2725 lines
65 KiB
Markdown
2725 lines
65 KiB
Markdown
---
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tags:
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- mteb
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- sentence_embedding
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- feature_extraction
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- sentence-transformers
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- transformers
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- transformers.js
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model-index:
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- name: UAE-Large-V1
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results:
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_counterfactual
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name: MTEB AmazonCounterfactualClassification (en)
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config: en
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split: test
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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metrics:
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- type: accuracy
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value: 75.55223880597015
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- type: ap
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value: 38.264070815317794
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- type: f1
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value: 69.40977934769845
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_polarity
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name: MTEB AmazonPolarityClassification
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config: default
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split: test
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046
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metrics:
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- type: accuracy
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value: 92.84267499999999
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- type: ap
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value: 89.57568507997713
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- type: f1
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value: 92.82590734337774
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- task:
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type: Classification
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dataset:
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type: mteb/amazon_reviews_multi
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name: MTEB AmazonReviewsClassification (en)
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config: en
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split: test
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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metrics:
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- type: accuracy
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value: 48.292
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- type: f1
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value: 47.90257816032778
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- task:
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type: Retrieval
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dataset:
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type: arguana
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name: MTEB ArguAna
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 42.105
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- type: map_at_10
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value: 58.181000000000004
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- type: map_at_100
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value: 58.653999999999996
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- type: map_at_1000
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value: 58.657000000000004
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- type: map_at_3
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value: 54.386
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- type: map_at_5
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value: 56.757999999999996
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- type: mrr_at_1
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value: 42.745
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- type: mrr_at_10
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value: 58.437
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- type: mrr_at_100
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value: 58.894999999999996
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- type: mrr_at_1000
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value: 58.897999999999996
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- type: mrr_at_3
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value: 54.635
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- type: mrr_at_5
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value: 56.99999999999999
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- type: ndcg_at_1
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value: 42.105
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- type: ndcg_at_10
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value: 66.14999999999999
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- type: ndcg_at_100
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value: 68.048
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- type: ndcg_at_1000
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value: 68.11399999999999
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- type: ndcg_at_3
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value: 58.477000000000004
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- type: ndcg_at_5
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value: 62.768
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- type: precision_at_1
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value: 42.105
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- type: precision_at_10
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value: 9.110999999999999
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- type: precision_at_100
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value: 0.991
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- type: precision_at_1000
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value: 0.1
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- type: precision_at_3
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value: 23.447000000000003
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- type: precision_at_5
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value: 16.159000000000002
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- type: recall_at_1
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value: 42.105
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- type: recall_at_10
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value: 91.11
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- type: recall_at_100
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value: 99.14699999999999
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- type: recall_at_1000
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value: 99.644
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- type: recall_at_3
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value: 70.341
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- type: recall_at_5
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value: 80.797
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- task:
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type: Clustering
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dataset:
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type: mteb/arxiv-clustering-p2p
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name: MTEB ArxivClusteringP2P
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config: default
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split: test
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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metrics:
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- type: v_measure
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value: 49.02580759154173
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- task:
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type: Clustering
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dataset:
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type: mteb/arxiv-clustering-s2s
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name: MTEB ArxivClusteringS2S
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config: default
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split: test
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
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metrics:
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- type: v_measure
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value: 43.093601280163554
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- task:
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type: Reranking
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dataset:
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type: mteb/askubuntudupquestions-reranking
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name: MTEB AskUbuntuDupQuestions
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config: default
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split: test
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revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
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metrics:
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- type: map
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value: 64.19590406875427
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- type: mrr
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value: 77.09547992788991
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- task:
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type: STS
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dataset:
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type: mteb/biosses-sts
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name: MTEB BIOSSES
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config: default
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split: test
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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metrics:
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- type: cos_sim_pearson
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value: 87.86678362843676
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- type: cos_sim_spearman
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value: 86.1423242570783
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- type: euclidean_pearson
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value: 85.98994198511751
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- type: euclidean_spearman
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value: 86.48209103503942
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- type: manhattan_pearson
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value: 85.6446436316182
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- type: manhattan_spearman
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value: 86.21039809734357
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- task:
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type: Classification
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dataset:
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type: mteb/banking77
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name: MTEB Banking77Classification
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config: default
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split: test
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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metrics:
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- type: accuracy
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value: 87.69155844155844
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- type: f1
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value: 87.68109381943547
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- task:
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type: Clustering
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dataset:
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type: mteb/biorxiv-clustering-p2p
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name: MTEB BiorxivClusteringP2P
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config: default
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split: test
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
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metrics:
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- type: v_measure
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value: 39.37501687500394
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- task:
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type: Clustering
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dataset:
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type: mteb/biorxiv-clustering-s2s
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name: MTEB BiorxivClusteringS2S
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config: default
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split: test
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
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metrics:
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- type: v_measure
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value: 37.23401405155885
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackAndroidRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 30.232
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- type: map_at_10
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value: 41.404999999999994
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- type: map_at_100
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value: 42.896
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- type: map_at_1000
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value: 43.028
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- type: map_at_3
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value: 37.925
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- type: map_at_5
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value: 39.865
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- type: mrr_at_1
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value: 36.338
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- type: mrr_at_10
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value: 46.969
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- type: mrr_at_100
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value: 47.684
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- type: mrr_at_1000
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value: 47.731
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- type: mrr_at_3
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value: 44.063
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- type: mrr_at_5
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value: 45.908
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- type: ndcg_at_1
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value: 36.338
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- type: ndcg_at_10
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value: 47.887
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- type: ndcg_at_100
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value: 53.357
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- type: ndcg_at_1000
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value: 55.376999999999995
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- type: ndcg_at_3
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value: 42.588
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- type: ndcg_at_5
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value: 45.132
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- type: precision_at_1
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value: 36.338
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- type: precision_at_10
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value: 9.17
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- type: precision_at_100
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value: 1.4909999999999999
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- type: precision_at_1000
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value: 0.196
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- type: precision_at_3
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value: 20.315
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- type: precision_at_5
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value: 14.793000000000001
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- type: recall_at_1
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value: 30.232
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- type: recall_at_10
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value: 60.67399999999999
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- type: recall_at_100
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value: 83.628
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- type: recall_at_1000
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value: 96.209
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- type: recall_at_3
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value: 45.48
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- type: recall_at_5
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value: 52.354
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackEnglishRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 32.237
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- type: map_at_10
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value: 42.829
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- type: map_at_100
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value: 44.065
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- type: map_at_1000
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value: 44.199
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- type: map_at_3
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value: 39.885999999999996
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- type: map_at_5
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value: 41.55
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- type: mrr_at_1
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value: 40.064
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- type: mrr_at_10
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value: 48.611
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- type: mrr_at_100
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value: 49.245
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- type: mrr_at_1000
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value: 49.29
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- type: mrr_at_3
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value: 46.561
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- type: mrr_at_5
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value: 47.771
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- type: ndcg_at_1
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value: 40.064
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- type: ndcg_at_10
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value: 48.388
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- type: ndcg_at_100
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value: 52.666999999999994
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- type: ndcg_at_1000
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value: 54.67100000000001
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- type: ndcg_at_3
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value: 44.504
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- type: ndcg_at_5
|
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value: 46.303
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- type: precision_at_1
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value: 40.064
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- type: precision_at_10
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value: 9.051
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- type: precision_at_100
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value: 1.4500000000000002
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- type: precision_at_1000
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value: 0.193
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- type: precision_at_3
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value: 21.444
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- type: precision_at_5
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value: 15.045
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- type: recall_at_1
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value: 32.237
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- type: recall_at_10
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value: 57.943999999999996
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- type: recall_at_100
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value: 75.98700000000001
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- type: recall_at_1000
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value: 88.453
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- type: recall_at_3
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value: 46.268
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- type: recall_at_5
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value: 51.459999999999994
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGamingRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 38.797
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- type: map_at_10
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value: 51.263000000000005
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- type: map_at_100
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value: 52.333
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- type: map_at_1000
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value: 52.393
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- type: map_at_3
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value: 47.936
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- type: map_at_5
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value: 49.844
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- type: mrr_at_1
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value: 44.389
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- type: mrr_at_10
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value: 54.601
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- type: mrr_at_100
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value: 55.300000000000004
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- type: mrr_at_1000
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value: 55.333
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- type: mrr_at_3
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value: 52.068999999999996
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- type: mrr_at_5
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value: 53.627
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- type: ndcg_at_1
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value: 44.389
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- type: ndcg_at_10
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value: 57.193000000000005
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- type: ndcg_at_100
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value: 61.307
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- type: ndcg_at_1000
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value: 62.529
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- type: ndcg_at_3
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value: 51.607
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- type: ndcg_at_5
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value: 54.409
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- type: precision_at_1
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value: 44.389
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- type: precision_at_10
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value: 9.26
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- type: precision_at_100
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value: 1.222
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- type: precision_at_1000
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value: 0.13699999999999998
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- type: precision_at_3
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value: 23.03
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- type: precision_at_5
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value: 15.887
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- type: recall_at_1
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value: 38.797
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- type: recall_at_10
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value: 71.449
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- type: recall_at_100
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value: 88.881
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- type: recall_at_1000
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value: 97.52
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- type: recall_at_3
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value: 56.503
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- type: recall_at_5
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value: 63.392
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- task:
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type: Retrieval
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dataset:
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type: BeIR/cqadupstack
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name: MTEB CQADupstackGisRetrieval
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config: default
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split: test
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revision: None
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metrics:
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- type: map_at_1
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value: 27.291999999999998
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- type: map_at_10
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value: 35.65
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- type: map_at_100
|
|
value: 36.689
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- type: map_at_1000
|
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value: 36.753
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- type: map_at_3
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value: 32.995000000000005
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- type: map_at_5
|
|
value: 34.409
|
|
- type: mrr_at_1
|
|
value: 29.04
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|
- type: mrr_at_10
|
|
value: 37.486000000000004
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|
- type: mrr_at_100
|
|
value: 38.394
|
|
- type: mrr_at_1000
|
|
value: 38.445
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|
- type: mrr_at_3
|
|
value: 35.028
|
|
- type: mrr_at_5
|
|
value: 36.305
|
|
- type: ndcg_at_1
|
|
value: 29.04
|
|
- type: ndcg_at_10
|
|
value: 40.613
|
|
- type: ndcg_at_100
|
|
value: 45.733000000000004
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|
- type: ndcg_at_1000
|
|
value: 47.447
|
|
- type: ndcg_at_3
|
|
value: 35.339999999999996
|
|
- type: ndcg_at_5
|
|
value: 37.706
|
|
- type: precision_at_1
|
|
value: 29.04
|
|
- type: precision_at_10
|
|
value: 6.192
|
|
- type: precision_at_100
|
|
value: 0.9249999999999999
|
|
- type: precision_at_1000
|
|
value: 0.11
|
|
- type: precision_at_3
|
|
value: 14.802000000000001
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|
- type: precision_at_5
|
|
value: 10.305
|
|
- type: recall_at_1
|
|
value: 27.291999999999998
|
|
- type: recall_at_10
|
|
value: 54.25299999999999
|
|
- type: recall_at_100
|
|
value: 77.773
|
|
- type: recall_at_1000
|
|
value: 90.795
|
|
- type: recall_at_3
|
|
value: 39.731
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- type: recall_at_5
|
|
value: 45.403999999999996
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|
- task:
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type: Retrieval
|
|
dataset:
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type: BeIR/cqadupstack
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|
name: MTEB CQADupstackMathematicaRetrieval
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config: default
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split: test
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revision: None
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|
metrics:
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|
- type: map_at_1
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|
value: 18.326
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|
- type: map_at_10
|
|
value: 26.290999999999997
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|
- type: map_at_100
|
|
value: 27.456999999999997
|
|
- type: map_at_1000
|
|
value: 27.583000000000002
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- type: map_at_3
|
|
value: 23.578
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- type: map_at_5
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|
value: 25.113000000000003
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- type: mrr_at_1
|
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value: 22.637
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- type: mrr_at_10
|
|
value: 31.139
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- type: mrr_at_100
|
|
value: 32.074999999999996
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- type: mrr_at_1000
|
|
value: 32.147
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- type: mrr_at_3
|
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value: 28.483000000000004
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- type: mrr_at_5
|
|
value: 29.963
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|
- type: ndcg_at_1
|
|
value: 22.637
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- type: ndcg_at_10
|
|
value: 31.717000000000002
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- type: ndcg_at_100
|
|
value: 37.201
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- type: ndcg_at_1000
|
|
value: 40.088
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- type: ndcg_at_3
|
|
value: 26.686
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- type: ndcg_at_5
|
|
value: 29.076999999999998
|
|
- type: precision_at_1
|
|
value: 22.637
|
|
- type: precision_at_10
|
|
value: 5.7090000000000005
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|
- type: precision_at_100
|
|
value: 0.979
|
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- type: precision_at_1000
|
|
value: 0.13799999999999998
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|
- type: precision_at_3
|
|
value: 12.894
|
|
- type: precision_at_5
|
|
value: 9.328
|
|
- type: recall_at_1
|
|
value: 18.326
|
|
- type: recall_at_10
|
|
value: 43.824999999999996
|
|
- type: recall_at_100
|
|
value: 67.316
|
|
- type: recall_at_1000
|
|
value: 87.481
|
|
- type: recall_at_3
|
|
value: 29.866999999999997
|
|
- type: recall_at_5
|
|
value: 35.961999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 29.875
|
|
- type: map_at_10
|
|
value: 40.458
|
|
- type: map_at_100
|
|
value: 41.772
|
|
- type: map_at_1000
|
|
value: 41.882999999999996
|
|
- type: map_at_3
|
|
value: 37.086999999999996
|
|
- type: map_at_5
|
|
value: 39.153
|
|
- type: mrr_at_1
|
|
value: 36.381
|
|
- type: mrr_at_10
|
|
value: 46.190999999999995
|
|
- type: mrr_at_100
|
|
value: 46.983999999999995
|
|
- type: mrr_at_1000
|
|
value: 47.032000000000004
|
|
- type: mrr_at_3
|
|
value: 43.486999999999995
|
|
- type: mrr_at_5
|
|
value: 45.249
|
|
- type: ndcg_at_1
|
|
value: 36.381
|
|
- type: ndcg_at_10
|
|
value: 46.602
|
|
- type: ndcg_at_100
|
|
value: 51.885999999999996
|
|
- type: ndcg_at_1000
|
|
value: 53.895
|
|
- type: ndcg_at_3
|
|
value: 41.155
|
|
- type: ndcg_at_5
|
|
value: 44.182
|
|
- type: precision_at_1
|
|
value: 36.381
|
|
- type: precision_at_10
|
|
value: 8.402
|
|
- type: precision_at_100
|
|
value: 1.278
|
|
- type: precision_at_1000
|
|
value: 0.16199999999999998
|
|
- type: precision_at_3
|
|
value: 19.346
|
|
- type: precision_at_5
|
|
value: 14.09
|
|
- type: recall_at_1
|
|
value: 29.875
|
|
- type: recall_at_10
|
|
value: 59.065999999999995
|
|
- type: recall_at_100
|
|
value: 80.923
|
|
- type: recall_at_1000
|
|
value: 93.927
|
|
- type: recall_at_3
|
|
value: 44.462
|
|
- type: recall_at_5
|
|
value: 51.89
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.94
|
|
- type: map_at_10
|
|
value: 35.125
|
|
- type: map_at_100
|
|
value: 36.476
|
|
- type: map_at_1000
|
|
value: 36.579
|
|
- type: map_at_3
|
|
value: 31.840000000000003
|
|
- type: map_at_5
|
|
value: 33.647
|
|
- type: mrr_at_1
|
|
value: 30.936000000000003
|
|
- type: mrr_at_10
|
|
value: 40.637
|
|
- type: mrr_at_100
|
|
value: 41.471000000000004
|
|
- type: mrr_at_1000
|
|
value: 41.525
|
|
- type: mrr_at_3
|
|
value: 38.013999999999996
|
|
- type: mrr_at_5
|
|
value: 39.469
|
|
- type: ndcg_at_1
|
|
value: 30.936000000000003
|
|
- type: ndcg_at_10
|
|
value: 41.295
|
|
- type: ndcg_at_100
|
|
value: 46.92
|
|
- type: ndcg_at_1000
|
|
value: 49.183
|
|
- type: ndcg_at_3
|
|
value: 35.811
|
|
- type: ndcg_at_5
|
|
value: 38.306000000000004
|
|
- type: precision_at_1
|
|
value: 30.936000000000003
|
|
- type: precision_at_10
|
|
value: 7.728
|
|
- type: precision_at_100
|
|
value: 1.226
|
|
- type: precision_at_1000
|
|
value: 0.158
|
|
- type: precision_at_3
|
|
value: 17.237
|
|
- type: precision_at_5
|
|
value: 12.42
|
|
- type: recall_at_1
|
|
value: 24.94
|
|
- type: recall_at_10
|
|
value: 54.235
|
|
- type: recall_at_100
|
|
value: 78.314
|
|
- type: recall_at_1000
|
|
value: 93.973
|
|
- type: recall_at_3
|
|
value: 38.925
|
|
- type: recall_at_5
|
|
value: 45.505
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.250833333333333
|
|
- type: map_at_10
|
|
value: 35.46875
|
|
- type: map_at_100
|
|
value: 36.667
|
|
- type: map_at_1000
|
|
value: 36.78025
|
|
- type: map_at_3
|
|
value: 32.56733333333334
|
|
- type: map_at_5
|
|
value: 34.20333333333333
|
|
- type: mrr_at_1
|
|
value: 30.8945
|
|
- type: mrr_at_10
|
|
value: 39.636833333333335
|
|
- type: mrr_at_100
|
|
value: 40.46508333333333
|
|
- type: mrr_at_1000
|
|
value: 40.521249999999995
|
|
- type: mrr_at_3
|
|
value: 37.140166666666666
|
|
- type: mrr_at_5
|
|
value: 38.60999999999999
|
|
- type: ndcg_at_1
|
|
value: 30.8945
|
|
- type: ndcg_at_10
|
|
value: 40.93441666666667
|
|
- type: ndcg_at_100
|
|
value: 46.062416666666664
|
|
- type: ndcg_at_1000
|
|
value: 48.28341666666667
|
|
- type: ndcg_at_3
|
|
value: 35.97575
|
|
- type: ndcg_at_5
|
|
value: 38.3785
|
|
- type: precision_at_1
|
|
value: 30.8945
|
|
- type: precision_at_10
|
|
value: 7.180250000000001
|
|
- type: precision_at_100
|
|
value: 1.1468333333333334
|
|
- type: precision_at_1000
|
|
value: 0.15283333333333332
|
|
- type: precision_at_3
|
|
value: 16.525583333333334
|
|
- type: precision_at_5
|
|
value: 11.798333333333332
|
|
- type: recall_at_1
|
|
value: 26.250833333333333
|
|
- type: recall_at_10
|
|
value: 52.96108333333333
|
|
- type: recall_at_100
|
|
value: 75.45908333333334
|
|
- type: recall_at_1000
|
|
value: 90.73924999999998
|
|
- type: recall_at_3
|
|
value: 39.25483333333333
|
|
- type: recall_at_5
|
|
value: 45.37950000000001
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.595
|
|
- type: map_at_10
|
|
value: 31.747999999999998
|
|
- type: map_at_100
|
|
value: 32.62
|
|
- type: map_at_1000
|
|
value: 32.713
|
|
- type: map_at_3
|
|
value: 29.48
|
|
- type: map_at_5
|
|
value: 30.635
|
|
- type: mrr_at_1
|
|
value: 27.607
|
|
- type: mrr_at_10
|
|
value: 34.449000000000005
|
|
- type: mrr_at_100
|
|
value: 35.182
|
|
- type: mrr_at_1000
|
|
value: 35.254000000000005
|
|
- type: mrr_at_3
|
|
value: 32.413
|
|
- type: mrr_at_5
|
|
value: 33.372
|
|
- type: ndcg_at_1
|
|
value: 27.607
|
|
- type: ndcg_at_10
|
|
value: 36.041000000000004
|
|
- type: ndcg_at_100
|
|
value: 40.514
|
|
- type: ndcg_at_1000
|
|
value: 42.851
|
|
- type: ndcg_at_3
|
|
value: 31.689
|
|
- type: ndcg_at_5
|
|
value: 33.479
|
|
- type: precision_at_1
|
|
value: 27.607
|
|
- type: precision_at_10
|
|
value: 5.66
|
|
- type: precision_at_100
|
|
value: 0.868
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 13.446
|
|
- type: precision_at_5
|
|
value: 9.264
|
|
- type: recall_at_1
|
|
value: 24.595
|
|
- type: recall_at_10
|
|
value: 46.79
|
|
- type: recall_at_100
|
|
value: 67.413
|
|
- type: recall_at_1000
|
|
value: 84.753
|
|
- type: recall_at_3
|
|
value: 34.644999999999996
|
|
- type: recall_at_5
|
|
value: 39.09
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.333000000000002
|
|
- type: map_at_10
|
|
value: 24.427
|
|
- type: map_at_100
|
|
value: 25.576
|
|
- type: map_at_1000
|
|
value: 25.692999999999998
|
|
- type: map_at_3
|
|
value: 22.002
|
|
- type: map_at_5
|
|
value: 23.249
|
|
- type: mrr_at_1
|
|
value: 20.716
|
|
- type: mrr_at_10
|
|
value: 28.072000000000003
|
|
- type: mrr_at_100
|
|
value: 29.067
|
|
- type: mrr_at_1000
|
|
value: 29.137
|
|
- type: mrr_at_3
|
|
value: 25.832
|
|
- type: mrr_at_5
|
|
value: 27.045
|
|
- type: ndcg_at_1
|
|
value: 20.716
|
|
- type: ndcg_at_10
|
|
value: 29.109
|
|
- type: ndcg_at_100
|
|
value: 34.797
|
|
- type: ndcg_at_1000
|
|
value: 37.503
|
|
- type: ndcg_at_3
|
|
value: 24.668
|
|
- type: ndcg_at_5
|
|
value: 26.552999999999997
|
|
- type: precision_at_1
|
|
value: 20.716
|
|
- type: precision_at_10
|
|
value: 5.351
|
|
- type: precision_at_100
|
|
value: 0.955
|
|
- type: precision_at_1000
|
|
value: 0.136
|
|
- type: precision_at_3
|
|
value: 11.584999999999999
|
|
- type: precision_at_5
|
|
value: 8.362
|
|
- type: recall_at_1
|
|
value: 17.333000000000002
|
|
- type: recall_at_10
|
|
value: 39.604
|
|
- type: recall_at_100
|
|
value: 65.525
|
|
- type: recall_at_1000
|
|
value: 84.651
|
|
- type: recall_at_3
|
|
value: 27.199
|
|
- type: recall_at_5
|
|
value: 32.019
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.342
|
|
- type: map_at_10
|
|
value: 35.349000000000004
|
|
- type: map_at_100
|
|
value: 36.443
|
|
- type: map_at_1000
|
|
value: 36.548
|
|
- type: map_at_3
|
|
value: 32.307
|
|
- type: map_at_5
|
|
value: 34.164
|
|
- type: mrr_at_1
|
|
value: 31.063000000000002
|
|
- type: mrr_at_10
|
|
value: 39.703
|
|
- type: mrr_at_100
|
|
value: 40.555
|
|
- type: mrr_at_1000
|
|
value: 40.614
|
|
- type: mrr_at_3
|
|
value: 37.141999999999996
|
|
- type: mrr_at_5
|
|
value: 38.812000000000005
|
|
- type: ndcg_at_1
|
|
value: 31.063000000000002
|
|
- type: ndcg_at_10
|
|
value: 40.873
|
|
- type: ndcg_at_100
|
|
value: 45.896
|
|
- type: ndcg_at_1000
|
|
value: 48.205999999999996
|
|
- type: ndcg_at_3
|
|
value: 35.522
|
|
- type: ndcg_at_5
|
|
value: 38.419
|
|
- type: precision_at_1
|
|
value: 31.063000000000002
|
|
- type: precision_at_10
|
|
value: 6.866
|
|
- type: precision_at_100
|
|
value: 1.053
|
|
- type: precision_at_1000
|
|
value: 0.13699999999999998
|
|
- type: precision_at_3
|
|
value: 16.014
|
|
- type: precision_at_5
|
|
value: 11.604000000000001
|
|
- type: recall_at_1
|
|
value: 26.342
|
|
- type: recall_at_10
|
|
value: 53.40200000000001
|
|
- type: recall_at_100
|
|
value: 75.251
|
|
- type: recall_at_1000
|
|
value: 91.13799999999999
|
|
- type: recall_at_3
|
|
value: 39.103
|
|
- type: recall_at_5
|
|
value: 46.357
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.71
|
|
- type: map_at_10
|
|
value: 32.153999999999996
|
|
- type: map_at_100
|
|
value: 33.821
|
|
- type: map_at_1000
|
|
value: 34.034
|
|
- type: map_at_3
|
|
value: 29.376
|
|
- type: map_at_5
|
|
value: 30.878
|
|
- type: mrr_at_1
|
|
value: 28.458
|
|
- type: mrr_at_10
|
|
value: 36.775999999999996
|
|
- type: mrr_at_100
|
|
value: 37.804
|
|
- type: mrr_at_1000
|
|
value: 37.858999999999995
|
|
- type: mrr_at_3
|
|
value: 34.123999999999995
|
|
- type: mrr_at_5
|
|
value: 35.596
|
|
- type: ndcg_at_1
|
|
value: 28.458
|
|
- type: ndcg_at_10
|
|
value: 37.858999999999995
|
|
- type: ndcg_at_100
|
|
value: 44.194
|
|
- type: ndcg_at_1000
|
|
value: 46.744
|
|
- type: ndcg_at_3
|
|
value: 33.348
|
|
- type: ndcg_at_5
|
|
value: 35.448
|
|
- type: precision_at_1
|
|
value: 28.458
|
|
- type: precision_at_10
|
|
value: 7.4510000000000005
|
|
- type: precision_at_100
|
|
value: 1.5
|
|
- type: precision_at_1000
|
|
value: 0.23700000000000002
|
|
- type: precision_at_3
|
|
value: 15.809999999999999
|
|
- type: precision_at_5
|
|
value: 11.462
|
|
- type: recall_at_1
|
|
value: 23.71
|
|
- type: recall_at_10
|
|
value: 48.272999999999996
|
|
- type: recall_at_100
|
|
value: 77.134
|
|
- type: recall_at_1000
|
|
value: 93.001
|
|
- type: recall_at_3
|
|
value: 35.480000000000004
|
|
- type: recall_at_5
|
|
value: 41.19
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 21.331
|
|
- type: map_at_10
|
|
value: 28.926000000000002
|
|
- type: map_at_100
|
|
value: 29.855999999999998
|
|
- type: map_at_1000
|
|
value: 29.957
|
|
- type: map_at_3
|
|
value: 26.395999999999997
|
|
- type: map_at_5
|
|
value: 27.933000000000003
|
|
- type: mrr_at_1
|
|
value: 23.105
|
|
- type: mrr_at_10
|
|
value: 31.008000000000003
|
|
- type: mrr_at_100
|
|
value: 31.819999999999997
|
|
- type: mrr_at_1000
|
|
value: 31.887999999999998
|
|
- type: mrr_at_3
|
|
value: 28.466
|
|
- type: mrr_at_5
|
|
value: 30.203000000000003
|
|
- type: ndcg_at_1
|
|
value: 23.105
|
|
- type: ndcg_at_10
|
|
value: 33.635999999999996
|
|
- type: ndcg_at_100
|
|
value: 38.277
|
|
- type: ndcg_at_1000
|
|
value: 40.907
|
|
- type: ndcg_at_3
|
|
value: 28.791
|
|
- type: ndcg_at_5
|
|
value: 31.528
|
|
- type: precision_at_1
|
|
value: 23.105
|
|
- type: precision_at_10
|
|
value: 5.323
|
|
- type: precision_at_100
|
|
value: 0.815
|
|
- type: precision_at_1000
|
|
value: 0.117
|
|
- type: precision_at_3
|
|
value: 12.384
|
|
- type: precision_at_5
|
|
value: 9.02
|
|
- type: recall_at_1
|
|
value: 21.331
|
|
- type: recall_at_10
|
|
value: 46.018
|
|
- type: recall_at_100
|
|
value: 67.364
|
|
- type: recall_at_1000
|
|
value: 86.97
|
|
- type: recall_at_3
|
|
value: 33.395
|
|
- type: recall_at_5
|
|
value: 39.931
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.011000000000003
|
|
- type: map_at_10
|
|
value: 28.816999999999997
|
|
- type: map_at_100
|
|
value: 30.761
|
|
- type: map_at_1000
|
|
value: 30.958000000000002
|
|
- type: map_at_3
|
|
value: 24.044999999999998
|
|
- type: map_at_5
|
|
value: 26.557
|
|
- type: mrr_at_1
|
|
value: 38.696999999999996
|
|
- type: mrr_at_10
|
|
value: 50.464
|
|
- type: mrr_at_100
|
|
value: 51.193999999999996
|
|
- type: mrr_at_1000
|
|
value: 51.219
|
|
- type: mrr_at_3
|
|
value: 47.339999999999996
|
|
- type: mrr_at_5
|
|
value: 49.346000000000004
|
|
- type: ndcg_at_1
|
|
value: 38.696999999999996
|
|
- type: ndcg_at_10
|
|
value: 38.53
|
|
- type: ndcg_at_100
|
|
value: 45.525
|
|
- type: ndcg_at_1000
|
|
value: 48.685
|
|
- type: ndcg_at_3
|
|
value: 32.282
|
|
- type: ndcg_at_5
|
|
value: 34.482
|
|
- type: precision_at_1
|
|
value: 38.696999999999996
|
|
- type: precision_at_10
|
|
value: 11.895999999999999
|
|
- type: precision_at_100
|
|
value: 1.95
|
|
- type: precision_at_1000
|
|
value: 0.254
|
|
- type: precision_at_3
|
|
value: 24.038999999999998
|
|
- type: precision_at_5
|
|
value: 18.332
|
|
- type: recall_at_1
|
|
value: 17.011000000000003
|
|
- type: recall_at_10
|
|
value: 44.452999999999996
|
|
- type: recall_at_100
|
|
value: 68.223
|
|
- type: recall_at_1000
|
|
value: 85.653
|
|
- type: recall_at_3
|
|
value: 28.784
|
|
- type: recall_at_5
|
|
value: 35.66
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 9.516
|
|
- type: map_at_10
|
|
value: 21.439
|
|
- type: map_at_100
|
|
value: 31.517
|
|
- type: map_at_1000
|
|
value: 33.267
|
|
- type: map_at_3
|
|
value: 15.004999999999999
|
|
- type: map_at_5
|
|
value: 17.793999999999997
|
|
- type: mrr_at_1
|
|
value: 71.25
|
|
- type: mrr_at_10
|
|
value: 79.071
|
|
- type: mrr_at_100
|
|
value: 79.325
|
|
- type: mrr_at_1000
|
|
value: 79.33
|
|
- type: mrr_at_3
|
|
value: 77.708
|
|
- type: mrr_at_5
|
|
value: 78.546
|
|
- type: ndcg_at_1
|
|
value: 58.62500000000001
|
|
- type: ndcg_at_10
|
|
value: 44.889
|
|
- type: ndcg_at_100
|
|
value: 50.536
|
|
- type: ndcg_at_1000
|
|
value: 57.724
|
|
- type: ndcg_at_3
|
|
value: 49.32
|
|
- type: ndcg_at_5
|
|
value: 46.775
|
|
- type: precision_at_1
|
|
value: 71.25
|
|
- type: precision_at_10
|
|
value: 36.175000000000004
|
|
- type: precision_at_100
|
|
value: 11.940000000000001
|
|
- type: precision_at_1000
|
|
value: 2.178
|
|
- type: precision_at_3
|
|
value: 53.583000000000006
|
|
- type: precision_at_5
|
|
value: 45.550000000000004
|
|
- type: recall_at_1
|
|
value: 9.516
|
|
- type: recall_at_10
|
|
value: 27.028000000000002
|
|
- type: recall_at_100
|
|
value: 57.581
|
|
- type: recall_at_1000
|
|
value: 80.623
|
|
- type: recall_at_3
|
|
value: 16.313
|
|
- type: recall_at_5
|
|
value: 20.674
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 51.74999999999999
|
|
- type: f1
|
|
value: 46.46706502669774
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 77.266
|
|
- type: map_at_10
|
|
value: 84.89999999999999
|
|
- type: map_at_100
|
|
value: 85.109
|
|
- type: map_at_1000
|
|
value: 85.123
|
|
- type: map_at_3
|
|
value: 83.898
|
|
- type: map_at_5
|
|
value: 84.541
|
|
- type: mrr_at_1
|
|
value: 83.138
|
|
- type: mrr_at_10
|
|
value: 89.37
|
|
- type: mrr_at_100
|
|
value: 89.432
|
|
- type: mrr_at_1000
|
|
value: 89.43299999999999
|
|
- type: mrr_at_3
|
|
value: 88.836
|
|
- type: mrr_at_5
|
|
value: 89.21
|
|
- type: ndcg_at_1
|
|
value: 83.138
|
|
- type: ndcg_at_10
|
|
value: 88.244
|
|
- type: ndcg_at_100
|
|
value: 88.98700000000001
|
|
- type: ndcg_at_1000
|
|
value: 89.21900000000001
|
|
- type: ndcg_at_3
|
|
value: 86.825
|
|
- type: ndcg_at_5
|
|
value: 87.636
|
|
- type: precision_at_1
|
|
value: 83.138
|
|
- type: precision_at_10
|
|
value: 10.47
|
|
- type: precision_at_100
|
|
value: 1.1079999999999999
|
|
- type: precision_at_1000
|
|
value: 0.11499999999999999
|
|
- type: precision_at_3
|
|
value: 32.933
|
|
- type: precision_at_5
|
|
value: 20.36
|
|
- type: recall_at_1
|
|
value: 77.266
|
|
- type: recall_at_10
|
|
value: 94.063
|
|
- type: recall_at_100
|
|
value: 96.993
|
|
- type: recall_at_1000
|
|
value: 98.414
|
|
- type: recall_at_3
|
|
value: 90.228
|
|
- type: recall_at_5
|
|
value: 92.328
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.319
|
|
- type: map_at_10
|
|
value: 36.943
|
|
- type: map_at_100
|
|
value: 38.951
|
|
- type: map_at_1000
|
|
value: 39.114
|
|
- type: map_at_3
|
|
value: 32.82
|
|
- type: map_at_5
|
|
value: 34.945
|
|
- type: mrr_at_1
|
|
value: 44.135999999999996
|
|
- type: mrr_at_10
|
|
value: 53.071999999999996
|
|
- type: mrr_at_100
|
|
value: 53.87
|
|
- type: mrr_at_1000
|
|
value: 53.90200000000001
|
|
- type: mrr_at_3
|
|
value: 50.77199999999999
|
|
- type: mrr_at_5
|
|
value: 52.129999999999995
|
|
- type: ndcg_at_1
|
|
value: 44.135999999999996
|
|
- type: ndcg_at_10
|
|
value: 44.836
|
|
- type: ndcg_at_100
|
|
value: 51.754
|
|
- type: ndcg_at_1000
|
|
value: 54.36
|
|
- type: ndcg_at_3
|
|
value: 41.658
|
|
- type: ndcg_at_5
|
|
value: 42.354
|
|
- type: precision_at_1
|
|
value: 44.135999999999996
|
|
- type: precision_at_10
|
|
value: 12.284
|
|
- type: precision_at_100
|
|
value: 1.952
|
|
- type: precision_at_1000
|
|
value: 0.242
|
|
- type: precision_at_3
|
|
value: 27.828999999999997
|
|
- type: precision_at_5
|
|
value: 20.093
|
|
- type: recall_at_1
|
|
value: 22.319
|
|
- type: recall_at_10
|
|
value: 51.528
|
|
- type: recall_at_100
|
|
value: 76.70700000000001
|
|
- type: recall_at_1000
|
|
value: 92.143
|
|
- type: recall_at_3
|
|
value: 38.641
|
|
- type: recall_at_5
|
|
value: 43.653999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 40.182
|
|
- type: map_at_10
|
|
value: 65.146
|
|
- type: map_at_100
|
|
value: 66.023
|
|
- type: map_at_1000
|
|
value: 66.078
|
|
- type: map_at_3
|
|
value: 61.617999999999995
|
|
- type: map_at_5
|
|
value: 63.82299999999999
|
|
- type: mrr_at_1
|
|
value: 80.365
|
|
- type: mrr_at_10
|
|
value: 85.79
|
|
- type: mrr_at_100
|
|
value: 85.963
|
|
- type: mrr_at_1000
|
|
value: 85.968
|
|
- type: mrr_at_3
|
|
value: 84.952
|
|
- type: mrr_at_5
|
|
value: 85.503
|
|
- type: ndcg_at_1
|
|
value: 80.365
|
|
- type: ndcg_at_10
|
|
value: 73.13499999999999
|
|
- type: ndcg_at_100
|
|
value: 76.133
|
|
- type: ndcg_at_1000
|
|
value: 77.151
|
|
- type: ndcg_at_3
|
|
value: 68.255
|
|
- type: ndcg_at_5
|
|
value: 70.978
|
|
- type: precision_at_1
|
|
value: 80.365
|
|
- type: precision_at_10
|
|
value: 15.359
|
|
- type: precision_at_100
|
|
value: 1.7690000000000001
|
|
- type: precision_at_1000
|
|
value: 0.19
|
|
- type: precision_at_3
|
|
value: 44.024
|
|
- type: precision_at_5
|
|
value: 28.555999999999997
|
|
- type: recall_at_1
|
|
value: 40.182
|
|
- type: recall_at_10
|
|
value: 76.793
|
|
- type: recall_at_100
|
|
value: 88.474
|
|
- type: recall_at_1000
|
|
value: 95.159
|
|
- type: recall_at_3
|
|
value: 66.036
|
|
- type: recall_at_5
|
|
value: 71.391
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.7796
|
|
- type: ap
|
|
value: 89.24883716810874
|
|
- type: f1
|
|
value: 92.7706903433313
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.016
|
|
- type: map_at_10
|
|
value: 34.408
|
|
- type: map_at_100
|
|
value: 35.592
|
|
- type: map_at_1000
|
|
value: 35.64
|
|
- type: map_at_3
|
|
value: 30.459999999999997
|
|
- type: map_at_5
|
|
value: 32.721000000000004
|
|
- type: mrr_at_1
|
|
value: 22.593
|
|
- type: mrr_at_10
|
|
value: 34.993
|
|
- type: mrr_at_100
|
|
value: 36.113
|
|
- type: mrr_at_1000
|
|
value: 36.156
|
|
- type: mrr_at_3
|
|
value: 31.101
|
|
- type: mrr_at_5
|
|
value: 33.364
|
|
- type: ndcg_at_1
|
|
value: 22.579
|
|
- type: ndcg_at_10
|
|
value: 41.404999999999994
|
|
- type: ndcg_at_100
|
|
value: 47.018
|
|
- type: ndcg_at_1000
|
|
value: 48.211999999999996
|
|
- type: ndcg_at_3
|
|
value: 33.389
|
|
- type: ndcg_at_5
|
|
value: 37.425000000000004
|
|
- type: precision_at_1
|
|
value: 22.579
|
|
- type: precision_at_10
|
|
value: 6.59
|
|
- type: precision_at_100
|
|
value: 0.938
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 14.241000000000001
|
|
- type: precision_at_5
|
|
value: 10.59
|
|
- type: recall_at_1
|
|
value: 22.016
|
|
- type: recall_at_10
|
|
value: 62.927
|
|
- type: recall_at_100
|
|
value: 88.72
|
|
- type: recall_at_1000
|
|
value: 97.80799999999999
|
|
- type: recall_at_3
|
|
value: 41.229
|
|
- type: recall_at_5
|
|
value: 50.88
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.01732786137711
|
|
- type: f1
|
|
value: 93.76353126402202
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.91746466028272
|
|
- type: f1
|
|
value: 57.715651682646765
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.5030262273033
|
|
- type: f1
|
|
value: 74.6693629986121
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 79.74781439139207
|
|
- type: f1
|
|
value: 79.96684171018774
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 33.2156206892017
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 31.180539484816137
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 32.51125957874274
|
|
- type: mrr
|
|
value: 33.777037359249995
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 7.248
|
|
- type: map_at_10
|
|
value: 15.340000000000002
|
|
- type: map_at_100
|
|
value: 19.591
|
|
- type: map_at_1000
|
|
value: 21.187
|
|
- type: map_at_3
|
|
value: 11.329
|
|
- type: map_at_5
|
|
value: 13.209999999999999
|
|
- type: mrr_at_1
|
|
value: 47.678
|
|
- type: mrr_at_10
|
|
value: 57.493
|
|
- type: mrr_at_100
|
|
value: 58.038999999999994
|
|
- type: mrr_at_1000
|
|
value: 58.07
|
|
- type: mrr_at_3
|
|
value: 55.36600000000001
|
|
- type: mrr_at_5
|
|
value: 56.635999999999996
|
|
- type: ndcg_at_1
|
|
value: 46.129999999999995
|
|
- type: ndcg_at_10
|
|
value: 38.653999999999996
|
|
- type: ndcg_at_100
|
|
value: 36.288
|
|
- type: ndcg_at_1000
|
|
value: 44.765
|
|
- type: ndcg_at_3
|
|
value: 43.553
|
|
- type: ndcg_at_5
|
|
value: 41.317
|
|
- type: precision_at_1
|
|
value: 47.368
|
|
- type: precision_at_10
|
|
value: 28.669
|
|
- type: precision_at_100
|
|
value: 9.158
|
|
- type: precision_at_1000
|
|
value: 2.207
|
|
- type: precision_at_3
|
|
value: 40.97
|
|
- type: precision_at_5
|
|
value: 35.604
|
|
- type: recall_at_1
|
|
value: 7.248
|
|
- type: recall_at_10
|
|
value: 19.46
|
|
- type: recall_at_100
|
|
value: 37.214000000000006
|
|
- type: recall_at_1000
|
|
value: 67.64099999999999
|
|
- type: recall_at_3
|
|
value: 12.025
|
|
- type: recall_at_5
|
|
value: 15.443999999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 31.595000000000002
|
|
- type: map_at_10
|
|
value: 47.815999999999995
|
|
- type: map_at_100
|
|
value: 48.811
|
|
- type: map_at_1000
|
|
value: 48.835
|
|
- type: map_at_3
|
|
value: 43.225
|
|
- type: map_at_5
|
|
value: 46.017
|
|
- type: mrr_at_1
|
|
value: 35.689
|
|
- type: mrr_at_10
|
|
value: 50.341
|
|
- type: mrr_at_100
|
|
value: 51.044999999999995
|
|
- type: mrr_at_1000
|
|
value: 51.062
|
|
- type: mrr_at_3
|
|
value: 46.553
|
|
- type: mrr_at_5
|
|
value: 48.918
|
|
- type: ndcg_at_1
|
|
value: 35.66
|
|
- type: ndcg_at_10
|
|
value: 55.859
|
|
- type: ndcg_at_100
|
|
value: 59.864
|
|
- type: ndcg_at_1000
|
|
value: 60.419999999999995
|
|
- type: ndcg_at_3
|
|
value: 47.371
|
|
- type: ndcg_at_5
|
|
value: 51.995000000000005
|
|
- type: precision_at_1
|
|
value: 35.66
|
|
- type: precision_at_10
|
|
value: 9.27
|
|
- type: precision_at_100
|
|
value: 1.1520000000000001
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 21.63
|
|
- type: precision_at_5
|
|
value: 15.655
|
|
- type: recall_at_1
|
|
value: 31.595000000000002
|
|
- type: recall_at_10
|
|
value: 77.704
|
|
- type: recall_at_100
|
|
value: 94.774
|
|
- type: recall_at_1000
|
|
value: 98.919
|
|
- type: recall_at_3
|
|
value: 56.052
|
|
- type: recall_at_5
|
|
value: 66.623
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 71.489
|
|
- type: map_at_10
|
|
value: 85.411
|
|
- type: map_at_100
|
|
value: 86.048
|
|
- type: map_at_1000
|
|
value: 86.064
|
|
- type: map_at_3
|
|
value: 82.587
|
|
- type: map_at_5
|
|
value: 84.339
|
|
- type: mrr_at_1
|
|
value: 82.28
|
|
- type: mrr_at_10
|
|
value: 88.27199999999999
|
|
- type: mrr_at_100
|
|
value: 88.362
|
|
- type: mrr_at_1000
|
|
value: 88.362
|
|
- type: mrr_at_3
|
|
value: 87.372
|
|
- type: mrr_at_5
|
|
value: 87.995
|
|
- type: ndcg_at_1
|
|
value: 82.27
|
|
- type: ndcg_at_10
|
|
value: 89.023
|
|
- type: ndcg_at_100
|
|
value: 90.191
|
|
- type: ndcg_at_1000
|
|
value: 90.266
|
|
- type: ndcg_at_3
|
|
value: 86.37
|
|
- type: ndcg_at_5
|
|
value: 87.804
|
|
- type: precision_at_1
|
|
value: 82.27
|
|
- type: precision_at_10
|
|
value: 13.469000000000001
|
|
- type: precision_at_100
|
|
value: 1.533
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_3
|
|
value: 37.797
|
|
- type: precision_at_5
|
|
value: 24.734
|
|
- type: recall_at_1
|
|
value: 71.489
|
|
- type: recall_at_10
|
|
value: 95.824
|
|
- type: recall_at_100
|
|
value: 99.70599999999999
|
|
- type: recall_at_1000
|
|
value: 99.979
|
|
- type: recall_at_3
|
|
value: 88.099
|
|
- type: recall_at_5
|
|
value: 92.285
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 60.52398807444541
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 65.34855891507871
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.188000000000001
|
|
- type: map_at_10
|
|
value: 13.987
|
|
- type: map_at_100
|
|
value: 16.438
|
|
- type: map_at_1000
|
|
value: 16.829
|
|
- type: map_at_3
|
|
value: 9.767000000000001
|
|
- type: map_at_5
|
|
value: 11.912
|
|
- type: mrr_at_1
|
|
value: 25.6
|
|
- type: mrr_at_10
|
|
value: 37.744
|
|
- type: mrr_at_100
|
|
value: 38.847
|
|
- type: mrr_at_1000
|
|
value: 38.894
|
|
- type: mrr_at_3
|
|
value: 34.166999999999994
|
|
- type: mrr_at_5
|
|
value: 36.207
|
|
- type: ndcg_at_1
|
|
value: 25.6
|
|
- type: ndcg_at_10
|
|
value: 22.980999999999998
|
|
- type: ndcg_at_100
|
|
value: 32.039
|
|
- type: ndcg_at_1000
|
|
value: 38.157000000000004
|
|
- type: ndcg_at_3
|
|
value: 21.567
|
|
- type: ndcg_at_5
|
|
value: 19.070999999999998
|
|
- type: precision_at_1
|
|
value: 25.6
|
|
- type: precision_at_10
|
|
value: 12.02
|
|
- type: precision_at_100
|
|
value: 2.5100000000000002
|
|
- type: precision_at_1000
|
|
value: 0.396
|
|
- type: precision_at_3
|
|
value: 20.333000000000002
|
|
- type: precision_at_5
|
|
value: 16.98
|
|
- type: recall_at_1
|
|
value: 5.188000000000001
|
|
- type: recall_at_10
|
|
value: 24.372
|
|
- type: recall_at_100
|
|
value: 50.934999999999995
|
|
- type: recall_at_1000
|
|
value: 80.477
|
|
- type: recall_at_3
|
|
value: 12.363
|
|
- type: recall_at_5
|
|
value: 17.203
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.24286275535398
|
|
- type: cos_sim_spearman
|
|
value: 82.62333770991818
|
|
- type: euclidean_pearson
|
|
value: 84.60353717637284
|
|
- type: euclidean_spearman
|
|
value: 82.32990108810047
|
|
- type: manhattan_pearson
|
|
value: 84.6089049738196
|
|
- type: manhattan_spearman
|
|
value: 82.33361785438936
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.87428858503165
|
|
- type: cos_sim_spearman
|
|
value: 79.09145886519929
|
|
- type: euclidean_pearson
|
|
value: 86.42669231664036
|
|
- type: euclidean_spearman
|
|
value: 80.03127375435449
|
|
- type: manhattan_pearson
|
|
value: 86.41330338305022
|
|
- type: manhattan_spearman
|
|
value: 80.02492538673368
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 88.67912277322645
|
|
- type: cos_sim_spearman
|
|
value: 89.6171319711762
|
|
- type: euclidean_pearson
|
|
value: 86.56571917398725
|
|
- type: euclidean_spearman
|
|
value: 87.71216907898948
|
|
- type: manhattan_pearson
|
|
value: 86.57459050182473
|
|
- type: manhattan_spearman
|
|
value: 87.71916648349993
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.71957379085862
|
|
- type: cos_sim_spearman
|
|
value: 85.01784075851465
|
|
- type: euclidean_pearson
|
|
value: 84.7407848472801
|
|
- type: euclidean_spearman
|
|
value: 84.61063091345538
|
|
- type: manhattan_pearson
|
|
value: 84.71494352494403
|
|
- type: manhattan_spearman
|
|
value: 84.58772077604254
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 88.40508326325175
|
|
- type: cos_sim_spearman
|
|
value: 89.50912897763186
|
|
- type: euclidean_pearson
|
|
value: 87.82349070086627
|
|
- type: euclidean_spearman
|
|
value: 88.44179162727521
|
|
- type: manhattan_pearson
|
|
value: 87.80181927025595
|
|
- type: manhattan_spearman
|
|
value: 88.43205129636243
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.35846741715478
|
|
- type: cos_sim_spearman
|
|
value: 86.61172476741842
|
|
- type: euclidean_pearson
|
|
value: 84.60123125491637
|
|
- type: euclidean_spearman
|
|
value: 85.3001948141827
|
|
- type: manhattan_pearson
|
|
value: 84.56231142658329
|
|
- type: manhattan_spearman
|
|
value: 85.23579900798813
|
|
- 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: 88.94539129818824
|
|
- type: cos_sim_spearman
|
|
value: 88.99349064256742
|
|
- type: euclidean_pearson
|
|
value: 88.7142444640351
|
|
- type: euclidean_spearman
|
|
value: 88.34120813505011
|
|
- type: manhattan_pearson
|
|
value: 88.70363008238084
|
|
- type: manhattan_spearman
|
|
value: 88.31952816956954
|
|
- 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: 68.29910260369893
|
|
- type: cos_sim_spearman
|
|
value: 68.79263346213466
|
|
- type: euclidean_pearson
|
|
value: 68.41627521422252
|
|
- type: euclidean_spearman
|
|
value: 66.61602587398579
|
|
- type: manhattan_pearson
|
|
value: 68.49402183447361
|
|
- type: manhattan_spearman
|
|
value: 66.80157792354453
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.43703906343708
|
|
- type: cos_sim_spearman
|
|
value: 89.06081805093662
|
|
- type: euclidean_pearson
|
|
value: 87.48311456299662
|
|
- type: euclidean_spearman
|
|
value: 88.07417597580013
|
|
- type: manhattan_pearson
|
|
value: 87.48202249768894
|
|
- type: manhattan_spearman
|
|
value: 88.04758031111642
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 87.49080620485203
|
|
- type: mrr
|
|
value: 96.19145378949301
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 59.317
|
|
- type: map_at_10
|
|
value: 69.296
|
|
- type: map_at_100
|
|
value: 69.738
|
|
- type: map_at_1000
|
|
value: 69.759
|
|
- type: map_at_3
|
|
value: 66.12599999999999
|
|
- type: map_at_5
|
|
value: 67.532
|
|
- type: mrr_at_1
|
|
value: 62
|
|
- type: mrr_at_10
|
|
value: 70.176
|
|
- type: mrr_at_100
|
|
value: 70.565
|
|
- type: mrr_at_1000
|
|
value: 70.583
|
|
- type: mrr_at_3
|
|
value: 67.833
|
|
- type: mrr_at_5
|
|
value: 68.93299999999999
|
|
- type: ndcg_at_1
|
|
value: 62
|
|
- type: ndcg_at_10
|
|
value: 74.069
|
|
- type: ndcg_at_100
|
|
value: 76.037
|
|
- type: ndcg_at_1000
|
|
value: 76.467
|
|
- type: ndcg_at_3
|
|
value: 68.628
|
|
- type: ndcg_at_5
|
|
value: 70.57600000000001
|
|
- type: precision_at_1
|
|
value: 62
|
|
- type: precision_at_10
|
|
value: 10
|
|
- type: precision_at_100
|
|
value: 1.097
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 26.667
|
|
- type: precision_at_5
|
|
value: 17.4
|
|
- type: recall_at_1
|
|
value: 59.317
|
|
- type: recall_at_10
|
|
value: 87.822
|
|
- type: recall_at_100
|
|
value: 96.833
|
|
- type: recall_at_1000
|
|
value: 100
|
|
- type: recall_at_3
|
|
value: 73.06099999999999
|
|
- type: recall_at_5
|
|
value: 77.928
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.88910891089108
|
|
- type: cos_sim_ap
|
|
value: 97.236958456951
|
|
- type: cos_sim_f1
|
|
value: 94.39999999999999
|
|
- type: cos_sim_precision
|
|
value: 94.39999999999999
|
|
- type: cos_sim_recall
|
|
value: 94.39999999999999
|
|
- type: dot_accuracy
|
|
value: 99.82574257425742
|
|
- type: dot_ap
|
|
value: 94.94344759441888
|
|
- type: dot_f1
|
|
value: 91.17352056168507
|
|
- type: dot_precision
|
|
value: 91.44869215291752
|
|
- type: dot_recall
|
|
value: 90.9
|
|
- type: euclidean_accuracy
|
|
value: 99.88415841584158
|
|
- type: euclidean_ap
|
|
value: 97.2044250782305
|
|
- type: euclidean_f1
|
|
value: 94.210786739238
|
|
- type: euclidean_precision
|
|
value: 93.24191968658178
|
|
- type: euclidean_recall
|
|
value: 95.19999999999999
|
|
- type: manhattan_accuracy
|
|
value: 99.88613861386139
|
|
- type: manhattan_ap
|
|
value: 97.20683205497689
|
|
- type: manhattan_f1
|
|
value: 94.2643391521197
|
|
- type: manhattan_precision
|
|
value: 94.02985074626866
|
|
- type: manhattan_recall
|
|
value: 94.5
|
|
- type: max_accuracy
|
|
value: 99.88910891089108
|
|
- type: max_ap
|
|
value: 97.236958456951
|
|
- type: max_f1
|
|
value: 94.39999999999999
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 66.53940781726187
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 36.71865011295108
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 55.3218674533331
|
|
- type: mrr
|
|
value: 56.28279910449028
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 30.723915667479673
|
|
- type: cos_sim_spearman
|
|
value: 32.029070449745234
|
|
- type: dot_pearson
|
|
value: 28.864944212481454
|
|
- type: dot_spearman
|
|
value: 27.939266999596725
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.231
|
|
- type: map_at_10
|
|
value: 1.949
|
|
- type: map_at_100
|
|
value: 10.023
|
|
- type: map_at_1000
|
|
value: 23.485
|
|
- type: map_at_3
|
|
value: 0.652
|
|
- type: map_at_5
|
|
value: 1.054
|
|
- type: mrr_at_1
|
|
value: 86
|
|
- type: mrr_at_10
|
|
value: 92.067
|
|
- type: mrr_at_100
|
|
value: 92.067
|
|
- type: mrr_at_1000
|
|
value: 92.067
|
|
- type: mrr_at_3
|
|
value: 91.667
|
|
- type: mrr_at_5
|
|
value: 92.067
|
|
- type: ndcg_at_1
|
|
value: 83
|
|
- type: ndcg_at_10
|
|
value: 76.32900000000001
|
|
- type: ndcg_at_100
|
|
value: 54.662
|
|
- type: ndcg_at_1000
|
|
value: 48.062
|
|
- type: ndcg_at_3
|
|
value: 81.827
|
|
- type: ndcg_at_5
|
|
value: 80.664
|
|
- type: precision_at_1
|
|
value: 86
|
|
- type: precision_at_10
|
|
value: 80
|
|
- type: precision_at_100
|
|
value: 55.48
|
|
- type: precision_at_1000
|
|
value: 20.938000000000002
|
|
- type: precision_at_3
|
|
value: 85.333
|
|
- type: precision_at_5
|
|
value: 84.39999999999999
|
|
- type: recall_at_1
|
|
value: 0.231
|
|
- type: recall_at_10
|
|
value: 2.158
|
|
- type: recall_at_100
|
|
value: 13.344000000000001
|
|
- type: recall_at_1000
|
|
value: 44.31
|
|
- type: recall_at_3
|
|
value: 0.6779999999999999
|
|
- type: recall_at_5
|
|
value: 1.13
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 2.524
|
|
- type: map_at_10
|
|
value: 10.183
|
|
- type: map_at_100
|
|
value: 16.625
|
|
- type: map_at_1000
|
|
value: 18.017
|
|
- type: map_at_3
|
|
value: 5.169
|
|
- type: map_at_5
|
|
value: 6.772
|
|
- type: mrr_at_1
|
|
value: 32.653
|
|
- type: mrr_at_10
|
|
value: 47.128
|
|
- type: mrr_at_100
|
|
value: 48.458
|
|
- type: mrr_at_1000
|
|
value: 48.473
|
|
- type: mrr_at_3
|
|
value: 44.897999999999996
|
|
- type: mrr_at_5
|
|
value: 45.306000000000004
|
|
- type: ndcg_at_1
|
|
value: 30.612000000000002
|
|
- type: ndcg_at_10
|
|
value: 24.928
|
|
- type: ndcg_at_100
|
|
value: 37.613
|
|
- type: ndcg_at_1000
|
|
value: 48.528
|
|
- type: ndcg_at_3
|
|
value: 28.829
|
|
- type: ndcg_at_5
|
|
value: 25.237
|
|
- type: precision_at_1
|
|
value: 32.653
|
|
- type: precision_at_10
|
|
value: 22.448999999999998
|
|
- type: precision_at_100
|
|
value: 8.02
|
|
- type: precision_at_1000
|
|
value: 1.537
|
|
- type: precision_at_3
|
|
value: 30.612000000000002
|
|
- type: precision_at_5
|
|
value: 24.490000000000002
|
|
- type: recall_at_1
|
|
value: 2.524
|
|
- type: recall_at_10
|
|
value: 16.38
|
|
- type: recall_at_100
|
|
value: 49.529
|
|
- type: recall_at_1000
|
|
value: 83.598
|
|
- type: recall_at_3
|
|
value: 6.411
|
|
- type: recall_at_5
|
|
value: 8.932
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.09020000000001
|
|
- type: ap
|
|
value: 14.451710060978993
|
|
- type: f1
|
|
value: 54.7874410609049
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 59.745331069609506
|
|
- type: f1
|
|
value: 60.08387848592697
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 51.71549485462037
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 87.39345532574357
|
|
- type: cos_sim_ap
|
|
value: 78.16796549696478
|
|
- type: cos_sim_f1
|
|
value: 71.27713276123171
|
|
- type: cos_sim_precision
|
|
value: 68.3115626511853
|
|
- type: cos_sim_recall
|
|
value: 74.51187335092348
|
|
- type: dot_accuracy
|
|
value: 85.12248912201228
|
|
- type: dot_ap
|
|
value: 69.26039256107077
|
|
- type: dot_f1
|
|
value: 65.04294321240867
|
|
- type: dot_precision
|
|
value: 63.251059586138126
|
|
- type: dot_recall
|
|
value: 66.93931398416886
|
|
- type: euclidean_accuracy
|
|
value: 87.07754664123503
|
|
- type: euclidean_ap
|
|
value: 77.7872176038945
|
|
- type: euclidean_f1
|
|
value: 70.85587801278899
|
|
- type: euclidean_precision
|
|
value: 66.3519115614924
|
|
- type: euclidean_recall
|
|
value: 76.01583113456465
|
|
- type: manhattan_accuracy
|
|
value: 87.07754664123503
|
|
- type: manhattan_ap
|
|
value: 77.7341400185556
|
|
- type: manhattan_f1
|
|
value: 70.80310880829015
|
|
- type: manhattan_precision
|
|
value: 69.54198473282443
|
|
- type: manhattan_recall
|
|
value: 72.1108179419525
|
|
- type: max_accuracy
|
|
value: 87.39345532574357
|
|
- type: max_ap
|
|
value: 78.16796549696478
|
|
- type: max_f1
|
|
value: 71.27713276123171
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 89.09457833663213
|
|
- type: cos_sim_ap
|
|
value: 86.33024314706873
|
|
- type: cos_sim_f1
|
|
value: 78.59623733719248
|
|
- type: cos_sim_precision
|
|
value: 74.13322413322413
|
|
- type: cos_sim_recall
|
|
value: 83.63104404065291
|
|
- type: dot_accuracy
|
|
value: 88.3086894089339
|
|
- type: dot_ap
|
|
value: 83.92225241805097
|
|
- type: dot_f1
|
|
value: 76.8721826377781
|
|
- type: dot_precision
|
|
value: 72.8168044077135
|
|
- type: dot_recall
|
|
value: 81.40591315060055
|
|
- type: euclidean_accuracy
|
|
value: 88.77052043311213
|
|
- type: euclidean_ap
|
|
value: 85.7410710218755
|
|
- type: euclidean_f1
|
|
value: 77.97705489398781
|
|
- type: euclidean_precision
|
|
value: 73.77713657598241
|
|
- type: euclidean_recall
|
|
value: 82.68401601478288
|
|
- type: manhattan_accuracy
|
|
value: 88.73753250281368
|
|
- type: manhattan_ap
|
|
value: 85.72867199072802
|
|
- type: manhattan_f1
|
|
value: 77.89774182922812
|
|
- type: manhattan_precision
|
|
value: 74.23787931635857
|
|
- type: manhattan_recall
|
|
value: 81.93717277486911
|
|
- type: max_accuracy
|
|
value: 89.09457833663213
|
|
- type: max_ap
|
|
value: 86.33024314706873
|
|
- type: max_f1
|
|
value: 78.59623733719248
|
|
license: mit
|
|
language:
|
|
- en
|
|
---
|
|
|
|
|
|
# [Universal AnglE Embedding](https://github.com/SeanLee97/AnglE)
|
|
|
|
📢 `WhereIsAI/UAE-Large-V1` **is licensed under MIT. Feel free to use it in any scenario.**
|
|
**If you use it for academic papers, you could cite us via 👉 [citation info](#citation).**
|
|
|
|
**🤝 Follow us on:**
|
|
|
|
- GitHub: https://github.com/SeanLee97/AnglE.
|
|
- Preprint Paper: [AnglE-optimized Text Embeddings](https://arxiv.org/abs/2309.12871)
|
|
- Conference Paper: [AoE: Angle-optimized Embeddings for Semantic Textual Similarity](https://aclanthology.org/2024.acl-long.101/) (ACL24)
|
|
- **📘 Documentation**: https://angle.readthedocs.io/en/latest/index.html
|
|
|
|
Welcome to using AnglE to train and infer powerful sentence embeddings.
|
|
|
|
**🏆 Achievements**
|
|
|
|
- 📅 May 16, 2024 | AnglE's paper is accepted by ACL 2024 Main Conference
|
|
- 📅 Dec 4, 2024 | 🔥 Our universal English sentence embedding `WhereIsAI/UAE-Large-V1` achieves **SOTA** on the [MTEB Leaderboard](https://huggingface.co/spaces/mteb/leaderboard) with an average score of 64.64!
|
|
|
|
|
|
|
|
|
|
**🧑🤝🧑 Siblings:**
|
|
|
|
- [WhereIsAI/UAE-Code-Large-V1](https://huggingface.co/WhereIsAI/UAE-Code-Large-V1): This model can be used for code or GitHub issue similarity measurement.
|
|
|
|
|
|
# Usage
|
|
## 1. angle_emb
|
|
|
|
```bash
|
|
python -m pip install -U angle-emb
|
|
```
|
|
|
|
1) Non-Retrieval Tasks
|
|
|
|
There is no need to specify any prompts.
|
|
|
|
```python
|
|
from angle_emb import AnglE
|
|
from angle_emb.utils import cosine_similarity
|
|
|
|
angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
|
|
doc_vecs = angle.encode([
|
|
'The weather is great!',
|
|
'The weather is very good!',
|
|
'i am going to bed'
|
|
], normalize_embedding=True)
|
|
|
|
for i, dv1 in enumerate(doc_vecs):
|
|
for dv2 in doc_vecs[i+1:]:
|
|
print(cosine_similarity(dv1, dv2))
|
|
```
|
|
|
|
2) Retrieval Tasks
|
|
|
|
For retrieval purposes, please use the prompt `Prompts.C` for query (not for document).
|
|
|
|
```python
|
|
from angle_emb import AnglE, Prompts
|
|
from angle_emb.utils import cosine_similarity
|
|
|
|
angle = AnglE.from_pretrained('WhereIsAI/UAE-Large-V1', pooling_strategy='cls').cuda()
|
|
qv = angle.encode(Prompts.C.format(text='what is the weather?'))
|
|
doc_vecs = angle.encode([
|
|
'The weather is great!',
|
|
'it is rainy today.',
|
|
'i am going to bed'
|
|
])
|
|
|
|
for dv in doc_vecs:
|
|
print(cosine_similarity(qv[0], dv))
|
|
```
|
|
|
|
## 2. sentence transformer
|
|
|
|
|
|
```python
|
|
from angle_emb import Prompts
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
model = SentenceTransformer("WhereIsAI/UAE-Large-V1").cuda()
|
|
|
|
qv = model.encode(Prompts.C.format(text='what is the weather?'))
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|
doc_vecs = model.encode([
|
|
'The weather is great!',
|
|
'it is rainy today.',
|
|
'i am going to bed'
|
|
])
|
|
|
|
for dv in doc_vecs:
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|
print(1 - spatial.distance.cosine(qv, dv))
|
|
```
|
|
|
|
|
|
## 3. Infinity
|
|
|
|
[Infinity](https://github.com/michaelfeil/infinity) is a MIT licensed server for OpenAI-compatible deployment.
|
|
```
|
|
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
|
|
michaelf34/infinity:latest \
|
|
v2 --model-id WhereIsAI/UAE-Large-V1 --revision "369c368f70f16a613f19f5598d4f12d9f44235d4" --dtype float16 --batch-size 32 --device cuda --engine torch --port 7997
|
|
```
|
|
|
|
# Citation
|
|
|
|
If you use our pre-trained models, welcome to support us by citing our work:
|
|
|
|
```
|
|
@article{li2023angle,
|
|
title={AnglE-optimized Text Embeddings},
|
|
author={Li, Xianming and Li, Jing},
|
|
journal={arXiv preprint arXiv:2309.12871},
|
|
year={2023}
|
|
}
|
|
``` |