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---
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tags:
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- mteb
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- Sentence Transformers
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- sentence-similarity
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- sentence-transformers
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model-index:
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- name: multilingual-e5-base
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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: 78.97014925373135
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- type: ap
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value: 43.69351129103008
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- type: f1
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value: 73.38075030070492
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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 (de)
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config: de
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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: 71.7237687366167
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- type: ap
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value: 82.22089859962671
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- type: f1
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value: 69.95532758884401
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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-ext)
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config: en-ext
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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: 79.65517241379312
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- type: ap
|
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value: 28.507918657094738
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- type: f1
|
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value: 66.84516013726119
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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 (ja)
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config: ja
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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: 73.32976445396146
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- type: ap
|
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value: 20.720481637566014
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- type: f1
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value: 59.78002763416003
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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: 90.63775
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- type: ap
|
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value: 87.22277903861716
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- type: f1
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value: 90.60378636386807
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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: 44.546
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- type: f1
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value: 44.05666638370923
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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 (de)
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config: de
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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: 41.828
|
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- type: f1
|
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value: 41.2710255644252
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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 (es)
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config: es
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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: 40.534
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- type: f1
|
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value: 39.820743174270326
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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 (fr)
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config: fr
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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: 39.684
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- type: f1
|
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value: 39.11052682815307
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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 (ja)
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config: ja
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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: 37.436
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- type: f1
|
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value: 37.07082931930871
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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 (zh)
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config: zh
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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: 37.226000000000006
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- type: f1
|
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value: 36.65372077739185
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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: 22.831000000000003
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- type: map_at_10
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value: 36.42
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- type: map_at_100
|
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value: 37.699
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- type: map_at_1000
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value: 37.724000000000004
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- type: map_at_3
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value: 32.207
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- type: map_at_5
|
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value: 34.312
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- type: mrr_at_1
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value: 23.257
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- type: mrr_at_10
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value: 36.574
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- type: mrr_at_100
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value: 37.854
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- type: mrr_at_1000
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value: 37.878
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- type: mrr_at_3
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value: 32.385000000000005
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- type: mrr_at_5
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value: 34.48
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- type: ndcg_at_1
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value: 22.831000000000003
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- type: ndcg_at_10
|
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value: 44.230000000000004
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- type: ndcg_at_100
|
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value: 49.974000000000004
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- type: ndcg_at_1000
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value: 50.522999999999996
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- type: ndcg_at_3
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value: 35.363
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- type: ndcg_at_5
|
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value: 39.164
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- type: precision_at_1
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value: 22.831000000000003
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- type: precision_at_10
|
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value: 6.935
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- type: precision_at_100
|
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value: 0.9520000000000001
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- type: precision_at_1000
|
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value: 0.099
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- type: precision_at_3
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value: 14.841
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- type: precision_at_5
|
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value: 10.754
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- type: recall_at_1
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value: 22.831000000000003
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- type: recall_at_10
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value: 69.346
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- type: recall_at_100
|
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value: 95.235
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- type: recall_at_1000
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value: 99.36
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- type: recall_at_3
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value: 44.523
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- type: recall_at_5
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value: 53.769999999999996
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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: 40.27789869854063
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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: 35.41979463347428
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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: 58.22752045109304
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- type: mrr
|
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value: 71.51112430198303
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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: 84.71147646622866
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- type: cos_sim_spearman
|
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value: 85.059167046486
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- type: euclidean_pearson
|
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value: 75.88421613600647
|
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- type: euclidean_spearman
|
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value: 75.12821787150585
|
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- type: manhattan_pearson
|
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value: 75.22005646957604
|
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- type: manhattan_spearman
|
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value: 74.42880434453272
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- task:
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type: BitextMining
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dataset:
|
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type: mteb/bucc-bitext-mining
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name: MTEB BUCC (de-en)
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config: de-en
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split: test
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revision: d51519689f32196a32af33b075a01d0e7c51e252
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metrics:
|
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- type: accuracy
|
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value: 99.23799582463465
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- type: f1
|
|
value: 99.12665274878218
|
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- type: precision
|
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value: 99.07098121085595
|
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- type: recall
|
|
value: 99.23799582463465
|
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- task:
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type: BitextMining
|
|
dataset:
|
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type: mteb/bucc-bitext-mining
|
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name: MTEB BUCC (fr-en)
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config: fr-en
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split: test
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revision: d51519689f32196a32af33b075a01d0e7c51e252
|
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metrics:
|
|
- type: accuracy
|
|
value: 97.88685890380806
|
|
- type: f1
|
|
value: 97.59336708489249
|
|
- type: precision
|
|
value: 97.44662117543473
|
|
- type: recall
|
|
value: 97.88685890380806
|
|
- task:
|
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type: BitextMining
|
|
dataset:
|
|
type: mteb/bucc-bitext-mining
|
|
name: MTEB BUCC (ru-en)
|
|
config: ru-en
|
|
split: test
|
|
revision: d51519689f32196a32af33b075a01d0e7c51e252
|
|
metrics:
|
|
- type: accuracy
|
|
value: 97.47142362313821
|
|
- type: f1
|
|
value: 97.1989377670015
|
|
- type: precision
|
|
value: 97.06384944001847
|
|
- type: recall
|
|
value: 97.47142362313821
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/bucc-bitext-mining
|
|
name: MTEB BUCC (zh-en)
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config: zh-en
|
|
split: test
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revision: d51519689f32196a32af33b075a01d0e7c51e252
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metrics:
|
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- type: accuracy
|
|
value: 98.4728804634018
|
|
- type: f1
|
|
value: 98.2973494821836
|
|
- type: precision
|
|
value: 98.2095839915745
|
|
- type: recall
|
|
value: 98.4728804634018
|
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- task:
|
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type: Classification
|
|
dataset:
|
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type: mteb/banking77
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name: MTEB Banking77Classification
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|
config: default
|
|
split: test
|
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
|
metrics:
|
|
- type: accuracy
|
|
value: 82.74025974025975
|
|
- type: f1
|
|
value: 82.67420447730439
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/biorxiv-clustering-p2p
|
|
name: MTEB BiorxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
|
metrics:
|
|
- type: v_measure
|
|
value: 35.0380848063507
|
|
- task:
|
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type: Clustering
|
|
dataset:
|
|
type: mteb/biorxiv-clustering-s2s
|
|
name: MTEB BiorxivClusteringS2S
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|
config: default
|
|
split: test
|
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
|
metrics:
|
|
- type: v_measure
|
|
value: 29.45956405670166
|
|
- task:
|
|
type: Retrieval
|
|
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
|
|
value: 32.122
|
|
- type: map_at_10
|
|
value: 42.03
|
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- type: map_at_100
|
|
value: 43.364000000000004
|
|
- type: map_at_1000
|
|
value: 43.474000000000004
|
|
- type: map_at_3
|
|
value: 38.804
|
|
- type: map_at_5
|
|
value: 40.585
|
|
- type: mrr_at_1
|
|
value: 39.914
|
|
- type: mrr_at_10
|
|
value: 48.227
|
|
- type: mrr_at_100
|
|
value: 49.018
|
|
- type: mrr_at_1000
|
|
value: 49.064
|
|
- type: mrr_at_3
|
|
value: 45.994
|
|
- type: mrr_at_5
|
|
value: 47.396
|
|
- type: ndcg_at_1
|
|
value: 39.914
|
|
- type: ndcg_at_10
|
|
value: 47.825
|
|
- type: ndcg_at_100
|
|
value: 52.852
|
|
- type: ndcg_at_1000
|
|
value: 54.891
|
|
- type: ndcg_at_3
|
|
value: 43.517
|
|
- type: ndcg_at_5
|
|
value: 45.493
|
|
- type: precision_at_1
|
|
value: 39.914
|
|
- type: precision_at_10
|
|
value: 8.956
|
|
- type: precision_at_100
|
|
value: 1.388
|
|
- type: precision_at_1000
|
|
value: 0.182
|
|
- type: precision_at_3
|
|
value: 20.791999999999998
|
|
- type: precision_at_5
|
|
value: 14.821000000000002
|
|
- type: recall_at_1
|
|
value: 32.122
|
|
- type: recall_at_10
|
|
value: 58.294999999999995
|
|
- type: recall_at_100
|
|
value: 79.726
|
|
- type: recall_at_1000
|
|
value: 93.099
|
|
- type: recall_at_3
|
|
value: 45.017
|
|
- type: recall_at_5
|
|
value: 51.002
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackEnglishRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 29.677999999999997
|
|
- type: map_at_10
|
|
value: 38.684000000000005
|
|
- type: map_at_100
|
|
value: 39.812999999999995
|
|
- type: map_at_1000
|
|
value: 39.945
|
|
- type: map_at_3
|
|
value: 35.831
|
|
- type: map_at_5
|
|
value: 37.446
|
|
- type: mrr_at_1
|
|
value: 37.771
|
|
- type: mrr_at_10
|
|
value: 44.936
|
|
- type: mrr_at_100
|
|
value: 45.583
|
|
- type: mrr_at_1000
|
|
value: 45.634
|
|
- type: mrr_at_3
|
|
value: 42.771
|
|
- type: mrr_at_5
|
|
value: 43.994
|
|
- type: ndcg_at_1
|
|
value: 37.771
|
|
- type: ndcg_at_10
|
|
value: 44.059
|
|
- type: ndcg_at_100
|
|
value: 48.192
|
|
- type: ndcg_at_1000
|
|
value: 50.375
|
|
- type: ndcg_at_3
|
|
value: 40.172000000000004
|
|
- type: ndcg_at_5
|
|
value: 41.899
|
|
- type: precision_at_1
|
|
value: 37.771
|
|
- type: precision_at_10
|
|
value: 8.286999999999999
|
|
- type: precision_at_100
|
|
value: 1.322
|
|
- type: precision_at_1000
|
|
value: 0.178
|
|
- type: precision_at_3
|
|
value: 19.406000000000002
|
|
- type: precision_at_5
|
|
value: 13.745
|
|
- type: recall_at_1
|
|
value: 29.677999999999997
|
|
- type: recall_at_10
|
|
value: 53.071
|
|
- type: recall_at_100
|
|
value: 70.812
|
|
- type: recall_at_1000
|
|
value: 84.841
|
|
- type: recall_at_3
|
|
value: 41.016000000000005
|
|
- type: recall_at_5
|
|
value: 46.22
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackGamingRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 42.675000000000004
|
|
- type: map_at_10
|
|
value: 53.93599999999999
|
|
- type: map_at_100
|
|
value: 54.806999999999995
|
|
- type: map_at_1000
|
|
value: 54.867
|
|
- type: map_at_3
|
|
value: 50.934000000000005
|
|
- type: map_at_5
|
|
value: 52.583
|
|
- type: mrr_at_1
|
|
value: 48.339
|
|
- type: mrr_at_10
|
|
value: 57.265
|
|
- type: mrr_at_100
|
|
value: 57.873
|
|
- type: mrr_at_1000
|
|
value: 57.906
|
|
- type: mrr_at_3
|
|
value: 55.193000000000005
|
|
- type: mrr_at_5
|
|
value: 56.303000000000004
|
|
- type: ndcg_at_1
|
|
value: 48.339
|
|
- type: ndcg_at_10
|
|
value: 59.19799999999999
|
|
- type: ndcg_at_100
|
|
value: 62.743
|
|
- type: ndcg_at_1000
|
|
value: 63.99399999999999
|
|
- type: ndcg_at_3
|
|
value: 54.367
|
|
- type: ndcg_at_5
|
|
value: 56.548
|
|
- type: precision_at_1
|
|
value: 48.339
|
|
- type: precision_at_10
|
|
value: 9.216000000000001
|
|
- type: precision_at_100
|
|
value: 1.1809999999999998
|
|
- type: precision_at_1000
|
|
value: 0.134
|
|
- type: precision_at_3
|
|
value: 23.72
|
|
- type: precision_at_5
|
|
value: 16.025
|
|
- type: recall_at_1
|
|
value: 42.675000000000004
|
|
- type: recall_at_10
|
|
value: 71.437
|
|
- type: recall_at_100
|
|
value: 86.803
|
|
- type: recall_at_1000
|
|
value: 95.581
|
|
- type: recall_at_3
|
|
value: 58.434
|
|
- type: recall_at_5
|
|
value: 63.754
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackGisRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.518
|
|
- type: map_at_10
|
|
value: 30.648999999999997
|
|
- type: map_at_100
|
|
value: 31.508999999999997
|
|
- type: map_at_1000
|
|
value: 31.604
|
|
- type: map_at_3
|
|
value: 28.247
|
|
- type: map_at_5
|
|
value: 29.65
|
|
- type: mrr_at_1
|
|
value: 25.650000000000002
|
|
- type: mrr_at_10
|
|
value: 32.771
|
|
- type: mrr_at_100
|
|
value: 33.554
|
|
- type: mrr_at_1000
|
|
value: 33.629999999999995
|
|
- type: mrr_at_3
|
|
value: 30.433
|
|
- type: mrr_at_5
|
|
value: 31.812
|
|
- type: ndcg_at_1
|
|
value: 25.650000000000002
|
|
- type: ndcg_at_10
|
|
value: 34.929
|
|
- type: ndcg_at_100
|
|
value: 39.382
|
|
- type: ndcg_at_1000
|
|
value: 41.913
|
|
- type: ndcg_at_3
|
|
value: 30.292
|
|
- type: ndcg_at_5
|
|
value: 32.629999999999995
|
|
- type: precision_at_1
|
|
value: 25.650000000000002
|
|
- type: precision_at_10
|
|
value: 5.311
|
|
- type: precision_at_100
|
|
value: 0.792
|
|
- type: precision_at_1000
|
|
value: 0.105
|
|
- type: precision_at_3
|
|
value: 12.58
|
|
- type: precision_at_5
|
|
value: 8.994
|
|
- type: recall_at_1
|
|
value: 23.518
|
|
- type: recall_at_10
|
|
value: 46.19
|
|
- type: recall_at_100
|
|
value: 67.123
|
|
- type: recall_at_1000
|
|
value: 86.442
|
|
- type: recall_at_3
|
|
value: 33.678000000000004
|
|
- type: recall_at_5
|
|
value: 39.244
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackMathematicaRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 15.891
|
|
- type: map_at_10
|
|
value: 22.464000000000002
|
|
- type: map_at_100
|
|
value: 23.483
|
|
- type: map_at_1000
|
|
value: 23.613
|
|
- type: map_at_3
|
|
value: 20.080000000000002
|
|
- type: map_at_5
|
|
value: 21.526
|
|
- type: mrr_at_1
|
|
value: 20.025000000000002
|
|
- type: mrr_at_10
|
|
value: 26.712999999999997
|
|
- type: mrr_at_100
|
|
value: 27.650000000000002
|
|
- type: mrr_at_1000
|
|
value: 27.737000000000002
|
|
- type: mrr_at_3
|
|
value: 24.274
|
|
- type: mrr_at_5
|
|
value: 25.711000000000002
|
|
- type: ndcg_at_1
|
|
value: 20.025000000000002
|
|
- type: ndcg_at_10
|
|
value: 27.028999999999996
|
|
- type: ndcg_at_100
|
|
value: 32.064
|
|
- type: ndcg_at_1000
|
|
value: 35.188
|
|
- type: ndcg_at_3
|
|
value: 22.512999999999998
|
|
- type: ndcg_at_5
|
|
value: 24.89
|
|
- type: precision_at_1
|
|
value: 20.025000000000002
|
|
- type: precision_at_10
|
|
value: 4.776
|
|
- type: precision_at_100
|
|
value: 0.8500000000000001
|
|
- type: precision_at_1000
|
|
value: 0.125
|
|
- type: precision_at_3
|
|
value: 10.531
|
|
- type: precision_at_5
|
|
value: 7.811
|
|
- type: recall_at_1
|
|
value: 15.891
|
|
- type: recall_at_10
|
|
value: 37.261
|
|
- type: recall_at_100
|
|
value: 59.12
|
|
- type: recall_at_1000
|
|
value: 81.356
|
|
- type: recall_at_3
|
|
value: 24.741
|
|
- type: recall_at_5
|
|
value: 30.753999999999998
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.544
|
|
- type: map_at_10
|
|
value: 36.283
|
|
- type: map_at_100
|
|
value: 37.467
|
|
- type: map_at_1000
|
|
value: 37.574000000000005
|
|
- type: map_at_3
|
|
value: 33.528999999999996
|
|
- type: map_at_5
|
|
value: 35.028999999999996
|
|
- type: mrr_at_1
|
|
value: 34.166999999999994
|
|
- type: mrr_at_10
|
|
value: 41.866
|
|
- type: mrr_at_100
|
|
value: 42.666
|
|
- type: mrr_at_1000
|
|
value: 42.716
|
|
- type: mrr_at_3
|
|
value: 39.541
|
|
- type: mrr_at_5
|
|
value: 40.768
|
|
- type: ndcg_at_1
|
|
value: 34.166999999999994
|
|
- type: ndcg_at_10
|
|
value: 41.577
|
|
- type: ndcg_at_100
|
|
value: 46.687
|
|
- type: ndcg_at_1000
|
|
value: 48.967
|
|
- type: ndcg_at_3
|
|
value: 37.177
|
|
- type: ndcg_at_5
|
|
value: 39.097
|
|
- type: precision_at_1
|
|
value: 34.166999999999994
|
|
- type: precision_at_10
|
|
value: 7.420999999999999
|
|
- type: precision_at_100
|
|
value: 1.165
|
|
- type: precision_at_1000
|
|
value: 0.154
|
|
- type: precision_at_3
|
|
value: 17.291999999999998
|
|
- type: precision_at_5
|
|
value: 12.166
|
|
- type: recall_at_1
|
|
value: 27.544
|
|
- type: recall_at_10
|
|
value: 51.99399999999999
|
|
- type: recall_at_100
|
|
value: 73.738
|
|
- type: recall_at_1000
|
|
value: 89.33
|
|
- type: recall_at_3
|
|
value: 39.179
|
|
- type: recall_at_5
|
|
value: 44.385999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.661
|
|
- type: map_at_10
|
|
value: 35.475
|
|
- type: map_at_100
|
|
value: 36.626999999999995
|
|
- type: map_at_1000
|
|
value: 36.741
|
|
- type: map_at_3
|
|
value: 32.818000000000005
|
|
- type: map_at_5
|
|
value: 34.397
|
|
- type: mrr_at_1
|
|
value: 32.647999999999996
|
|
- type: mrr_at_10
|
|
value: 40.784
|
|
- type: mrr_at_100
|
|
value: 41.602
|
|
- type: mrr_at_1000
|
|
value: 41.661
|
|
- type: mrr_at_3
|
|
value: 38.68
|
|
- type: mrr_at_5
|
|
value: 39.838
|
|
- type: ndcg_at_1
|
|
value: 32.647999999999996
|
|
- type: ndcg_at_10
|
|
value: 40.697
|
|
- type: ndcg_at_100
|
|
value: 45.799
|
|
- type: ndcg_at_1000
|
|
value: 48.235
|
|
- type: ndcg_at_3
|
|
value: 36.516
|
|
- type: ndcg_at_5
|
|
value: 38.515
|
|
- type: precision_at_1
|
|
value: 32.647999999999996
|
|
- type: precision_at_10
|
|
value: 7.202999999999999
|
|
- type: precision_at_100
|
|
value: 1.1360000000000001
|
|
- type: precision_at_1000
|
|
value: 0.151
|
|
- type: precision_at_3
|
|
value: 17.314
|
|
- type: precision_at_5
|
|
value: 12.145999999999999
|
|
- type: recall_at_1
|
|
value: 26.661
|
|
- type: recall_at_10
|
|
value: 50.995000000000005
|
|
- type: recall_at_100
|
|
value: 73.065
|
|
- type: recall_at_1000
|
|
value: 89.781
|
|
- type: recall_at_3
|
|
value: 39.073
|
|
- type: recall_at_5
|
|
value: 44.395
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.946583333333333
|
|
- type: map_at_10
|
|
value: 33.79725
|
|
- type: map_at_100
|
|
value: 34.86408333333333
|
|
- type: map_at_1000
|
|
value: 34.9795
|
|
- type: map_at_3
|
|
value: 31.259999999999998
|
|
- type: map_at_5
|
|
value: 32.71541666666666
|
|
- type: mrr_at_1
|
|
value: 30.863749999999996
|
|
- type: mrr_at_10
|
|
value: 37.99183333333333
|
|
- type: mrr_at_100
|
|
value: 38.790499999999994
|
|
- type: mrr_at_1000
|
|
value: 38.85575000000001
|
|
- type: mrr_at_3
|
|
value: 35.82083333333333
|
|
- type: mrr_at_5
|
|
value: 37.07533333333333
|
|
- type: ndcg_at_1
|
|
value: 30.863749999999996
|
|
- type: ndcg_at_10
|
|
value: 38.52141666666667
|
|
- type: ndcg_at_100
|
|
value: 43.17966666666667
|
|
- type: ndcg_at_1000
|
|
value: 45.64608333333333
|
|
- type: ndcg_at_3
|
|
value: 34.333000000000006
|
|
- type: ndcg_at_5
|
|
value: 36.34975
|
|
- type: precision_at_1
|
|
value: 30.863749999999996
|
|
- type: precision_at_10
|
|
value: 6.598999999999999
|
|
- type: precision_at_100
|
|
value: 1.0502500000000001
|
|
- type: precision_at_1000
|
|
value: 0.14400000000000002
|
|
- type: precision_at_3
|
|
value: 15.557583333333334
|
|
- type: precision_at_5
|
|
value: 11.020000000000001
|
|
- type: recall_at_1
|
|
value: 25.946583333333333
|
|
- type: recall_at_10
|
|
value: 48.36991666666666
|
|
- type: recall_at_100
|
|
value: 69.02408333333334
|
|
- type: recall_at_1000
|
|
value: 86.43858333333331
|
|
- type: recall_at_3
|
|
value: 36.4965
|
|
- type: recall_at_5
|
|
value: 41.76258333333334
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.431
|
|
- type: map_at_10
|
|
value: 28.889
|
|
- type: map_at_100
|
|
value: 29.642000000000003
|
|
- type: map_at_1000
|
|
value: 29.742
|
|
- type: map_at_3
|
|
value: 26.998
|
|
- type: map_at_5
|
|
value: 28.172000000000004
|
|
- type: mrr_at_1
|
|
value: 25.307000000000002
|
|
- type: mrr_at_10
|
|
value: 31.763
|
|
- type: mrr_at_100
|
|
value: 32.443
|
|
- type: mrr_at_1000
|
|
value: 32.531
|
|
- type: mrr_at_3
|
|
value: 29.959000000000003
|
|
- type: mrr_at_5
|
|
value: 31.063000000000002
|
|
- type: ndcg_at_1
|
|
value: 25.307000000000002
|
|
- type: ndcg_at_10
|
|
value: 32.586999999999996
|
|
- type: ndcg_at_100
|
|
value: 36.5
|
|
- type: ndcg_at_1000
|
|
value: 39.133
|
|
- type: ndcg_at_3
|
|
value: 29.25
|
|
- type: ndcg_at_5
|
|
value: 31.023
|
|
- type: precision_at_1
|
|
value: 25.307000000000002
|
|
- type: precision_at_10
|
|
value: 4.954
|
|
- type: precision_at_100
|
|
value: 0.747
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 12.577
|
|
- type: precision_at_5
|
|
value: 8.741999999999999
|
|
- type: recall_at_1
|
|
value: 22.431
|
|
- type: recall_at_10
|
|
value: 41.134
|
|
- type: recall_at_100
|
|
value: 59.28600000000001
|
|
- type: recall_at_1000
|
|
value: 78.857
|
|
- type: recall_at_3
|
|
value: 31.926
|
|
- type: recall_at_5
|
|
value: 36.335
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.586
|
|
- type: map_at_10
|
|
value: 23.304
|
|
- type: map_at_100
|
|
value: 24.159
|
|
- type: map_at_1000
|
|
value: 24.281
|
|
- type: map_at_3
|
|
value: 21.316
|
|
- type: map_at_5
|
|
value: 22.383
|
|
- type: mrr_at_1
|
|
value: 21.645
|
|
- type: mrr_at_10
|
|
value: 27.365000000000002
|
|
- type: mrr_at_100
|
|
value: 28.108
|
|
- type: mrr_at_1000
|
|
value: 28.192
|
|
- type: mrr_at_3
|
|
value: 25.482
|
|
- type: mrr_at_5
|
|
value: 26.479999999999997
|
|
- type: ndcg_at_1
|
|
value: 21.645
|
|
- type: ndcg_at_10
|
|
value: 27.306
|
|
- type: ndcg_at_100
|
|
value: 31.496000000000002
|
|
- type: ndcg_at_1000
|
|
value: 34.53
|
|
- type: ndcg_at_3
|
|
value: 23.73
|
|
- type: ndcg_at_5
|
|
value: 25.294
|
|
- type: precision_at_1
|
|
value: 21.645
|
|
- type: precision_at_10
|
|
value: 4.797
|
|
- type: precision_at_100
|
|
value: 0.8059999999999999
|
|
- type: precision_at_1000
|
|
value: 0.121
|
|
- type: precision_at_3
|
|
value: 10.850999999999999
|
|
- type: precision_at_5
|
|
value: 7.736
|
|
- type: recall_at_1
|
|
value: 17.586
|
|
- type: recall_at_10
|
|
value: 35.481
|
|
- type: recall_at_100
|
|
value: 54.534000000000006
|
|
- type: recall_at_1000
|
|
value: 76.456
|
|
- type: recall_at_3
|
|
value: 25.335
|
|
- type: recall_at_5
|
|
value: 29.473
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.095
|
|
- type: map_at_10
|
|
value: 32.374
|
|
- type: map_at_100
|
|
value: 33.537
|
|
- type: map_at_1000
|
|
value: 33.634
|
|
- type: map_at_3
|
|
value: 30.089
|
|
- type: map_at_5
|
|
value: 31.433
|
|
- type: mrr_at_1
|
|
value: 29.198
|
|
- type: mrr_at_10
|
|
value: 36.01
|
|
- type: mrr_at_100
|
|
value: 37.022
|
|
- type: mrr_at_1000
|
|
value: 37.083
|
|
- type: mrr_at_3
|
|
value: 33.94
|
|
- type: mrr_at_5
|
|
value: 35.148
|
|
- type: ndcg_at_1
|
|
value: 29.198
|
|
- type: ndcg_at_10
|
|
value: 36.729
|
|
- type: ndcg_at_100
|
|
value: 42.114000000000004
|
|
- type: ndcg_at_1000
|
|
value: 44.592
|
|
- type: ndcg_at_3
|
|
value: 32.644
|
|
- type: ndcg_at_5
|
|
value: 34.652
|
|
- type: precision_at_1
|
|
value: 29.198
|
|
- type: precision_at_10
|
|
value: 5.970000000000001
|
|
- type: precision_at_100
|
|
value: 0.967
|
|
- type: precision_at_1000
|
|
value: 0.129
|
|
- type: precision_at_3
|
|
value: 14.396999999999998
|
|
- type: precision_at_5
|
|
value: 10.093
|
|
- type: recall_at_1
|
|
value: 25.095
|
|
- type: recall_at_10
|
|
value: 46.392
|
|
- type: recall_at_100
|
|
value: 69.706
|
|
- type: recall_at_1000
|
|
value: 87.738
|
|
- type: recall_at_3
|
|
value: 35.303000000000004
|
|
- type: recall_at_5
|
|
value: 40.441
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.857999999999997
|
|
- type: map_at_10
|
|
value: 34.066
|
|
- type: map_at_100
|
|
value: 35.671
|
|
- type: map_at_1000
|
|
value: 35.881
|
|
- type: map_at_3
|
|
value: 31.304
|
|
- type: map_at_5
|
|
value: 32.885
|
|
- type: mrr_at_1
|
|
value: 32.411
|
|
- type: mrr_at_10
|
|
value: 38.987
|
|
- type: mrr_at_100
|
|
value: 39.894
|
|
- type: mrr_at_1000
|
|
value: 39.959
|
|
- type: mrr_at_3
|
|
value: 36.626999999999995
|
|
- type: mrr_at_5
|
|
value: 38.011
|
|
- type: ndcg_at_1
|
|
value: 32.411
|
|
- type: ndcg_at_10
|
|
value: 39.208
|
|
- type: ndcg_at_100
|
|
value: 44.626
|
|
- type: ndcg_at_1000
|
|
value: 47.43
|
|
- type: ndcg_at_3
|
|
value: 35.091
|
|
- type: ndcg_at_5
|
|
value: 37.119
|
|
- type: precision_at_1
|
|
value: 32.411
|
|
- type: precision_at_10
|
|
value: 7.51
|
|
- type: precision_at_100
|
|
value: 1.486
|
|
- type: precision_at_1000
|
|
value: 0.234
|
|
- type: precision_at_3
|
|
value: 16.14
|
|
- type: precision_at_5
|
|
value: 11.976
|
|
- type: recall_at_1
|
|
value: 26.857999999999997
|
|
- type: recall_at_10
|
|
value: 47.407
|
|
- type: recall_at_100
|
|
value: 72.236
|
|
- type: recall_at_1000
|
|
value: 90.77
|
|
- type: recall_at_3
|
|
value: 35.125
|
|
- type: recall_at_5
|
|
value: 40.522999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 21.3
|
|
- type: map_at_10
|
|
value: 27.412999999999997
|
|
- type: map_at_100
|
|
value: 28.29
|
|
- type: map_at_1000
|
|
value: 28.398
|
|
- type: map_at_3
|
|
value: 25.169999999999998
|
|
- type: map_at_5
|
|
value: 26.496
|
|
- type: mrr_at_1
|
|
value: 23.29
|
|
- type: mrr_at_10
|
|
value: 29.215000000000003
|
|
- type: mrr_at_100
|
|
value: 30.073
|
|
- type: mrr_at_1000
|
|
value: 30.156
|
|
- type: mrr_at_3
|
|
value: 26.956000000000003
|
|
- type: mrr_at_5
|
|
value: 28.38
|
|
- type: ndcg_at_1
|
|
value: 23.29
|
|
- type: ndcg_at_10
|
|
value: 31.113000000000003
|
|
- type: ndcg_at_100
|
|
value: 35.701
|
|
- type: ndcg_at_1000
|
|
value: 38.505
|
|
- type: ndcg_at_3
|
|
value: 26.727
|
|
- type: ndcg_at_5
|
|
value: 29.037000000000003
|
|
- type: precision_at_1
|
|
value: 23.29
|
|
- type: precision_at_10
|
|
value: 4.787
|
|
- type: precision_at_100
|
|
value: 0.763
|
|
- type: precision_at_1000
|
|
value: 0.11100000000000002
|
|
- type: precision_at_3
|
|
value: 11.091
|
|
- type: precision_at_5
|
|
value: 7.985
|
|
- type: recall_at_1
|
|
value: 21.3
|
|
- type: recall_at_10
|
|
value: 40.782000000000004
|
|
- type: recall_at_100
|
|
value: 62.13999999999999
|
|
- type: recall_at_1000
|
|
value: 83.012
|
|
- type: recall_at_3
|
|
value: 29.131
|
|
- type: recall_at_5
|
|
value: 34.624
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 9.631
|
|
- type: map_at_10
|
|
value: 16.634999999999998
|
|
- type: map_at_100
|
|
value: 18.23
|
|
- type: map_at_1000
|
|
value: 18.419
|
|
- type: map_at_3
|
|
value: 13.66
|
|
- type: map_at_5
|
|
value: 15.173
|
|
- type: mrr_at_1
|
|
value: 21.368000000000002
|
|
- type: mrr_at_10
|
|
value: 31.56
|
|
- type: mrr_at_100
|
|
value: 32.58
|
|
- type: mrr_at_1000
|
|
value: 32.633
|
|
- type: mrr_at_3
|
|
value: 28.241
|
|
- type: mrr_at_5
|
|
value: 30.225
|
|
- type: ndcg_at_1
|
|
value: 21.368000000000002
|
|
- type: ndcg_at_10
|
|
value: 23.855999999999998
|
|
- type: ndcg_at_100
|
|
value: 30.686999999999998
|
|
- type: ndcg_at_1000
|
|
value: 34.327000000000005
|
|
- type: ndcg_at_3
|
|
value: 18.781
|
|
- type: ndcg_at_5
|
|
value: 20.73
|
|
- type: precision_at_1
|
|
value: 21.368000000000002
|
|
- type: precision_at_10
|
|
value: 7.564
|
|
- type: precision_at_100
|
|
value: 1.496
|
|
- type: precision_at_1000
|
|
value: 0.217
|
|
- type: precision_at_3
|
|
value: 13.876
|
|
- type: precision_at_5
|
|
value: 11.062
|
|
- type: recall_at_1
|
|
value: 9.631
|
|
- type: recall_at_10
|
|
value: 29.517
|
|
- type: recall_at_100
|
|
value: 53.452
|
|
- type: recall_at_1000
|
|
value: 74.115
|
|
- type: recall_at_3
|
|
value: 17.605999999999998
|
|
- type: recall_at_5
|
|
value: 22.505
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 8.885
|
|
- type: map_at_10
|
|
value: 18.798000000000002
|
|
- type: map_at_100
|
|
value: 26.316
|
|
- type: map_at_1000
|
|
value: 27.869
|
|
- type: map_at_3
|
|
value: 13.719000000000001
|
|
- type: map_at_5
|
|
value: 15.716
|
|
- type: mrr_at_1
|
|
value: 66
|
|
- type: mrr_at_10
|
|
value: 74.263
|
|
- type: mrr_at_100
|
|
value: 74.519
|
|
- type: mrr_at_1000
|
|
value: 74.531
|
|
- type: mrr_at_3
|
|
value: 72.458
|
|
- type: mrr_at_5
|
|
value: 73.321
|
|
- type: ndcg_at_1
|
|
value: 53.87499999999999
|
|
- type: ndcg_at_10
|
|
value: 40.355999999999995
|
|
- type: ndcg_at_100
|
|
value: 44.366
|
|
- type: ndcg_at_1000
|
|
value: 51.771
|
|
- type: ndcg_at_3
|
|
value: 45.195
|
|
- type: ndcg_at_5
|
|
value: 42.187000000000005
|
|
- type: precision_at_1
|
|
value: 66
|
|
- type: precision_at_10
|
|
value: 31.75
|
|
- type: precision_at_100
|
|
value: 10.11
|
|
- type: precision_at_1000
|
|
value: 1.9800000000000002
|
|
- type: precision_at_3
|
|
value: 48.167
|
|
- type: precision_at_5
|
|
value: 40.050000000000004
|
|
- type: recall_at_1
|
|
value: 8.885
|
|
- type: recall_at_10
|
|
value: 24.471999999999998
|
|
- type: recall_at_100
|
|
value: 49.669000000000004
|
|
- type: recall_at_1000
|
|
value: 73.383
|
|
- type: recall_at_3
|
|
value: 14.872
|
|
- type: recall_at_5
|
|
value: 18.262999999999998
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 45.18
|
|
- type: f1
|
|
value: 40.26878691789978
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 62.751999999999995
|
|
- type: map_at_10
|
|
value: 74.131
|
|
- type: map_at_100
|
|
value: 74.407
|
|
- type: map_at_1000
|
|
value: 74.423
|
|
- type: map_at_3
|
|
value: 72.329
|
|
- type: map_at_5
|
|
value: 73.555
|
|
- type: mrr_at_1
|
|
value: 67.282
|
|
- type: mrr_at_10
|
|
value: 78.292
|
|
- type: mrr_at_100
|
|
value: 78.455
|
|
- type: mrr_at_1000
|
|
value: 78.458
|
|
- type: mrr_at_3
|
|
value: 76.755
|
|
- type: mrr_at_5
|
|
value: 77.839
|
|
- type: ndcg_at_1
|
|
value: 67.282
|
|
- type: ndcg_at_10
|
|
value: 79.443
|
|
- type: ndcg_at_100
|
|
value: 80.529
|
|
- type: ndcg_at_1000
|
|
value: 80.812
|
|
- type: ndcg_at_3
|
|
value: 76.281
|
|
- type: ndcg_at_5
|
|
value: 78.235
|
|
- type: precision_at_1
|
|
value: 67.282
|
|
- type: precision_at_10
|
|
value: 10.078
|
|
- type: precision_at_100
|
|
value: 1.082
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 30.178
|
|
- type: precision_at_5
|
|
value: 19.232
|
|
- type: recall_at_1
|
|
value: 62.751999999999995
|
|
- type: recall_at_10
|
|
value: 91.521
|
|
- type: recall_at_100
|
|
value: 95.997
|
|
- type: recall_at_1000
|
|
value: 97.775
|
|
- type: recall_at_3
|
|
value: 83.131
|
|
- type: recall_at_5
|
|
value: 87.93299999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 18.861
|
|
- type: map_at_10
|
|
value: 30.252000000000002
|
|
- type: map_at_100
|
|
value: 32.082
|
|
- type: map_at_1000
|
|
value: 32.261
|
|
- type: map_at_3
|
|
value: 25.909
|
|
- type: map_at_5
|
|
value: 28.296
|
|
- type: mrr_at_1
|
|
value: 37.346000000000004
|
|
- type: mrr_at_10
|
|
value: 45.802
|
|
- type: mrr_at_100
|
|
value: 46.611999999999995
|
|
- type: mrr_at_1000
|
|
value: 46.659
|
|
- type: mrr_at_3
|
|
value: 43.056
|
|
- type: mrr_at_5
|
|
value: 44.637
|
|
- type: ndcg_at_1
|
|
value: 37.346000000000004
|
|
- type: ndcg_at_10
|
|
value: 38.169
|
|
- type: ndcg_at_100
|
|
value: 44.864
|
|
- type: ndcg_at_1000
|
|
value: 47.974
|
|
- type: ndcg_at_3
|
|
value: 33.619
|
|
- type: ndcg_at_5
|
|
value: 35.317
|
|
- type: precision_at_1
|
|
value: 37.346000000000004
|
|
- type: precision_at_10
|
|
value: 10.693999999999999
|
|
- type: precision_at_100
|
|
value: 1.775
|
|
- type: precision_at_1000
|
|
value: 0.231
|
|
- type: precision_at_3
|
|
value: 22.325
|
|
- type: precision_at_5
|
|
value: 16.852
|
|
- type: recall_at_1
|
|
value: 18.861
|
|
- type: recall_at_10
|
|
value: 45.672000000000004
|
|
- type: recall_at_100
|
|
value: 70.60499999999999
|
|
- type: recall_at_1000
|
|
value: 89.216
|
|
- type: recall_at_3
|
|
value: 30.361
|
|
- type: recall_at_5
|
|
value: 36.998999999999995
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 37.852999999999994
|
|
- type: map_at_10
|
|
value: 59.961
|
|
- type: map_at_100
|
|
value: 60.78
|
|
- type: map_at_1000
|
|
value: 60.843
|
|
- type: map_at_3
|
|
value: 56.39999999999999
|
|
- type: map_at_5
|
|
value: 58.646
|
|
- type: mrr_at_1
|
|
value: 75.70599999999999
|
|
- type: mrr_at_10
|
|
value: 82.321
|
|
- type: mrr_at_100
|
|
value: 82.516
|
|
- type: mrr_at_1000
|
|
value: 82.525
|
|
- type: mrr_at_3
|
|
value: 81.317
|
|
- type: mrr_at_5
|
|
value: 81.922
|
|
- type: ndcg_at_1
|
|
value: 75.70599999999999
|
|
- type: ndcg_at_10
|
|
value: 68.557
|
|
- type: ndcg_at_100
|
|
value: 71.485
|
|
- type: ndcg_at_1000
|
|
value: 72.71600000000001
|
|
- type: ndcg_at_3
|
|
value: 63.524
|
|
- type: ndcg_at_5
|
|
value: 66.338
|
|
- type: precision_at_1
|
|
value: 75.70599999999999
|
|
- type: precision_at_10
|
|
value: 14.463000000000001
|
|
- type: precision_at_100
|
|
value: 1.677
|
|
- type: precision_at_1000
|
|
value: 0.184
|
|
- type: precision_at_3
|
|
value: 40.806
|
|
- type: precision_at_5
|
|
value: 26.709
|
|
- type: recall_at_1
|
|
value: 37.852999999999994
|
|
- type: recall_at_10
|
|
value: 72.316
|
|
- type: recall_at_100
|
|
value: 83.842
|
|
- type: recall_at_1000
|
|
value: 91.999
|
|
- type: recall_at_3
|
|
value: 61.209
|
|
- type: recall_at_5
|
|
value: 66.77199999999999
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 85.46039999999999
|
|
- type: ap
|
|
value: 79.9812521351881
|
|
- type: f1
|
|
value: 85.31722909702084
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.704
|
|
- type: map_at_10
|
|
value: 35.329
|
|
- type: map_at_100
|
|
value: 36.494
|
|
- type: map_at_1000
|
|
value: 36.541000000000004
|
|
- type: map_at_3
|
|
value: 31.476
|
|
- type: map_at_5
|
|
value: 33.731
|
|
- type: mrr_at_1
|
|
value: 23.294999999999998
|
|
- type: mrr_at_10
|
|
value: 35.859
|
|
- type: mrr_at_100
|
|
value: 36.968
|
|
- type: mrr_at_1000
|
|
value: 37.008
|
|
- type: mrr_at_3
|
|
value: 32.085
|
|
- type: mrr_at_5
|
|
value: 34.299
|
|
- type: ndcg_at_1
|
|
value: 23.324
|
|
- type: ndcg_at_10
|
|
value: 42.274
|
|
- type: ndcg_at_100
|
|
value: 47.839999999999996
|
|
- type: ndcg_at_1000
|
|
value: 48.971
|
|
- type: ndcg_at_3
|
|
value: 34.454
|
|
- type: ndcg_at_5
|
|
value: 38.464
|
|
- type: precision_at_1
|
|
value: 23.324
|
|
- type: precision_at_10
|
|
value: 6.648
|
|
- type: precision_at_100
|
|
value: 0.9440000000000001
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 14.674999999999999
|
|
- type: precision_at_5
|
|
value: 10.850999999999999
|
|
- type: recall_at_1
|
|
value: 22.704
|
|
- type: recall_at_10
|
|
value: 63.660000000000004
|
|
- type: recall_at_100
|
|
value: 89.29899999999999
|
|
- type: recall_at_1000
|
|
value: 97.88900000000001
|
|
- type: recall_at_3
|
|
value: 42.441
|
|
- type: recall_at_5
|
|
value: 52.04
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.1326949384405
|
|
- type: f1
|
|
value: 92.89743579612082
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (de)
|
|
config: de
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 89.62524654832347
|
|
- type: f1
|
|
value: 88.65106082263151
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (es)
|
|
config: es
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 90.59039359573046
|
|
- type: f1
|
|
value: 90.31532892105662
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (fr)
|
|
config: fr
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 86.21046038208581
|
|
- type: f1
|
|
value: 86.41459529813113
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (hi)
|
|
config: hi
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 87.3180351380423
|
|
- type: f1
|
|
value: 86.71383078226444
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (th)
|
|
config: th
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 86.24231464737792
|
|
- type: f1
|
|
value: 86.31845567592403
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 75.27131782945736
|
|
- type: f1
|
|
value: 57.52079940417103
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (de)
|
|
config: de
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.2341504649197
|
|
- type: f1
|
|
value: 51.349951558039244
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (es)
|
|
config: es
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.27418278852569
|
|
- type: f1
|
|
value: 50.1714985749095
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (fr)
|
|
config: fr
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 67.68243031631694
|
|
- type: f1
|
|
value: 50.1066160836192
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (hi)
|
|
config: hi
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 69.2362854069559
|
|
- type: f1
|
|
value: 48.821279948766424
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (th)
|
|
config: th
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.71428571428571
|
|
- type: f1
|
|
value: 53.94611389496195
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (af)
|
|
config: af
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 59.97646267652992
|
|
- type: f1
|
|
value: 57.26797883561521
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (am)
|
|
config: am
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 53.65501008742435
|
|
- type: f1
|
|
value: 50.416258382177034
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (ar)
|
|
config: ar
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 57.45796906523201
|
|
- type: f1
|
|
value: 53.306690547422185
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (az)
|
|
config: az
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 62.59246805648957
|
|
- type: f1
|
|
value: 59.818381969051494
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (bn)
|
|
config: bn
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 61.126429051782104
|
|
- type: f1
|
|
value: 58.25993593933026
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (cy)
|
|
config: cy
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 50.057162071284466
|
|
- type: f1
|
|
value: 46.96095728790911
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (da)
|
|
config: da
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 66.64425016812375
|
|
- type: f1
|
|
value: 62.858291698755764
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (de)
|
|
config: de
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 66.08944182918628
|
|
- type: f1
|
|
value: 62.44639030604241
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (el)
|
|
config: el
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 64.68056489576328
|
|
- type: f1
|
|
value: 61.775326758789504
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 72.11163416274377
|
|
- type: f1
|
|
value: 69.70789096927015
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (es)
|
|
config: es
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 68.40282447881641
|
|
- type: f1
|
|
value: 66.38492065671895
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (fa)
|
|
config: fa
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 67.24613315400134
|
|
- type: f1
|
|
value: 64.3348019501336
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (fi)
|
|
config: fi
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 65.78345662407531
|
|
- type: f1
|
|
value: 62.21279452354622
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (fr)
|
|
config: fr
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 67.9455279085407
|
|
- type: f1
|
|
value: 65.48193124964094
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (he)
|
|
config: he
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 62.05110961667788
|
|
- type: f1
|
|
value: 58.097856564684534
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (hi)
|
|
config: hi
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 64.95292535305985
|
|
- type: f1
|
|
value: 62.09182174767901
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (hu)
|
|
config: hu
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 64.97310020174848
|
|
- type: f1
|
|
value: 61.14252567730396
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (hy)
|
|
config: hy
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 60.08069939475453
|
|
- type: f1
|
|
value: 57.044041742492034
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (id)
|
|
config: id
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 66.63752521856085
|
|
- type: f1
|
|
value: 63.889340907205316
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (is)
|
|
config: is
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 56.385339609952936
|
|
- type: f1
|
|
value: 53.449033750088304
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (it)
|
|
config: it
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 68.93073301950234
|
|
- type: f1
|
|
value: 65.9884357824104
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (ja)
|
|
config: ja
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 68.94418291862812
|
|
- type: f1
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
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|
- task:
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
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|
|
value: 28.91697191169135
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 28.434000079573313
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 30.96683513343383
|
|
- type: mrr
|
|
value: 31.967364078714834
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.5280000000000005
|
|
- type: map_at_10
|
|
value: 11.793
|
|
- type: map_at_100
|
|
value: 14.496999999999998
|
|
- type: map_at_1000
|
|
value: 15.783
|
|
- type: map_at_3
|
|
value: 8.838
|
|
- type: map_at_5
|
|
value: 10.07
|
|
- type: mrr_at_1
|
|
value: 43.653
|
|
- type: mrr_at_10
|
|
value: 51.531000000000006
|
|
- type: mrr_at_100
|
|
value: 52.205
|
|
- type: mrr_at_1000
|
|
value: 52.242999999999995
|
|
- type: mrr_at_3
|
|
value: 49.431999999999995
|
|
- type: mrr_at_5
|
|
value: 50.470000000000006
|
|
- type: ndcg_at_1
|
|
value: 42.415000000000006
|
|
- type: ndcg_at_10
|
|
value: 32.464999999999996
|
|
- type: ndcg_at_100
|
|
value: 28.927999999999997
|
|
- type: ndcg_at_1000
|
|
value: 37.629000000000005
|
|
- type: ndcg_at_3
|
|
value: 37.845
|
|
- type: ndcg_at_5
|
|
value: 35.147
|
|
- type: precision_at_1
|
|
value: 43.653
|
|
- type: precision_at_10
|
|
value: 23.932000000000002
|
|
- type: precision_at_100
|
|
value: 7.17
|
|
- type: precision_at_1000
|
|
value: 1.967
|
|
- type: precision_at_3
|
|
value: 35.397
|
|
- type: precision_at_5
|
|
value: 29.907
|
|
- type: recall_at_1
|
|
value: 5.5280000000000005
|
|
- type: recall_at_10
|
|
value: 15.568000000000001
|
|
- type: recall_at_100
|
|
value: 28.54
|
|
- type: recall_at_1000
|
|
value: 59.864
|
|
- type: recall_at_3
|
|
value: 9.822000000000001
|
|
- type: recall_at_5
|
|
value: 11.726
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 37.041000000000004
|
|
- type: map_at_10
|
|
value: 52.664
|
|
- type: map_at_100
|
|
value: 53.477
|
|
- type: map_at_1000
|
|
value: 53.505
|
|
- type: map_at_3
|
|
value: 48.510999999999996
|
|
- type: map_at_5
|
|
value: 51.036
|
|
- type: mrr_at_1
|
|
value: 41.338
|
|
- type: mrr_at_10
|
|
value: 55.071000000000005
|
|
- type: mrr_at_100
|
|
value: 55.672
|
|
- type: mrr_at_1000
|
|
value: 55.689
|
|
- type: mrr_at_3
|
|
value: 51.82
|
|
- type: mrr_at_5
|
|
value: 53.852
|
|
- type: ndcg_at_1
|
|
value: 41.338
|
|
- type: ndcg_at_10
|
|
value: 60.01800000000001
|
|
- type: ndcg_at_100
|
|
value: 63.409000000000006
|
|
- type: ndcg_at_1000
|
|
value: 64.017
|
|
- type: ndcg_at_3
|
|
value: 52.44799999999999
|
|
- type: ndcg_at_5
|
|
value: 56.571000000000005
|
|
- type: precision_at_1
|
|
value: 41.338
|
|
- type: precision_at_10
|
|
value: 9.531
|
|
- type: precision_at_100
|
|
value: 1.145
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 23.416
|
|
- type: precision_at_5
|
|
value: 16.46
|
|
- type: recall_at_1
|
|
value: 37.041000000000004
|
|
- type: recall_at_10
|
|
value: 79.76299999999999
|
|
- type: recall_at_100
|
|
value: 94.39
|
|
- type: recall_at_1000
|
|
value: 98.851
|
|
- type: recall_at_3
|
|
value: 60.465
|
|
- type: recall_at_5
|
|
value: 69.906
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 69.952
|
|
- type: map_at_10
|
|
value: 83.758
|
|
- type: map_at_100
|
|
value: 84.406
|
|
- type: map_at_1000
|
|
value: 84.425
|
|
- type: map_at_3
|
|
value: 80.839
|
|
- type: map_at_5
|
|
value: 82.646
|
|
- type: mrr_at_1
|
|
value: 80.62
|
|
- type: mrr_at_10
|
|
value: 86.947
|
|
- type: mrr_at_100
|
|
value: 87.063
|
|
- type: mrr_at_1000
|
|
value: 87.064
|
|
- type: mrr_at_3
|
|
value: 85.96000000000001
|
|
- type: mrr_at_5
|
|
value: 86.619
|
|
- type: ndcg_at_1
|
|
value: 80.63
|
|
- type: ndcg_at_10
|
|
value: 87.64800000000001
|
|
- type: ndcg_at_100
|
|
value: 88.929
|
|
- type: ndcg_at_1000
|
|
value: 89.054
|
|
- type: ndcg_at_3
|
|
value: 84.765
|
|
- type: ndcg_at_5
|
|
value: 86.291
|
|
- type: precision_at_1
|
|
value: 80.63
|
|
- type: precision_at_10
|
|
value: 13.314
|
|
- type: precision_at_100
|
|
value: 1.525
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_3
|
|
value: 37.1
|
|
- type: precision_at_5
|
|
value: 24.372
|
|
- type: recall_at_1
|
|
value: 69.952
|
|
- type: recall_at_10
|
|
value: 94.955
|
|
- type: recall_at_100
|
|
value: 99.38
|
|
- type: recall_at_1000
|
|
value: 99.96000000000001
|
|
- type: recall_at_3
|
|
value: 86.60600000000001
|
|
- type: recall_at_5
|
|
value: 90.997
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 42.41329517878427
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 55.171278362748666
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 4.213
|
|
- type: map_at_10
|
|
value: 9.895
|
|
- type: map_at_100
|
|
value: 11.776
|
|
- type: map_at_1000
|
|
value: 12.084
|
|
- type: map_at_3
|
|
value: 7.2669999999999995
|
|
- type: map_at_5
|
|
value: 8.620999999999999
|
|
- type: mrr_at_1
|
|
value: 20.8
|
|
- type: mrr_at_10
|
|
value: 31.112000000000002
|
|
- type: mrr_at_100
|
|
value: 32.274
|
|
- type: mrr_at_1000
|
|
value: 32.35
|
|
- type: mrr_at_3
|
|
value: 28.133000000000003
|
|
- type: mrr_at_5
|
|
value: 29.892999999999997
|
|
- type: ndcg_at_1
|
|
value: 20.8
|
|
- type: ndcg_at_10
|
|
value: 17.163999999999998
|
|
- type: ndcg_at_100
|
|
value: 24.738
|
|
- type: ndcg_at_1000
|
|
value: 30.316
|
|
- type: ndcg_at_3
|
|
value: 16.665
|
|
- type: ndcg_at_5
|
|
value: 14.478
|
|
- type: precision_at_1
|
|
value: 20.8
|
|
- type: precision_at_10
|
|
value: 8.74
|
|
- type: precision_at_100
|
|
value: 1.963
|
|
- type: precision_at_1000
|
|
value: 0.33
|
|
- type: precision_at_3
|
|
value: 15.467
|
|
- type: precision_at_5
|
|
value: 12.6
|
|
- type: recall_at_1
|
|
value: 4.213
|
|
- type: recall_at_10
|
|
value: 17.698
|
|
- type: recall_at_100
|
|
value: 39.838
|
|
- type: recall_at_1000
|
|
value: 66.893
|
|
- type: recall_at_3
|
|
value: 9.418
|
|
- type: recall_at_5
|
|
value: 12.773000000000001
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.90453315738294
|
|
- type: cos_sim_spearman
|
|
value: 78.51197850080254
|
|
- type: euclidean_pearson
|
|
value: 80.09647123597748
|
|
- type: euclidean_spearman
|
|
value: 78.63548011514061
|
|
- type: manhattan_pearson
|
|
value: 80.10645285675231
|
|
- type: manhattan_spearman
|
|
value: 78.57861806068901
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.2616156846401
|
|
- type: cos_sim_spearman
|
|
value: 76.69713867850156
|
|
- type: euclidean_pearson
|
|
value: 77.97948563800394
|
|
- type: euclidean_spearman
|
|
value: 74.2371211567807
|
|
- type: manhattan_pearson
|
|
value: 77.69697879669705
|
|
- type: manhattan_spearman
|
|
value: 73.86529778022278
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 77.0293269315045
|
|
- type: cos_sim_spearman
|
|
value: 78.02555120584198
|
|
- type: euclidean_pearson
|
|
value: 78.25398100379078
|
|
- type: euclidean_spearman
|
|
value: 78.66963870599464
|
|
- type: manhattan_pearson
|
|
value: 78.14314682167348
|
|
- type: manhattan_spearman
|
|
value: 78.57692322969135
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 79.16989925136942
|
|
- type: cos_sim_spearman
|
|
value: 76.5996225327091
|
|
- type: euclidean_pearson
|
|
value: 77.8319003279786
|
|
- type: euclidean_spearman
|
|
value: 76.42824009468998
|
|
- type: manhattan_pearson
|
|
value: 77.69118862737736
|
|
- type: manhattan_spearman
|
|
value: 76.25568104762812
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.42012286935325
|
|
- type: cos_sim_spearman
|
|
value: 88.15654297884122
|
|
- type: euclidean_pearson
|
|
value: 87.34082819427852
|
|
- type: euclidean_spearman
|
|
value: 88.06333589547084
|
|
- type: manhattan_pearson
|
|
value: 87.25115596784842
|
|
- type: manhattan_spearman
|
|
value: 87.9559927695203
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.88222044996712
|
|
- type: cos_sim_spearman
|
|
value: 84.28476589061077
|
|
- type: euclidean_pearson
|
|
value: 83.17399758058309
|
|
- type: euclidean_spearman
|
|
value: 83.85497357244542
|
|
- type: manhattan_pearson
|
|
value: 83.0308397703786
|
|
- type: manhattan_spearman
|
|
value: 83.71554539935046
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (ko-ko)
|
|
config: ko-ko
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 80.20682986257339
|
|
- type: cos_sim_spearman
|
|
value: 79.94567120362092
|
|
- type: euclidean_pearson
|
|
value: 79.43122480368902
|
|
- type: euclidean_spearman
|
|
value: 79.94802077264987
|
|
- type: manhattan_pearson
|
|
value: 79.32653021527081
|
|
- type: manhattan_spearman
|
|
value: 79.80961146709178
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (ar-ar)
|
|
config: ar-ar
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 74.46578144394383
|
|
- type: cos_sim_spearman
|
|
value: 74.52496637472179
|
|
- type: euclidean_pearson
|
|
value: 72.2903807076809
|
|
- type: euclidean_spearman
|
|
value: 73.55549359771645
|
|
- type: manhattan_pearson
|
|
value: 72.09324837709393
|
|
- type: manhattan_spearman
|
|
value: 73.36743103606581
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (en-ar)
|
|
config: en-ar
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 71.37272335116
|
|
- type: cos_sim_spearman
|
|
value: 71.26702117766037
|
|
- type: euclidean_pearson
|
|
value: 67.114829954434
|
|
- type: euclidean_spearman
|
|
value: 66.37938893947761
|
|
- type: manhattan_pearson
|
|
value: 66.79688574095246
|
|
- type: manhattan_spearman
|
|
value: 66.17292828079667
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (en-de)
|
|
config: en-de
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 80.61016770129092
|
|
- type: cos_sim_spearman
|
|
value: 82.08515426632214
|
|
- type: euclidean_pearson
|
|
value: 80.557340361131
|
|
- type: euclidean_spearman
|
|
value: 80.37585812266175
|
|
- type: manhattan_pearson
|
|
value: 80.6782873404285
|
|
- type: manhattan_spearman
|
|
value: 80.6678073032024
|
|
- 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: 87.00150745350108
|
|
- type: cos_sim_spearman
|
|
value: 87.83441972211425
|
|
- type: euclidean_pearson
|
|
value: 87.94826702308792
|
|
- type: euclidean_spearman
|
|
value: 87.46143974860725
|
|
- type: manhattan_pearson
|
|
value: 87.97560344306105
|
|
- type: manhattan_spearman
|
|
value: 87.5267102829796
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (en-tr)
|
|
config: en-tr
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 64.76325252267235
|
|
- type: cos_sim_spearman
|
|
value: 63.32615095463905
|
|
- type: euclidean_pearson
|
|
value: 64.07920669155716
|
|
- type: euclidean_spearman
|
|
value: 61.21409893072176
|
|
- type: manhattan_pearson
|
|
value: 64.26308625680016
|
|
- type: manhattan_spearman
|
|
value: 61.2438185254079
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (es-en)
|
|
config: es-en
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 75.82644463022595
|
|
- type: cos_sim_spearman
|
|
value: 76.50381269945073
|
|
- type: euclidean_pearson
|
|
value: 75.1328548315934
|
|
- type: euclidean_spearman
|
|
value: 75.63761139408453
|
|
- type: manhattan_pearson
|
|
value: 75.18610101241407
|
|
- type: manhattan_spearman
|
|
value: 75.30669266354164
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (es-es)
|
|
config: es-es
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.49994164686832
|
|
- type: cos_sim_spearman
|
|
value: 86.73743986245549
|
|
- type: euclidean_pearson
|
|
value: 86.8272894387145
|
|
- type: euclidean_spearman
|
|
value: 85.97608491000507
|
|
- type: manhattan_pearson
|
|
value: 86.74960140396779
|
|
- type: manhattan_spearman
|
|
value: 85.79285984190273
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (fr-en)
|
|
config: fr-en
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 79.58172210788469
|
|
- type: cos_sim_spearman
|
|
value: 80.17516468334607
|
|
- type: euclidean_pearson
|
|
value: 77.56537843470504
|
|
- type: euclidean_spearman
|
|
value: 77.57264627395521
|
|
- type: manhattan_pearson
|
|
value: 78.09703521695943
|
|
- type: manhattan_spearman
|
|
value: 78.15942760916954
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (it-en)
|
|
config: it-en
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 79.7589932931751
|
|
- type: cos_sim_spearman
|
|
value: 80.15210089028162
|
|
- type: euclidean_pearson
|
|
value: 77.54135223516057
|
|
- type: euclidean_spearman
|
|
value: 77.52697996368764
|
|
- type: manhattan_pearson
|
|
value: 77.65734439572518
|
|
- type: manhattan_spearman
|
|
value: 77.77702992016121
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (nl-en)
|
|
config: nl-en
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 79.16682365511267
|
|
- type: cos_sim_spearman
|
|
value: 79.25311267628506
|
|
- type: euclidean_pearson
|
|
value: 77.54882036762244
|
|
- type: euclidean_spearman
|
|
value: 77.33212935194827
|
|
- type: manhattan_pearson
|
|
value: 77.98405516064015
|
|
- type: manhattan_spearman
|
|
value: 77.85075717865719
|
|
- 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: 59.10473294775917
|
|
- type: cos_sim_spearman
|
|
value: 61.82780474476838
|
|
- type: euclidean_pearson
|
|
value: 45.885111672377256
|
|
- type: euclidean_spearman
|
|
value: 56.88306351932454
|
|
- type: manhattan_pearson
|
|
value: 46.101218127323186
|
|
- type: manhattan_spearman
|
|
value: 56.80953694186333
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (de)
|
|
config: de
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 45.781923079584146
|
|
- type: cos_sim_spearman
|
|
value: 55.95098449691107
|
|
- type: euclidean_pearson
|
|
value: 25.4571031323205
|
|
- type: euclidean_spearman
|
|
value: 49.859978118078935
|
|
- type: manhattan_pearson
|
|
value: 25.624938455041384
|
|
- type: manhattan_spearman
|
|
value: 49.99546185049401
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (es)
|
|
config: es
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 60.00618133997907
|
|
- type: cos_sim_spearman
|
|
value: 66.57896677718321
|
|
- type: euclidean_pearson
|
|
value: 42.60118466388821
|
|
- type: euclidean_spearman
|
|
value: 62.8210759715209
|
|
- type: manhattan_pearson
|
|
value: 42.63446860604094
|
|
- type: manhattan_spearman
|
|
value: 62.73803068925271
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (pl)
|
|
config: pl
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 28.460759121626943
|
|
- type: cos_sim_spearman
|
|
value: 34.13459007469131
|
|
- type: euclidean_pearson
|
|
value: 6.0917739325525195
|
|
- type: euclidean_spearman
|
|
value: 27.9947262664867
|
|
- type: manhattan_pearson
|
|
value: 6.16877864169911
|
|
- type: manhattan_spearman
|
|
value: 28.00664163971514
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (tr)
|
|
config: tr
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 57.42546621771696
|
|
- type: cos_sim_spearman
|
|
value: 63.699663168970474
|
|
- type: euclidean_pearson
|
|
value: 38.12085278789738
|
|
- type: euclidean_spearman
|
|
value: 58.12329140741536
|
|
- type: manhattan_pearson
|
|
value: 37.97364549443335
|
|
- type: manhattan_spearman
|
|
value: 57.81545502318733
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (ar)
|
|
config: ar
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 46.82241380954213
|
|
- type: cos_sim_spearman
|
|
value: 57.86569456006391
|
|
- type: euclidean_pearson
|
|
value: 31.80480070178813
|
|
- type: euclidean_spearman
|
|
value: 52.484000620130104
|
|
- type: manhattan_pearson
|
|
value: 31.952708554646097
|
|
- type: manhattan_spearman
|
|
value: 52.8560972356195
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (ru)
|
|
config: ru
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 52.00447170498087
|
|
- type: cos_sim_spearman
|
|
value: 60.664116225735164
|
|
- type: euclidean_pearson
|
|
value: 33.87382555421702
|
|
- type: euclidean_spearman
|
|
value: 55.74649067458667
|
|
- type: manhattan_pearson
|
|
value: 33.99117246759437
|
|
- type: manhattan_spearman
|
|
value: 55.98749034923899
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (zh)
|
|
config: zh
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 58.06497233105448
|
|
- type: cos_sim_spearman
|
|
value: 65.62968801135676
|
|
- type: euclidean_pearson
|
|
value: 47.482076613243905
|
|
- type: euclidean_spearman
|
|
value: 62.65137791498299
|
|
- type: manhattan_pearson
|
|
value: 47.57052626104093
|
|
- type: manhattan_spearman
|
|
value: 62.436916516613294
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (fr)
|
|
config: fr
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 70.49397298562575
|
|
- type: cos_sim_spearman
|
|
value: 74.79604041187868
|
|
- type: euclidean_pearson
|
|
value: 49.661891561317795
|
|
- type: euclidean_spearman
|
|
value: 70.31535537621006
|
|
- type: manhattan_pearson
|
|
value: 49.553715741850006
|
|
- type: manhattan_spearman
|
|
value: 70.24779344636806
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (de-en)
|
|
config: de-en
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 55.640574515348696
|
|
- type: cos_sim_spearman
|
|
value: 54.927959317689
|
|
- type: euclidean_pearson
|
|
value: 29.00139666967476
|
|
- type: euclidean_spearman
|
|
value: 41.86386566971605
|
|
- type: manhattan_pearson
|
|
value: 29.47411067730344
|
|
- type: manhattan_spearman
|
|
value: 42.337438424952786
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (es-en)
|
|
config: es-en
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 68.14095292259312
|
|
- type: cos_sim_spearman
|
|
value: 73.99017581234789
|
|
- type: euclidean_pearson
|
|
value: 46.46304297872084
|
|
- type: euclidean_spearman
|
|
value: 60.91834114800041
|
|
- type: manhattan_pearson
|
|
value: 47.07072666338692
|
|
- type: manhattan_spearman
|
|
value: 61.70415727977926
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (it)
|
|
config: it
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 73.27184653359575
|
|
- type: cos_sim_spearman
|
|
value: 77.76070252418626
|
|
- type: euclidean_pearson
|
|
value: 62.30586577544778
|
|
- type: euclidean_spearman
|
|
value: 75.14246629110978
|
|
- type: manhattan_pearson
|
|
value: 62.328196884927046
|
|
- type: manhattan_spearman
|
|
value: 75.1282792981433
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (pl-en)
|
|
config: pl-en
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 71.59448528829957
|
|
- type: cos_sim_spearman
|
|
value: 70.37277734222123
|
|
- type: euclidean_pearson
|
|
value: 57.63145565721123
|
|
- type: euclidean_spearman
|
|
value: 66.10113048304427
|
|
- type: manhattan_pearson
|
|
value: 57.18897811586808
|
|
- type: manhattan_spearman
|
|
value: 66.5595511215901
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (zh-en)
|
|
config: zh-en
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 66.37520607720838
|
|
- type: cos_sim_spearman
|
|
value: 69.92282148997948
|
|
- type: euclidean_pearson
|
|
value: 40.55768770125291
|
|
- type: euclidean_spearman
|
|
value: 55.189128944669605
|
|
- type: manhattan_pearson
|
|
value: 41.03566433468883
|
|
- type: manhattan_spearman
|
|
value: 55.61251893174558
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (es-it)
|
|
config: es-it
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 57.791929533771835
|
|
- type: cos_sim_spearman
|
|
value: 66.45819707662093
|
|
- type: euclidean_pearson
|
|
value: 39.03686018511092
|
|
- type: euclidean_spearman
|
|
value: 56.01282695640428
|
|
- type: manhattan_pearson
|
|
value: 38.91586623619632
|
|
- type: manhattan_spearman
|
|
value: 56.69394943612747
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (de-fr)
|
|
config: de-fr
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 47.82224468473866
|
|
- type: cos_sim_spearman
|
|
value: 59.467307194781164
|
|
- type: euclidean_pearson
|
|
value: 27.428459190256145
|
|
- type: euclidean_spearman
|
|
value: 60.83463107397519
|
|
- type: manhattan_pearson
|
|
value: 27.487391578496638
|
|
- type: manhattan_spearman
|
|
value: 61.281380460246496
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (de-pl)
|
|
config: de-pl
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 16.306666792752644
|
|
- type: cos_sim_spearman
|
|
value: 39.35486427252405
|
|
- type: euclidean_pearson
|
|
value: -2.7887154897955435
|
|
- type: euclidean_spearman
|
|
value: 27.1296051831719
|
|
- type: manhattan_pearson
|
|
value: -3.202291270581297
|
|
- type: manhattan_spearman
|
|
value: 26.32895849218158
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (fr-pl)
|
|
config: fr-pl
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 59.67006803805076
|
|
- type: cos_sim_spearman
|
|
value: 73.24670207647144
|
|
- type: euclidean_pearson
|
|
value: 46.91884681500483
|
|
- type: euclidean_spearman
|
|
value: 16.903085094570333
|
|
- type: manhattan_pearson
|
|
value: 46.88391675325812
|
|
- type: manhattan_spearman
|
|
value: 28.17180849095055
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.79555591223837
|
|
- type: cos_sim_spearman
|
|
value: 85.63658602085185
|
|
- type: euclidean_pearson
|
|
value: 85.22080894037671
|
|
- type: euclidean_spearman
|
|
value: 85.54113580167038
|
|
- type: manhattan_pearson
|
|
value: 85.1639505960118
|
|
- type: manhattan_spearman
|
|
value: 85.43502665436196
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 80.73900991689766
|
|
- type: mrr
|
|
value: 94.81624131133934
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 55.678000000000004
|
|
- type: map_at_10
|
|
value: 65.135
|
|
- type: map_at_100
|
|
value: 65.824
|
|
- type: map_at_1000
|
|
value: 65.852
|
|
- type: map_at_3
|
|
value: 62.736000000000004
|
|
- type: map_at_5
|
|
value: 64.411
|
|
- type: mrr_at_1
|
|
value: 58.333
|
|
- type: mrr_at_10
|
|
value: 66.5
|
|
- type: mrr_at_100
|
|
value: 67.053
|
|
- type: mrr_at_1000
|
|
value: 67.08
|
|
- type: mrr_at_3
|
|
value: 64.944
|
|
- type: mrr_at_5
|
|
value: 65.89399999999999
|
|
- type: ndcg_at_1
|
|
value: 58.333
|
|
- type: ndcg_at_10
|
|
value: 69.34700000000001
|
|
- type: ndcg_at_100
|
|
value: 72.32
|
|
- type: ndcg_at_1000
|
|
value: 73.014
|
|
- type: ndcg_at_3
|
|
value: 65.578
|
|
- type: ndcg_at_5
|
|
value: 67.738
|
|
- type: precision_at_1
|
|
value: 58.333
|
|
- type: precision_at_10
|
|
value: 9.033
|
|
- type: precision_at_100
|
|
value: 1.0670000000000002
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 25.444
|
|
- type: precision_at_5
|
|
value: 16.933
|
|
- type: recall_at_1
|
|
value: 55.678000000000004
|
|
- type: recall_at_10
|
|
value: 80.72200000000001
|
|
- type: recall_at_100
|
|
value: 93.93299999999999
|
|
- type: recall_at_1000
|
|
value: 99.333
|
|
- type: recall_at_3
|
|
value: 70.783
|
|
- type: recall_at_5
|
|
value: 75.978
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.74653465346535
|
|
- type: cos_sim_ap
|
|
value: 93.01476369929063
|
|
- type: cos_sim_f1
|
|
value: 86.93009118541033
|
|
- type: cos_sim_precision
|
|
value: 88.09034907597535
|
|
- type: cos_sim_recall
|
|
value: 85.8
|
|
- type: dot_accuracy
|
|
value: 99.22970297029703
|
|
- type: dot_ap
|
|
value: 51.58725659485144
|
|
- type: dot_f1
|
|
value: 53.51351351351352
|
|
- type: dot_precision
|
|
value: 58.235294117647065
|
|
- type: dot_recall
|
|
value: 49.5
|
|
- type: euclidean_accuracy
|
|
value: 99.74356435643564
|
|
- type: euclidean_ap
|
|
value: 92.40332894384368
|
|
- type: euclidean_f1
|
|
value: 86.97838109602817
|
|
- type: euclidean_precision
|
|
value: 87.46208291203236
|
|
- type: euclidean_recall
|
|
value: 86.5
|
|
- type: manhattan_accuracy
|
|
value: 99.73069306930694
|
|
- type: manhattan_ap
|
|
value: 92.01320815721121
|
|
- type: manhattan_f1
|
|
value: 86.4135864135864
|
|
- type: manhattan_precision
|
|
value: 86.32734530938124
|
|
- type: manhattan_recall
|
|
value: 86.5
|
|
- type: max_accuracy
|
|
value: 99.74653465346535
|
|
- type: max_ap
|
|
value: 93.01476369929063
|
|
- type: max_f1
|
|
value: 86.97838109602817
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 55.2660514302523
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 30.4637783572547
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 49.41377758357637
|
|
- type: mrr
|
|
value: 50.138451213818854
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 28.887846011166594
|
|
- type: cos_sim_spearman
|
|
value: 30.10823258355903
|
|
- type: dot_pearson
|
|
value: 12.888049550236385
|
|
- type: dot_spearman
|
|
value: 12.827495903098123
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.21
|
|
- type: map_at_10
|
|
value: 1.667
|
|
- type: map_at_100
|
|
value: 9.15
|
|
- type: map_at_1000
|
|
value: 22.927
|
|
- type: map_at_3
|
|
value: 0.573
|
|
- type: map_at_5
|
|
value: 0.915
|
|
- type: mrr_at_1
|
|
value: 80
|
|
- type: mrr_at_10
|
|
value: 87.167
|
|
- type: mrr_at_100
|
|
value: 87.167
|
|
- type: mrr_at_1000
|
|
value: 87.167
|
|
- type: mrr_at_3
|
|
value: 85.667
|
|
- type: mrr_at_5
|
|
value: 87.167
|
|
- type: ndcg_at_1
|
|
value: 76
|
|
- type: ndcg_at_10
|
|
value: 69.757
|
|
- type: ndcg_at_100
|
|
value: 52.402
|
|
- type: ndcg_at_1000
|
|
value: 47.737
|
|
- type: ndcg_at_3
|
|
value: 71.866
|
|
- type: ndcg_at_5
|
|
value: 72.225
|
|
- type: precision_at_1
|
|
value: 80
|
|
- type: precision_at_10
|
|
value: 75
|
|
- type: precision_at_100
|
|
value: 53.959999999999994
|
|
- type: precision_at_1000
|
|
value: 21.568
|
|
- type: precision_at_3
|
|
value: 76.667
|
|
- type: precision_at_5
|
|
value: 78
|
|
- type: recall_at_1
|
|
value: 0.21
|
|
- type: recall_at_10
|
|
value: 1.9189999999999998
|
|
- type: recall_at_100
|
|
value: 12.589
|
|
- type: recall_at_1000
|
|
value: 45.312000000000005
|
|
- type: recall_at_3
|
|
value: 0.61
|
|
- type: recall_at_5
|
|
value: 1.019
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (sqi-eng)
|
|
config: sqi-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.10000000000001
|
|
- type: f1
|
|
value: 90.06
|
|
- type: precision
|
|
value: 89.17333333333333
|
|
- type: recall
|
|
value: 92.10000000000001
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (fry-eng)
|
|
config: fry-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 56.06936416184971
|
|
- type: f1
|
|
value: 50.87508028259473
|
|
- type: precision
|
|
value: 48.97398843930635
|
|
- type: recall
|
|
value: 56.06936416184971
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (kur-eng)
|
|
config: kur-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 57.3170731707317
|
|
- type: f1
|
|
value: 52.96080139372822
|
|
- type: precision
|
|
value: 51.67861124382864
|
|
- type: recall
|
|
value: 57.3170731707317
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (tur-eng)
|
|
config: tur-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.3
|
|
- type: f1
|
|
value: 92.67333333333333
|
|
- type: precision
|
|
value: 91.90833333333333
|
|
- type: recall
|
|
value: 94.3
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (deu-eng)
|
|
config: deu-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 97.7
|
|
- type: f1
|
|
value: 97.07333333333332
|
|
- type: precision
|
|
value: 96.79500000000002
|
|
- type: recall
|
|
value: 97.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (nld-eng)
|
|
config: nld-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.69999999999999
|
|
- type: f1
|
|
value: 93.2
|
|
- type: precision
|
|
value: 92.48333333333333
|
|
- type: recall
|
|
value: 94.69999999999999
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ron-eng)
|
|
config: ron-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.9
|
|
- type: f1
|
|
value: 91.26666666666667
|
|
- type: precision
|
|
value: 90.59444444444445
|
|
- type: recall
|
|
value: 92.9
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ang-eng)
|
|
config: ang-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 34.32835820895522
|
|
- type: f1
|
|
value: 29.074180380150533
|
|
- type: precision
|
|
value: 28.068207322920596
|
|
- type: recall
|
|
value: 34.32835820895522
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ido-eng)
|
|
config: ido-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 78.5
|
|
- type: f1
|
|
value: 74.3945115995116
|
|
- type: precision
|
|
value: 72.82967843459222
|
|
- type: recall
|
|
value: 78.5
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (jav-eng)
|
|
config: jav-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 66.34146341463415
|
|
- type: f1
|
|
value: 61.2469400518181
|
|
- type: precision
|
|
value: 59.63977756660683
|
|
- type: recall
|
|
value: 66.34146341463415
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (isl-eng)
|
|
config: isl-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 80.9
|
|
- type: f1
|
|
value: 76.90349206349207
|
|
- type: precision
|
|
value: 75.32921568627451
|
|
- type: recall
|
|
value: 80.9
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (slv-eng)
|
|
config: slv-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 84.93317132442284
|
|
- type: f1
|
|
value: 81.92519105034295
|
|
- type: precision
|
|
value: 80.71283920615635
|
|
- type: recall
|
|
value: 84.93317132442284
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (cym-eng)
|
|
config: cym-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.1304347826087
|
|
- type: f1
|
|
value: 65.22394755003451
|
|
- type: precision
|
|
value: 62.912422360248435
|
|
- type: recall
|
|
value: 71.1304347826087
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (kaz-eng)
|
|
config: kaz-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 79.82608695652173
|
|
- type: f1
|
|
value: 75.55693581780538
|
|
- type: precision
|
|
value: 73.79420289855072
|
|
- type: recall
|
|
value: 79.82608695652173
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (est-eng)
|
|
config: est-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 74
|
|
- type: f1
|
|
value: 70.51022222222223
|
|
- type: precision
|
|
value: 69.29673599347512
|
|
- type: recall
|
|
value: 74
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (heb-eng)
|
|
config: heb-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 78.7
|
|
- type: f1
|
|
value: 74.14238095238095
|
|
- type: precision
|
|
value: 72.27214285714285
|
|
- type: recall
|
|
value: 78.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (gla-eng)
|
|
config: gla-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 48.97466827503016
|
|
- type: f1
|
|
value: 43.080330405420874
|
|
- type: precision
|
|
value: 41.36505499593557
|
|
- type: recall
|
|
value: 48.97466827503016
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (mar-eng)
|
|
config: mar-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 89.60000000000001
|
|
- type: f1
|
|
value: 86.62333333333333
|
|
- type: precision
|
|
value: 85.225
|
|
- type: recall
|
|
value: 89.60000000000001
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (lat-eng)
|
|
config: lat-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 45.2
|
|
- type: f1
|
|
value: 39.5761253006253
|
|
- type: precision
|
|
value: 37.991358436312
|
|
- type: recall
|
|
value: 45.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (bel-eng)
|
|
config: bel-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 89.5
|
|
- type: f1
|
|
value: 86.70333333333333
|
|
- type: precision
|
|
value: 85.53166666666667
|
|
- type: recall
|
|
value: 89.5
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (pms-eng)
|
|
config: pms-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 50.095238095238095
|
|
- type: f1
|
|
value: 44.60650460650461
|
|
- type: precision
|
|
value: 42.774116796477045
|
|
- type: recall
|
|
value: 50.095238095238095
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (gle-eng)
|
|
config: gle-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 63.4
|
|
- type: f1
|
|
value: 58.35967261904762
|
|
- type: precision
|
|
value: 56.54857142857143
|
|
- type: recall
|
|
value: 63.4
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (pes-eng)
|
|
config: pes-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 89.2
|
|
- type: f1
|
|
value: 87.075
|
|
- type: precision
|
|
value: 86.12095238095239
|
|
- type: recall
|
|
value: 89.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (nob-eng)
|
|
config: nob-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 96.8
|
|
- type: f1
|
|
value: 95.90333333333334
|
|
- type: precision
|
|
value: 95.50833333333333
|
|
- type: recall
|
|
value: 96.8
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (bul-eng)
|
|
config: bul-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 90.9
|
|
- type: f1
|
|
value: 88.6288888888889
|
|
- type: precision
|
|
value: 87.61607142857142
|
|
- type: recall
|
|
value: 90.9
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (cbk-eng)
|
|
config: cbk-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 65.2
|
|
- type: f1
|
|
value: 60.54377630539395
|
|
- type: precision
|
|
value: 58.89434482711381
|
|
- type: recall
|
|
value: 65.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (hun-eng)
|
|
config: hun-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 87
|
|
- type: f1
|
|
value: 84.32412698412699
|
|
- type: precision
|
|
value: 83.25527777777778
|
|
- type: recall
|
|
value: 87
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (uig-eng)
|
|
config: uig-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 68.7
|
|
- type: f1
|
|
value: 63.07883541295306
|
|
- type: precision
|
|
value: 61.06117424242426
|
|
- type: recall
|
|
value: 68.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (rus-eng)
|
|
config: rus-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.7
|
|
- type: f1
|
|
value: 91.78333333333335
|
|
- type: precision
|
|
value: 90.86666666666667
|
|
- type: recall
|
|
value: 93.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (spa-eng)
|
|
config: spa-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 97.7
|
|
- type: f1
|
|
value: 96.96666666666667
|
|
- type: precision
|
|
value: 96.61666666666667
|
|
- type: recall
|
|
value: 97.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (hye-eng)
|
|
config: hye-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 88.27493261455525
|
|
- type: f1
|
|
value: 85.90745732255168
|
|
- type: precision
|
|
value: 84.91389637616052
|
|
- type: recall
|
|
value: 88.27493261455525
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (tel-eng)
|
|
config: tel-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 90.5982905982906
|
|
- type: f1
|
|
value: 88.4900284900285
|
|
- type: precision
|
|
value: 87.57122507122507
|
|
- type: recall
|
|
value: 90.5982905982906
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (afr-eng)
|
|
config: afr-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 89.5
|
|
- type: f1
|
|
value: 86.90769841269842
|
|
- type: precision
|
|
value: 85.80178571428571
|
|
- type: recall
|
|
value: 89.5
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (mon-eng)
|
|
config: mon-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 82.5
|
|
- type: f1
|
|
value: 78.36796536796538
|
|
- type: precision
|
|
value: 76.82196969696969
|
|
- type: recall
|
|
value: 82.5
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (arz-eng)
|
|
config: arz-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.48846960167715
|
|
- type: f1
|
|
value: 66.78771089148448
|
|
- type: precision
|
|
value: 64.98302885095339
|
|
- type: recall
|
|
value: 71.48846960167715
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (hrv-eng)
|
|
config: hrv-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.1
|
|
- type: f1
|
|
value: 92.50333333333333
|
|
- type: precision
|
|
value: 91.77499999999999
|
|
- type: recall
|
|
value: 94.1
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (nov-eng)
|
|
config: nov-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.20622568093385
|
|
- type: f1
|
|
value: 66.83278891450098
|
|
- type: precision
|
|
value: 65.35065777283677
|
|
- type: recall
|
|
value: 71.20622568093385
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (gsw-eng)
|
|
config: gsw-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 48.717948717948715
|
|
- type: f1
|
|
value: 43.53146853146853
|
|
- type: precision
|
|
value: 42.04721204721204
|
|
- type: recall
|
|
value: 48.717948717948715
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (nds-eng)
|
|
config: nds-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 58.5
|
|
- type: f1
|
|
value: 53.8564991863928
|
|
- type: precision
|
|
value: 52.40329436122275
|
|
- type: recall
|
|
value: 58.5
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ukr-eng)
|
|
config: ukr-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 90.8
|
|
- type: f1
|
|
value: 88.29
|
|
- type: precision
|
|
value: 87.09166666666667
|
|
- type: recall
|
|
value: 90.8
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (uzb-eng)
|
|
config: uzb-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 67.28971962616822
|
|
- type: f1
|
|
value: 62.63425307817832
|
|
- type: precision
|
|
value: 60.98065939771546
|
|
- type: recall
|
|
value: 67.28971962616822
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (lit-eng)
|
|
config: lit-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 78.7
|
|
- type: f1
|
|
value: 75.5264472455649
|
|
- type: precision
|
|
value: 74.38205086580086
|
|
- type: recall
|
|
value: 78.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ina-eng)
|
|
config: ina-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 88.7
|
|
- type: f1
|
|
value: 86.10809523809525
|
|
- type: precision
|
|
value: 85.07602564102565
|
|
- type: recall
|
|
value: 88.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (lfn-eng)
|
|
config: lfn-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 56.99999999999999
|
|
- type: f1
|
|
value: 52.85487521402737
|
|
- type: precision
|
|
value: 51.53985162713104
|
|
- type: recall
|
|
value: 56.99999999999999
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (zsm-eng)
|
|
config: zsm-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94
|
|
- type: f1
|
|
value: 92.45333333333333
|
|
- type: precision
|
|
value: 91.79166666666667
|
|
- type: recall
|
|
value: 94
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ita-eng)
|
|
config: ita-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.30000000000001
|
|
- type: f1
|
|
value: 90.61333333333333
|
|
- type: precision
|
|
value: 89.83333333333331
|
|
- type: recall
|
|
value: 92.30000000000001
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (cmn-eng)
|
|
config: cmn-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.69999999999999
|
|
- type: f1
|
|
value: 93.34555555555555
|
|
- type: precision
|
|
value: 92.75416666666668
|
|
- type: recall
|
|
value: 94.69999999999999
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (lvs-eng)
|
|
config: lvs-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 80.2
|
|
- type: f1
|
|
value: 76.6563035113035
|
|
- type: precision
|
|
value: 75.3014652014652
|
|
- type: recall
|
|
value: 80.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (glg-eng)
|
|
config: glg-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 84.7
|
|
- type: f1
|
|
value: 82.78689263765207
|
|
- type: precision
|
|
value: 82.06705086580087
|
|
- type: recall
|
|
value: 84.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ceb-eng)
|
|
config: ceb-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 50.33333333333333
|
|
- type: f1
|
|
value: 45.461523661523664
|
|
- type: precision
|
|
value: 43.93545574795575
|
|
- type: recall
|
|
value: 50.33333333333333
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (bre-eng)
|
|
config: bre-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 6.6000000000000005
|
|
- type: f1
|
|
value: 5.442121400446441
|
|
- type: precision
|
|
value: 5.146630385487529
|
|
- type: recall
|
|
value: 6.6000000000000005
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ben-eng)
|
|
config: ben-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 85
|
|
- type: f1
|
|
value: 81.04666666666667
|
|
- type: precision
|
|
value: 79.25
|
|
- type: recall
|
|
value: 85
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (swg-eng)
|
|
config: swg-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 47.32142857142857
|
|
- type: f1
|
|
value: 42.333333333333336
|
|
- type: precision
|
|
value: 40.69196428571429
|
|
- type: recall
|
|
value: 47.32142857142857
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (arq-eng)
|
|
config: arq-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 30.735455543358945
|
|
- type: f1
|
|
value: 26.73616790022338
|
|
- type: precision
|
|
value: 25.397823220451283
|
|
- type: recall
|
|
value: 30.735455543358945
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (kab-eng)
|
|
config: kab-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 25.1
|
|
- type: f1
|
|
value: 21.975989896371022
|
|
- type: precision
|
|
value: 21.059885632257203
|
|
- type: recall
|
|
value: 25.1
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (fra-eng)
|
|
config: fra-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.3
|
|
- type: f1
|
|
value: 92.75666666666666
|
|
- type: precision
|
|
value: 92.06166666666665
|
|
- type: recall
|
|
value: 94.3
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (por-eng)
|
|
config: por-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.1
|
|
- type: f1
|
|
value: 92.74
|
|
- type: precision
|
|
value: 92.09166666666667
|
|
- type: recall
|
|
value: 94.1
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (tat-eng)
|
|
config: tat-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.3
|
|
- type: f1
|
|
value: 66.922442002442
|
|
- type: precision
|
|
value: 65.38249567099568
|
|
- type: recall
|
|
value: 71.3
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (oci-eng)
|
|
config: oci-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 40.300000000000004
|
|
- type: f1
|
|
value: 35.78682789299971
|
|
- type: precision
|
|
value: 34.66425128716588
|
|
- type: recall
|
|
value: 40.300000000000004
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (pol-eng)
|
|
config: pol-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 96
|
|
- type: f1
|
|
value: 94.82333333333334
|
|
- type: precision
|
|
value: 94.27833333333334
|
|
- type: recall
|
|
value: 96
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (war-eng)
|
|
config: war-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 51.1
|
|
- type: f1
|
|
value: 47.179074753133584
|
|
- type: precision
|
|
value: 46.06461044702424
|
|
- type: recall
|
|
value: 51.1
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (aze-eng)
|
|
config: aze-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 87.7
|
|
- type: f1
|
|
value: 84.71
|
|
- type: precision
|
|
value: 83.46166666666667
|
|
- type: recall
|
|
value: 87.7
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (vie-eng)
|
|
config: vie-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 95.8
|
|
- type: f1
|
|
value: 94.68333333333334
|
|
- type: precision
|
|
value: 94.13333333333334
|
|
- type: recall
|
|
value: 95.8
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (nno-eng)
|
|
config: nno-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 85.39999999999999
|
|
- type: f1
|
|
value: 82.5577380952381
|
|
- type: precision
|
|
value: 81.36833333333334
|
|
- type: recall
|
|
value: 85.39999999999999
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (cha-eng)
|
|
config: cha-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 21.16788321167883
|
|
- type: f1
|
|
value: 16.948865627297987
|
|
- type: precision
|
|
value: 15.971932568647897
|
|
- type: recall
|
|
value: 21.16788321167883
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (mhr-eng)
|
|
config: mhr-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 6.9
|
|
- type: f1
|
|
value: 5.515526831658907
|
|
- type: precision
|
|
value: 5.141966366966367
|
|
- type: recall
|
|
value: 6.9
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (dan-eng)
|
|
config: dan-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.2
|
|
- type: f1
|
|
value: 91.39666666666668
|
|
- type: precision
|
|
value: 90.58666666666667
|
|
- type: recall
|
|
value: 93.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ell-eng)
|
|
config: ell-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.2
|
|
- type: f1
|
|
value: 89.95666666666666
|
|
- type: precision
|
|
value: 88.92833333333333
|
|
- type: recall
|
|
value: 92.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (amh-eng)
|
|
config: amh-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 79.76190476190477
|
|
- type: f1
|
|
value: 74.93386243386244
|
|
- type: precision
|
|
value: 73.11011904761904
|
|
- type: recall
|
|
value: 79.76190476190477
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (pam-eng)
|
|
config: pam-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 8.799999999999999
|
|
- type: f1
|
|
value: 6.921439712248537
|
|
- type: precision
|
|
value: 6.489885109680683
|
|
- type: recall
|
|
value: 8.799999999999999
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (hsb-eng)
|
|
config: hsb-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 45.75569358178054
|
|
- type: f1
|
|
value: 40.34699501312631
|
|
- type: precision
|
|
value: 38.57886764719063
|
|
- type: recall
|
|
value: 45.75569358178054
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (srp-eng)
|
|
config: srp-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 91.4
|
|
- type: f1
|
|
value: 89.08333333333333
|
|
- type: precision
|
|
value: 88.01666666666668
|
|
- type: recall
|
|
value: 91.4
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (epo-eng)
|
|
config: epo-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.60000000000001
|
|
- type: f1
|
|
value: 92.06690476190477
|
|
- type: precision
|
|
value: 91.45095238095239
|
|
- type: recall
|
|
value: 93.60000000000001
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (kzj-eng)
|
|
config: kzj-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 7.5
|
|
- type: f1
|
|
value: 6.200363129378736
|
|
- type: precision
|
|
value: 5.89115314822466
|
|
- type: recall
|
|
value: 7.5
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (awa-eng)
|
|
config: awa-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 73.59307359307358
|
|
- type: f1
|
|
value: 68.38933553219267
|
|
- type: precision
|
|
value: 66.62698412698413
|
|
- type: recall
|
|
value: 73.59307359307358
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (fao-eng)
|
|
config: fao-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 69.8473282442748
|
|
- type: f1
|
|
value: 64.72373682297346
|
|
- type: precision
|
|
value: 62.82834214131924
|
|
- type: recall
|
|
value: 69.8473282442748
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (mal-eng)
|
|
config: mal-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 97.5254730713246
|
|
- type: f1
|
|
value: 96.72489082969432
|
|
- type: precision
|
|
value: 96.33672974284326
|
|
- type: recall
|
|
value: 97.5254730713246
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ile-eng)
|
|
config: ile-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 75.6
|
|
- type: f1
|
|
value: 72.42746031746033
|
|
- type: precision
|
|
value: 71.14036630036631
|
|
- type: recall
|
|
value: 75.6
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (bos-eng)
|
|
config: bos-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 91.24293785310734
|
|
- type: f1
|
|
value: 88.86064030131826
|
|
- type: precision
|
|
value: 87.73540489642184
|
|
- type: recall
|
|
value: 91.24293785310734
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (cor-eng)
|
|
config: cor-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 6.2
|
|
- type: f1
|
|
value: 4.383083659794954
|
|
- type: precision
|
|
value: 4.027861324289673
|
|
- type: recall
|
|
value: 6.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (cat-eng)
|
|
config: cat-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 86.8
|
|
- type: f1
|
|
value: 84.09428571428572
|
|
- type: precision
|
|
value: 83.00333333333333
|
|
- type: recall
|
|
value: 86.8
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (eus-eng)
|
|
config: eus-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 60.699999999999996
|
|
- type: f1
|
|
value: 56.1584972394755
|
|
- type: precision
|
|
value: 54.713456330903135
|
|
- type: recall
|
|
value: 60.699999999999996
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (yue-eng)
|
|
config: yue-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 84.2
|
|
- type: f1
|
|
value: 80.66190476190475
|
|
- type: precision
|
|
value: 79.19690476190476
|
|
- type: recall
|
|
value: 84.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (swe-eng)
|
|
config: swe-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.2
|
|
- type: f1
|
|
value: 91.33
|
|
- type: precision
|
|
value: 90.45
|
|
- type: recall
|
|
value: 93.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (dtp-eng)
|
|
config: dtp-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 6.3
|
|
- type: f1
|
|
value: 5.126828976748276
|
|
- type: precision
|
|
value: 4.853614328966668
|
|
- type: recall
|
|
value: 6.3
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (kat-eng)
|
|
config: kat-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 81.76943699731903
|
|
- type: f1
|
|
value: 77.82873739308057
|
|
- type: precision
|
|
value: 76.27622452019234
|
|
- type: recall
|
|
value: 81.76943699731903
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (jpn-eng)
|
|
config: jpn-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.30000000000001
|
|
- type: f1
|
|
value: 90.29666666666665
|
|
- type: precision
|
|
value: 89.40333333333334
|
|
- type: recall
|
|
value: 92.30000000000001
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (csb-eng)
|
|
config: csb-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 29.249011857707508
|
|
- type: f1
|
|
value: 24.561866096392947
|
|
- type: precision
|
|
value: 23.356583740215456
|
|
- type: recall
|
|
value: 29.249011857707508
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (xho-eng)
|
|
config: xho-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.46478873239437
|
|
- type: f1
|
|
value: 73.23943661971832
|
|
- type: precision
|
|
value: 71.66666666666667
|
|
- type: recall
|
|
value: 77.46478873239437
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (orv-eng)
|
|
config: orv-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 20.35928143712575
|
|
- type: f1
|
|
value: 15.997867865075824
|
|
- type: precision
|
|
value: 14.882104658301346
|
|
- type: recall
|
|
value: 20.35928143712575
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ind-eng)
|
|
config: ind-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.2
|
|
- type: f1
|
|
value: 90.25999999999999
|
|
- type: precision
|
|
value: 89.45333333333335
|
|
- type: recall
|
|
value: 92.2
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (tuk-eng)
|
|
config: tuk-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 23.15270935960591
|
|
- type: f1
|
|
value: 19.65673625772148
|
|
- type: precision
|
|
value: 18.793705293464992
|
|
- type: recall
|
|
value: 23.15270935960591
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (max-eng)
|
|
config: max-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 59.154929577464785
|
|
- type: f1
|
|
value: 52.3868463305083
|
|
- type: precision
|
|
value: 50.14938113529662
|
|
- type: recall
|
|
value: 59.154929577464785
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (swh-eng)
|
|
config: swh-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 70.51282051282051
|
|
- type: f1
|
|
value: 66.8089133089133
|
|
- type: precision
|
|
value: 65.37645687645687
|
|
- type: recall
|
|
value: 70.51282051282051
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (hin-eng)
|
|
config: hin-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.6
|
|
- type: f1
|
|
value: 93
|
|
- type: precision
|
|
value: 92.23333333333333
|
|
- type: recall
|
|
value: 94.6
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (dsb-eng)
|
|
config: dsb-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 38.62212943632568
|
|
- type: f1
|
|
value: 34.3278276962583
|
|
- type: precision
|
|
value: 33.07646935732408
|
|
- type: recall
|
|
value: 38.62212943632568
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ber-eng)
|
|
config: ber-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 28.1
|
|
- type: f1
|
|
value: 23.579609223054604
|
|
- type: precision
|
|
value: 22.39622774921555
|
|
- type: recall
|
|
value: 28.1
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (tam-eng)
|
|
config: tam-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 88.27361563517914
|
|
- type: f1
|
|
value: 85.12486427795874
|
|
- type: precision
|
|
value: 83.71335504885994
|
|
- type: recall
|
|
value: 88.27361563517914
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (slk-eng)
|
|
config: slk-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 88.6
|
|
- type: f1
|
|
value: 86.39928571428571
|
|
- type: precision
|
|
value: 85.4947557997558
|
|
- type: recall
|
|
value: 88.6
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (tgl-eng)
|
|
config: tgl-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 86.5
|
|
- type: f1
|
|
value: 83.77952380952381
|
|
- type: precision
|
|
value: 82.67602564102565
|
|
- type: recall
|
|
value: 86.5
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ast-eng)
|
|
config: ast-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 79.52755905511812
|
|
- type: f1
|
|
value: 75.3055868016498
|
|
- type: precision
|
|
value: 73.81889763779527
|
|
- type: recall
|
|
value: 79.52755905511812
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (mkd-eng)
|
|
config: mkd-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.9
|
|
- type: f1
|
|
value: 73.76261904761905
|
|
- type: precision
|
|
value: 72.11670995670995
|
|
- type: recall
|
|
value: 77.9
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (khm-eng)
|
|
config: khm-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 53.8781163434903
|
|
- type: f1
|
|
value: 47.25804051288816
|
|
- type: precision
|
|
value: 45.0603482390186
|
|
- type: recall
|
|
value: 53.8781163434903
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ces-eng)
|
|
config: ces-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 91.10000000000001
|
|
- type: f1
|
|
value: 88.88
|
|
- type: precision
|
|
value: 87.96333333333334
|
|
- type: recall
|
|
value: 91.10000000000001
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (tzl-eng)
|
|
config: tzl-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 38.46153846153847
|
|
- type: f1
|
|
value: 34.43978243978244
|
|
- type: precision
|
|
value: 33.429487179487175
|
|
- type: recall
|
|
value: 38.46153846153847
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (urd-eng)
|
|
config: urd-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 88.9
|
|
- type: f1
|
|
value: 86.19888888888887
|
|
- type: precision
|
|
value: 85.07440476190476
|
|
- type: recall
|
|
value: 88.9
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (ara-eng)
|
|
config: ara-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 85.9
|
|
- type: f1
|
|
value: 82.58857142857143
|
|
- type: precision
|
|
value: 81.15666666666667
|
|
- type: recall
|
|
value: 85.9
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (kor-eng)
|
|
config: kor-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 86.8
|
|
- type: f1
|
|
value: 83.36999999999999
|
|
- type: precision
|
|
value: 81.86833333333333
|
|
- type: recall
|
|
value: 86.8
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (yid-eng)
|
|
config: yid-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 68.51415094339622
|
|
- type: f1
|
|
value: 63.195000099481234
|
|
- type: precision
|
|
value: 61.394033442972116
|
|
- type: recall
|
|
value: 68.51415094339622
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (fin-eng)
|
|
config: fin-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 88.5
|
|
- type: f1
|
|
value: 86.14603174603175
|
|
- type: precision
|
|
value: 85.1162037037037
|
|
- type: recall
|
|
value: 88.5
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (tha-eng)
|
|
config: tha-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 95.62043795620438
|
|
- type: f1
|
|
value: 94.40389294403892
|
|
- type: precision
|
|
value: 93.7956204379562
|
|
- type: recall
|
|
value: 95.62043795620438
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/tatoeba-bitext-mining
|
|
name: MTEB Tatoeba (wuu-eng)
|
|
config: wuu-eng
|
|
split: test
|
|
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
|
|
metrics:
|
|
- type: accuracy
|
|
value: 81.8
|
|
- type: f1
|
|
value: 78.6532178932179
|
|
- type: precision
|
|
value: 77.46348795840176
|
|
- type: recall
|
|
value: 81.8
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 2.603
|
|
- type: map_at_10
|
|
value: 8.5
|
|
- type: map_at_100
|
|
value: 12.985
|
|
- type: map_at_1000
|
|
value: 14.466999999999999
|
|
- type: map_at_3
|
|
value: 4.859999999999999
|
|
- type: map_at_5
|
|
value: 5.817
|
|
- type: mrr_at_1
|
|
value: 28.571
|
|
- type: mrr_at_10
|
|
value: 42.331
|
|
- type: mrr_at_100
|
|
value: 43.592999999999996
|
|
- type: mrr_at_1000
|
|
value: 43.592999999999996
|
|
- type: mrr_at_3
|
|
value: 38.435
|
|
- type: mrr_at_5
|
|
value: 39.966
|
|
- type: ndcg_at_1
|
|
value: 26.531
|
|
- type: ndcg_at_10
|
|
value: 21.353
|
|
- type: ndcg_at_100
|
|
value: 31.087999999999997
|
|
- type: ndcg_at_1000
|
|
value: 43.163000000000004
|
|
- type: ndcg_at_3
|
|
value: 22.999
|
|
- type: ndcg_at_5
|
|
value: 21.451
|
|
- type: precision_at_1
|
|
value: 28.571
|
|
- type: precision_at_10
|
|
value: 19.387999999999998
|
|
- type: precision_at_100
|
|
value: 6.265
|
|
- type: precision_at_1000
|
|
value: 1.4160000000000001
|
|
- type: precision_at_3
|
|
value: 24.490000000000002
|
|
- type: precision_at_5
|
|
value: 21.224
|
|
- type: recall_at_1
|
|
value: 2.603
|
|
- type: recall_at_10
|
|
value: 14.474
|
|
- type: recall_at_100
|
|
value: 40.287
|
|
- type: recall_at_1000
|
|
value: 76.606
|
|
- type: recall_at_3
|
|
value: 5.978
|
|
- type: recall_at_5
|
|
value: 7.819
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 69.7848
|
|
- type: ap
|
|
value: 13.661023167088224
|
|
- type: f1
|
|
value: 53.61686134460943
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 61.28183361629882
|
|
- type: f1
|
|
value: 61.55481034919965
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 35.972128420092396
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 85.59933241938367
|
|
- type: cos_sim_ap
|
|
value: 72.20760361208136
|
|
- type: cos_sim_f1
|
|
value: 66.4447731755424
|
|
- type: cos_sim_precision
|
|
value: 62.35539102267469
|
|
- type: cos_sim_recall
|
|
value: 71.10817941952506
|
|
- type: dot_accuracy
|
|
value: 78.98313166835548
|
|
- type: dot_ap
|
|
value: 44.492521645493795
|
|
- type: dot_f1
|
|
value: 45.814889336016094
|
|
- type: dot_precision
|
|
value: 37.02439024390244
|
|
- type: dot_recall
|
|
value: 60.07915567282321
|
|
- type: euclidean_accuracy
|
|
value: 85.3907134767837
|
|
- type: euclidean_ap
|
|
value: 71.53847289080343
|
|
- type: euclidean_f1
|
|
value: 65.95952206778834
|
|
- type: euclidean_precision
|
|
value: 61.31006346328196
|
|
- type: euclidean_recall
|
|
value: 71.37203166226914
|
|
- type: manhattan_accuracy
|
|
value: 85.40859510043511
|
|
- type: manhattan_ap
|
|
value: 71.49664104395515
|
|
- type: manhattan_f1
|
|
value: 65.98569969356485
|
|
- type: manhattan_precision
|
|
value: 63.928748144482924
|
|
- type: manhattan_recall
|
|
value: 68.17941952506597
|
|
- type: max_accuracy
|
|
value: 85.59933241938367
|
|
- type: max_ap
|
|
value: 72.20760361208136
|
|
- type: max_f1
|
|
value: 66.4447731755424
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 88.83261536073273
|
|
- type: cos_sim_ap
|
|
value: 85.48178133644264
|
|
- type: cos_sim_f1
|
|
value: 77.87816307403935
|
|
- type: cos_sim_precision
|
|
value: 75.88953021114926
|
|
- type: cos_sim_recall
|
|
value: 79.97382198952879
|
|
- type: dot_accuracy
|
|
value: 79.76287499514883
|
|
- type: dot_ap
|
|
value: 59.17438838475084
|
|
- type: dot_f1
|
|
value: 56.34566667855996
|
|
- type: dot_precision
|
|
value: 52.50349092359864
|
|
- type: dot_recall
|
|
value: 60.794579611949494
|
|
- type: euclidean_accuracy
|
|
value: 88.76857996662397
|
|
- type: euclidean_ap
|
|
value: 85.22764834359887
|
|
- type: euclidean_f1
|
|
value: 77.65379751543554
|
|
- type: euclidean_precision
|
|
value: 75.11152683839401
|
|
- type: euclidean_recall
|
|
value: 80.37419156144134
|
|
- type: manhattan_accuracy
|
|
value: 88.6987231730508
|
|
- type: manhattan_ap
|
|
value: 85.18907981724007
|
|
- type: manhattan_f1
|
|
value: 77.51967028849757
|
|
- type: manhattan_precision
|
|
value: 75.49992701795358
|
|
- type: manhattan_recall
|
|
value: 79.65044656606098
|
|
- type: max_accuracy
|
|
value: 88.83261536073273
|
|
- type: max_ap
|
|
value: 85.48178133644264
|
|
- type: max_f1
|
|
value: 77.87816307403935
|
|
language:
|
|
- multilingual
|
|
- af
|
|
- am
|
|
- ar
|
|
- as
|
|
- az
|
|
- be
|
|
- bg
|
|
- bn
|
|
- br
|
|
- bs
|
|
- ca
|
|
- cs
|
|
- cy
|
|
- da
|
|
- de
|
|
- el
|
|
- en
|
|
- eo
|
|
- es
|
|
- et
|
|
- eu
|
|
- fa
|
|
- fi
|
|
- fr
|
|
- fy
|
|
- ga
|
|
- gd
|
|
- gl
|
|
- gu
|
|
- ha
|
|
- he
|
|
- hi
|
|
- hr
|
|
- hu
|
|
- hy
|
|
- id
|
|
- is
|
|
- it
|
|
- ja
|
|
- jv
|
|
- ka
|
|
- kk
|
|
- km
|
|
- kn
|
|
- ko
|
|
- ku
|
|
- ky
|
|
- la
|
|
- lo
|
|
- lt
|
|
- lv
|
|
- mg
|
|
- mk
|
|
- ml
|
|
- mn
|
|
- mr
|
|
- ms
|
|
- my
|
|
- ne
|
|
- nl
|
|
- 'no'
|
|
- om
|
|
- or
|
|
- pa
|
|
- pl
|
|
- ps
|
|
- pt
|
|
- ro
|
|
- ru
|
|
- sa
|
|
- sd
|
|
- si
|
|
- sk
|
|
- sl
|
|
- so
|
|
- sq
|
|
- sr
|
|
- su
|
|
- sv
|
|
- sw
|
|
- ta
|
|
- te
|
|
- th
|
|
- tl
|
|
- tr
|
|
- ug
|
|
- uk
|
|
- ur
|
|
- uz
|
|
- vi
|
|
- xh
|
|
- yi
|
|
- zh
|
|
license: mit
|
|
---
|
|
|
|
## Multilingual-E5-base
|
|
|
|
[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
|
|
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
|
|
|
|
This model has 12 layers and the embedding size is 768.
|
|
|
|
## Usage
|
|
|
|
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
|
|
|
|
```python
|
|
import torch.nn.functional as F
|
|
|
|
from torch import Tensor
|
|
from transformers import AutoTokenizer, AutoModel
|
|
|
|
|
|
def average_pool(last_hidden_states: Tensor,
|
|
attention_mask: Tensor) -> Tensor:
|
|
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
|
|
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
|
|
|
|
|
|
# Each input text should start with "query: " or "passage: ", even for non-English texts.
|
|
# For tasks other than retrieval, you can simply use the "query: " prefix.
|
|
input_texts = ['query: how much protein should a female eat',
|
|
'query: 南瓜的家常做法',
|
|
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
|
|
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右,放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"]
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-base')
|
|
model = AutoModel.from_pretrained('intfloat/multilingual-e5-base')
|
|
|
|
# Tokenize the input texts
|
|
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
|
|
|
|
outputs = model(**batch_dict)
|
|
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
|
|
|
|
# normalize embeddings
|
|
embeddings = F.normalize(embeddings, p=2, dim=1)
|
|
scores = (embeddings[:2] @ embeddings[2:].T) * 100
|
|
print(scores.tolist())
|
|
```
|
|
|
|
## Supported Languages
|
|
|
|
This model is initialized from [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
|
|
and continually trained on a mixture of multilingual datasets.
|
|
It supports 100 languages from xlm-roberta,
|
|
but low-resource languages may see performance degradation.
|
|
|
|
## Training Details
|
|
|
|
**Initialization**: [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
|
|
|
|
**First stage**: contrastive pre-training with weak supervision
|
|
|
|
| Dataset | Weak supervision | # of text pairs |
|
|
|--------------------------------------------------------------------------------------------------------|---------------------------------------|-----------------|
|
|
| Filtered [mC4](https://huggingface.co/datasets/mc4) | (title, page content) | 1B |
|
|
| [CC News](https://huggingface.co/datasets/intfloat/multilingual_cc_news) | (title, news content) | 400M |
|
|
| [NLLB](https://huggingface.co/datasets/allenai/nllb) | translation pairs | 2.4B |
|
|
| [Wikipedia](https://huggingface.co/datasets/intfloat/wikipedia) | (hierarchical section title, passage) | 150M |
|
|
| Filtered [Reddit](https://www.reddit.com/) | (comment, response) | 800M |
|
|
| [S2ORC](https://github.com/allenai/s2orc) | (title, abstract) and citation pairs | 100M |
|
|
| [Stackexchange](https://stackexchange.com/) | (question, answer) | 50M |
|
|
| [xP3](https://huggingface.co/datasets/bigscience/xP3) | (input prompt, response) | 80M |
|
|
| [Miscellaneous unsupervised SBERT data](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | - | 10M |
|
|
|
|
**Second stage**: supervised fine-tuning
|
|
|
|
| Dataset | Language | # of text pairs |
|
|
|----------------------------------------------------------------------------------------|--------------|-----------------|
|
|
| [MS MARCO](https://microsoft.github.io/msmarco/) | English | 500k |
|
|
| [NQ](https://github.com/facebookresearch/DPR) | English | 70k |
|
|
| [Trivia QA](https://github.com/facebookresearch/DPR) | English | 60k |
|
|
| [NLI from SimCSE](https://github.com/princeton-nlp/SimCSE) | English | <300k |
|
|
| [ELI5](https://huggingface.co/datasets/eli5) | English | 500k |
|
|
| [DuReader Retrieval](https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval) | Chinese | 86k |
|
|
| [KILT Fever](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
|
|
| [KILT HotpotQA](https://huggingface.co/datasets/kilt_tasks) | English | 70k |
|
|
| [SQuAD](https://huggingface.co/datasets/squad) | English | 87k |
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| [Quora](https://huggingface.co/datasets/quora) | English | 150k |
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| [Mr. TyDi](https://huggingface.co/datasets/castorini/mr-tydi) | 11 languages | 50k |
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| [MIRACL](https://huggingface.co/datasets/miracl/miracl) | 16 languages | 40k |
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For all labeled datasets, we only use its training set for fine-tuning.
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For other training details, please refer to our paper at [https://arxiv.org/pdf/2402.05672](https://arxiv.org/pdf/2402.05672).
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## Benchmark Results on [Mr. TyDi](https://arxiv.org/abs/2108.08787)
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| Model | Avg MRR@10 | | ar | bn | en | fi | id | ja | ko | ru | sw | te | th |
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|-----------------------|------------|-------|------| --- | --- | --- | --- | --- | --- | --- |------| --- | --- |
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| BM25 | 33.3 | | 36.7 | 41.3 | 15.1 | 28.8 | 38.2 | 21.7 | 28.1 | 32.9 | 39.6 | 42.4 | 41.7 |
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| mDPR | 16.7 | | 26.0 | 25.8 | 16.2 | 11.3 | 14.6 | 18.1 | 21.9 | 18.5 | 7.3 | 10.6 | 13.5 |
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| BM25 + mDPR | 41.7 | | 49.1 | 53.5 | 28.4 | 36.5 | 45.5 | 35.5 | 36.2 | 42.7 | 40.5 | 42.0 | 49.2 |
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| | |
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| multilingual-e5-small | 64.4 | | 71.5 | 66.3 | 54.5 | 57.7 | 63.2 | 55.4 | 54.3 | 60.8 | 65.4 | 89.1 | 70.1 |
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| multilingual-e5-base | 65.9 | | 72.3 | 65.0 | 58.5 | 60.8 | 64.9 | 56.6 | 55.8 | 62.7 | 69.0 | 86.6 | 72.7 |
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| multilingual-e5-large | **70.5** | | 77.5 | 73.2 | 60.8 | 66.8 | 68.5 | 62.5 | 61.6 | 65.8 | 72.7 | 90.2 | 76.2 |
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## MTEB Benchmark Evaluation
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Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results
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on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316).
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## Support for Sentence Transformers
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Below is an example for usage with sentence_transformers.
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```python
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer('intfloat/multilingual-e5-base')
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input_texts = [
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'query: how much protein should a female eat',
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'query: 南瓜的家常做法',
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"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
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"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
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]
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embeddings = model.encode(input_texts, normalize_embeddings=True)
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```
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Package requirements
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`pip install sentence_transformers~=2.2.2`
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Contributors: [michaelfeil](https://huggingface.co/michaelfeil)
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## FAQ
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**1. Do I need to add the prefix "query: " and "passage: " to input texts?**
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Yes, this is how the model is trained, otherwise you will see a performance degradation.
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Here are some rules of thumb:
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- Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.
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- Use "query: " prefix for symmetric tasks such as semantic similarity, bitext mining, paraphrase retrieval.
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- Use "query: " prefix if you want to use embeddings as features, such as linear probing classification, clustering.
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**2. Why are my reproduced results slightly different from reported in the model card?**
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Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences.
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**3. Why does the cosine similarity scores distribute around 0.7 to 1.0?**
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This is a known and expected behavior as we use a low temperature 0.01 for InfoNCE contrastive loss.
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For text embedding tasks like text retrieval or semantic similarity,
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what matters is the relative order of the scores instead of the absolute values,
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so this should not be an issue.
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## Citation
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If you find our paper or models helpful, please consider cite as follows:
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|
|
```
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@article{wang2024multilingual,
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title={Multilingual E5 Text Embeddings: A Technical Report},
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author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
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journal={arXiv preprint arXiv:2402.05672},
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year={2024}
|
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}
|
|
```
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## Limitations
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Long texts will be truncated to at most 512 tokens.
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|
