2781 lines
69 KiB
Markdown
2781 lines
69 KiB
Markdown
---
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- mteb
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datasets:
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- jinaai/negation-dataset
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language: en
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inference: false
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license: apache-2.0
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model-index:
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- name: jina-embedding-s-en-v2
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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: 71.35820895522387
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- type: ap
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value: 33.99931933598115
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- type: f1
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value: 65.3853685535555
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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: 82.90140000000001
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- type: ap
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value: 78.01434597815617
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- type: f1
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value: 82.83357802722676
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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: 40.88999999999999
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- type: f1
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value: 39.209432767163456
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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: 23.257
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- type: map_at_10
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value: 37.946000000000005
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- type: map_at_100
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value: 39.17
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- type: map_at_1000
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value: 39.181
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- type: map_at_3
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value: 32.99
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- type: map_at_5
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value: 35.467999999999996
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- type: mrr_at_1
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value: 23.541999999999998
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- type: mrr_at_10
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value: 38.057
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- type: mrr_at_100
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value: 39.289
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- type: mrr_at_1000
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value: 39.299
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- type: mrr_at_3
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value: 33.096
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- type: mrr_at_5
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value: 35.628
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- type: ndcg_at_1
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value: 23.257
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- type: ndcg_at_10
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value: 46.729
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- type: ndcg_at_100
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value: 51.900999999999996
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- type: ndcg_at_1000
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value: 52.16
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- type: ndcg_at_3
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value: 36.323
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- type: ndcg_at_5
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value: 40.766999999999996
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- type: precision_at_1
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value: 23.257
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- type: precision_at_10
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value: 7.510999999999999
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- type: precision_at_100
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value: 0.976
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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: 15.339
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- type: precision_at_5
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value: 11.350999999999999
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- type: recall_at_1
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value: 23.257
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- type: recall_at_10
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value: 75.107
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- type: recall_at_100
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value: 97.58200000000001
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- type: recall_at_1000
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value: 99.57300000000001
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- type: recall_at_3
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value: 46.017
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- type: recall_at_5
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value: 56.757000000000005
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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: 44.02420878391967
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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: 35.16136856000258
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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: 59.61809790513646
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- type: mrr
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value: 73.07215406938397
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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: 82.0167350090749
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- type: cos_sim_spearman
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value: 80.51569002630401
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- type: euclidean_pearson
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value: 81.46820525099726
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- type: euclidean_spearman
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value: 80.51569002630401
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- type: manhattan_pearson
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value: 81.35596555056757
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- type: manhattan_spearman
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value: 80.12592210903303
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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: 78.25
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- type: f1
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value: 77.34950913540605
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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: 35.57238596005698
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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: 29.066444306196683
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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: 31.891000000000002
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- type: map_at_10
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value: 42.772
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- type: map_at_100
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value: 44.108999999999995
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- type: map_at_1000
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value: 44.236
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- type: map_at_3
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value: 39.289
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- type: map_at_5
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value: 41.113
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- type: mrr_at_1
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value: 39.342
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- type: mrr_at_10
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value: 48.852000000000004
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- type: mrr_at_100
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value: 49.534
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- type: mrr_at_1000
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value: 49.582
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- type: mrr_at_3
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value: 46.089999999999996
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- type: mrr_at_5
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value: 47.685
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- type: ndcg_at_1
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value: 39.342
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- type: ndcg_at_10
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value: 48.988
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- type: ndcg_at_100
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value: 53.854
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- type: ndcg_at_1000
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value: 55.955
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- type: ndcg_at_3
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value: 43.877
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- type: ndcg_at_5
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value: 46.027
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- type: precision_at_1
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value: 39.342
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- type: precision_at_10
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value: 9.285
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- type: precision_at_100
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value: 1.488
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- type: precision_at_1000
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value: 0.194
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- type: precision_at_3
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value: 20.696
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- type: precision_at_5
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value: 14.878
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- type: recall_at_1
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value: 31.891000000000002
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- type: recall_at_10
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value: 60.608
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- type: recall_at_100
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value: 81.025
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- type: recall_at_1000
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value: 94.883
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- type: recall_at_3
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value: 45.694
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- type: recall_at_5
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value: 51.684
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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: 28.778
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- type: map_at_10
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value: 37.632
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- type: map_at_100
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value: 38.800000000000004
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- type: map_at_1000
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value: 38.934999999999995
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- type: map_at_3
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value: 35.293
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- type: map_at_5
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value: 36.547000000000004
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- type: mrr_at_1
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value: 35.35
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- type: mrr_at_10
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value: 42.936
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- type: mrr_at_100
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value: 43.69
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- type: mrr_at_1000
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value: 43.739
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- type: mrr_at_3
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value: 41.062
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- type: mrr_at_5
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value: 42.097
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- type: ndcg_at_1
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value: 35.35
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- type: ndcg_at_10
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value: 42.528
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- type: ndcg_at_100
|
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value: 46.983000000000004
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- type: ndcg_at_1000
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value: 49.187999999999995
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- type: ndcg_at_3
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value: 39.271
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- type: ndcg_at_5
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value: 40.654
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- type: precision_at_1
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value: 35.35
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- type: precision_at_10
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value: 7.828
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- type: precision_at_100
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value: 1.3010000000000002
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- type: precision_at_1000
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value: 0.17700000000000002
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- type: precision_at_3
|
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value: 18.96
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- type: precision_at_5
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value: 13.120999999999999
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- type: recall_at_1
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value: 28.778
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- type: recall_at_10
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value: 50.775000000000006
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- type: recall_at_100
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value: 69.66799999999999
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- type: recall_at_1000
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value: 83.638
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- type: recall_at_3
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value: 40.757
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- type: recall_at_5
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value: 44.86
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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: 37.584
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- type: map_at_10
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value: 49.69
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- type: map_at_100
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value: 50.639
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- type: map_at_1000
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value: 50.702999999999996
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- type: map_at_3
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value: 46.61
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- type: map_at_5
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value: 48.486000000000004
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- type: mrr_at_1
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value: 43.009
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- type: mrr_at_10
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value: 52.949999999999996
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- type: mrr_at_100
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value: 53.618
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- type: mrr_at_1000
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value: 53.65299999999999
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- type: mrr_at_3
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value: 50.605999999999995
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- type: mrr_at_5
|
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value: 52.095
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- type: ndcg_at_1
|
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value: 43.009
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- type: ndcg_at_10
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value: 55.278000000000006
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- type: ndcg_at_100
|
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value: 59.134
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- type: ndcg_at_1000
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value: 60.528999999999996
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- type: ndcg_at_3
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value: 50.184
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- type: ndcg_at_5
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value: 52.919000000000004
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- type: precision_at_1
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value: 43.009
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- type: precision_at_10
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value: 8.821
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- type: precision_at_100
|
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value: 1.161
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- type: precision_at_1000
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value: 0.133
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- type: precision_at_3
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value: 22.424
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- type: precision_at_5
|
|
value: 15.436
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- type: recall_at_1
|
|
value: 37.584
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- type: recall_at_10
|
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value: 68.514
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- type: recall_at_100
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value: 85.099
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- type: recall_at_1000
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value: 95.123
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- type: recall_at_3
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value: 55.007
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- type: recall_at_5
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value: 61.714999999999996
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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: 24.7
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- type: map_at_10
|
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value: 32.804
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- type: map_at_100
|
|
value: 33.738
|
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- type: map_at_1000
|
|
value: 33.825
|
|
- type: map_at_3
|
|
value: 30.639
|
|
- type: map_at_5
|
|
value: 31.781
|
|
- type: mrr_at_1
|
|
value: 26.328000000000003
|
|
- type: mrr_at_10
|
|
value: 34.679
|
|
- type: mrr_at_100
|
|
value: 35.510000000000005
|
|
- type: mrr_at_1000
|
|
value: 35.577999999999996
|
|
- type: mrr_at_3
|
|
value: 32.58
|
|
- type: mrr_at_5
|
|
value: 33.687
|
|
- type: ndcg_at_1
|
|
value: 26.328000000000003
|
|
- type: ndcg_at_10
|
|
value: 37.313
|
|
- type: ndcg_at_100
|
|
value: 42.004000000000005
|
|
- type: ndcg_at_1000
|
|
value: 44.232
|
|
- type: ndcg_at_3
|
|
value: 33.076
|
|
- type: ndcg_at_5
|
|
value: 34.966
|
|
- type: precision_at_1
|
|
value: 26.328000000000003
|
|
- type: precision_at_10
|
|
value: 5.627
|
|
- type: precision_at_100
|
|
value: 0.8410000000000001
|
|
- type: precision_at_1000
|
|
value: 0.106
|
|
- type: precision_at_3
|
|
value: 14.011000000000001
|
|
- type: precision_at_5
|
|
value: 9.582
|
|
- type: recall_at_1
|
|
value: 24.7
|
|
- type: recall_at_10
|
|
value: 49.324
|
|
- type: recall_at_100
|
|
value: 71.018
|
|
- type: recall_at_1000
|
|
value: 87.905
|
|
- type: recall_at_3
|
|
value: 37.7
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- type: recall_at_5
|
|
value: 42.281
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|
- task:
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type: Retrieval
|
|
dataset:
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type: BeIR/cqadupstack
|
|
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:
|
|
- type: map_at_1
|
|
value: 14.350999999999999
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- type: map_at_10
|
|
value: 21.745
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|
- type: map_at_100
|
|
value: 22.731
|
|
- type: map_at_1000
|
|
value: 22.852
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- type: map_at_3
|
|
value: 19.245
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- type: map_at_5
|
|
value: 20.788
|
|
- type: mrr_at_1
|
|
value: 18.159
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|
- type: mrr_at_10
|
|
value: 25.833000000000002
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|
- type: mrr_at_100
|
|
value: 26.728
|
|
- type: mrr_at_1000
|
|
value: 26.802
|
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- type: mrr_at_3
|
|
value: 23.383000000000003
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- type: mrr_at_5
|
|
value: 24.887999999999998
|
|
- type: ndcg_at_1
|
|
value: 18.159
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- type: ndcg_at_10
|
|
value: 26.518000000000004
|
|
- type: ndcg_at_100
|
|
value: 31.473000000000003
|
|
- type: ndcg_at_1000
|
|
value: 34.576
|
|
- type: ndcg_at_3
|
|
value: 21.907
|
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- type: ndcg_at_5
|
|
value: 24.39
|
|
- type: precision_at_1
|
|
value: 18.159
|
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- type: precision_at_10
|
|
value: 4.938
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- type: precision_at_100
|
|
value: 0.853
|
|
- type: precision_at_1000
|
|
value: 0.125
|
|
- type: precision_at_3
|
|
value: 10.655000000000001
|
|
- type: precision_at_5
|
|
value: 7.985
|
|
- type: recall_at_1
|
|
value: 14.350999999999999
|
|
- type: recall_at_10
|
|
value: 37.284
|
|
- type: recall_at_100
|
|
value: 59.11300000000001
|
|
- type: recall_at_1000
|
|
value: 81.634
|
|
- type: recall_at_3
|
|
value: 24.753
|
|
- type: recall_at_5
|
|
value: 30.979
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.978
|
|
- type: map_at_10
|
|
value: 36.276
|
|
- type: map_at_100
|
|
value: 37.547000000000004
|
|
- type: map_at_1000
|
|
value: 37.678
|
|
- type: map_at_3
|
|
value: 33.674
|
|
- type: map_at_5
|
|
value: 35.119
|
|
- type: mrr_at_1
|
|
value: 32.916000000000004
|
|
- type: mrr_at_10
|
|
value: 41.798
|
|
- type: mrr_at_100
|
|
value: 42.72
|
|
- type: mrr_at_1000
|
|
value: 42.778
|
|
- type: mrr_at_3
|
|
value: 39.493
|
|
- type: mrr_at_5
|
|
value: 40.927
|
|
- type: ndcg_at_1
|
|
value: 32.916000000000004
|
|
- type: ndcg_at_10
|
|
value: 41.81
|
|
- type: ndcg_at_100
|
|
value: 47.284
|
|
- type: ndcg_at_1000
|
|
value: 49.702
|
|
- type: ndcg_at_3
|
|
value: 37.486999999999995
|
|
- type: ndcg_at_5
|
|
value: 39.597
|
|
- type: precision_at_1
|
|
value: 32.916000000000004
|
|
- type: precision_at_10
|
|
value: 7.411
|
|
- type: precision_at_100
|
|
value: 1.189
|
|
- type: precision_at_1000
|
|
value: 0.158
|
|
- type: precision_at_3
|
|
value: 17.581
|
|
- type: precision_at_5
|
|
value: 12.397
|
|
- type: recall_at_1
|
|
value: 26.978
|
|
- type: recall_at_10
|
|
value: 52.869
|
|
- type: recall_at_100
|
|
value: 75.78399999999999
|
|
- type: recall_at_1000
|
|
value: 91.545
|
|
- type: recall_at_3
|
|
value: 40.717
|
|
- type: recall_at_5
|
|
value: 46.168
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.641
|
|
- type: map_at_10
|
|
value: 32.916000000000004
|
|
- type: map_at_100
|
|
value: 34.165
|
|
- type: map_at_1000
|
|
value: 34.286
|
|
- type: map_at_3
|
|
value: 30.335
|
|
- type: map_at_5
|
|
value: 31.569000000000003
|
|
- type: mrr_at_1
|
|
value: 30.593999999999998
|
|
- type: mrr_at_10
|
|
value: 38.448
|
|
- type: mrr_at_100
|
|
value: 39.299
|
|
- type: mrr_at_1000
|
|
value: 39.362
|
|
- type: mrr_at_3
|
|
value: 36.244
|
|
- type: mrr_at_5
|
|
value: 37.232
|
|
- type: ndcg_at_1
|
|
value: 30.593999999999998
|
|
- type: ndcg_at_10
|
|
value: 38.2
|
|
- type: ndcg_at_100
|
|
value: 43.742
|
|
- type: ndcg_at_1000
|
|
value: 46.217000000000006
|
|
- type: ndcg_at_3
|
|
value: 33.925
|
|
- type: ndcg_at_5
|
|
value: 35.394
|
|
- type: precision_at_1
|
|
value: 30.593999999999998
|
|
- type: precision_at_10
|
|
value: 6.895
|
|
- type: precision_at_100
|
|
value: 1.1320000000000001
|
|
- type: precision_at_1000
|
|
value: 0.153
|
|
- type: precision_at_3
|
|
value: 16.096
|
|
- type: precision_at_5
|
|
value: 11.05
|
|
- type: recall_at_1
|
|
value: 24.641
|
|
- type: recall_at_10
|
|
value: 48.588
|
|
- type: recall_at_100
|
|
value: 72.841
|
|
- type: recall_at_1000
|
|
value: 89.535
|
|
- type: recall_at_3
|
|
value: 36.087
|
|
- type: recall_at_5
|
|
value: 40.346
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.79425
|
|
- type: map_at_10
|
|
value: 33.12033333333333
|
|
- type: map_at_100
|
|
value: 34.221333333333334
|
|
- type: map_at_1000
|
|
value: 34.3435
|
|
- type: map_at_3
|
|
value: 30.636583333333338
|
|
- type: map_at_5
|
|
value: 31.974083333333326
|
|
- type: mrr_at_1
|
|
value: 29.242416666666664
|
|
- type: mrr_at_10
|
|
value: 37.11675
|
|
- type: mrr_at_100
|
|
value: 37.93783333333334
|
|
- type: mrr_at_1000
|
|
value: 38.003083333333336
|
|
- type: mrr_at_3
|
|
value: 34.904666666666664
|
|
- type: mrr_at_5
|
|
value: 36.12916666666667
|
|
- type: ndcg_at_1
|
|
value: 29.242416666666664
|
|
- type: ndcg_at_10
|
|
value: 38.03416666666667
|
|
- type: ndcg_at_100
|
|
value: 42.86674999999999
|
|
- type: ndcg_at_1000
|
|
value: 45.34550000000001
|
|
- type: ndcg_at_3
|
|
value: 33.76466666666666
|
|
- type: ndcg_at_5
|
|
value: 35.668666666666674
|
|
- type: precision_at_1
|
|
value: 29.242416666666664
|
|
- type: precision_at_10
|
|
value: 6.589833333333334
|
|
- type: precision_at_100
|
|
value: 1.0693333333333332
|
|
- type: precision_at_1000
|
|
value: 0.14641666666666667
|
|
- type: precision_at_3
|
|
value: 15.430749999999998
|
|
- type: precision_at_5
|
|
value: 10.833833333333333
|
|
- type: recall_at_1
|
|
value: 24.79425
|
|
- type: recall_at_10
|
|
value: 48.582916666666655
|
|
- type: recall_at_100
|
|
value: 69.88499999999999
|
|
- type: recall_at_1000
|
|
value: 87.211
|
|
- type: recall_at_3
|
|
value: 36.625499999999995
|
|
- type: recall_at_5
|
|
value: 41.553999999999995
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.767
|
|
- type: map_at_10
|
|
value: 28.450999999999997
|
|
- type: map_at_100
|
|
value: 29.332
|
|
- type: map_at_1000
|
|
value: 29.426000000000002
|
|
- type: map_at_3
|
|
value: 26.379
|
|
- type: map_at_5
|
|
value: 27.584999999999997
|
|
- type: mrr_at_1
|
|
value: 25.46
|
|
- type: mrr_at_10
|
|
value: 30.974
|
|
- type: mrr_at_100
|
|
value: 31.784000000000002
|
|
- type: mrr_at_1000
|
|
value: 31.857999999999997
|
|
- type: mrr_at_3
|
|
value: 28.962
|
|
- type: mrr_at_5
|
|
value: 30.066
|
|
- type: ndcg_at_1
|
|
value: 25.46
|
|
- type: ndcg_at_10
|
|
value: 32.041
|
|
- type: ndcg_at_100
|
|
value: 36.522
|
|
- type: ndcg_at_1000
|
|
value: 39.101
|
|
- type: ndcg_at_3
|
|
value: 28.152
|
|
- type: ndcg_at_5
|
|
value: 30.03
|
|
- type: precision_at_1
|
|
value: 25.46
|
|
- type: precision_at_10
|
|
value: 4.893
|
|
- type: precision_at_100
|
|
value: 0.77
|
|
- type: precision_at_1000
|
|
value: 0.107
|
|
- type: precision_at_3
|
|
value: 11.605
|
|
- type: precision_at_5
|
|
value: 8.19
|
|
- type: recall_at_1
|
|
value: 22.767
|
|
- type: recall_at_10
|
|
value: 40.71
|
|
- type: recall_at_100
|
|
value: 61.334999999999994
|
|
- type: recall_at_1000
|
|
value: 80.567
|
|
- type: recall_at_3
|
|
value: 30.198000000000004
|
|
- type: recall_at_5
|
|
value: 34.803
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 16.722
|
|
- type: map_at_10
|
|
value: 22.794
|
|
- type: map_at_100
|
|
value: 23.7
|
|
- type: map_at_1000
|
|
value: 23.822
|
|
- type: map_at_3
|
|
value: 20.781
|
|
- type: map_at_5
|
|
value: 22.024
|
|
- type: mrr_at_1
|
|
value: 20.061999999999998
|
|
- type: mrr_at_10
|
|
value: 26.346999999999998
|
|
- type: mrr_at_100
|
|
value: 27.153
|
|
- type: mrr_at_1000
|
|
value: 27.233
|
|
- type: mrr_at_3
|
|
value: 24.375
|
|
- type: mrr_at_5
|
|
value: 25.593
|
|
- type: ndcg_at_1
|
|
value: 20.061999999999998
|
|
- type: ndcg_at_10
|
|
value: 26.785999999999998
|
|
- type: ndcg_at_100
|
|
value: 31.319999999999997
|
|
- type: ndcg_at_1000
|
|
value: 34.346
|
|
- type: ndcg_at_3
|
|
value: 23.219
|
|
- type: ndcg_at_5
|
|
value: 25.107000000000003
|
|
- type: precision_at_1
|
|
value: 20.061999999999998
|
|
- type: precision_at_10
|
|
value: 4.78
|
|
- type: precision_at_100
|
|
value: 0.83
|
|
- type: precision_at_1000
|
|
value: 0.125
|
|
- type: precision_at_3
|
|
value: 10.874
|
|
- type: precision_at_5
|
|
value: 7.956
|
|
- type: recall_at_1
|
|
value: 16.722
|
|
- type: recall_at_10
|
|
value: 35.204
|
|
- type: recall_at_100
|
|
value: 55.797
|
|
- type: recall_at_1000
|
|
value: 77.689
|
|
- type: recall_at_3
|
|
value: 25.245
|
|
- type: recall_at_5
|
|
value: 30.115
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.842
|
|
- type: map_at_10
|
|
value: 32.917
|
|
- type: map_at_100
|
|
value: 33.961000000000006
|
|
- type: map_at_1000
|
|
value: 34.069
|
|
- type: map_at_3
|
|
value: 30.595
|
|
- type: map_at_5
|
|
value: 31.837
|
|
- type: mrr_at_1
|
|
value: 29.011
|
|
- type: mrr_at_10
|
|
value: 36.977
|
|
- type: mrr_at_100
|
|
value: 37.814
|
|
- type: mrr_at_1000
|
|
value: 37.885999999999996
|
|
- type: mrr_at_3
|
|
value: 34.966
|
|
- type: mrr_at_5
|
|
value: 36.043
|
|
- type: ndcg_at_1
|
|
value: 29.011
|
|
- type: ndcg_at_10
|
|
value: 37.735
|
|
- type: ndcg_at_100
|
|
value: 42.683
|
|
- type: ndcg_at_1000
|
|
value: 45.198
|
|
- type: ndcg_at_3
|
|
value: 33.650000000000006
|
|
- type: ndcg_at_5
|
|
value: 35.386
|
|
- type: precision_at_1
|
|
value: 29.011
|
|
- type: precision_at_10
|
|
value: 6.259
|
|
- type: precision_at_100
|
|
value: 0.984
|
|
- type: precision_at_1000
|
|
value: 0.13
|
|
- type: precision_at_3
|
|
value: 15.329999999999998
|
|
- type: precision_at_5
|
|
value: 10.541
|
|
- type: recall_at_1
|
|
value: 24.842
|
|
- type: recall_at_10
|
|
value: 48.304
|
|
- type: recall_at_100
|
|
value: 70.04899999999999
|
|
- type: recall_at_1000
|
|
value: 87.82600000000001
|
|
- type: recall_at_3
|
|
value: 36.922
|
|
- type: recall_at_5
|
|
value: 41.449999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 24.252000000000002
|
|
- type: map_at_10
|
|
value: 32.293
|
|
- type: map_at_100
|
|
value: 33.816
|
|
- type: map_at_1000
|
|
value: 34.053
|
|
- type: map_at_3
|
|
value: 29.781999999999996
|
|
- type: map_at_5
|
|
value: 31.008000000000003
|
|
- type: mrr_at_1
|
|
value: 29.051
|
|
- type: mrr_at_10
|
|
value: 36.722
|
|
- type: mrr_at_100
|
|
value: 37.663000000000004
|
|
- type: mrr_at_1000
|
|
value: 37.734
|
|
- type: mrr_at_3
|
|
value: 34.354
|
|
- type: mrr_at_5
|
|
value: 35.609
|
|
- type: ndcg_at_1
|
|
value: 29.051
|
|
- type: ndcg_at_10
|
|
value: 37.775999999999996
|
|
- type: ndcg_at_100
|
|
value: 43.221
|
|
- type: ndcg_at_1000
|
|
value: 46.116
|
|
- type: ndcg_at_3
|
|
value: 33.403
|
|
- type: ndcg_at_5
|
|
value: 35.118
|
|
- type: precision_at_1
|
|
value: 29.051
|
|
- type: precision_at_10
|
|
value: 7.332
|
|
- type: precision_at_100
|
|
value: 1.49
|
|
- type: precision_at_1000
|
|
value: 0.23600000000000002
|
|
- type: precision_at_3
|
|
value: 15.415000000000001
|
|
- type: precision_at_5
|
|
value: 11.107
|
|
- type: recall_at_1
|
|
value: 24.252000000000002
|
|
- type: recall_at_10
|
|
value: 47.861
|
|
- type: recall_at_100
|
|
value: 72.21600000000001
|
|
- type: recall_at_1000
|
|
value: 90.886
|
|
- type: recall_at_3
|
|
value: 35.533
|
|
- type: recall_at_5
|
|
value: 39.959
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 20.025000000000002
|
|
- type: map_at_10
|
|
value: 27.154
|
|
- type: map_at_100
|
|
value: 28.118
|
|
- type: map_at_1000
|
|
value: 28.237000000000002
|
|
- type: map_at_3
|
|
value: 25.017
|
|
- type: map_at_5
|
|
value: 25.832
|
|
- type: mrr_at_1
|
|
value: 21.627
|
|
- type: mrr_at_10
|
|
value: 28.884999999999998
|
|
- type: mrr_at_100
|
|
value: 29.741
|
|
- type: mrr_at_1000
|
|
value: 29.831999999999997
|
|
- type: mrr_at_3
|
|
value: 26.741
|
|
- type: mrr_at_5
|
|
value: 27.628000000000004
|
|
- type: ndcg_at_1
|
|
value: 21.627
|
|
- type: ndcg_at_10
|
|
value: 31.436999999999998
|
|
- type: ndcg_at_100
|
|
value: 36.181000000000004
|
|
- type: ndcg_at_1000
|
|
value: 38.986
|
|
- type: ndcg_at_3
|
|
value: 27.025
|
|
- type: ndcg_at_5
|
|
value: 28.436
|
|
- type: precision_at_1
|
|
value: 21.627
|
|
- type: precision_at_10
|
|
value: 5.009
|
|
- type: precision_at_100
|
|
value: 0.7929999999999999
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 11.522
|
|
- type: precision_at_5
|
|
value: 7.763000000000001
|
|
- type: recall_at_1
|
|
value: 20.025000000000002
|
|
- type: recall_at_10
|
|
value: 42.954
|
|
- type: recall_at_100
|
|
value: 64.67500000000001
|
|
- type: recall_at_1000
|
|
value: 85.301
|
|
- type: recall_at_3
|
|
value: 30.892999999999997
|
|
- type: recall_at_5
|
|
value: 34.288000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 10.079
|
|
- type: map_at_10
|
|
value: 16.930999999999997
|
|
- type: map_at_100
|
|
value: 18.398999999999997
|
|
- type: map_at_1000
|
|
value: 18.561
|
|
- type: map_at_3
|
|
value: 14.294
|
|
- type: map_at_5
|
|
value: 15.579
|
|
- type: mrr_at_1
|
|
value: 22.606
|
|
- type: mrr_at_10
|
|
value: 32.513
|
|
- type: mrr_at_100
|
|
value: 33.463
|
|
- type: mrr_at_1000
|
|
value: 33.513999999999996
|
|
- type: mrr_at_3
|
|
value: 29.479
|
|
- type: mrr_at_5
|
|
value: 31.3
|
|
- type: ndcg_at_1
|
|
value: 22.606
|
|
- type: ndcg_at_10
|
|
value: 24.053
|
|
- type: ndcg_at_100
|
|
value: 30.258000000000003
|
|
- type: ndcg_at_1000
|
|
value: 33.516
|
|
- type: ndcg_at_3
|
|
value: 19.721
|
|
- type: ndcg_at_5
|
|
value: 21.144
|
|
- type: precision_at_1
|
|
value: 22.606
|
|
- type: precision_at_10
|
|
value: 7.55
|
|
- type: precision_at_100
|
|
value: 1.399
|
|
- type: precision_at_1000
|
|
value: 0.2
|
|
- type: precision_at_3
|
|
value: 14.701
|
|
- type: precision_at_5
|
|
value: 11.192
|
|
- type: recall_at_1
|
|
value: 10.079
|
|
- type: recall_at_10
|
|
value: 28.970000000000002
|
|
- type: recall_at_100
|
|
value: 50.805
|
|
- type: recall_at_1000
|
|
value: 69.378
|
|
- type: recall_at_3
|
|
value: 18.199
|
|
- type: recall_at_5
|
|
value: 22.442
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 7.794
|
|
- type: map_at_10
|
|
value: 15.165999999999999
|
|
- type: map_at_100
|
|
value: 20.508000000000003
|
|
- type: map_at_1000
|
|
value: 21.809
|
|
- type: map_at_3
|
|
value: 11.568000000000001
|
|
- type: map_at_5
|
|
value: 13.059000000000001
|
|
- type: mrr_at_1
|
|
value: 56.49999999999999
|
|
- type: mrr_at_10
|
|
value: 65.90899999999999
|
|
- type: mrr_at_100
|
|
value: 66.352
|
|
- type: mrr_at_1000
|
|
value: 66.369
|
|
- type: mrr_at_3
|
|
value: 64.0
|
|
- type: mrr_at_5
|
|
value: 65.10000000000001
|
|
- type: ndcg_at_1
|
|
value: 44.25
|
|
- type: ndcg_at_10
|
|
value: 32.649
|
|
- type: ndcg_at_100
|
|
value: 36.668
|
|
- type: ndcg_at_1000
|
|
value: 43.918
|
|
- type: ndcg_at_3
|
|
value: 37.096000000000004
|
|
- type: ndcg_at_5
|
|
value: 34.048
|
|
- type: precision_at_1
|
|
value: 56.49999999999999
|
|
- type: precision_at_10
|
|
value: 25.45
|
|
- type: precision_at_100
|
|
value: 8.055
|
|
- type: precision_at_1000
|
|
value: 1.7489999999999999
|
|
- type: precision_at_3
|
|
value: 41.0
|
|
- type: precision_at_5
|
|
value: 32.85
|
|
- type: recall_at_1
|
|
value: 7.794
|
|
- type: recall_at_10
|
|
value: 20.101
|
|
- type: recall_at_100
|
|
value: 42.448
|
|
- type: recall_at_1000
|
|
value: 65.88000000000001
|
|
- type: recall_at_3
|
|
value: 12.753
|
|
- type: recall_at_5
|
|
value: 15.307
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 44.01
|
|
- type: f1
|
|
value: 38.659680951114964
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 49.713
|
|
- type: map_at_10
|
|
value: 61.79
|
|
- type: map_at_100
|
|
value: 62.28
|
|
- type: map_at_1000
|
|
value: 62.297000000000004
|
|
- type: map_at_3
|
|
value: 59.361
|
|
- type: map_at_5
|
|
value: 60.92100000000001
|
|
- type: mrr_at_1
|
|
value: 53.405
|
|
- type: mrr_at_10
|
|
value: 65.79899999999999
|
|
- type: mrr_at_100
|
|
value: 66.219
|
|
- type: mrr_at_1000
|
|
value: 66.227
|
|
- type: mrr_at_3
|
|
value: 63.431000000000004
|
|
- type: mrr_at_5
|
|
value: 64.98
|
|
- type: ndcg_at_1
|
|
value: 53.405
|
|
- type: ndcg_at_10
|
|
value: 68.01899999999999
|
|
- type: ndcg_at_100
|
|
value: 70.197
|
|
- type: ndcg_at_1000
|
|
value: 70.571
|
|
- type: ndcg_at_3
|
|
value: 63.352
|
|
- type: ndcg_at_5
|
|
value: 66.018
|
|
- type: precision_at_1
|
|
value: 53.405
|
|
- type: precision_at_10
|
|
value: 9.119
|
|
- type: precision_at_100
|
|
value: 1.03
|
|
- type: precision_at_1000
|
|
value: 0.107
|
|
- type: precision_at_3
|
|
value: 25.602999999999998
|
|
- type: precision_at_5
|
|
value: 16.835
|
|
- type: recall_at_1
|
|
value: 49.713
|
|
- type: recall_at_10
|
|
value: 83.306
|
|
- type: recall_at_100
|
|
value: 92.92
|
|
- type: recall_at_1000
|
|
value: 95.577
|
|
- type: recall_at_3
|
|
value: 70.798
|
|
- type: recall_at_5
|
|
value: 77.254
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 15.310000000000002
|
|
- type: map_at_10
|
|
value: 26.204
|
|
- type: map_at_100
|
|
value: 27.932000000000002
|
|
- type: map_at_1000
|
|
value: 28.121000000000002
|
|
- type: map_at_3
|
|
value: 22.481
|
|
- type: map_at_5
|
|
value: 24.678
|
|
- type: mrr_at_1
|
|
value: 29.784
|
|
- type: mrr_at_10
|
|
value: 39.582
|
|
- type: mrr_at_100
|
|
value: 40.52
|
|
- type: mrr_at_1000
|
|
value: 40.568
|
|
- type: mrr_at_3
|
|
value: 37.114000000000004
|
|
- type: mrr_at_5
|
|
value: 38.596000000000004
|
|
- type: ndcg_at_1
|
|
value: 29.784
|
|
- type: ndcg_at_10
|
|
value: 33.432
|
|
- type: ndcg_at_100
|
|
value: 40.281
|
|
- type: ndcg_at_1000
|
|
value: 43.653999999999996
|
|
- type: ndcg_at_3
|
|
value: 29.612
|
|
- type: ndcg_at_5
|
|
value: 31.223
|
|
- type: precision_at_1
|
|
value: 29.784
|
|
- type: precision_at_10
|
|
value: 9.645
|
|
- type: precision_at_100
|
|
value: 1.645
|
|
- type: precision_at_1000
|
|
value: 0.22499999999999998
|
|
- type: precision_at_3
|
|
value: 20.165
|
|
- type: precision_at_5
|
|
value: 15.401000000000002
|
|
- type: recall_at_1
|
|
value: 15.310000000000002
|
|
- type: recall_at_10
|
|
value: 40.499
|
|
- type: recall_at_100
|
|
value: 66.643
|
|
- type: recall_at_1000
|
|
value: 87.059
|
|
- type: recall_at_3
|
|
value: 27.492
|
|
- type: recall_at_5
|
|
value: 33.748
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 33.599000000000004
|
|
- type: map_at_10
|
|
value: 47.347
|
|
- type: map_at_100
|
|
value: 48.191
|
|
- type: map_at_1000
|
|
value: 48.263
|
|
- type: map_at_3
|
|
value: 44.698
|
|
- type: map_at_5
|
|
value: 46.278999999999996
|
|
- type: mrr_at_1
|
|
value: 67.19800000000001
|
|
- type: mrr_at_10
|
|
value: 74.054
|
|
- type: mrr_at_100
|
|
value: 74.376
|
|
- type: mrr_at_1000
|
|
value: 74.392
|
|
- type: mrr_at_3
|
|
value: 72.849
|
|
- type: mrr_at_5
|
|
value: 73.643
|
|
- type: ndcg_at_1
|
|
value: 67.19800000000001
|
|
- type: ndcg_at_10
|
|
value: 56.482
|
|
- type: ndcg_at_100
|
|
value: 59.694
|
|
- type: ndcg_at_1000
|
|
value: 61.204
|
|
- type: ndcg_at_3
|
|
value: 52.43299999999999
|
|
- type: ndcg_at_5
|
|
value: 54.608000000000004
|
|
- type: precision_at_1
|
|
value: 67.19800000000001
|
|
- type: precision_at_10
|
|
value: 11.613999999999999
|
|
- type: precision_at_100
|
|
value: 1.415
|
|
- type: precision_at_1000
|
|
value: 0.16199999999999998
|
|
- type: precision_at_3
|
|
value: 32.726
|
|
- type: precision_at_5
|
|
value: 21.349999999999998
|
|
- type: recall_at_1
|
|
value: 33.599000000000004
|
|
- type: recall_at_10
|
|
value: 58.069
|
|
- type: recall_at_100
|
|
value: 70.736
|
|
- type: recall_at_1000
|
|
value: 80.804
|
|
- type: recall_at_3
|
|
value: 49.088
|
|
- type: recall_at_5
|
|
value: 53.376000000000005
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 73.64359999999999
|
|
- type: ap
|
|
value: 67.54685976014599
|
|
- type: f1
|
|
value: 73.55148707559482
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 19.502
|
|
- type: map_at_10
|
|
value: 30.816
|
|
- type: map_at_100
|
|
value: 32.007999999999996
|
|
- type: map_at_1000
|
|
value: 32.067
|
|
- type: map_at_3
|
|
value: 27.215
|
|
- type: map_at_5
|
|
value: 29.304000000000002
|
|
- type: mrr_at_1
|
|
value: 20.072000000000003
|
|
- type: mrr_at_10
|
|
value: 31.406
|
|
- type: mrr_at_100
|
|
value: 32.549
|
|
- type: mrr_at_1000
|
|
value: 32.602
|
|
- type: mrr_at_3
|
|
value: 27.839000000000002
|
|
- type: mrr_at_5
|
|
value: 29.926000000000002
|
|
- type: ndcg_at_1
|
|
value: 20.086000000000002
|
|
- type: ndcg_at_10
|
|
value: 37.282
|
|
- type: ndcg_at_100
|
|
value: 43.206
|
|
- type: ndcg_at_1000
|
|
value: 44.690000000000005
|
|
- type: ndcg_at_3
|
|
value: 29.932
|
|
- type: ndcg_at_5
|
|
value: 33.668
|
|
- type: precision_at_1
|
|
value: 20.086000000000002
|
|
- type: precision_at_10
|
|
value: 5.961
|
|
- type: precision_at_100
|
|
value: 0.898
|
|
- type: precision_at_1000
|
|
value: 0.10200000000000001
|
|
- type: precision_at_3
|
|
value: 12.856000000000002
|
|
- type: precision_at_5
|
|
value: 9.596
|
|
- type: recall_at_1
|
|
value: 19.502
|
|
- type: recall_at_10
|
|
value: 57.182
|
|
- type: recall_at_100
|
|
value: 84.952
|
|
- type: recall_at_1000
|
|
value: 96.34700000000001
|
|
- type: recall_at_3
|
|
value: 37.193
|
|
- type: recall_at_5
|
|
value: 46.157
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.96488828089375
|
|
- type: f1
|
|
value: 93.32119260543482
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 72.4965800273598
|
|
- type: f1
|
|
value: 49.34896217536082
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 67.60928043039678
|
|
- type: f1
|
|
value: 64.34244712074538
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 69.75453934095493
|
|
- type: f1
|
|
value: 68.39224867489249
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 31.862573504920082
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 27.511123551196803
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 30.99145104942086
|
|
- type: mrr
|
|
value: 32.03606480418627
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.015
|
|
- type: map_at_10
|
|
value: 11.054
|
|
- type: map_at_100
|
|
value: 13.773
|
|
- type: map_at_1000
|
|
value: 15.082999999999998
|
|
- type: map_at_3
|
|
value: 8.253
|
|
- type: map_at_5
|
|
value: 9.508999999999999
|
|
- type: mrr_at_1
|
|
value: 42.105
|
|
- type: mrr_at_10
|
|
value: 50.44499999999999
|
|
- type: mrr_at_100
|
|
value: 51.080000000000005
|
|
- type: mrr_at_1000
|
|
value: 51.129999999999995
|
|
- type: mrr_at_3
|
|
value: 48.555
|
|
- type: mrr_at_5
|
|
value: 49.84
|
|
- type: ndcg_at_1
|
|
value: 40.402
|
|
- type: ndcg_at_10
|
|
value: 30.403000000000002
|
|
- type: ndcg_at_100
|
|
value: 28.216
|
|
- type: ndcg_at_1000
|
|
value: 37.021
|
|
- type: ndcg_at_3
|
|
value: 35.53
|
|
- type: ndcg_at_5
|
|
value: 33.202999999999996
|
|
- type: precision_at_1
|
|
value: 42.105
|
|
- type: precision_at_10
|
|
value: 22.353
|
|
- type: precision_at_100
|
|
value: 7.266
|
|
- type: precision_at_1000
|
|
value: 2.011
|
|
- type: precision_at_3
|
|
value: 32.921
|
|
- type: precision_at_5
|
|
value: 28.297
|
|
- type: recall_at_1
|
|
value: 5.015
|
|
- type: recall_at_10
|
|
value: 14.393
|
|
- type: recall_at_100
|
|
value: 28.893
|
|
- type: recall_at_1000
|
|
value: 60.18
|
|
- type: recall_at_3
|
|
value: 9.184000000000001
|
|
- type: recall_at_5
|
|
value: 11.39
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 29.524
|
|
- type: map_at_10
|
|
value: 44.182
|
|
- type: map_at_100
|
|
value: 45.228
|
|
- type: map_at_1000
|
|
value: 45.265
|
|
- type: map_at_3
|
|
value: 39.978
|
|
- type: map_at_5
|
|
value: 42.482
|
|
- type: mrr_at_1
|
|
value: 33.256
|
|
- type: mrr_at_10
|
|
value: 46.661
|
|
- type: mrr_at_100
|
|
value: 47.47
|
|
- type: mrr_at_1000
|
|
value: 47.496
|
|
- type: mrr_at_3
|
|
value: 43.187999999999995
|
|
- type: mrr_at_5
|
|
value: 45.330999999999996
|
|
- type: ndcg_at_1
|
|
value: 33.227000000000004
|
|
- type: ndcg_at_10
|
|
value: 51.589
|
|
- type: ndcg_at_100
|
|
value: 56.043
|
|
- type: ndcg_at_1000
|
|
value: 56.937000000000005
|
|
- type: ndcg_at_3
|
|
value: 43.751
|
|
- type: ndcg_at_5
|
|
value: 47.937000000000005
|
|
- type: precision_at_1
|
|
value: 33.227000000000004
|
|
- type: precision_at_10
|
|
value: 8.556999999999999
|
|
- type: precision_at_100
|
|
value: 1.103
|
|
- type: precision_at_1000
|
|
value: 0.11900000000000001
|
|
- type: precision_at_3
|
|
value: 19.921
|
|
- type: precision_at_5
|
|
value: 14.396999999999998
|
|
- type: recall_at_1
|
|
value: 29.524
|
|
- type: recall_at_10
|
|
value: 71.615
|
|
- type: recall_at_100
|
|
value: 91.056
|
|
- type: recall_at_1000
|
|
value: 97.72800000000001
|
|
- type: recall_at_3
|
|
value: 51.451
|
|
- type: recall_at_5
|
|
value: 61.119
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 69.596
|
|
- type: map_at_10
|
|
value: 83.281
|
|
- type: map_at_100
|
|
value: 83.952
|
|
- type: map_at_1000
|
|
value: 83.97200000000001
|
|
- type: map_at_3
|
|
value: 80.315
|
|
- type: map_at_5
|
|
value: 82.223
|
|
- type: mrr_at_1
|
|
value: 80.17
|
|
- type: mrr_at_10
|
|
value: 86.522
|
|
- type: mrr_at_100
|
|
value: 86.644
|
|
- type: mrr_at_1000
|
|
value: 86.64500000000001
|
|
- type: mrr_at_3
|
|
value: 85.438
|
|
- type: mrr_at_5
|
|
value: 86.21799999999999
|
|
- type: ndcg_at_1
|
|
value: 80.19
|
|
- type: ndcg_at_10
|
|
value: 87.19
|
|
- type: ndcg_at_100
|
|
value: 88.567
|
|
- type: ndcg_at_1000
|
|
value: 88.70400000000001
|
|
- type: ndcg_at_3
|
|
value: 84.17999999999999
|
|
- type: ndcg_at_5
|
|
value: 85.931
|
|
- type: precision_at_1
|
|
value: 80.19
|
|
- type: precision_at_10
|
|
value: 13.209000000000001
|
|
- type: precision_at_100
|
|
value: 1.518
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_3
|
|
value: 36.717
|
|
- type: precision_at_5
|
|
value: 24.248
|
|
- type: recall_at_1
|
|
value: 69.596
|
|
- type: recall_at_10
|
|
value: 94.533
|
|
- type: recall_at_100
|
|
value: 99.322
|
|
- type: recall_at_1000
|
|
value: 99.965
|
|
- type: recall_at_3
|
|
value: 85.911
|
|
- type: recall_at_5
|
|
value: 90.809
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 49.27650627571912
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 57.08550946534183
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 4.568
|
|
- type: map_at_10
|
|
value: 10.862
|
|
- type: map_at_100
|
|
value: 12.757
|
|
- type: map_at_1000
|
|
value: 13.031
|
|
- type: map_at_3
|
|
value: 7.960000000000001
|
|
- type: map_at_5
|
|
value: 9.337
|
|
- type: mrr_at_1
|
|
value: 22.5
|
|
- type: mrr_at_10
|
|
value: 32.6
|
|
- type: mrr_at_100
|
|
value: 33.603
|
|
- type: mrr_at_1000
|
|
value: 33.672000000000004
|
|
- type: mrr_at_3
|
|
value: 29.299999999999997
|
|
- type: mrr_at_5
|
|
value: 31.25
|
|
- type: ndcg_at_1
|
|
value: 22.5
|
|
- type: ndcg_at_10
|
|
value: 18.605
|
|
- type: ndcg_at_100
|
|
value: 26.029999999999998
|
|
- type: ndcg_at_1000
|
|
value: 31.256
|
|
- type: ndcg_at_3
|
|
value: 17.873
|
|
- type: ndcg_at_5
|
|
value: 15.511
|
|
- type: precision_at_1
|
|
value: 22.5
|
|
- type: precision_at_10
|
|
value: 9.58
|
|
- type: precision_at_100
|
|
value: 2.033
|
|
- type: precision_at_1000
|
|
value: 0.33
|
|
- type: precision_at_3
|
|
value: 16.633
|
|
- type: precision_at_5
|
|
value: 13.54
|
|
- type: recall_at_1
|
|
value: 4.568
|
|
- type: recall_at_10
|
|
value: 19.402
|
|
- type: recall_at_100
|
|
value: 41.277
|
|
- type: recall_at_1000
|
|
value: 66.963
|
|
- type: recall_at_3
|
|
value: 10.112
|
|
- type: recall_at_5
|
|
value: 13.712
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.31992291680787
|
|
- type: cos_sim_spearman
|
|
value: 76.7212346922664
|
|
- type: euclidean_pearson
|
|
value: 80.42189271706478
|
|
- type: euclidean_spearman
|
|
value: 76.7212342532493
|
|
- type: manhattan_pearson
|
|
value: 80.33171093031578
|
|
- type: manhattan_spearman
|
|
value: 76.63192883074694
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.16654278886763
|
|
- type: cos_sim_spearman
|
|
value: 73.66390263429565
|
|
- type: euclidean_pearson
|
|
value: 79.7485360086639
|
|
- type: euclidean_spearman
|
|
value: 73.66389870373436
|
|
- type: manhattan_pearson
|
|
value: 79.73652237443706
|
|
- type: manhattan_spearman
|
|
value: 73.65296117151647
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.40389689929246
|
|
- type: cos_sim_spearman
|
|
value: 83.29727595993955
|
|
- type: euclidean_pearson
|
|
value: 82.23970587854079
|
|
- type: euclidean_spearman
|
|
value: 83.29727595993955
|
|
- type: manhattan_pearson
|
|
value: 82.18823600831897
|
|
- type: manhattan_spearman
|
|
value: 83.20746192209594
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 81.73505246913413
|
|
- type: cos_sim_spearman
|
|
value: 79.1686548248754
|
|
- type: euclidean_pearson
|
|
value: 80.48889135993412
|
|
- type: euclidean_spearman
|
|
value: 79.16864112930354
|
|
- type: manhattan_pearson
|
|
value: 80.40720651057302
|
|
- type: manhattan_spearman
|
|
value: 79.0640155089286
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.3953512879065
|
|
- type: cos_sim_spearman
|
|
value: 87.29947322714338
|
|
- type: euclidean_pearson
|
|
value: 86.59759438529645
|
|
- type: euclidean_spearman
|
|
value: 87.29947511092824
|
|
- type: manhattan_pearson
|
|
value: 86.52097806169155
|
|
- type: manhattan_spearman
|
|
value: 87.22987242146534
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.48565753792056
|
|
- type: cos_sim_spearman
|
|
value: 83.6049720319893
|
|
- type: euclidean_pearson
|
|
value: 82.56452023172913
|
|
- type: euclidean_spearman
|
|
value: 83.60490168191697
|
|
- type: manhattan_pearson
|
|
value: 82.58079941137872
|
|
- type: manhattan_spearman
|
|
value: 83.60975807374051
|
|
- 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: 88.18239976618212
|
|
- type: cos_sim_spearman
|
|
value: 88.23061724730616
|
|
- type: euclidean_pearson
|
|
value: 87.78482472776658
|
|
- type: euclidean_spearman
|
|
value: 88.23061724730616
|
|
- type: manhattan_pearson
|
|
value: 87.75059641730239
|
|
- type: manhattan_spearman
|
|
value: 88.22527413524622
|
|
- 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: 63.42816418706765
|
|
- type: cos_sim_spearman
|
|
value: 63.4569864520124
|
|
- type: euclidean_pearson
|
|
value: 64.35405409953853
|
|
- type: euclidean_spearman
|
|
value: 63.4569864520124
|
|
- type: manhattan_pearson
|
|
value: 63.96649236073056
|
|
- type: manhattan_spearman
|
|
value: 63.01448583722708
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.41659638047614
|
|
- type: cos_sim_spearman
|
|
value: 84.03893866106175
|
|
- type: euclidean_pearson
|
|
value: 84.2251203953798
|
|
- type: euclidean_spearman
|
|
value: 84.03893866106175
|
|
- type: manhattan_pearson
|
|
value: 84.22733643205514
|
|
- type: manhattan_spearman
|
|
value: 84.06504411263612
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 79.75608022582414
|
|
- type: mrr
|
|
value: 94.0947732369301
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 50.161
|
|
- type: map_at_10
|
|
value: 59.458999999999996
|
|
- type: map_at_100
|
|
value: 60.156
|
|
- type: map_at_1000
|
|
value: 60.194
|
|
- type: map_at_3
|
|
value: 56.45400000000001
|
|
- type: map_at_5
|
|
value: 58.165
|
|
- type: mrr_at_1
|
|
value: 53.333
|
|
- type: mrr_at_10
|
|
value: 61.050000000000004
|
|
- type: mrr_at_100
|
|
value: 61.586
|
|
- type: mrr_at_1000
|
|
value: 61.624
|
|
- type: mrr_at_3
|
|
value: 58.889
|
|
- type: mrr_at_5
|
|
value: 60.122
|
|
- type: ndcg_at_1
|
|
value: 53.333
|
|
- type: ndcg_at_10
|
|
value: 63.888999999999996
|
|
- type: ndcg_at_100
|
|
value: 66.963
|
|
- type: ndcg_at_1000
|
|
value: 68.062
|
|
- type: ndcg_at_3
|
|
value: 59.01
|
|
- type: ndcg_at_5
|
|
value: 61.373999999999995
|
|
- type: precision_at_1
|
|
value: 53.333
|
|
- type: precision_at_10
|
|
value: 8.633000000000001
|
|
- type: precision_at_100
|
|
value: 1.027
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 23.111
|
|
- type: precision_at_5
|
|
value: 15.467
|
|
- type: recall_at_1
|
|
value: 50.161
|
|
- type: recall_at_10
|
|
value: 75.922
|
|
- type: recall_at_100
|
|
value: 90.0
|
|
- type: recall_at_1000
|
|
value: 98.667
|
|
- type: recall_at_3
|
|
value: 62.90599999999999
|
|
- type: recall_at_5
|
|
value: 68.828
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.81188118811882
|
|
- type: cos_sim_ap
|
|
value: 95.11619225962413
|
|
- type: cos_sim_f1
|
|
value: 90.35840484603736
|
|
- type: cos_sim_precision
|
|
value: 91.23343527013252
|
|
- type: cos_sim_recall
|
|
value: 89.5
|
|
- type: dot_accuracy
|
|
value: 99.81188118811882
|
|
- type: dot_ap
|
|
value: 95.11619225962413
|
|
- type: dot_f1
|
|
value: 90.35840484603736
|
|
- type: dot_precision
|
|
value: 91.23343527013252
|
|
- type: dot_recall
|
|
value: 89.5
|
|
- type: euclidean_accuracy
|
|
value: 99.81188118811882
|
|
- type: euclidean_ap
|
|
value: 95.11619225962413
|
|
- type: euclidean_f1
|
|
value: 90.35840484603736
|
|
- type: euclidean_precision
|
|
value: 91.23343527013252
|
|
- type: euclidean_recall
|
|
value: 89.5
|
|
- type: manhattan_accuracy
|
|
value: 99.80891089108911
|
|
- type: manhattan_ap
|
|
value: 95.07294266220966
|
|
- type: manhattan_f1
|
|
value: 90.21794221996959
|
|
- type: manhattan_precision
|
|
value: 91.46968139773895
|
|
- type: manhattan_recall
|
|
value: 89.0
|
|
- type: max_accuracy
|
|
value: 99.81188118811882
|
|
- type: max_ap
|
|
value: 95.11619225962413
|
|
- type: max_f1
|
|
value: 90.35840484603736
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 55.3481874105239
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 34.421291695525
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 49.98746633276634
|
|
- type: mrr
|
|
value: 50.63143249724133
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 31.009961979844036
|
|
- type: cos_sim_spearman
|
|
value: 30.558416108881044
|
|
- type: dot_pearson
|
|
value: 31.009964941134253
|
|
- type: dot_spearman
|
|
value: 30.545760761761393
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.207
|
|
- type: map_at_10
|
|
value: 1.6
|
|
- type: map_at_100
|
|
value: 8.594
|
|
- type: map_at_1000
|
|
value: 20.213
|
|
- type: map_at_3
|
|
value: 0.585
|
|
- type: map_at_5
|
|
value: 0.9039999999999999
|
|
- type: mrr_at_1
|
|
value: 78.0
|
|
- type: mrr_at_10
|
|
value: 87.4
|
|
- type: mrr_at_100
|
|
value: 87.4
|
|
- type: mrr_at_1000
|
|
value: 87.4
|
|
- type: mrr_at_3
|
|
value: 86.667
|
|
- type: mrr_at_5
|
|
value: 87.06700000000001
|
|
- type: ndcg_at_1
|
|
value: 73.0
|
|
- type: ndcg_at_10
|
|
value: 65.18
|
|
- type: ndcg_at_100
|
|
value: 49.631
|
|
- type: ndcg_at_1000
|
|
value: 43.498999999999995
|
|
- type: ndcg_at_3
|
|
value: 71.83800000000001
|
|
- type: ndcg_at_5
|
|
value: 69.271
|
|
- type: precision_at_1
|
|
value: 78.0
|
|
- type: precision_at_10
|
|
value: 69.19999999999999
|
|
- type: precision_at_100
|
|
value: 50.980000000000004
|
|
- type: precision_at_1000
|
|
value: 19.426
|
|
- type: precision_at_3
|
|
value: 77.333
|
|
- type: precision_at_5
|
|
value: 74.0
|
|
- type: recall_at_1
|
|
value: 0.207
|
|
- type: recall_at_10
|
|
value: 1.822
|
|
- type: recall_at_100
|
|
value: 11.849
|
|
- type: recall_at_1000
|
|
value: 40.492
|
|
- type: recall_at_3
|
|
value: 0.622
|
|
- type: recall_at_5
|
|
value: 0.9809999999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 2.001
|
|
- type: map_at_10
|
|
value: 10.376000000000001
|
|
- type: map_at_100
|
|
value: 16.936999999999998
|
|
- type: map_at_1000
|
|
value: 18.615000000000002
|
|
- type: map_at_3
|
|
value: 5.335999999999999
|
|
- type: map_at_5
|
|
value: 7.374
|
|
- type: mrr_at_1
|
|
value: 20.408
|
|
- type: mrr_at_10
|
|
value: 38.29
|
|
- type: mrr_at_100
|
|
value: 39.33
|
|
- type: mrr_at_1000
|
|
value: 39.347
|
|
- type: mrr_at_3
|
|
value: 32.993
|
|
- type: mrr_at_5
|
|
value: 36.973
|
|
- type: ndcg_at_1
|
|
value: 17.347
|
|
- type: ndcg_at_10
|
|
value: 23.515
|
|
- type: ndcg_at_100
|
|
value: 37.457
|
|
- type: ndcg_at_1000
|
|
value: 49.439
|
|
- type: ndcg_at_3
|
|
value: 22.762999999999998
|
|
- type: ndcg_at_5
|
|
value: 22.622
|
|
- type: precision_at_1
|
|
value: 20.408
|
|
- type: precision_at_10
|
|
value: 22.448999999999998
|
|
- type: precision_at_100
|
|
value: 8.184
|
|
- type: precision_at_1000
|
|
value: 1.608
|
|
- type: precision_at_3
|
|
value: 25.85
|
|
- type: precision_at_5
|
|
value: 25.306
|
|
- type: recall_at_1
|
|
value: 2.001
|
|
- type: recall_at_10
|
|
value: 17.422
|
|
- type: recall_at_100
|
|
value: 51.532999999999994
|
|
- type: recall_at_1000
|
|
value: 87.466
|
|
- type: recall_at_3
|
|
value: 6.861000000000001
|
|
- type: recall_at_5
|
|
value: 10.502
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.54419999999999
|
|
- type: ap
|
|
value: 14.372170450843907
|
|
- type: f1
|
|
value: 54.94420257390529
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 59.402942840973395
|
|
- type: f1
|
|
value: 59.4166538875571
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 41.569064336457906
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 85.31322644096085
|
|
- type: cos_sim_ap
|
|
value: 72.14518894837381
|
|
- type: cos_sim_f1
|
|
value: 66.67489813557229
|
|
- type: cos_sim_precision
|
|
value: 62.65954977953121
|
|
- type: cos_sim_recall
|
|
value: 71.2401055408971
|
|
- type: dot_accuracy
|
|
value: 85.31322644096085
|
|
- type: dot_ap
|
|
value: 72.14521480685293
|
|
- type: dot_f1
|
|
value: 66.67489813557229
|
|
- type: dot_precision
|
|
value: 62.65954977953121
|
|
- type: dot_recall
|
|
value: 71.2401055408971
|
|
- type: euclidean_accuracy
|
|
value: 85.31322644096085
|
|
- type: euclidean_ap
|
|
value: 72.14520820485349
|
|
- type: euclidean_f1
|
|
value: 66.67489813557229
|
|
- type: euclidean_precision
|
|
value: 62.65954977953121
|
|
- type: euclidean_recall
|
|
value: 71.2401055408971
|
|
- type: manhattan_accuracy
|
|
value: 85.21785778148656
|
|
- type: manhattan_ap
|
|
value: 72.01177147657364
|
|
- type: manhattan_f1
|
|
value: 66.62594673833374
|
|
- type: manhattan_precision
|
|
value: 62.0336669699727
|
|
- type: manhattan_recall
|
|
value: 71.95250659630607
|
|
- type: max_accuracy
|
|
value: 85.31322644096085
|
|
- type: max_ap
|
|
value: 72.14521480685293
|
|
- type: max_f1
|
|
value: 66.67489813557229
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 89.12756626693057
|
|
- type: cos_sim_ap
|
|
value: 86.05430786440826
|
|
- type: cos_sim_f1
|
|
value: 78.27759692216631
|
|
- type: cos_sim_precision
|
|
value: 75.33466248931929
|
|
- type: cos_sim_recall
|
|
value: 81.45980905451185
|
|
- type: dot_accuracy
|
|
value: 89.12950673341872
|
|
- type: dot_ap
|
|
value: 86.05431161145492
|
|
- type: dot_f1
|
|
value: 78.27759692216631
|
|
- type: dot_precision
|
|
value: 75.33466248931929
|
|
- type: dot_recall
|
|
value: 81.45980905451185
|
|
- type: euclidean_accuracy
|
|
value: 89.12756626693057
|
|
- type: euclidean_ap
|
|
value: 86.05431303247397
|
|
- type: euclidean_f1
|
|
value: 78.27759692216631
|
|
- type: euclidean_precision
|
|
value: 75.33466248931929
|
|
- type: euclidean_recall
|
|
value: 81.45980905451185
|
|
- type: manhattan_accuracy
|
|
value: 89.04994760740482
|
|
- type: manhattan_ap
|
|
value: 86.00860610892074
|
|
- type: manhattan_f1
|
|
value: 78.1846776005392
|
|
- type: manhattan_precision
|
|
value: 76.10438839480975
|
|
- type: manhattan_recall
|
|
value: 80.3818909762858
|
|
- type: max_accuracy
|
|
value: 89.12950673341872
|
|
- type: max_ap
|
|
value: 86.05431303247397
|
|
- type: max_f1
|
|
value: 78.27759692216631
|
|
---
|
|
<!-- TODO: add evaluation results here -->
|
|
<br><br>
|
|
|
|
<p align="center">
|
|
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
|
|
</p>
|
|
|
|
|
|
<p align="center">
|
|
<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
|
|
</p>
|
|
|
|
## Quick Start
|
|
|
|
The easiest way to starting using `jina-embeddings-v2-small-en` is to use Jina AI's [Embedding API](https://jina.ai/embeddings/).
|
|
|
|
|
|
## Intended Usage & Model Info
|
|
|
|
`jina-embeddings-v2-small-en` is an English, monolingual **embedding model** supporting **8192 sequence length**.
|
|
It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of [ALiBi](https://arxiv.org/abs/2108.12409) to allow longer sequence length.
|
|
The backbone `jina-bert-v2-small-en` is pretrained on the C4 dataset.
|
|
The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives.
|
|
These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
|
|
|
|
The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi.
|
|
This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
|
|
|
|
This model has 33 million parameters, which enables lightning-fast and memory efficient inference, while still delivering impressive performance.
|
|
Additionally, we provide the following embedding models:
|
|
|
|
- [`jina-embeddings-v2-small-en`](https://huggingface.co/jinaai/jina-embeddings-v2-small-en): 33 million parameters **(you are here)**.
|
|
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
|
|
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English Bilingual embeddings.
|
|
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English Bilingual embeddings.
|
|
- [`jina-embeddings-v2-base-es`](): Spanish-English Bilingual embeddings (soon).
|
|
|
|
## Data & Parameters
|
|
|
|
Jina Embeddings V2 [technical report](https://arxiv.org/abs/2310.19923)
|
|
|
|
## Usage
|
|
|
|
**<details><summary>Please apply mean pooling when integrating the model.</summary>**
|
|
<p>
|
|
|
|
### Why mean pooling?
|
|
|
|
`mean poooling` takes all token embeddings from model output and averaging them at sentence/paragraph level.
|
|
It has been proved to be the most effective way to produce high-quality sentence embeddings.
|
|
We offer an `encode` function to deal with this.
|
|
|
|
However, if you would like to do it without using the default `encode` function:
|
|
|
|
```python
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from transformers import AutoTokenizer, AutoModel
|
|
|
|
def mean_pooling(model_output, attention_mask):
|
|
token_embeddings = model_output[0]
|
|
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
|
|
|
|
sentences = ['How is the weather today?', 'What is the current weather like today?']
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|
|
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tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en')
|
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model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True)
|
|
|
|
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
|
|
|
|
with torch.no_grad():
|
|
model_output = model(**encoded_input)
|
|
|
|
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
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|
embeddings = F.normalize(embeddings, p=2, dim=1)
|
|
```
|
|
|
|
</p>
|
|
</details>
|
|
|
|
You can use Jina Embedding models directly from transformers package.
|
|
|
|
```python
|
|
!pip install transformers
|
|
from transformers import AutoModel
|
|
from numpy.linalg import norm
|
|
|
|
cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
|
|
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method
|
|
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
|
|
print(cos_sim(embeddings[0], embeddings[1]))
|
|
```
|
|
|
|
If you only want to handle shorter sequence, such as 2k, pass the `max_length` parameter to the `encode` function:
|
|
|
|
```python
|
|
embeddings = model.encode(
|
|
['Very long ... document'],
|
|
max_length=2048
|
|
)
|
|
```
|
|
|
|
The latest sentence-transformers also supports Jina embeddings:
|
|
|
|
```python
|
|
!pip install -U sentence-transformers
|
|
from sentence_transformers import SentenceTransformer
|
|
from sentence_transformers.util import cos_sim
|
|
|
|
model = SentenceTransformer(
|
|
"jinaai/jina-embeddings-v2-small-en", # switch to en/zh for English or Chinese
|
|
trust_remote_code=True
|
|
)
|
|
|
|
# control your input sequence length up to 8192
|
|
model.max_seq_length = 1024
|
|
|
|
embeddings = model.encode([
|
|
'How is the weather today?',
|
|
'What is the current weather like today?'
|
|
])
|
|
print(cos_sim(embeddings[0], embeddings[1]))
|
|
```
|
|
|
|
## Alternatives to Using Transformers Package
|
|
|
|
1. _Managed SaaS_: Get started with a free key on Jina AI's [Embedding API](https://jina.ai/embeddings/).
|
|
2. _Private and high-performance deployment_: Get started by picking from our suite of models and deploy them on [AWS Sagemaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-stch2ludm6vgy).
|
|
|
|
## RAG Performance
|
|
|
|
According to the latest blog post from [LLamaIndex](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83),
|
|
|
|
> In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.
|
|
|
|
|
|
<img src="https://miro.medium.com/v2/resize:fit:4800/format:webp/1*ZP2RVejCZovF3FDCg-Bx3A.png" width="780px">
|
|
|
|
## Plans
|
|
|
|
1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
|
|
2. Multimodal embedding models enable Multimodal RAG applications.
|
|
3. High-performt rerankers.
|
|
|
|
## Trouble Shooting
|
|
|
|
**Loading of Model Code failed**
|
|
|
|
If you forgot to pass the `trust_remote_code=True` flag when calling `AutoModel.from_pretrained` or initializing the model via the `SentenceTransformer` class, you will receive an error that the model weights could not be initialized.
|
|
This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:
|
|
|
|
```bash
|
|
Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...
|
|
```
|
|
|
|
## Contact
|
|
|
|
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
|
|
|
|
## Citation
|
|
|
|
If you find Jina Embeddings useful in your research, please cite the following paper:
|
|
|
|
```
|
|
@misc{günther2023jina,
|
|
title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents},
|
|
author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao},
|
|
year={2023},
|
|
eprint={2310.19923},
|
|
archivePrefix={arXiv},
|
|
primaryClass={cs.CL}
|
|
}
|
|
``` |