000006
- type: map_at_5 value: 42.937
- type: mrr_at_1 value: 40.916000000000004
- type: mrr_at_10 value: 51.317
- type: mrr_at_100 value: 52.003
- type: mrr_at_1000 value: 52.044999999999995
- type: mrr_at_3 value: 48.569
- type: mrr_at_5 value: 50.322
- type: ndcg_at_1 value: 40.916000000000004
- type: ndcg_at_10 value: 51.353
- type: ndcg_at_100 value: 56.762
- type: ndcg_at_1000 value: 58.555
- type: ndcg_at_3 value: 46.064
- type: ndcg_at_5 value: 48.677
- type: precision_at_1 value: 40.916000000000004
- type: precision_at_10 value: 9.927999999999999
- type: precision_at_100 value: 1.592
- type: precision_at_1000 value: 0.20600000000000002
- type: precision_at_3 value: 22.078999999999997
- type: precision_at_5 value: 16.08
- type: recall_at_1 value: 32.557
- type: recall_at_10 value: 63.942
- type: recall_at_100 value: 86.436
- type: recall_at_1000 value: 97.547
- type: recall_at_3 value: 48.367
- type: recall_at_5 value: 55.818
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 32.106
- type: map_at_10 value: 42.55
- type: map_at_100 value: 43.818
- type: map_at_1000 value: 43.952999999999996
- type: map_at_3 value: 39.421
- type: map_at_5 value: 41.276
- type: mrr_at_1 value: 39.936
- type: mrr_at_10 value: 48.484
- type: mrr_at_100 value: 49.123
- type: mrr_at_1000 value: 49.163000000000004
- type: mrr_at_3 value: 46.221000000000004
- type: mrr_at_5 value: 47.603
- type: ndcg_at_1 value: 39.936
- type: ndcg_at_10 value: 48.25
- type: ndcg_at_100 value: 52.674
- type: ndcg_at_1000 value: 54.638
- type: ndcg_at_3 value: 44.05
- type: ndcg_at_5 value: 46.125
- type: precision_at_1 value: 39.936
- type: precision_at_10 value: 9.096
- type: precision_at_100 value: 1.473
- type: precision_at_1000 value: 0.19499999999999998
- type: precision_at_3 value: 21.295
- type: precision_at_5 value: 15.121
- type: recall_at_1 value: 32.106
- type: recall_at_10 value: 58.107
- type: recall_at_100 value: 76.873
- type: recall_at_1000 value: 89.079
- type: recall_at_3 value: 45.505
- type: recall_at_5 value: 51.479
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 41.513
- type: map_at_10 value: 54.571999999999996
- type: map_at_100 value: 55.579
- type: map_at_1000 value: 55.626
- type: map_at_3 value: 51.127
- type: map_at_5 value: 53.151
- type: mrr_at_1 value: 47.398
- type: mrr_at_10 value: 57.82000000000001
- type: mrr_at_100 value: 58.457
- type: mrr_at_1000 value: 58.479000000000006
- type: mrr_at_3 value: 55.32899999999999
- type: mrr_at_5 value: 56.89999999999999
- type: ndcg_at_1 value: 47.398
- type: ndcg_at_10 value: 60.599000000000004
- type: ndcg_at_100 value: 64.366
- type: ndcg_at_1000 value: 65.333
- type: ndcg_at_3 value: 54.98
- type: ndcg_at_5 value: 57.874
- type: precision_at_1 value: 47.398
- type: precision_at_10 value: 9.806
- type: precision_at_100 value: 1.2590000000000001
- type: precision_at_1000 value: 0.13799999999999998
- type: precision_at_3 value: 24.619
- type: precision_at_5 value: 16.878
- type: recall_at_1 value: 41.513
- type: recall_at_10 value: 74.91799999999999
- type: recall_at_100 value: 90.96
- type: recall_at_1000 value: 97.923
- type: recall_at_3 value: 60.013000000000005
- type: recall_at_5 value: 67.245
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 26.319
- type: map_at_10 value: 35.766999999999996
- type: map_at_100 value: 36.765
- type: map_at_1000 value: 36.829
- type: map_at_3 value: 32.888
- type: map_at_5 value: 34.538999999999994
- type: mrr_at_1 value: 28.249000000000002
- type: mrr_at_10 value: 37.766
- type: mrr_at_100 value: 38.62
- type: mrr_at_1000 value: 38.667
- type: mrr_at_3 value: 35.009
- type: mrr_at_5 value: 36.608000000000004
- type: ndcg_at_1 value: 28.249000000000002
- type: ndcg_at_10 value: 41.215
- type: ndcg_at_100 value: 46.274
- type: ndcg_at_1000 value: 48.007
- type: ndcg_at_3 value: 35.557
- type: ndcg_at_5 value: 38.344
- type: precision_at_1 value: 28.249000000000002
- type: precision_at_10 value: 6.429
- type: precision_at_100 value: 0.9480000000000001
- type: precision_at_1000 value: 0.11399999999999999
- type: precision_at_3 value: 15.179
- type: precision_at_5 value: 10.734
- type: recall_at_1 value: 26.319
- type: recall_at_10 value: 56.157999999999994
- type: recall_at_100 value: 79.65
- type: recall_at_1000 value: 92.73
- type: recall_at_3 value: 40.738
- type: recall_at_5 value: 47.418
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 18.485
- type: map_at_10 value: 27.400999999999996
- type: map_at_100 value: 28.665000000000003
- type: map_at_1000 value: 28.79
- type: map_at_3 value: 24.634
- type: map_at_5 value: 26.313
- type: mrr_at_1 value: 23.134
- type: mrr_at_10 value: 32.332
- type: mrr_at_100 value: 33.318
- type: mrr_at_1000 value: 33.384
- type: mrr_at_3 value: 29.664
- type: mrr_at_5 value: 31.262
- type: ndcg_at_1 value: 23.134
- type: ndcg_at_10 value: 33.016
- type: ndcg_at_100 value: 38.763
- type: ndcg_at_1000 value: 41.619
- type: ndcg_at_3 value: 28.017999999999997
- type: ndcg_at_5 value: 30.576999999999998
- type: precision_at_1 value: 23.134
- type: precision_at_10 value: 6.069999999999999
- type: precision_at_100 value: 1.027
- type: precision_at_1000 value: 0.14200000000000002
- type: precision_at_3 value: 13.599
- type: precision_at_5 value: 9.975000000000001
- type: recall_at_1 value: 18.485
- type: recall_at_10 value: 45.39
- type: recall_at_100 value: 69.876
- type: recall_at_1000 value: 90.023
- type: recall_at_3 value: 31.587
- type: recall_at_5 value: 38.164
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 30.676
- type: map_at_10 value: 41.785
- type: map_at_100 value: 43.169000000000004
- type: map_at_1000 value: 43.272
- type: map_at_3 value: 38.462
- type: map_at_5 value: 40.32
- type: mrr_at_1 value: 37.729
- type: mrr_at_10 value: 47.433
- type: mrr_at_100 value: 48.303000000000004
- type: mrr_at_1000 value: 48.337
- type: mrr_at_3 value: 45.011
- type: mrr_at_5 value: 46.455
- type: ndcg_at_1 value: 37.729
- type: ndcg_at_10 value: 47.921
- type: ndcg_at_100 value: 53.477
- type: ndcg_at_1000 value: 55.300000000000004
- type: ndcg_at_3 value: 42.695
- type: ndcg_at_5 value: 45.175
- type: precision_at_1 value: 37.729
- type: precision_at_10 value: 8.652999999999999
- type: precision_at_100 value: 1.336
- type: precision_at_1000 value: 0.168
- type: precision_at_3 value: 20.18
- type: precision_at_5 value: 14.302000000000001
- type: recall_at_1 value: 30.676
- type: recall_at_10 value: 60.441
- type: recall_at_100 value: 83.37
- type: recall_at_1000 value: 95.092
- type: recall_at_3 value: 45.964
- type: recall_at_5 value: 52.319
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 24.978
- type: map_at_10 value: 35.926
- type: map_at_100 value: 37.341
- type: map_at_1000 value: 37.445
- type: map_at_3 value: 32.748
- type: map_at_5 value: 34.207
- type: mrr_at_1 value: 31.163999999999998
- type: mrr_at_10 value: 41.394
- type: mrr_at_100 value: 42.321
- type: mrr_at_1000 value: 42.368
- type: mrr_at_3 value: 38.964999999999996
- type: mrr_at_5 value: 40.135
- type: ndcg_at_1 value: 31.163999999999998
- type: ndcg_at_10 value: 42.191
- type: ndcg_at_100 value: 48.083999999999996
- type: ndcg_at_1000 value: 50.21
- type: ndcg_at_3 value: 36.979
- type: ndcg_at_5 value: 38.823
- type: precision_at_1 value: 31.163999999999998
- type: precision_at_10 value: 7.968
- type: precision_at_100 value: 1.2550000000000001
- type: precision_at_1000 value: 0.16199999999999998
- type: precision_at_3 value: 18.075
- type: precision_at_5 value: 12.626000000000001
- type: recall_at_1 value: 24.978
- type: recall_at_10 value: 55.410000000000004
- type: recall_at_100 value: 80.562
- type: recall_at_1000 value: 94.77600000000001
- type: recall_at_3 value: 40.359
- type: recall_at_5 value: 45.577
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 26.812166666666666
- type: map_at_10 value: 36.706916666666665
- type: map_at_100 value: 37.94016666666666
- type: map_at_1000 value: 38.05358333333333
- type: map_at_3 value: 33.72408333333334
- type: map_at_5 value: 35.36508333333333
- type: mrr_at_1 value: 31.91516666666667
- type: mrr_at_10 value: 41.09716666666666
- type: mrr_at_100 value: 41.931916666666666
- type: mrr_at_1000 value: 41.98458333333333
- type: mrr_at_3 value: 38.60183333333333
- type: mrr_at_5 value: 40.031916666666675
- type: ndcg_at_1 value: 31.91516666666667
- type: ndcg_at_10 value: 42.38725
- type: ndcg_at_100 value: 47.56291666666667
- type: ndcg_at_1000 value: 49.716499999999996
- type: ndcg_at_3 value: 37.36491666666667
- type: ndcg_at_5 value: 39.692166666666665
- type: precision_at_1 value: 31.91516666666667
- type: precision_at_10 value: 7.476749999999999
- type: precision_at_100 value: 1.1869166666666668
- type: precision_at_1000 value: 0.157
- type: precision_at_3 value: 17.275249999999996
- type: precision_at_5 value: 12.25825
- type: recall_at_1 value: 26.812166666666666
- type: recall_at_10 value: 54.82933333333333
- type: recall_at_100 value: 77.36508333333333
- type: recall_at_1000 value: 92.13366666666667
- type: recall_at_3 value: 40.83508333333334
- type: recall_at_5 value: 46.85083333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 25.352999999999998
- type: map_at_10 value: 33.025999999999996
- type: map_at_100 value: 33.882
- type: map_at_1000 value: 33.983999999999995
- type: map_at_3 value: 30.995
- type: map_at_5 value: 32.113
- type: mrr_at_1 value: 28.834
- type: mrr_at_10 value: 36.14
- type: mrr_at_100 value: 36.815
- type: mrr_at_1000 value: 36.893
- type: mrr_at_3 value: 34.305
- type: mrr_at_5 value: 35.263
- type: ndcg_at_1 value: 28.834
- type: ndcg_at_10 value: 37.26
- type: ndcg_at_100 value: 41.723
- type: ndcg_at_1000 value: 44.314
- type: ndcg_at_3 value: 33.584
- type: ndcg_at_5 value: 35.302
- type: precision_at_1 value: 28.834
- type: precision_at_10 value: 5.736
- type: precision_at_100 value: 0.876
- type: precision_at_1000 value: 0.117
- type: precision_at_3 value: 14.468
- type: precision_at_5 value: 9.847
- type: recall_at_1 value: 25.352999999999998
- type: recall_at_10 value: 47.155
- type: recall_at_100 value: 68.024
- type: recall_at_1000 value: 87.26899999999999
- type: recall_at_3 value: 37.074
- type: recall_at_5 value: 41.352
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 17.845
- type: map_at_10 value: 25.556
- type: map_at_100 value: 26.787
- type: map_at_1000 value: 26.913999999999998
- type: map_at_3 value: 23.075000000000003
- type: map_at_5 value: 24.308
- type: mrr_at_1 value: 21.714
- type: mrr_at_10 value: 29.543999999999997
- type: mrr_at_100 value: 30.543
- type: mrr_at_1000 value: 30.618000000000002
- type: mrr_at_3 value: 27.174
- type: mrr_at_5 value: 28.409000000000002
- type: ndcg_at_1 value: 21.714
- type: ndcg_at_10 value: 30.562
- type: ndcg_at_100 value: 36.27
- type: ndcg_at_1000 value: 39.033
- type: ndcg_at_3 value: 26.006
- type: ndcg_at_5 value: 27.843
- type: precision_at_1 value: 21.714
- type: precision_at_10 value: 5.657
- type: precision_at_100 value: 1
- type: precision_at_1000 value: 0.14100000000000001
- type: precision_at_3 value: 12.4
- type: precision_at_5 value: 8.863999999999999
- type: recall_at_1 value: 17.845
- type: recall_at_10 value: 41.72
- type: recall_at_100 value: 67.06400000000001
- type: recall_at_1000 value: 86.515
- type: recall_at_3 value: 28.78
- type: recall_at_5 value: 33.629999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 26.695
- type: map_at_10 value: 36.205999999999996
- type: map_at_100 value: 37.346000000000004
- type: map_at_1000 value: 37.447
- type: map_at_3 value: 32.84
- type: map_at_5 value: 34.733000000000004
- type: mrr_at_1 value: 31.343
- type: mrr_at_10 value: 40.335
- type: mrr_at_100 value: 41.162
- type: mrr_at_1000 value: 41.221000000000004
- type: mrr_at_3 value: 37.329
- type: mrr_at_5 value: 39.068999999999996
- type: ndcg_at_1 value: 31.343
- type: ndcg_at_10 value: 41.996
- type: ndcg_at_100 value: 47.096
- type: ndcg_at_1000 value: 49.4
- type: ndcg_at_3 value: 35.902
- type: ndcg_at_5 value: 38.848
- type: precision_at_1 value: 31.343
- type: precision_at_10 value: 7.146
- type: precision_at_100 value: 1.098
- type: precision_at_1000 value: 0.14100000000000001
- type: precision_at_3 value: 16.014
- type: precision_at_5 value: 11.735
- type: recall_at_1 value: 26.695
- type: recall_at_10 value: 55.525000000000006
- type: recall_at_100 value: 77.376
- type: recall_at_1000 value: 93.476
- type: recall_at_3 value: 39.439
- type: recall_at_5 value: 46.501
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 24.196
- type: map_at_10 value: 33.516
- type: map_at_100 value: 35.202
- type: map_at_1000 value: 35.426
- type: map_at_3 value: 30.561
- type: map_at_5 value: 31.961000000000002
- type: mrr_at_1 value: 29.644
- type: mrr_at_10 value: 38.769
- type: mrr_at_100 value: 39.843
- type: mrr_at_1000 value: 39.888
- type: mrr_at_3 value: 36.132999999999996
- type: mrr_at_5 value: 37.467
- type: ndcg_at_1 value: 29.644
- type: ndcg_at_10 value: 39.584
- type: ndcg_at_100 value: 45.964
- type: ndcg_at_1000 value: 48.27
- type: ndcg_at_3 value: 34.577999999999996
- type: ndcg_at_5 value: 36.498000000000005
- type: precision_at_1 value: 29.644
- type: precision_at_10 value: 7.668
- type: precision_at_100 value: 1.545
- type: precision_at_1000 value: 0.242
- type: precision_at_3 value: 16.271
- type: precision_at_5 value: 11.620999999999999
- type: recall_at_1 value: 24.196
- type: recall_at_10 value: 51.171
- type: recall_at_100 value: 79.212
- type: recall_at_1000 value: 92.976
- type: recall_at_3 value: 36.797999999999995
- type: recall_at_5 value: 42.006
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 21.023
- type: map_at_10 value: 29.677
- type: map_at_100 value: 30.618000000000002
- type: map_at_1000 value: 30.725
- type: map_at_3 value: 27.227
- type: map_at_5 value: 28.523
- type: mrr_at_1 value: 22.921
- type: mrr_at_10 value: 31.832
- type: mrr_at_100 value: 32.675
- type: mrr_at_1000 value: 32.751999999999995
- type: mrr_at_3 value: 29.513
- type: mrr_at_5 value: 30.89
- type: ndcg_at_1 value: 22.921
- type: ndcg_at_10 value: 34.699999999999996
- type: ndcg_at_100 value: 39.302
- type: ndcg_at_1000 value: 41.919000000000004
- type: ndcg_at_3 value: 29.965999999999998
- type: ndcg_at_5 value: 32.22
- type: precision_at_1 value: 22.921
- type: precision_at_10 value: 5.564
- type: precision_at_100 value: 0.8340000000000001
- type: precision_at_1000 value: 0.11800000000000001
- type: precision_at_3 value: 13.123999999999999
- type: precision_at_5 value: 9.316
- type: recall_at_1 value: 21.023
- type: recall_at_10 value: 48.015
- type: recall_at_100 value: 68.978
- type: recall_at_1000 value: 88.198
- type: recall_at_3 value: 35.397
- type: recall_at_5 value: 40.701
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 11.198
- type: map_at_10 value: 19.336000000000002
- type: map_at_100 value: 21.382
- type: map_at_1000 value: 21.581
- type: map_at_3 value: 15.992
- type: map_at_5 value: 17.613
- type: mrr_at_1 value: 25.080999999999996
- type: mrr_at_10 value: 36.032
- type: mrr_at_100 value: 37.1
- type: mrr_at_1000 value: 37.145
- type: mrr_at_3 value: 32.595
- type: mrr_at_5 value: 34.553
- type: ndcg_at_1 value: 25.080999999999996
- type: ndcg_at_10 value: 27.290999999999997
- type: ndcg_at_100 value: 35.31
- type: ndcg_at_1000 value: 38.885
- type: ndcg_at_3 value: 21.895999999999997
- type: ndcg_at_5 value: 23.669999999999998
- type: precision_at_1 value: 25.080999999999996
- type: precision_at_10 value: 8.645
- type: precision_at_100 value: 1.7209999999999999
- type: precision_at_1000 value: 0.23900000000000002
- type: precision_at_3 value: 16.287
- type: precision_at_5 value: 12.625
- type: recall_at_1 value: 11.198
- type: recall_at_10 value: 33.355000000000004
- type: recall_at_100 value: 60.912
- type: recall_at_1000 value: 80.89
- type: recall_at_3 value: 20.055
- type: recall_at_5 value: 25.14
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 9.228
- type: map_at_10 value: 20.018
- type: map_at_100 value: 28.388999999999996
- type: map_at_1000 value: 30.073
- type: map_at_3 value: 14.366999999999999
- type: map_at_5 value: 16.705000000000002
- type: mrr_at_1 value: 69
- type: mrr_at_10 value: 77.058
- type: mrr_at_100 value: 77.374
- type: mrr_at_1000 value: 77.384
- type: mrr_at_3 value: 75.708
- type: mrr_at_5 value: 76.608
- type: ndcg_at_1 value: 57.49999999999999
- type: ndcg_at_10 value: 41.792
- type: ndcg_at_100 value: 47.374
- type: ndcg_at_1000 value: 55.13
- type: ndcg_at_3 value: 46.353
- type: ndcg_at_5 value: 43.702000000000005
- type: precision_at_1 value: 69
- type: precision_at_10 value: 32.85
- type: precision_at_100 value: 10.708
- type: precision_at_1000 value: 2.024
- type: precision_at_3 value: 49.5
- type: precision_at_5 value: 42.05
- type: recall_at_1 value: 9.228
- type: recall_at_10 value: 25.635
- type: recall_at_100 value: 54.894
- type: recall_at_1000 value: 79.38
- type: recall_at_3 value: 15.68
- type: recall_at_5 value: 19.142
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy value: 52.035
- type: f1 value: 46.85325505614071
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 70.132
- type: map_at_10 value: 79.527
- type: map_at_100 value: 79.81200000000001
- type: map_at_1000 value: 79.828
- type: map_at_3 value: 78.191
- type: map_at_5 value: 79.092
- type: mrr_at_1 value: 75.563
- type: mrr_at_10 value: 83.80199999999999
- type: mrr_at_100 value: 83.93
- type: mrr_at_1000 value: 83.933
- type: mrr_at_3 value: 82.818
- type: mrr_at_5 value: 83.505
- type: ndcg_at_1 value: 75.563
- type: ndcg_at_10 value: 83.692
- type: ndcg_at_100 value: 84.706
- type: ndcg_at_1000 value: 85.001
- type: ndcg_at_3 value: 81.51
- type: ndcg_at_5 value: 82.832
- type: precision_at_1 value: 75.563
- type: precision_at_10 value: 10.245
- type: precision_at_100 value: 1.0959999999999999
- type: precision_at_1000 value: 0.11399999999999999
- type: precision_at_3 value: 31.518
- type: precision_at_5 value: 19.772000000000002
- type: recall_at_1 value: 70.132
- type: recall_at_10 value: 92.204
- type: recall_at_100 value: 96.261
- type: recall_at_1000 value: 98.17399999999999
- type: recall_at_3 value: 86.288
- type: recall_at_5 value: 89.63799999999999
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 22.269
- type: map_at_10 value: 36.042
- type: map_at_100 value: 37.988
- type: map_at_1000 value: 38.162
- type: map_at_3 value: 31.691000000000003
- type: map_at_5 value: 33.988
- type: mrr_at_1 value: 44.907000000000004
- type: mrr_at_10 value: 53.348
- type: mrr_at_100 value: 54.033
- type: mrr_at_1000 value: 54.064
- type: mrr_at_3 value: 50.977
- type: mrr_at_5 value: 52.112
- type: ndcg_at_1 value: 44.907000000000004
- type: ndcg_at_10 value: 44.302
- type: ndcg_at_100 value: 51.054
- type: ndcg_at_1000 value: 53.822
- type: ndcg_at_3 value: 40.615
- type: ndcg_at_5 value: 41.455999999999996
- type: precision_at_1 value: 44.907000000000004
- type: precision_at_10 value: 12.176
- type: precision_at_100 value: 1.931
- type: precision_at_1000 value: 0.243
- type: precision_at_3 value: 27.16
- type: precision_at_5 value: 19.567999999999998
- type: recall_at_1 value: 22.269
- type: recall_at_10 value: 51.188
- type: recall_at_100 value: 75.924
- type: recall_at_1000 value: 92.525
- type: recall_at_3 value: 36.643
- type: recall_at_5 value: 42.27
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 40.412
- type: map_at_10 value: 66.376
- type: map_at_100 value: 67.217
- type: map_at_1000 value: 67.271
- type: map_at_3 value: 62.741
- type: map_at_5 value: 65.069
- type: mrr_at_1 value: 80.824
- type: mrr_at_10 value: 86.53
- type: mrr_at_100 value: 86.67399999999999
- type: mrr_at_1000 value: 86.678
- type: mrr_at_3 value: 85.676
- type: mrr_at_5 value: 86.256
- type: ndcg_at_1 value: 80.824
- type: ndcg_at_10 value: 74.332
- type: ndcg_at_100 value: 77.154
- type: ndcg_at_1000 value: 78.12400000000001
- type: ndcg_at_3 value: 69.353
- type: ndcg_at_5 value: 72.234
- type: precision_at_1 value: 80.824
- type: precision_at_10 value: 15.652
- type: precision_at_100 value: 1.7840000000000003
- type: precision_at_1000 value: 0.191
- type: precision_at_3 value: 44.911
- type: precision_at_5 value: 29.221000000000004
- type: recall_at_1 value: 40.412
- type: recall_at_10 value: 78.25800000000001
- type: recall_at_100 value: 89.196
- type: recall_at_1000 value: 95.544
- type: recall_at_3 value: 67.367
- type: recall_at_5 value: 73.05199999999999
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy value: 92.78880000000001
- type: ap value: 89.39251741048801
- type: f1 value: 92.78019950076781
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 22.888
- type: map_at_10 value: 35.146
- type: map_at_100 value: 36.325
- type: map_at_1000 value: 36.372
- type: map_at_3 value: 31.3
- type: map_at_5 value: 33.533
- type: mrr_at_1 value: 23.480999999999998
- type: mrr_at_10 value: 35.777
- type: mrr_at_100 value: 36.887
- type: mrr_at_1000 value: 36.928
- type: mrr_at_3 value: 31.989
- type: mrr_at_5 value: 34.202
- type: ndcg_at_1 value: 23.496
- type: ndcg_at_10 value: 42.028999999999996
- type: ndcg_at_100 value: 47.629
- type: ndcg_at_1000 value: 48.785000000000004
- type: ndcg_at_3 value: 34.227000000000004
- type: ndcg_at_5 value: 38.207
- type: precision_at_1 value: 23.496
- type: precision_at_10 value: 6.596
- type: precision_at_100 value: 0.9400000000000001
- type: precision_at_1000 value: 0.104
- type: precision_at_3 value: 14.513000000000002
- type: precision_at_5 value: 10.711
- type: recall_at_1 value: 22.888
- type: recall_at_10 value: 63.129999999999995
- type: recall_at_100 value: 88.90299999999999
- type: recall_at_1000 value: 97.69
- type: recall_at_3 value: 42.014
- type: recall_at_5 value: 51.554
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 94.59188326493388
- type: f1 value: 94.36568950290486
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy value: 79.25672594619242
- type: f1 value: 59.52405059722216
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy value: 77.4142568930733
- type: f1 value: 75.23044196543388
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy value: 80.44720914593141
- type: f1 value: 80.41049641537015
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure value: 31.960921474993775
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure value: 30.88042240204361
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map value: 32.27071371606404
- type: mrr value: 33.541450459533856
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 6.551
- type: map_at_10 value: 14.359
- type: map_at_100 value: 18.157
- type: map_at_1000 value: 19.659
- type: map_at_3 value: 10.613999999999999
- type: map_at_5 value: 12.296
- type: mrr_at_1 value: 47.368
- type: mrr_at_10 value: 56.689
- type: mrr_at_100 value: 57.24399999999999
- type: mrr_at_1000 value: 57.284
- type: mrr_at_3 value: 54.489
- type: mrr_at_5 value: 55.928999999999995
- type: ndcg_at_1 value: 45.511
- type: ndcg_at_10 value: 36.911
- type: ndcg_at_100 value: 34.241
- type: ndcg_at_1000 value: 43.064
- type: ndcg_at_3 value: 42.348
- type: ndcg_at_5 value: 39.884
- type: precision_at_1 value: 46.749
- type: precision_at_10 value: 27.028000000000002
- type: precision_at_100 value: 8.52
- type: precision_at_1000 value: 2.154
- type: precision_at_3 value: 39.525
- type: precision_at_5 value: 34.18
- type: recall_at_1 value: 6.551
- type: recall_at_10 value: 18.602
- type: recall_at_100 value: 34.882999999999996
- type: recall_at_1000 value: 66.049
- type: recall_at_3 value: 11.872
- type: recall_at_5 value: 14.74
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 27.828999999999997
- type: map_at_10 value: 43.606
- type: map_at_100 value: 44.656
- type: map_at_1000 value: 44.690000000000005
- type: map_at_3 value: 39.015
- type: map_at_5 value: 41.625
- type: mrr_at_1 value: 31.518
- type: mrr_at_10 value: 46.047
- type: mrr_at_100 value: 46.846
- type: mrr_at_1000 value: 46.867999999999995
- type: mrr_at_3 value: 42.154
- type: mrr_at_5 value: 44.468999999999994
- type: ndcg_at_1 value: 31.518
- type: ndcg_at_10 value: 51.768
- type: ndcg_at_100 value: 56.184999999999995
- type: ndcg_at_1000 value: 56.92
- type: ndcg_at_3 value: 43.059999999999995
- type: ndcg_at_5 value: 47.481
- type: precision_at_1 value: 31.518
- type: precision_at_10 value: 8.824
- type: precision_at_100 value: 1.131
- type: precision_at_1000 value: 0.12
- type: precision_at_3 value: 19.969
- type: precision_at_5 value: 14.502
- type: recall_at_1 value: 27.828999999999997
- type: recall_at_10 value: 74.244
- type: recall_at_100 value: 93.325
- type: recall_at_1000 value: 98.71799999999999
- type: recall_at_3 value: 51.601
- type: recall_at_5 value: 61.841
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 71.54
- type: map_at_10 value: 85.509
- type: map_at_100 value: 86.137
- type: map_at_1000 value: 86.151
- type: map_at_3 value: 82.624
- type: map_at_5 value: 84.425
- type: mrr_at_1 value: 82.45
- type: mrr_at_10 value: 88.344
- type: mrr_at_100 value: 88.437
- type: mrr_at_1000 value: 88.437
- type: mrr_at_3 value: 87.417
- type: mrr_at_5 value: 88.066
- type: ndcg_at_1 value: 82.45
- type: ndcg_at_10 value: 89.092
- type: ndcg_at_100 value: 90.252
- type: ndcg_at_1000 value: 90.321
- type: ndcg_at_3 value: 86.404
- type: ndcg_at_5 value: 87.883
- type: precision_at_1 value: 82.45
- type: precision_at_10 value: 13.496
- type: precision_at_100 value: 1.536
- type: precision_at_1000 value: 0.157
- type: precision_at_3 value: 37.833
- type: precision_at_5 value: 24.79
- type: recall_at_1 value: 71.54
- type: recall_at_10 value: 95.846
- type: recall_at_100 value: 99.715
- type: recall_at_1000 value: 99.979
- type: recall_at_3 value: 88.01299999999999
- type: recall_at_5 value: 92.32000000000001
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure value: 57.60557586253866
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure value: 64.0287172242051
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 3.9849999999999994
- type: map_at_10 value: 11.397
- type: map_at_100 value: 13.985
- type: map_at_1000 value: 14.391000000000002
- type: map_at_3 value: 7.66
- type: map_at_5 value: 9.46
- type: mrr_at_1 value: 19.8
- type: mrr_at_10 value: 31.958
- type: mrr_at_100 value: 33.373999999999995
- type: mrr_at_1000 value: 33.411
- type: mrr_at_3 value: 28.316999999999997
- type: mrr_at_5 value: 30.297
- type: ndcg_at_1 value: 19.8
- type: ndcg_at_10 value: 19.580000000000002
- type: ndcg_at_100 value: 29.555999999999997
- type: ndcg_at_1000 value: 35.882
- type: ndcg_at_3 value: 17.544
- type: ndcg_at_5 value: 15.815999999999999
- type: precision_at_1 value: 19.8
- type: precision_at_10 value: 10.61
- type: precision_at_100 value: 2.501
- type: precision_at_1000 value: 0.40099999999999997
- type: precision_at_3 value: 16.900000000000002
- type: precision_at_5 value: 14.44
- type: recall_at_1 value: 3.9849999999999994
- type: recall_at_10 value: 21.497
- type: recall_at_100 value: 50.727999999999994
- type: recall_at_1000 value: 81.27499999999999
- type: recall_at_3 value: 10.263
- type: recall_at_5 value: 14.643
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson value: 85.0087509585503
- type: cos_sim_spearman value: 81.74697270664319
- type: euclidean_pearson value: 81.80424382731947
- type: euclidean_spearman value: 81.29794251968431
- type: manhattan_pearson value: 81.81524666226125
- type: manhattan_spearman value: 81.29475370198963
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson value: 86.44442736429552
- type: cos_sim_spearman value: 78.51011398910948
- type: euclidean_pearson value: 83.36181801196723
- type: euclidean_spearman value: 79.47272621331535
- type: manhattan_pearson value: 83.3660113483837
- type: manhattan_spearman value: 79.47695922566032
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson value: 85.82923943323635
- type: cos_sim_spearman value: 86.62037823380983
- type: euclidean_pearson value: 83.56369548403958
- type: euclidean_spearman value: 84.2176755481191
- type: manhattan_pearson value: 83.55460702084464
- type: manhattan_spearman value: 84.18617930921467
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson value: 84.09071068110103
- type: cos_sim_spearman value: 83.05697553913335
- type: euclidean_pearson value: 81.1377457216497
- type: euclidean_spearman value: 81.74714169016676
- type: manhattan_pearson value: 81.0893424142723
- type: manhattan_spearman value: 81.7058918219677
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson value: 87.61132157220429
- type: cos_sim_spearman value: 88.38581627185445
- type: euclidean_pearson value: 86.14904510913374
- type: euclidean_spearman value: 86.5452758925542
- type: manhattan_pearson value: 86.1484025377679
- type: manhattan_spearman value: 86.55483841566252
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson value: 85.46195145161064
- type: cos_sim_spearman value: 86.82409112251158
- type: euclidean_pearson value: 84.75479672288957
- type: euclidean_spearman value: 85.41144307151548
- type: manhattan_pearson value: 84.70914329694165
- type: manhattan_spearman value: 85.38477943384089
- 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: 88.06351289930238
- type: cos_sim_spearman value: 87.90311138579116
- type: euclidean_pearson value: 86.17651467063077
- type: euclidean_spearman value: 84.89447802019073
- type: manhattan_pearson value: 86.3267677479595
- type: manhattan_spearman value: 85.00472295103874
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson value: 67.78311975978767
- type: cos_sim_spearman value: 66.76465685245887
- type: euclidean_pearson value: 67.21687806595443
- type: euclidean_spearman value: 65.05776733534435
- type: manhattan_pearson value: 67.14008143635883
- type: manhattan_spearman value: 65.25247076149701
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson value: 86.7403488889418
- type: cos_sim_spearman value: 87.76870289783061
- type: euclidean_pearson value: 84.83171077794671
- type: euclidean_spearman value: 85.50579695091902
- type: manhattan_pearson value: 84.83074260180555
- type: manhattan_spearman value: 85.47589026938667
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map value: 87.56234016237356
- type: mrr value: 96.26124238869338
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 59.660999999999994
- type: map_at_10 value: 69.105
- type: map_at_100 value: 69.78
- type: map_at_1000 value: 69.80199999999999
- type: map_at_3 value: 65.991
- type: map_at_5 value: 68.02
- type: mrr_at_1 value: 62.666999999999994
- type: mrr_at_10 value: 70.259
- type: mrr_at_100 value: 70.776
- type: mrr_at_1000 value: 70.796
- type: mrr_at_3 value: 67.889
- type: mrr_at_5 value: 69.52199999999999
- type: ndcg_at_1 value: 62.666999999999994
- type: ndcg_at_10 value: 73.425
- type: ndcg_at_100 value: 75.955
- type: ndcg_at_1000 value: 76.459
- type: ndcg_at_3 value: 68.345
- type: ndcg_at_5 value: 71.319
- type: precision_at_1 value: 62.666999999999994
- type: precision_at_10 value: 9.667
- type: precision_at_100 value: 1.09
- type: precision_at_1000 value: 0.11299999999999999
- type: precision_at_3 value: 26.333000000000002
- type: precision_at_5 value: 17.732999999999997
- type: recall_at_1 value: 59.660999999999994
- type: recall_at_10 value: 85.422
- type: recall_at_100 value: 96.167
- type: recall_at_1000 value: 100
- type: recall_at_3 value: 72.044
- type: recall_at_5 value: 79.428
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy value: 99.86435643564356
- type: cos_sim_ap value: 96.83057412333741
- type: cos_sim_f1 value: 93.04215337734891
- type: cos_sim_precision value: 94.53044375644994
- type: cos_sim_recall value: 91.60000000000001
- type: dot_accuracy value: 99.7910891089109
- type: dot_ap value: 94.10681982106397
- type: dot_f1 value: 89.34881373043918
- type: dot_precision value: 90.21406727828746
- type: dot_recall value: 88.5
- type: euclidean_accuracy value: 99.85544554455446
- type: euclidean_ap value: 96.78545104478602
- type: euclidean_f1 value: 92.65143992055613
- type: euclidean_precision value: 92.01183431952663
- type: euclidean_recall value: 93.30000000000001
- type: manhattan_accuracy value: 99.85841584158416
- type: manhattan_ap value: 96.80748903307823
- type: manhattan_f1 value: 92.78247884519662
- type: manhattan_precision value: 92.36868186323092
- type: manhattan_recall value: 93.2
- type: max_accuracy value: 99.86435643564356
- type: max_ap value: 96.83057412333741
- type: max_f1 value: 93.04215337734891
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure value: 65.53971025855282
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure value: 33.97791591490788
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map value: 55.852215301355066
- type: mrr value: 56.85527809608691
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson value: 31.21442519856758
- type: cos_sim_spearman value: 30.822536216936825
- type: dot_pearson value: 28.661325528121807
- type: dot_spearman value: 28.1435226478879
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 0.183
- type: map_at_10 value: 1.526
- type: map_at_100 value: 7.915
- type: map_at_1000 value: 19.009
- type: map_at_3 value: 0.541
- type: map_at_5 value: 0.8659999999999999
- type: mrr_at_1 value: 68
- type: mrr_at_10 value: 81.186
- type: mrr_at_100 value: 81.186
- type: mrr_at_1000 value: 81.186
- type: mrr_at_3 value: 80
- type: mrr_at_5 value: 80.9
- type: ndcg_at_1 value: 64
- type: ndcg_at_10 value: 64.13799999999999
- type: ndcg_at_100 value: 47.632000000000005
- type: ndcg_at_1000 value: 43.037
- type: ndcg_at_3 value: 67.542
- type: ndcg_at_5 value: 67.496
- type: precision_at_1 value: 68
- type: precision_at_10 value: 67.80000000000001
- type: precision_at_100 value: 48.980000000000004
- type: precision_at_1000 value: 19.036
- type: precision_at_3 value: 72
- type: precision_at_5 value: 71.2
- type: recall_at_1 value: 0.183
- type: recall_at_10 value: 1.799
- type: recall_at_100 value: 11.652999999999999
- type: recall_at_1000 value: 40.086
- type: recall_at_3 value: 0.5930000000000001
- type: recall_at_5 value: 0.983
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1 value: 2.29
- type: map_at_10 value: 9.489
- type: map_at_100 value: 15.051
- type: map_at_1000 value: 16.561999999999998
- type: map_at_3 value: 5.137
- type: map_at_5 value: 6.7989999999999995
- type: mrr_at_1 value: 28.571
- type: mrr_at_10 value: 45.699
- type: mrr_at_100 value: 46.461000000000006
- type: mrr_at_1000 value: 46.461000000000006
- type: mrr_at_3 value: 41.837
- type: mrr_at_5 value: 43.163000000000004
- type: ndcg_at_1 value: 23.469
- type: ndcg_at_10 value: 23.544999999999998
- type: ndcg_at_100 value: 34.572
- type: ndcg_at_1000 value: 46.035
- type: ndcg_at_3 value: 27.200000000000003
- type: ndcg_at_5 value: 25.266
- type: precision_at_1 value: 28.571
- type: precision_at_10 value: 22.041
- type: precision_at_100 value: 7.3469999999999995
- type: precision_at_1000 value: 1.484
- type: precision_at_3 value: 29.932
- type: precision_at_5 value: 26.531
- type: recall_at_1 value: 2.29
- type: recall_at_10 value: 15.895999999999999
- type: recall_at_100 value: 45.518
- type: recall_at_1000 value: 80.731
- type: recall_at_3 value: 6.433
- type: recall_at_5 value: 9.484
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy value: 71.4178
- type: ap value: 14.575240629602373
- type: f1 value: 55.02449563229096
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy value: 60.00282965478212
- type: f1 value: 60.34413028768773
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure value: 50.409448342549936
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy value: 87.62591643321214
- type: cos_sim_ap value: 79.28766491329633
- type: cos_sim_f1 value: 71.98772064466617
- type: cos_sim_precision value: 69.8609731876862
- type: cos_sim_recall value: 74.24802110817942
- type: dot_accuracy value: 84.75293556654945
- type: dot_ap value: 69.72705761174353
- type: dot_f1 value: 65.08692852543464
- type: dot_precision value: 63.57232704402516
- type: dot_recall value: 66.6754617414248
- type: euclidean_accuracy value: 87.44710019669786
- type: euclidean_ap value: 79.11021477292638
- type: euclidean_f1 value: 71.5052389470994
- type: euclidean_precision value: 69.32606541129832
- type: euclidean_recall value: 73.82585751978891
- type: manhattan_accuracy value: 87.42325803182929
- type: manhattan_ap value: 79.05094494327616
- type: manhattan_f1 value: 71.36333985649055
- type: manhattan_precision value: 70.58064516129032
- type: manhattan_recall value: 72.16358839050132
- type: max_accuracy value: 87.62591643321214
- type: max_ap value: 79.28766491329633
- type: max_f1 value: 71.98772064466617
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy value: 88.85202002561415
- type: cos_sim_ap value: 85.9835303311168
- type: cos_sim_f1 value: 78.25741142443962
- type: cos_sim_precision value: 73.76635768811342
- type: cos_sim_recall value: 83.3307668617185
- type: dot_accuracy value: 88.20584468506229
- type: dot_ap value: 83.591632302697
- type: dot_f1 value: 76.81739705396173
- type: dot_precision value: 73.45275728837373
- type: dot_recall value: 80.50508161379734
- type: euclidean_accuracy value: 88.64633057787093
- type: euclidean_ap value: 85.25705123182283
- type: euclidean_f1 value: 77.18535726329199
- type: euclidean_precision value: 75.17699437997226
- type: euclidean_recall value: 79.30397289805975
- type: manhattan_accuracy value: 88.63274731245392
- type: manhattan_ap value: 85.2376825633018
- type: manhattan_f1 value: 77.15810785937788
- type: manhattan_precision value: 73.92255061014319
- type: manhattan_recall value: 80.68986757006468
- type: max_accuracy value: 88.85202002561415
- type: max_ap value: 85.9835303311168
- type: max_f1 value: 78.25741142443962
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
ember-v1
This model has been trained on an extensive corpus of text pairs that encompass a broad spectrum of domains, including finance, science, medicine, law, and various others. During the training process, we incorporated techniques derived from the RetroMAE and SetFit research papers.
Plans
- The research paper will be published soon.
- The v2 of the model is currently in development and will feature an extended maximum sequence length of 4,000 tokens.
Usage
Use with transformers:
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
input_texts = [
"This is an example sentence",
"Each sentence is converted"
]
tokenizer = AutoTokenizer.from_pretrained("llmrails/ember-v1")
model = AutoModel.from_pretrained("llmrails/ember-v1")
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
Use with sentence-transformers:
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = [
"This is an example sentence",
"Each sentence is converted"
]
model = SentenceTransformer('llmrails/ember-v1')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
Massive Text Embedding Benchmark (MTEB) Evaluation
Our model achieve state-of-the-art performance on MTEB leaderboard
| Model Name | Dimension | Sequence Length | Average (56) |
|---|---|---|---|
| ember-v1 | 1024 | 512 | 63.54 |
| bge-large-en-v1.5 | 1024 | 512 | 63.23 |
| bge-base-en-v1.5 | 768 | 512 | 63.05 |
| text-embedding-ada-002 | 1536 | 8191 | 60.99 |
Limitation
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
License
MIT
Citation
@misc{nur2024emberv1,
title={ember-v1: SOTA embedding model},
author={Enrike Nur and Anar Aliyev},
year={2023},
}
Jina Embeddings V3
Jina Embeddings V3 is a multilingual sentence embedding model supporting over 100 languages, specializing in sentence similarity and feature extraction tasks.
Text Embedding
Transformers Supports Multiple Languages
Transformers Supports Multiple LanguagesJ
jinaai
3.7M
911
Ms Marco MiniLM L6 V2
Apache-2.0
A cross-encoder model trained on the MS Marco passage ranking task for query-passage relevance scoring in information retrieval
Text Embedding English
M
cross-encoder
2.5M
86
Opensearch Neural Sparse Encoding Doc V2 Distill
Apache-2.0
A sparse retrieval model based on distillation technology, optimized for OpenSearch, supporting inference-free document encoding with improved search relevance and efficiency over V1
Text Embedding Transformers English
Transformers EnglishO
opensearch-project
1.8M
7
Sapbert From PubMedBERT Fulltext
Apache-2.0
A biomedical entity representation model based on PubMedBERT, optimized for semantic relation capture through self-aligned pre-training
Text Embedding English
S
cambridgeltl
1.7M
49
Gte Large
MIT
GTE-Large is a powerful sentence transformer model focused on sentence similarity and text embedding tasks, excelling in multiple benchmark tests.
Text Embedding English
G
thenlper
1.5M
278
Gte Base En V1.5
Apache-2.0
GTE-base-en-v1.5 is an English sentence transformer model focused on sentence similarity tasks, excelling in multiple text embedding benchmarks.
Text Embedding Transformers Supports Multiple Languages
Transformers Supports Multiple LanguagesG
Alibaba-NLP
1.5M
63
Gte Multilingual Base
Apache-2.0
GTE Multilingual Base is a multilingual sentence embedding model supporting over 50 languages, suitable for tasks like sentence similarity calculation.
Text Embedding Transformers Supports Multiple Languages
Transformers Supports Multiple LanguagesG
Alibaba-NLP
1.2M
246
Polybert
polyBERT is a chemical language model designed to achieve fully machine-driven ultrafast polymer informatics. It maps PSMILES strings into 600-dimensional dense fingerprints to numerically represent polymer chemical structures.
Text Embedding Transformers
TransformersP
kuelumbus
1.0M
5
Bert Base Turkish Cased Mean Nli Stsb Tr
Apache-2.0
A sentence embedding model based on Turkish BERT, optimized for semantic similarity tasks
Text Embedding Transformers Other
Transformers OtherB
emrecan
1.0M
40
GIST Small Embedding V0
MIT
A text embedding model fine-tuned based on BAAI/bge-small-en-v1.5, trained with the MEDI dataset and MTEB classification task datasets, optimized for query encoding in retrieval tasks.
Text Embedding
Safetensors English
Safetensors EnglishG
avsolatorio
945.68k
29
Featured Recommended AI Models