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---
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tags:
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- mteb
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- sentence-transformers
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- transformers
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model-index:
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- name: multilingual-e5-large-instruct
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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
|
|
value: 76.23880597014924
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|
- type: ap
|
|
value: 39.07351965022687
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- type: f1
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value: 70.04836733862683
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- task:
|
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type: Classification
|
|
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
|
|
value: 66.71306209850107
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- type: ap
|
|
value: 79.01499914759529
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|
- type: f1
|
|
value: 64.81951817560703
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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
|
|
value: 73.85307346326837
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- type: ap
|
|
value: 22.447519885878737
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- type: f1
|
|
value: 61.0162730745633
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- task:
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type: Classification
|
|
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
|
|
value: 76.04925053533191
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- type: ap
|
|
value: 23.44983217128922
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- type: f1
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value: 62.5723230907759
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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
|
|
value: 96.28742500000001
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- type: ap
|
|
value: 94.8449918887462
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- type: f1
|
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value: 96.28680923610432
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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: 56.716
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- type: f1
|
|
value: 55.76510398266401
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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:
|
|
- type: accuracy
|
|
value: 52.99999999999999
|
|
- type: f1
|
|
value: 52.00829994765178
|
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- task:
|
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type: Classification
|
|
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
|
|
value: 48.806000000000004
|
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- type: f1
|
|
value: 48.082345914983634
|
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- task:
|
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type: Classification
|
|
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
|
|
value: 48.507999999999996
|
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- type: f1
|
|
value: 47.68752844642045
|
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- task:
|
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type: Classification
|
|
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:
|
|
- type: accuracy
|
|
value: 47.709999999999994
|
|
- type: f1
|
|
value: 47.05870376637181
|
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- task:
|
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type: Classification
|
|
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
|
|
metrics:
|
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- type: accuracy
|
|
value: 44.662000000000006
|
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- type: f1
|
|
value: 43.42371965372771
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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
|
|
value: 31.721
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- type: map_at_10
|
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value: 49.221
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- type: map_at_100
|
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value: 49.884
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- type: map_at_1000
|
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value: 49.888
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- type: map_at_3
|
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value: 44.31
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- type: map_at_5
|
|
value: 47.276
|
|
- type: mrr_at_1
|
|
value: 32.432
|
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- type: mrr_at_10
|
|
value: 49.5
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- type: mrr_at_100
|
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value: 50.163000000000004
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- type: mrr_at_1000
|
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value: 50.166
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- type: mrr_at_3
|
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value: 44.618
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- type: mrr_at_5
|
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value: 47.541
|
|
- type: ndcg_at_1
|
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value: 31.721
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- type: ndcg_at_10
|
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value: 58.384
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- type: ndcg_at_100
|
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value: 61.111000000000004
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- type: ndcg_at_1000
|
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value: 61.187999999999995
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- type: ndcg_at_3
|
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value: 48.386
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- type: ndcg_at_5
|
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value: 53.708999999999996
|
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- type: precision_at_1
|
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value: 31.721
|
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- type: precision_at_10
|
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value: 8.741
|
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- type: precision_at_100
|
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value: 0.991
|
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- type: precision_at_1000
|
|
value: 0.1
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- type: precision_at_3
|
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value: 20.057
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- type: precision_at_5
|
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value: 14.609
|
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- type: recall_at_1
|
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value: 31.721
|
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- type: recall_at_10
|
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value: 87.411
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- type: recall_at_100
|
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value: 99.075
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- type: recall_at_1000
|
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value: 99.644
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- type: recall_at_3
|
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value: 60.171
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- type: recall_at_5
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value: 73.044
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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
|
|
value: 46.40419580759799
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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: 40.48593255007969
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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
|
|
value: 63.889179122289995
|
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- type: mrr
|
|
value: 77.61146286769556
|
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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
|
|
value: 88.15075203727929
|
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- type: cos_sim_spearman
|
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value: 86.9622224570873
|
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- type: euclidean_pearson
|
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value: 86.70473853624121
|
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- type: euclidean_spearman
|
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value: 86.9622224570873
|
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- type: manhattan_pearson
|
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value: 86.21089380980065
|
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- type: manhattan_spearman
|
|
value: 86.75318154937008
|
|
- task:
|
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type: BitextMining
|
|
dataset:
|
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type: mteb/bucc-bitext-mining
|
|
name: MTEB BUCC (de-en)
|
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config: de-en
|
|
split: test
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revision: d51519689f32196a32af33b075a01d0e7c51e252
|
|
metrics:
|
|
- type: accuracy
|
|
value: 99.65553235908142
|
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- type: f1
|
|
value: 99.60681976339595
|
|
- type: precision
|
|
value: 99.58246346555325
|
|
- type: recall
|
|
value: 99.65553235908142
|
|
- task:
|
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type: BitextMining
|
|
dataset:
|
|
type: mteb/bucc-bitext-mining
|
|
name: MTEB BUCC (fr-en)
|
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config: fr-en
|
|
split: test
|
|
revision: d51519689f32196a32af33b075a01d0e7c51e252
|
|
metrics:
|
|
- type: accuracy
|
|
value: 99.26260180497468
|
|
- type: f1
|
|
value: 99.14520507740848
|
|
- type: precision
|
|
value: 99.08650671362535
|
|
- type: recall
|
|
value: 99.26260180497468
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/bucc-bitext-mining
|
|
name: MTEB BUCC (ru-en)
|
|
config: ru-en
|
|
split: test
|
|
revision: d51519689f32196a32af33b075a01d0e7c51e252
|
|
metrics:
|
|
- type: accuracy
|
|
value: 98.07412538967787
|
|
- type: f1
|
|
value: 97.86629719431936
|
|
- type: precision
|
|
value: 97.76238309664012
|
|
- type: recall
|
|
value: 98.07412538967787
|
|
- task:
|
|
type: BitextMining
|
|
dataset:
|
|
type: mteb/bucc-bitext-mining
|
|
name: MTEB BUCC (zh-en)
|
|
config: zh-en
|
|
split: test
|
|
revision: d51519689f32196a32af33b075a01d0e7c51e252
|
|
metrics:
|
|
- type: accuracy
|
|
value: 99.42074776197998
|
|
- type: f1
|
|
value: 99.38564156573635
|
|
- type: precision
|
|
value: 99.36808846761454
|
|
- type: recall
|
|
value: 99.42074776197998
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/banking77
|
|
name: MTEB Banking77Classification
|
|
config: default
|
|
split: test
|
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
|
metrics:
|
|
- type: accuracy
|
|
value: 85.73376623376623
|
|
- type: f1
|
|
value: 85.68480707214599
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/biorxiv-clustering-p2p
|
|
name: MTEB BiorxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
|
metrics:
|
|
- type: v_measure
|
|
value: 40.935218072113855
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/biorxiv-clustering-s2s
|
|
name: MTEB BiorxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
|
metrics:
|
|
- type: v_measure
|
|
value: 36.276389017675264
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
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|
name: MTEB CQADupstackRetrieval
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config: default
|
|
split: test
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revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.764166666666668
|
|
- type: map_at_10
|
|
value: 37.298166666666674
|
|
- type: map_at_100
|
|
value: 38.530166666666666
|
|
- type: map_at_1000
|
|
value: 38.64416666666667
|
|
- type: map_at_3
|
|
value: 34.484833333333334
|
|
- type: map_at_5
|
|
value: 36.0385
|
|
- type: mrr_at_1
|
|
value: 32.93558333333333
|
|
- type: mrr_at_10
|
|
value: 41.589749999999995
|
|
- type: mrr_at_100
|
|
value: 42.425333333333334
|
|
- type: mrr_at_1000
|
|
value: 42.476333333333336
|
|
- type: mrr_at_3
|
|
value: 39.26825
|
|
- type: mrr_at_5
|
|
value: 40.567083333333336
|
|
- type: ndcg_at_1
|
|
value: 32.93558333333333
|
|
- type: ndcg_at_10
|
|
value: 42.706583333333334
|
|
- type: ndcg_at_100
|
|
value: 47.82483333333333
|
|
- type: ndcg_at_1000
|
|
value: 49.95733333333334
|
|
- type: ndcg_at_3
|
|
value: 38.064750000000004
|
|
- type: ndcg_at_5
|
|
value: 40.18158333333333
|
|
- type: precision_at_1
|
|
value: 32.93558333333333
|
|
- type: precision_at_10
|
|
value: 7.459833333333334
|
|
- type: precision_at_100
|
|
value: 1.1830833333333335
|
|
- type: precision_at_1000
|
|
value: 0.15608333333333332
|
|
- type: precision_at_3
|
|
value: 17.5235
|
|
- type: precision_at_5
|
|
value: 12.349833333333333
|
|
- type: recall_at_1
|
|
value: 27.764166666666668
|
|
- type: recall_at_10
|
|
value: 54.31775
|
|
- type: recall_at_100
|
|
value: 76.74350000000001
|
|
- type: recall_at_1000
|
|
value: 91.45208333333332
|
|
- type: recall_at_3
|
|
value: 41.23425
|
|
- type: recall_at_5
|
|
value: 46.73983333333334
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 12.969
|
|
- type: map_at_10
|
|
value: 21.584999999999997
|
|
- type: map_at_100
|
|
value: 23.3
|
|
- type: map_at_1000
|
|
value: 23.5
|
|
- type: map_at_3
|
|
value: 18.218999999999998
|
|
- type: map_at_5
|
|
value: 19.983
|
|
- type: mrr_at_1
|
|
value: 29.316
|
|
- type: mrr_at_10
|
|
value: 40.033
|
|
- type: mrr_at_100
|
|
value: 40.96
|
|
- type: mrr_at_1000
|
|
value: 41.001
|
|
- type: mrr_at_3
|
|
value: 37.123
|
|
- type: mrr_at_5
|
|
value: 38.757999999999996
|
|
- type: ndcg_at_1
|
|
value: 29.316
|
|
- type: ndcg_at_10
|
|
value: 29.858
|
|
- type: ndcg_at_100
|
|
value: 36.756
|
|
- type: ndcg_at_1000
|
|
value: 40.245999999999995
|
|
- type: ndcg_at_3
|
|
value: 24.822
|
|
- type: ndcg_at_5
|
|
value: 26.565
|
|
- type: precision_at_1
|
|
value: 29.316
|
|
- type: precision_at_10
|
|
value: 9.186
|
|
- type: precision_at_100
|
|
value: 1.6549999999999998
|
|
- type: precision_at_1000
|
|
value: 0.22999999999999998
|
|
- type: precision_at_3
|
|
value: 18.436
|
|
- type: precision_at_5
|
|
value: 13.876
|
|
- type: recall_at_1
|
|
value: 12.969
|
|
- type: recall_at_10
|
|
value: 35.142
|
|
- type: recall_at_100
|
|
value: 59.143
|
|
- type: recall_at_1000
|
|
value: 78.594
|
|
- type: recall_at_3
|
|
value: 22.604
|
|
- type: recall_at_5
|
|
value: 27.883000000000003
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: dbpedia-entity
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 8.527999999999999
|
|
- type: map_at_10
|
|
value: 17.974999999999998
|
|
- type: map_at_100
|
|
value: 25.665
|
|
- type: map_at_1000
|
|
value: 27.406000000000002
|
|
- type: map_at_3
|
|
value: 13.017999999999999
|
|
- type: map_at_5
|
|
value: 15.137
|
|
- type: mrr_at_1
|
|
value: 62.5
|
|
- type: mrr_at_10
|
|
value: 71.891
|
|
- type: mrr_at_100
|
|
value: 72.294
|
|
- type: mrr_at_1000
|
|
value: 72.296
|
|
- type: mrr_at_3
|
|
value: 69.958
|
|
- type: mrr_at_5
|
|
value: 71.121
|
|
- type: ndcg_at_1
|
|
value: 50.875
|
|
- type: ndcg_at_10
|
|
value: 38.36
|
|
- type: ndcg_at_100
|
|
value: 44.235
|
|
- type: ndcg_at_1000
|
|
value: 52.154
|
|
- type: ndcg_at_3
|
|
value: 43.008
|
|
- type: ndcg_at_5
|
|
value: 40.083999999999996
|
|
- type: precision_at_1
|
|
value: 62.5
|
|
- type: precision_at_10
|
|
value: 30.0
|
|
- type: precision_at_100
|
|
value: 10.038
|
|
- type: precision_at_1000
|
|
value: 2.0869999999999997
|
|
- type: precision_at_3
|
|
value: 46.833000000000006
|
|
- type: precision_at_5
|
|
value: 38.800000000000004
|
|
- type: recall_at_1
|
|
value: 8.527999999999999
|
|
- type: recall_at_10
|
|
value: 23.828
|
|
- type: recall_at_100
|
|
value: 52.322
|
|
- type: recall_at_1000
|
|
value: 77.143
|
|
- type: recall_at_3
|
|
value: 14.136000000000001
|
|
- type: recall_at_5
|
|
value: 17.761
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 51.51
|
|
- type: f1
|
|
value: 47.632159862049896
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 60.734
|
|
- type: map_at_10
|
|
value: 72.442
|
|
- type: map_at_100
|
|
value: 72.735
|
|
- type: map_at_1000
|
|
value: 72.75
|
|
- type: map_at_3
|
|
value: 70.41199999999999
|
|
- type: map_at_5
|
|
value: 71.80499999999999
|
|
- type: mrr_at_1
|
|
value: 65.212
|
|
- type: mrr_at_10
|
|
value: 76.613
|
|
- type: mrr_at_100
|
|
value: 76.79899999999999
|
|
- type: mrr_at_1000
|
|
value: 76.801
|
|
- type: mrr_at_3
|
|
value: 74.8
|
|
- type: mrr_at_5
|
|
value: 76.12400000000001
|
|
- type: ndcg_at_1
|
|
value: 65.212
|
|
- type: ndcg_at_10
|
|
value: 77.988
|
|
- type: ndcg_at_100
|
|
value: 79.167
|
|
- type: ndcg_at_1000
|
|
value: 79.452
|
|
- type: ndcg_at_3
|
|
value: 74.362
|
|
- type: ndcg_at_5
|
|
value: 76.666
|
|
- type: precision_at_1
|
|
value: 65.212
|
|
- type: precision_at_10
|
|
value: 10.003
|
|
- type: precision_at_100
|
|
value: 1.077
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 29.518
|
|
- type: precision_at_5
|
|
value: 19.016
|
|
- type: recall_at_1
|
|
value: 60.734
|
|
- type: recall_at_10
|
|
value: 90.824
|
|
- type: recall_at_100
|
|
value: 95.71600000000001
|
|
- type: recall_at_1000
|
|
value: 97.577
|
|
- type: recall_at_3
|
|
value: 81.243
|
|
- type: recall_at_5
|
|
value: 86.90299999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.845
|
|
- type: map_at_10
|
|
value: 39.281
|
|
- type: map_at_100
|
|
value: 41.422
|
|
- type: map_at_1000
|
|
value: 41.593
|
|
- type: map_at_3
|
|
value: 34.467
|
|
- type: map_at_5
|
|
value: 37.017
|
|
- type: mrr_at_1
|
|
value: 47.531
|
|
- type: mrr_at_10
|
|
value: 56.204
|
|
- type: mrr_at_100
|
|
value: 56.928999999999995
|
|
- type: mrr_at_1000
|
|
value: 56.962999999999994
|
|
- type: mrr_at_3
|
|
value: 54.115
|
|
- type: mrr_at_5
|
|
value: 55.373000000000005
|
|
- type: ndcg_at_1
|
|
value: 47.531
|
|
- type: ndcg_at_10
|
|
value: 47.711999999999996
|
|
- type: ndcg_at_100
|
|
value: 54.510999999999996
|
|
- type: ndcg_at_1000
|
|
value: 57.103
|
|
- type: ndcg_at_3
|
|
value: 44.145
|
|
- type: ndcg_at_5
|
|
value: 45.032
|
|
- type: precision_at_1
|
|
value: 47.531
|
|
- type: precision_at_10
|
|
value: 13.194
|
|
- type: precision_at_100
|
|
value: 2.045
|
|
- type: precision_at_1000
|
|
value: 0.249
|
|
- type: precision_at_3
|
|
value: 29.424
|
|
- type: precision_at_5
|
|
value: 21.451
|
|
- type: recall_at_1
|
|
value: 23.845
|
|
- type: recall_at_10
|
|
value: 54.967
|
|
- type: recall_at_100
|
|
value: 79.11399999999999
|
|
- type: recall_at_1000
|
|
value: 94.56700000000001
|
|
- type: recall_at_3
|
|
value: 40.256
|
|
- type: recall_at_5
|
|
value: 46.215
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 37.819
|
|
- type: map_at_10
|
|
value: 60.889
|
|
- type: map_at_100
|
|
value: 61.717999999999996
|
|
- type: map_at_1000
|
|
value: 61.778
|
|
- type: map_at_3
|
|
value: 57.254000000000005
|
|
- type: map_at_5
|
|
value: 59.541
|
|
- type: mrr_at_1
|
|
value: 75.638
|
|
- type: mrr_at_10
|
|
value: 82.173
|
|
- type: mrr_at_100
|
|
value: 82.362
|
|
- type: mrr_at_1000
|
|
value: 82.37
|
|
- type: mrr_at_3
|
|
value: 81.089
|
|
- type: mrr_at_5
|
|
value: 81.827
|
|
- type: ndcg_at_1
|
|
value: 75.638
|
|
- type: ndcg_at_10
|
|
value: 69.317
|
|
- type: ndcg_at_100
|
|
value: 72.221
|
|
- type: ndcg_at_1000
|
|
value: 73.382
|
|
- type: ndcg_at_3
|
|
value: 64.14
|
|
- type: ndcg_at_5
|
|
value: 67.07600000000001
|
|
- type: precision_at_1
|
|
value: 75.638
|
|
- type: precision_at_10
|
|
value: 14.704999999999998
|
|
- type: precision_at_100
|
|
value: 1.698
|
|
- type: precision_at_1000
|
|
value: 0.185
|
|
- type: precision_at_3
|
|
value: 41.394999999999996
|
|
- type: precision_at_5
|
|
value: 27.162999999999997
|
|
- type: recall_at_1
|
|
value: 37.819
|
|
- type: recall_at_10
|
|
value: 73.52499999999999
|
|
- type: recall_at_100
|
|
value: 84.875
|
|
- type: recall_at_1000
|
|
value: 92.559
|
|
- type: recall_at_3
|
|
value: 62.092999999999996
|
|
- type: recall_at_5
|
|
value: 67.907
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 94.60079999999999
|
|
- type: ap
|
|
value: 92.67396345347356
|
|
- type: f1
|
|
value: 94.5988098167121
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 21.285
|
|
- type: map_at_10
|
|
value: 33.436
|
|
- type: map_at_100
|
|
value: 34.63
|
|
- type: map_at_1000
|
|
value: 34.681
|
|
- type: map_at_3
|
|
value: 29.412
|
|
- type: map_at_5
|
|
value: 31.715
|
|
- type: mrr_at_1
|
|
value: 21.848
|
|
- type: mrr_at_10
|
|
value: 33.979
|
|
- type: mrr_at_100
|
|
value: 35.118
|
|
- type: mrr_at_1000
|
|
value: 35.162
|
|
- type: mrr_at_3
|
|
value: 30.036
|
|
- type: mrr_at_5
|
|
value: 32.298
|
|
- type: ndcg_at_1
|
|
value: 21.862000000000002
|
|
- type: ndcg_at_10
|
|
value: 40.43
|
|
- type: ndcg_at_100
|
|
value: 46.17
|
|
- type: ndcg_at_1000
|
|
value: 47.412
|
|
- type: ndcg_at_3
|
|
value: 32.221
|
|
- type: ndcg_at_5
|
|
value: 36.332
|
|
- type: precision_at_1
|
|
value: 21.862000000000002
|
|
- type: precision_at_10
|
|
value: 6.491
|
|
- type: precision_at_100
|
|
value: 0.935
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 13.744
|
|
- type: precision_at_5
|
|
value: 10.331999999999999
|
|
- type: recall_at_1
|
|
value: 21.285
|
|
- type: recall_at_10
|
|
value: 62.083
|
|
- type: recall_at_100
|
|
value: 88.576
|
|
- type: recall_at_1000
|
|
value: 98.006
|
|
- type: recall_at_3
|
|
value: 39.729
|
|
- type: recall_at_5
|
|
value: 49.608000000000004
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 93.92612859097127
|
|
- type: f1
|
|
value: 93.82370333372853
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (de)
|
|
config: de
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.67681036911807
|
|
- type: f1
|
|
value: 92.14191382411472
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (es)
|
|
config: es
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.26817878585723
|
|
- type: f1
|
|
value: 91.92824250337878
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (fr)
|
|
config: fr
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 89.96554963983714
|
|
- type: f1
|
|
value: 90.02859329630792
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (hi)
|
|
config: hi
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 90.02509860164935
|
|
- type: f1
|
|
value: 89.30665159182062
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (th)
|
|
config: th
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 87.55515370705244
|
|
- type: f1
|
|
value: 87.94449232331907
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 82.4623803009576
|
|
- type: f1
|
|
value: 66.06738378772725
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (de)
|
|
config: de
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 79.3716539870386
|
|
- type: f1
|
|
value: 60.37614033396853
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (es)
|
|
config: es
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 80.34022681787857
|
|
- type: f1
|
|
value: 58.302008026952
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (fr)
|
|
config: fr
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.72095208268087
|
|
- type: f1
|
|
value: 59.64524724009049
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (hi)
|
|
config: hi
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.87020437432773
|
|
- type: f1
|
|
value: 57.80202694670567
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (th)
|
|
config: th
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.73598553345387
|
|
- type: f1
|
|
value: 58.19628250675031
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (af)
|
|
config: af
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 67.6630800268998
|
|
- type: f1
|
|
value: 65.00996668051691
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (am)
|
|
config: am
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 60.7128446536651
|
|
- type: f1
|
|
value: 57.95860594874963
|
|
- 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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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
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|
|
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|
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|
|
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|
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|
|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
|
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|
|
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|
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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: 67.29731681109291
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (nb)
|
|
config: nb
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.20914593140552
|
|
- type: f1
|
|
value: 77.07066497935367
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (nl)
|
|
config: nl
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 78.52387357094821
|
|
- type: f1
|
|
value: 78.5259569473291
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (pl)
|
|
config: pl
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.6913248150639
|
|
- type: f1
|
|
value: 76.91201656350455
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (pt)
|
|
config: pt
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.1217215870881
|
|
- type: f1
|
|
value: 77.41179937912504
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (ro)
|
|
config: ro
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 75.25891055817083
|
|
- type: f1
|
|
value: 75.8089244542887
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (ru)
|
|
config: ru
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.70679219905851
|
|
- type: f1
|
|
value: 78.21459594517711
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (sl)
|
|
config: sl
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 74.83523873570948
|
|
- type: f1
|
|
value: 74.86847028401978
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (sq)
|
|
config: sq
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 74.71755211835911
|
|
- type: f1
|
|
value: 74.0214326485662
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (sv)
|
|
config: sv
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 79.06523201075991
|
|
- type: f1
|
|
value: 79.10545620325138
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (sw)
|
|
config: sw
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 67.91862811028918
|
|
- type: f1
|
|
value: 66.50386121217983
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (ta)
|
|
config: ta
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 70.93140551445865
|
|
- type: f1
|
|
value: 70.755435928495
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (te)
|
|
config: te
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 72.40753194351042
|
|
- type: f1
|
|
value: 71.61816115782923
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (th)
|
|
config: th
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 75.1815736381977
|
|
- type: f1
|
|
value: 75.08016717887205
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (tl)
|
|
config: tl
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 72.86482851378614
|
|
- type: f1
|
|
value: 72.39521180006291
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (tr)
|
|
config: tr
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 76.46940147948891
|
|
- type: f1
|
|
value: 76.70044085362349
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (ur)
|
|
config: ur
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.89307330195024
|
|
- type: f1
|
|
value: 71.5721825332298
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (vi)
|
|
config: vi
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 74.7511768661735
|
|
- type: f1
|
|
value: 75.17918654541515
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (zh-CN)
|
|
config: zh-CN
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 78.69535978480162
|
|
- type: f1
|
|
value: 78.90019070153316
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (zh-TW)
|
|
config: zh-TW
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 75.45729657027572
|
|
- type: f1
|
|
value: 76.19578371794672
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 36.92715354123554
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 35.53536244162518
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 33.08507884504006
|
|
- type: mrr
|
|
value: 34.32436977159129
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.935
|
|
- type: map_at_10
|
|
value: 13.297
|
|
- type: map_at_100
|
|
value: 16.907
|
|
- type: map_at_1000
|
|
value: 18.391
|
|
- type: map_at_3
|
|
value: 9.626999999999999
|
|
- type: map_at_5
|
|
value: 11.190999999999999
|
|
- type: mrr_at_1
|
|
value: 46.129999999999995
|
|
- type: mrr_at_10
|
|
value: 54.346000000000004
|
|
- type: mrr_at_100
|
|
value: 55.067
|
|
- type: mrr_at_1000
|
|
value: 55.1
|
|
- type: mrr_at_3
|
|
value: 51.961
|
|
- type: mrr_at_5
|
|
value: 53.246
|
|
- type: ndcg_at_1
|
|
value: 44.118
|
|
- type: ndcg_at_10
|
|
value: 35.534
|
|
- type: ndcg_at_100
|
|
value: 32.946999999999996
|
|
- type: ndcg_at_1000
|
|
value: 41.599000000000004
|
|
- type: ndcg_at_3
|
|
value: 40.25
|
|
- type: ndcg_at_5
|
|
value: 37.978
|
|
- type: precision_at_1
|
|
value: 46.129999999999995
|
|
- type: precision_at_10
|
|
value: 26.842
|
|
- type: precision_at_100
|
|
value: 8.427
|
|
- type: precision_at_1000
|
|
value: 2.128
|
|
- type: precision_at_3
|
|
value: 37.977
|
|
- type: precision_at_5
|
|
value: 32.879000000000005
|
|
- type: recall_at_1
|
|
value: 5.935
|
|
- type: recall_at_10
|
|
value: 17.211000000000002
|
|
- type: recall_at_100
|
|
value: 34.33
|
|
- type: recall_at_1000
|
|
value: 65.551
|
|
- type: recall_at_3
|
|
value: 10.483
|
|
- type: recall_at_5
|
|
value: 13.078999999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 35.231
|
|
- type: map_at_10
|
|
value: 50.202000000000005
|
|
- type: map_at_100
|
|
value: 51.154999999999994
|
|
- type: map_at_1000
|
|
value: 51.181
|
|
- type: map_at_3
|
|
value: 45.774
|
|
- type: map_at_5
|
|
value: 48.522
|
|
- type: mrr_at_1
|
|
value: 39.687
|
|
- type: mrr_at_10
|
|
value: 52.88
|
|
- type: mrr_at_100
|
|
value: 53.569
|
|
- type: mrr_at_1000
|
|
value: 53.58500000000001
|
|
- type: mrr_at_3
|
|
value: 49.228
|
|
- type: mrr_at_5
|
|
value: 51.525
|
|
- type: ndcg_at_1
|
|
value: 39.687
|
|
- type: ndcg_at_10
|
|
value: 57.754000000000005
|
|
- type: ndcg_at_100
|
|
value: 61.597
|
|
- type: ndcg_at_1000
|
|
value: 62.18900000000001
|
|
- type: ndcg_at_3
|
|
value: 49.55
|
|
- type: ndcg_at_5
|
|
value: 54.11899999999999
|
|
- type: precision_at_1
|
|
value: 39.687
|
|
- type: precision_at_10
|
|
value: 9.313
|
|
- type: precision_at_100
|
|
value: 1.146
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 22.229
|
|
- type: precision_at_5
|
|
value: 15.939
|
|
- type: recall_at_1
|
|
value: 35.231
|
|
- type: recall_at_10
|
|
value: 78.083
|
|
- type: recall_at_100
|
|
value: 94.42099999999999
|
|
- type: recall_at_1000
|
|
value: 98.81
|
|
- type: recall_at_3
|
|
value: 57.047000000000004
|
|
- type: recall_at_5
|
|
value: 67.637
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 71.241
|
|
- type: map_at_10
|
|
value: 85.462
|
|
- type: map_at_100
|
|
value: 86.083
|
|
- type: map_at_1000
|
|
value: 86.09700000000001
|
|
- type: map_at_3
|
|
value: 82.49499999999999
|
|
- type: map_at_5
|
|
value: 84.392
|
|
- type: mrr_at_1
|
|
value: 82.09
|
|
- type: mrr_at_10
|
|
value: 88.301
|
|
- type: mrr_at_100
|
|
value: 88.383
|
|
- type: mrr_at_1000
|
|
value: 88.384
|
|
- type: mrr_at_3
|
|
value: 87.37
|
|
- type: mrr_at_5
|
|
value: 88.035
|
|
- type: ndcg_at_1
|
|
value: 82.12
|
|
- type: ndcg_at_10
|
|
value: 89.149
|
|
- type: ndcg_at_100
|
|
value: 90.235
|
|
- type: ndcg_at_1000
|
|
value: 90.307
|
|
- type: ndcg_at_3
|
|
value: 86.37599999999999
|
|
- type: ndcg_at_5
|
|
value: 87.964
|
|
- type: precision_at_1
|
|
value: 82.12
|
|
- type: precision_at_10
|
|
value: 13.56
|
|
- type: precision_at_100
|
|
value: 1.539
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_3
|
|
value: 37.88
|
|
- type: precision_at_5
|
|
value: 24.92
|
|
- type: recall_at_1
|
|
value: 71.241
|
|
- type: recall_at_10
|
|
value: 96.128
|
|
- type: recall_at_100
|
|
value: 99.696
|
|
- type: recall_at_1000
|
|
value: 99.994
|
|
- type: recall_at_3
|
|
value: 88.181
|
|
- type: recall_at_5
|
|
value: 92.694
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 56.59757799655151
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 64.27391998854624
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 4.243
|
|
- type: map_at_10
|
|
value: 10.965
|
|
- type: map_at_100
|
|
value: 12.934999999999999
|
|
- type: map_at_1000
|
|
value: 13.256
|
|
- type: map_at_3
|
|
value: 7.907
|
|
- type: map_at_5
|
|
value: 9.435
|
|
- type: mrr_at_1
|
|
value: 20.9
|
|
- type: mrr_at_10
|
|
value: 31.849
|
|
- type: mrr_at_100
|
|
value: 32.964
|
|
- type: mrr_at_1000
|
|
value: 33.024
|
|
- type: mrr_at_3
|
|
value: 28.517
|
|
- type: mrr_at_5
|
|
value: 30.381999999999998
|
|
- type: ndcg_at_1
|
|
value: 20.9
|
|
- type: ndcg_at_10
|
|
value: 18.723
|
|
- type: ndcg_at_100
|
|
value: 26.384999999999998
|
|
- type: ndcg_at_1000
|
|
value: 32.114
|
|
- type: ndcg_at_3
|
|
value: 17.753
|
|
- type: ndcg_at_5
|
|
value: 15.558
|
|
- type: precision_at_1
|
|
value: 20.9
|
|
- type: precision_at_10
|
|
value: 9.8
|
|
- type: precision_at_100
|
|
value: 2.078
|
|
- type: precision_at_1000
|
|
value: 0.345
|
|
- type: precision_at_3
|
|
value: 16.900000000000002
|
|
- type: precision_at_5
|
|
value: 13.88
|
|
- type: recall_at_1
|
|
value: 4.243
|
|
- type: recall_at_10
|
|
value: 19.885
|
|
- type: recall_at_100
|
|
value: 42.17
|
|
- type: recall_at_1000
|
|
value: 70.12
|
|
- type: recall_at_3
|
|
value: 10.288
|
|
- type: recall_at_5
|
|
value: 14.072000000000001
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.84209174935282
|
|
- type: cos_sim_spearman
|
|
value: 81.73248048438833
|
|
- type: euclidean_pearson
|
|
value: 83.02810070308149
|
|
- type: euclidean_spearman
|
|
value: 81.73248295679514
|
|
- type: manhattan_pearson
|
|
value: 82.95368060376002
|
|
- type: manhattan_spearman
|
|
value: 81.60277910998718
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 88.52628804556943
|
|
- type: cos_sim_spearman
|
|
value: 82.5713913555672
|
|
- type: euclidean_pearson
|
|
value: 85.8796774746988
|
|
- type: euclidean_spearman
|
|
value: 82.57137506803424
|
|
- type: manhattan_pearson
|
|
value: 85.79671002960058
|
|
- type: manhattan_spearman
|
|
value: 82.49445981618027
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.23682503505542
|
|
- type: cos_sim_spearman
|
|
value: 87.15008956711806
|
|
- type: euclidean_pearson
|
|
value: 86.79805401524959
|
|
- type: euclidean_spearman
|
|
value: 87.15008956711806
|
|
- type: manhattan_pearson
|
|
value: 86.65298502699244
|
|
- type: manhattan_spearman
|
|
value: 86.97677821948562
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.63370304677802
|
|
- type: cos_sim_spearman
|
|
value: 84.97105553540318
|
|
- type: euclidean_pearson
|
|
value: 85.28896108687721
|
|
- type: euclidean_spearman
|
|
value: 84.97105553540318
|
|
- type: manhattan_pearson
|
|
value: 85.09663190337331
|
|
- type: manhattan_spearman
|
|
value: 84.79126831644619
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 90.2614838800733
|
|
- type: cos_sim_spearman
|
|
value: 91.0509162991835
|
|
- type: euclidean_pearson
|
|
value: 90.33098317533373
|
|
- type: euclidean_spearman
|
|
value: 91.05091625871644
|
|
- type: manhattan_pearson
|
|
value: 90.26250435151107
|
|
- type: manhattan_spearman
|
|
value: 90.97999594417519
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.80480973335091
|
|
- type: cos_sim_spearman
|
|
value: 87.313695492969
|
|
- type: euclidean_pearson
|
|
value: 86.49267251576939
|
|
- type: euclidean_spearman
|
|
value: 87.313695492969
|
|
- type: manhattan_pearson
|
|
value: 86.44019901831935
|
|
- type: manhattan_spearman
|
|
value: 87.24205395460392
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts17-crosslingual-sts
|
|
name: MTEB STS17 (en-en)
|
|
config: en-en
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 90.05662789380672
|
|
- type: cos_sim_spearman
|
|
value: 90.02759424426651
|
|
- type: euclidean_pearson
|
|
value: 90.4042483422981
|
|
- type: euclidean_spearman
|
|
value: 90.02759424426651
|
|
- type: manhattan_pearson
|
|
value: 90.51446975000226
|
|
- type: manhattan_spearman
|
|
value: 90.08832889933616
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (en)
|
|
config: en
|
|
split: test
|
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 67.5975528273532
|
|
- type: cos_sim_spearman
|
|
value: 67.62969861411354
|
|
- type: euclidean_pearson
|
|
value: 69.224275734323
|
|
- type: euclidean_spearman
|
|
value: 67.62969861411354
|
|
- type: manhattan_pearson
|
|
value: 69.3761447059927
|
|
- type: manhattan_spearman
|
|
value: 67.90921005611467
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.11244327231684
|
|
- type: cos_sim_spearman
|
|
value: 88.37902438979035
|
|
- type: euclidean_pearson
|
|
value: 87.86054279847336
|
|
- type: euclidean_spearman
|
|
value: 88.37902438979035
|
|
- type: manhattan_pearson
|
|
value: 87.77257757320378
|
|
- type: manhattan_spearman
|
|
value: 88.25208966098123
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 85.87174608143563
|
|
- type: mrr
|
|
value: 96.12836872640794
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 57.760999999999996
|
|
- type: map_at_10
|
|
value: 67.258
|
|
- type: map_at_100
|
|
value: 67.757
|
|
- type: map_at_1000
|
|
value: 67.78800000000001
|
|
- type: map_at_3
|
|
value: 64.602
|
|
- type: map_at_5
|
|
value: 65.64
|
|
- type: mrr_at_1
|
|
value: 60.667
|
|
- type: mrr_at_10
|
|
value: 68.441
|
|
- type: mrr_at_100
|
|
value: 68.825
|
|
- type: mrr_at_1000
|
|
value: 68.853
|
|
- type: mrr_at_3
|
|
value: 66.444
|
|
- type: mrr_at_5
|
|
value: 67.26100000000001
|
|
- type: ndcg_at_1
|
|
value: 60.667
|
|
- type: ndcg_at_10
|
|
value: 71.852
|
|
- type: ndcg_at_100
|
|
value: 73.9
|
|
- type: ndcg_at_1000
|
|
value: 74.628
|
|
- type: ndcg_at_3
|
|
value: 67.093
|
|
- type: ndcg_at_5
|
|
value: 68.58
|
|
- type: precision_at_1
|
|
value: 60.667
|
|
- type: precision_at_10
|
|
value: 9.6
|
|
- type: precision_at_100
|
|
value: 1.0670000000000002
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_3
|
|
value: 26.111
|
|
- type: precision_at_5
|
|
value: 16.733
|
|
- type: recall_at_1
|
|
value: 57.760999999999996
|
|
- type: recall_at_10
|
|
value: 84.967
|
|
- type: recall_at_100
|
|
value: 93.833
|
|
- type: recall_at_1000
|
|
value: 99.333
|
|
- type: recall_at_3
|
|
value: 71.589
|
|
- type: recall_at_5
|
|
value: 75.483
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.66633663366336
|
|
- type: cos_sim_ap
|
|
value: 91.17685358899108
|
|
- type: cos_sim_f1
|
|
value: 82.16818642350559
|
|
- type: cos_sim_precision
|
|
value: 83.26488706365504
|
|
- type: cos_sim_recall
|
|
value: 81.10000000000001
|
|
- type: dot_accuracy
|
|
value: 99.66633663366336
|
|
- type: dot_ap
|
|
value: 91.17663411119032
|
|
- type: dot_f1
|
|
value: 82.16818642350559
|
|
- type: dot_precision
|
|
value: 83.26488706365504
|
|
- type: dot_recall
|
|
value: 81.10000000000001
|
|
- type: euclidean_accuracy
|
|
value: 99.66633663366336
|
|
- type: euclidean_ap
|
|
value: 91.17685189882275
|
|
- type: euclidean_f1
|
|
value: 82.16818642350559
|
|
- type: euclidean_precision
|
|
value: 83.26488706365504
|
|
- type: euclidean_recall
|
|
value: 81.10000000000001
|
|
- type: manhattan_accuracy
|
|
value: 99.66633663366336
|
|
- type: manhattan_ap
|
|
value: 91.2241619496737
|
|
- type: manhattan_f1
|
|
value: 82.20472440944883
|
|
- type: manhattan_precision
|
|
value: 86.51933701657458
|
|
- type: manhattan_recall
|
|
value: 78.3
|
|
- type: max_accuracy
|
|
value: 99.66633663366336
|
|
- type: max_ap
|
|
value: 91.2241619496737
|
|
- type: max_f1
|
|
value: 82.20472440944883
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 66.85101268897951
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 42.461184054706905
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 51.44542568873886
|
|
- type: mrr
|
|
value: 52.33656151854681
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 30.75982974997539
|
|
- type: cos_sim_spearman
|
|
value: 30.385405026539914
|
|
- type: dot_pearson
|
|
value: 30.75982433546523
|
|
- type: dot_spearman
|
|
value: 30.385405026539914
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.22799999999999998
|
|
- type: map_at_10
|
|
value: 2.064
|
|
- type: map_at_100
|
|
value: 13.056000000000001
|
|
- type: map_at_1000
|
|
value: 31.747999999999998
|
|
- type: map_at_3
|
|
value: 0.67
|
|
- type: map_at_5
|
|
value: 1.097
|
|
- type: mrr_at_1
|
|
value: 90.0
|
|
- type: mrr_at_10
|
|
value: 94.667
|
|
- type: mrr_at_100
|
|
value: 94.667
|
|
- type: mrr_at_1000
|
|
value: 94.667
|
|
- type: mrr_at_3
|
|
value: 94.667
|
|
- type: mrr_at_5
|
|
value: 94.667
|
|
- type: ndcg_at_1
|
|
value: 86.0
|
|
- type: ndcg_at_10
|
|
value: 82.0
|
|
- type: ndcg_at_100
|
|
value: 64.307
|
|
- type: ndcg_at_1000
|
|
value: 57.023999999999994
|
|
- type: ndcg_at_3
|
|
value: 85.816
|
|
- type: ndcg_at_5
|
|
value: 84.904
|
|
- type: precision_at_1
|
|
value: 90.0
|
|
- type: precision_at_10
|
|
value: 85.8
|
|
- type: precision_at_100
|
|
value: 66.46
|
|
- type: precision_at_1000
|
|
value: 25.202
|
|
- type: precision_at_3
|
|
value: 90.0
|
|
- type: precision_at_5
|
|
value: 89.2
|
|
- type: recall_at_1
|
|
value: 0.22799999999999998
|
|
- type: recall_at_10
|
|
value: 2.235
|
|
- type: recall_at_100
|
|
value: 16.185
|
|
- type: recall_at_1000
|
|
value: 53.620999999999995
|
|
- type: recall_at_3
|
|
value: 0.7040000000000001
|
|
- type: recall_at_5
|
|
value: 1.172
|
|
- 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: 97.39999999999999
|
|
- type: f1
|
|
value: 96.75
|
|
- type: precision
|
|
value: 96.45
|
|
- type: recall
|
|
value: 97.39999999999999
|
|
- 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: 85.54913294797689
|
|
- type: f1
|
|
value: 82.46628131021194
|
|
- type: precision
|
|
value: 81.1175337186898
|
|
- type: recall
|
|
value: 85.54913294797689
|
|
- 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: 81.21951219512195
|
|
- type: f1
|
|
value: 77.33333333333334
|
|
- type: precision
|
|
value: 75.54878048780488
|
|
- type: recall
|
|
value: 81.21951219512195
|
|
- 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: 98.6
|
|
- type: f1
|
|
value: 98.26666666666665
|
|
- type: precision
|
|
value: 98.1
|
|
- type: recall
|
|
value: 98.6
|
|
- 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: 99.5
|
|
- type: f1
|
|
value: 99.33333333333333
|
|
- type: precision
|
|
value: 99.25
|
|
- type: recall
|
|
value: 99.5
|
|
- 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: 97.8
|
|
- type: f1
|
|
value: 97.2
|
|
- type: precision
|
|
value: 96.89999999999999
|
|
- type: recall
|
|
value: 97.8
|
|
- 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: 97.8
|
|
- type: f1
|
|
value: 97.18333333333334
|
|
- type: precision
|
|
value: 96.88333333333333
|
|
- type: recall
|
|
value: 97.8
|
|
- 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: 77.61194029850746
|
|
- type: f1
|
|
value: 72.81094527363183
|
|
- type: precision
|
|
value: 70.83333333333333
|
|
- type: recall
|
|
value: 77.61194029850746
|
|
- 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: 93.7
|
|
- type: f1
|
|
value: 91.91666666666667
|
|
- type: precision
|
|
value: 91.08333333333334
|
|
- type: recall
|
|
value: 93.7
|
|
- 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: 88.29268292682927
|
|
- type: f1
|
|
value: 85.27642276422765
|
|
- type: precision
|
|
value: 84.01277584204414
|
|
- type: recall
|
|
value: 88.29268292682927
|
|
- 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: 96.1
|
|
- type: f1
|
|
value: 95.0
|
|
- type: precision
|
|
value: 94.46666666666668
|
|
- type: recall
|
|
value: 96.1
|
|
- 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: 93.681652490887
|
|
- type: f1
|
|
value: 91.90765492102065
|
|
- type: precision
|
|
value: 91.05913325232888
|
|
- type: recall
|
|
value: 93.681652490887
|
|
- 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: 92.17391304347827
|
|
- type: f1
|
|
value: 89.97101449275361
|
|
- type: precision
|
|
value: 88.96811594202899
|
|
- type: recall
|
|
value: 92.17391304347827
|
|
- 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: 90.43478260869566
|
|
- type: f1
|
|
value: 87.72173913043478
|
|
- type: precision
|
|
value: 86.42028985507245
|
|
- type: recall
|
|
value: 90.43478260869566
|
|
- 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: 90.4
|
|
- type: f1
|
|
value: 88.03
|
|
- type: precision
|
|
value: 86.95
|
|
- type: recall
|
|
value: 90.4
|
|
- 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: 93.4
|
|
- type: f1
|
|
value: 91.45666666666666
|
|
- type: precision
|
|
value: 90.525
|
|
- type: recall
|
|
value: 93.4
|
|
- 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: 81.9059107358263
|
|
- type: f1
|
|
value: 78.32557872364869
|
|
- type: precision
|
|
value: 76.78260286824823
|
|
- type: recall
|
|
value: 81.9059107358263
|
|
- 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: 94.3
|
|
- type: f1
|
|
value: 92.58333333333333
|
|
- type: precision
|
|
value: 91.73333333333332
|
|
- type: recall
|
|
value: 94.3
|
|
- 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: 79.10000000000001
|
|
- type: f1
|
|
value: 74.50500000000001
|
|
- type: precision
|
|
value: 72.58928571428571
|
|
- type: recall
|
|
value: 79.10000000000001
|
|
- 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: 96.6
|
|
- type: f1
|
|
value: 95.55
|
|
- type: precision
|
|
value: 95.05
|
|
- type: recall
|
|
value: 96.6
|
|
- 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: 82.0952380952381
|
|
- type: f1
|
|
value: 77.98458049886621
|
|
- type: precision
|
|
value: 76.1968253968254
|
|
- type: recall
|
|
value: 82.0952380952381
|
|
- 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: 87.9
|
|
- type: f1
|
|
value: 84.99190476190476
|
|
- type: precision
|
|
value: 83.65
|
|
- type: recall
|
|
value: 87.9
|
|
- 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: 95.7
|
|
- type: f1
|
|
value: 94.56666666666666
|
|
- type: precision
|
|
value: 94.01666666666667
|
|
- type: recall
|
|
value: 95.7
|
|
- 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: 98.6
|
|
- type: f1
|
|
value: 98.2
|
|
- type: precision
|
|
value: 98.0
|
|
- type: recall
|
|
value: 98.6
|
|
- 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: 95.6
|
|
- type: f1
|
|
value: 94.38333333333334
|
|
- type: precision
|
|
value: 93.78333333333335
|
|
- type: recall
|
|
value: 95.6
|
|
- 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: 87.4
|
|
- type: f1
|
|
value: 84.10380952380952
|
|
- type: precision
|
|
value: 82.67
|
|
- type: recall
|
|
value: 87.4
|
|
- 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: 95.5
|
|
- type: f1
|
|
value: 94.33333333333334
|
|
- type: precision
|
|
value: 93.78333333333333
|
|
- type: recall
|
|
value: 95.5
|
|
- 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: 89.4
|
|
- type: f1
|
|
value: 86.82000000000001
|
|
- type: precision
|
|
value: 85.64500000000001
|
|
- type: recall
|
|
value: 89.4
|
|
- 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: 95.1
|
|
- type: f1
|
|
value: 93.56666666666668
|
|
- type: precision
|
|
value: 92.81666666666666
|
|
- type: recall
|
|
value: 95.1
|
|
- 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: 98.9
|
|
- type: f1
|
|
value: 98.6
|
|
- type: precision
|
|
value: 98.45
|
|
- type: recall
|
|
value: 98.9
|
|
- 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: 95.01347708894879
|
|
- type: f1
|
|
value: 93.51752021563343
|
|
- type: precision
|
|
value: 92.82794249775381
|
|
- type: recall
|
|
value: 95.01347708894879
|
|
- 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: 97.00854700854701
|
|
- type: f1
|
|
value: 96.08262108262107
|
|
- type: precision
|
|
value: 95.65527065527067
|
|
- type: recall
|
|
value: 97.00854700854701
|
|
- 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: 96.5
|
|
- type: f1
|
|
value: 95.39999999999999
|
|
- type: precision
|
|
value: 94.88333333333333
|
|
- type: recall
|
|
value: 96.5
|
|
- 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: 96.5909090909091
|
|
- type: f1
|
|
value: 95.49242424242425
|
|
- type: precision
|
|
value: 94.9621212121212
|
|
- type: recall
|
|
value: 96.5909090909091
|
|
- 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: 84.90566037735849
|
|
- type: f1
|
|
value: 81.85883997204752
|
|
- type: precision
|
|
value: 80.54507337526205
|
|
- type: recall
|
|
value: 84.90566037735849
|
|
- 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: 97.5
|
|
- type: f1
|
|
value: 96.75
|
|
- type: precision
|
|
value: 96.38333333333333
|
|
- type: recall
|
|
value: 97.5
|
|
- 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: 86.7704280155642
|
|
- type: f1
|
|
value: 82.99610894941635
|
|
- type: precision
|
|
value: 81.32295719844358
|
|
- type: recall
|
|
value: 86.7704280155642
|
|
- 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: 67.52136752136752
|
|
- type: f1
|
|
value: 61.89662189662191
|
|
- type: precision
|
|
value: 59.68660968660969
|
|
- type: recall
|
|
value: 67.52136752136752
|
|
- 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: 89.2
|
|
- type: f1
|
|
value: 86.32
|
|
- type: precision
|
|
value: 85.015
|
|
- type: recall
|
|
value: 89.2
|
|
- 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: 96.0
|
|
- type: f1
|
|
value: 94.78333333333333
|
|
- type: precision
|
|
value: 94.18333333333334
|
|
- type: recall
|
|
value: 96.0
|
|
- 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: 83.8785046728972
|
|
- type: f1
|
|
value: 80.54517133956385
|
|
- type: precision
|
|
value: 79.154984423676
|
|
- type: recall
|
|
value: 83.8785046728972
|
|
- 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: 93.60000000000001
|
|
- type: f1
|
|
value: 92.01333333333334
|
|
- type: precision
|
|
value: 91.28333333333333
|
|
- type: recall
|
|
value: 93.60000000000001
|
|
- 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: 97.1
|
|
- type: f1
|
|
value: 96.26666666666667
|
|
- type: precision
|
|
value: 95.85000000000001
|
|
- type: recall
|
|
value: 97.1
|
|
- 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: 84.3
|
|
- type: f1
|
|
value: 80.67833333333333
|
|
- type: precision
|
|
value: 79.03928571428571
|
|
- type: recall
|
|
value: 84.3
|
|
- 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: 97.3
|
|
- type: f1
|
|
value: 96.48333333333332
|
|
- type: precision
|
|
value: 96.08333333333331
|
|
- type: recall
|
|
value: 97.3
|
|
- 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: 95.7
|
|
- type: f1
|
|
value: 94.66666666666667
|
|
- type: precision
|
|
value: 94.16666666666667
|
|
- type: recall
|
|
value: 95.7
|
|
- 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: 97.2
|
|
- type: f1
|
|
value: 96.36666666666667
|
|
- type: precision
|
|
value: 95.96666666666668
|
|
- type: recall
|
|
value: 97.2
|
|
- 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: 94.3
|
|
- type: f1
|
|
value: 92.80666666666667
|
|
- type: precision
|
|
value: 92.12833333333333
|
|
- type: recall
|
|
value: 94.3
|
|
- 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: 97.0
|
|
- type: f1
|
|
value: 96.22333333333334
|
|
- type: precision
|
|
value: 95.875
|
|
- type: recall
|
|
value: 97.0
|
|
- 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: 74.33333333333333
|
|
- type: f1
|
|
value: 70.78174603174602
|
|
- type: precision
|
|
value: 69.28333333333332
|
|
- type: recall
|
|
value: 74.33333333333333
|
|
- 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: 37.6
|
|
- type: f1
|
|
value: 32.938348952090365
|
|
- type: precision
|
|
value: 31.2811038961039
|
|
- type: recall
|
|
value: 37.6
|
|
- 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: 91.5
|
|
- type: f1
|
|
value: 89.13333333333333
|
|
- type: precision
|
|
value: 88.03333333333333
|
|
- type: recall
|
|
value: 91.5
|
|
- 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: 82.14285714285714
|
|
- type: f1
|
|
value: 77.67857142857143
|
|
- type: precision
|
|
value: 75.59523809523809
|
|
- type: recall
|
|
value: 82.14285714285714
|
|
- 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: 69.0450054884742
|
|
- type: f1
|
|
value: 63.070409283362075
|
|
- type: precision
|
|
value: 60.58992781824835
|
|
- type: recall
|
|
value: 69.0450054884742
|
|
- 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: 63.1
|
|
- type: f1
|
|
value: 57.848333333333336
|
|
- type: precision
|
|
value: 55.69500000000001
|
|
- type: recall
|
|
value: 63.1
|
|
- 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: 96.1
|
|
- type: f1
|
|
value: 95.01666666666667
|
|
- type: precision
|
|
value: 94.5
|
|
- type: recall
|
|
value: 96.1
|
|
- 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: 95.89999999999999
|
|
- type: f1
|
|
value: 94.90666666666667
|
|
- type: precision
|
|
value: 94.425
|
|
- type: recall
|
|
value: 95.89999999999999
|
|
- 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: 87.6
|
|
- type: f1
|
|
value: 84.61333333333333
|
|
- type: precision
|
|
value: 83.27
|
|
- type: recall
|
|
value: 87.6
|
|
- 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: 76.4
|
|
- type: f1
|
|
value: 71.90746031746032
|
|
- type: precision
|
|
value: 70.07027777777778
|
|
- type: recall
|
|
value: 76.4
|
|
- 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: 97.89999999999999
|
|
- type: f1
|
|
value: 97.26666666666667
|
|
- type: precision
|
|
value: 96.95
|
|
- type: recall
|
|
value: 97.89999999999999
|
|
- 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: 78.8
|
|
- type: f1
|
|
value: 74.39555555555555
|
|
- type: precision
|
|
value: 72.59416666666667
|
|
- type: recall
|
|
value: 78.8
|
|
- 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: 95.19999999999999
|
|
- type: f1
|
|
value: 93.78999999999999
|
|
- type: precision
|
|
value: 93.125
|
|
- type: recall
|
|
value: 95.19999999999999
|
|
- 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: 97.8
|
|
- type: f1
|
|
value: 97.1
|
|
- type: precision
|
|
value: 96.75
|
|
- type: recall
|
|
value: 97.8
|
|
- 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: 95.6
|
|
- type: f1
|
|
value: 94.25666666666666
|
|
- type: precision
|
|
value: 93.64166666666668
|
|
- type: recall
|
|
value: 95.6
|
|
- 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: 56.934306569343065
|
|
- type: f1
|
|
value: 51.461591936044485
|
|
- type: precision
|
|
value: 49.37434827945776
|
|
- type: recall
|
|
value: 56.934306569343065
|
|
- 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: 20.200000000000003
|
|
- type: f1
|
|
value: 16.91799284049284
|
|
- type: precision
|
|
value: 15.791855158730158
|
|
- type: recall
|
|
value: 20.200000000000003
|
|
- 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: 96.2
|
|
- type: f1
|
|
value: 95.3
|
|
- type: precision
|
|
value: 94.85
|
|
- type: recall
|
|
value: 96.2
|
|
- 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: 96.3
|
|
- type: f1
|
|
value: 95.11666666666667
|
|
- type: precision
|
|
value: 94.53333333333333
|
|
- type: recall
|
|
value: 96.3
|
|
- 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: 89.88095238095238
|
|
- type: f1
|
|
value: 87.14285714285714
|
|
- type: precision
|
|
value: 85.96230158730161
|
|
- type: recall
|
|
value: 89.88095238095238
|
|
- 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: 24.099999999999998
|
|
- type: f1
|
|
value: 19.630969083349783
|
|
- type: precision
|
|
value: 18.275094905094907
|
|
- type: recall
|
|
value: 24.099999999999998
|
|
- 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: 83.4368530020704
|
|
- type: f1
|
|
value: 79.45183870649709
|
|
- type: precision
|
|
value: 77.7432712215321
|
|
- type: recall
|
|
value: 83.4368530020704
|
|
- 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: 95.8
|
|
- type: f1
|
|
value: 94.53333333333333
|
|
- type: precision
|
|
value: 93.91666666666666
|
|
- type: recall
|
|
value: 95.8
|
|
- 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: 98.8
|
|
- type: f1
|
|
value: 98.48333333333332
|
|
- type: precision
|
|
value: 98.33333333333334
|
|
- type: recall
|
|
value: 98.8
|
|
- 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: 17.5
|
|
- type: f1
|
|
value: 14.979285714285714
|
|
- type: precision
|
|
value: 14.23235060690943
|
|
- type: recall
|
|
value: 17.5
|
|
- 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: 93.93939393939394
|
|
- type: f1
|
|
value: 91.991341991342
|
|
- type: precision
|
|
value: 91.05339105339105
|
|
- type: recall
|
|
value: 93.93939393939394
|
|
- 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: 89.31297709923665
|
|
- type: f1
|
|
value: 86.76844783715012
|
|
- type: precision
|
|
value: 85.63613231552164
|
|
- type: recall
|
|
value: 89.31297709923665
|
|
- 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: 99.12663755458514
|
|
- type: f1
|
|
value: 98.93255701115964
|
|
- type: precision
|
|
value: 98.83551673944687
|
|
- type: recall
|
|
value: 99.12663755458514
|
|
- 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: 92.0
|
|
- type: f1
|
|
value: 89.77999999999999
|
|
- type: precision
|
|
value: 88.78333333333333
|
|
- type: recall
|
|
value: 92.0
|
|
- 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: 96.89265536723164
|
|
- type: f1
|
|
value: 95.85687382297553
|
|
- type: precision
|
|
value: 95.33898305084746
|
|
- type: recall
|
|
value: 96.89265536723164
|
|
- 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: 14.6
|
|
- type: f1
|
|
value: 11.820611790170615
|
|
- type: precision
|
|
value: 11.022616224355355
|
|
- type: recall
|
|
value: 14.6
|
|
- 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: 95.89999999999999
|
|
- type: f1
|
|
value: 94.93333333333334
|
|
- type: precision
|
|
value: 94.48666666666666
|
|
- type: recall
|
|
value: 95.89999999999999
|
|
- 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: 87.6
|
|
- type: f1
|
|
value: 84.72333333333334
|
|
- type: precision
|
|
value: 83.44166666666666
|
|
- type: recall
|
|
value: 87.6
|
|
- 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: 94.8
|
|
- type: f1
|
|
value: 93.47333333333333
|
|
- type: precision
|
|
value: 92.875
|
|
- type: recall
|
|
value: 94.8
|
|
- 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: 96.6
|
|
- type: f1
|
|
value: 95.71666666666665
|
|
- type: precision
|
|
value: 95.28333333333335
|
|
- type: recall
|
|
value: 96.6
|
|
- 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: 17.8
|
|
- type: f1
|
|
value: 14.511074040901628
|
|
- type: precision
|
|
value: 13.503791000666002
|
|
- type: recall
|
|
value: 17.8
|
|
- 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: 94.10187667560321
|
|
- type: f1
|
|
value: 92.46648793565683
|
|
- type: precision
|
|
value: 91.71134941912423
|
|
- type: recall
|
|
value: 94.10187667560321
|
|
- 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: 97.0
|
|
- type: f1
|
|
value: 96.11666666666666
|
|
- type: precision
|
|
value: 95.68333333333334
|
|
- type: recall
|
|
value: 97.0
|
|
- 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: 72.72727272727273
|
|
- type: f1
|
|
value: 66.58949745906267
|
|
- type: precision
|
|
value: 63.86693017127799
|
|
- type: recall
|
|
value: 72.72727272727273
|
|
- 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: 90.14084507042254
|
|
- type: f1
|
|
value: 88.26291079812206
|
|
- type: precision
|
|
value: 87.32394366197182
|
|
- type: recall
|
|
value: 90.14084507042254
|
|
- 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: 64.67065868263472
|
|
- type: f1
|
|
value: 58.2876627696987
|
|
- type: precision
|
|
value: 55.79255774165953
|
|
- type: recall
|
|
value: 64.67065868263472
|
|
- 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: 95.6
|
|
- type: f1
|
|
value: 94.41666666666667
|
|
- type: precision
|
|
value: 93.85
|
|
- type: recall
|
|
value: 95.6
|
|
- 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: 55.172413793103445
|
|
- type: f1
|
|
value: 49.63992493549144
|
|
- type: precision
|
|
value: 47.71405113769646
|
|
- type: recall
|
|
value: 55.172413793103445
|
|
- 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: 77.46478873239437
|
|
- type: f1
|
|
value: 73.4417616811983
|
|
- type: precision
|
|
value: 71.91607981220658
|
|
- type: recall
|
|
value: 77.46478873239437
|
|
- 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: 84.61538461538461
|
|
- type: f1
|
|
value: 80.91452991452994
|
|
- type: precision
|
|
value: 79.33760683760683
|
|
- type: recall
|
|
value: 84.61538461538461
|
|
- 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: 98.2
|
|
- type: f1
|
|
value: 97.6
|
|
- type: precision
|
|
value: 97.3
|
|
- type: recall
|
|
value: 98.2
|
|
- 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: 75.5741127348643
|
|
- type: f1
|
|
value: 72.00417536534445
|
|
- type: precision
|
|
value: 70.53467872883321
|
|
- type: recall
|
|
value: 75.5741127348643
|
|
- 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: 62.2
|
|
- type: f1
|
|
value: 55.577460317460314
|
|
- type: precision
|
|
value: 52.98583333333333
|
|
- type: recall
|
|
value: 62.2
|
|
- 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: 92.18241042345277
|
|
- type: f1
|
|
value: 90.6468124709167
|
|
- type: precision
|
|
value: 89.95656894679696
|
|
- type: recall
|
|
value: 92.18241042345277
|
|
- 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: 96.1
|
|
- type: f1
|
|
value: 95.13333333333333
|
|
- type: precision
|
|
value: 94.66666666666667
|
|
- type: recall
|
|
value: 96.1
|
|
- 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: 96.8
|
|
- type: f1
|
|
value: 95.85000000000001
|
|
- type: precision
|
|
value: 95.39999999999999
|
|
- type: recall
|
|
value: 96.8
|
|
- 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: 92.1259842519685
|
|
- type: f1
|
|
value: 89.76377952755905
|
|
- type: precision
|
|
value: 88.71391076115485
|
|
- type: recall
|
|
value: 92.1259842519685
|
|
- 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: 94.1
|
|
- type: f1
|
|
value: 92.49
|
|
- type: precision
|
|
value: 91.725
|
|
- type: recall
|
|
value: 94.1
|
|
- 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: 77.5623268698061
|
|
- type: f1
|
|
value: 73.27364463791058
|
|
- type: precision
|
|
value: 71.51947852086357
|
|
- type: recall
|
|
value: 77.5623268698061
|
|
- 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: 97.39999999999999
|
|
- type: f1
|
|
value: 96.56666666666666
|
|
- type: precision
|
|
value: 96.16666666666667
|
|
- type: recall
|
|
value: 97.39999999999999
|
|
- 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: 66.34615384615384
|
|
- type: f1
|
|
value: 61.092032967032964
|
|
- type: precision
|
|
value: 59.27197802197802
|
|
- type: recall
|
|
value: 66.34615384615384
|
|
- 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: 94.89999999999999
|
|
- type: f1
|
|
value: 93.41190476190476
|
|
- type: precision
|
|
value: 92.7
|
|
- type: recall
|
|
value: 94.89999999999999
|
|
- 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: 93.10000000000001
|
|
- type: f1
|
|
value: 91.10000000000001
|
|
- type: precision
|
|
value: 90.13333333333333
|
|
- type: recall
|
|
value: 93.10000000000001
|
|
- 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: 93.7
|
|
- type: f1
|
|
value: 91.97333333333334
|
|
- type: precision
|
|
value: 91.14166666666667
|
|
- type: recall
|
|
value: 93.7
|
|
- 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: 92.21698113207547
|
|
- type: f1
|
|
value: 90.3796046720575
|
|
- type: precision
|
|
value: 89.56367924528303
|
|
- type: recall
|
|
value: 92.21698113207547
|
|
- 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: 97.6
|
|
- type: f1
|
|
value: 96.91666666666667
|
|
- type: precision
|
|
value: 96.6
|
|
- type: recall
|
|
value: 97.6
|
|
- 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: 97.44525547445255
|
|
- type: f1
|
|
value: 96.71532846715328
|
|
- type: precision
|
|
value: 96.35036496350365
|
|
- type: recall
|
|
value: 97.44525547445255
|
|
- 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: 94.1
|
|
- type: f1
|
|
value: 92.34000000000002
|
|
- type: precision
|
|
value: 91.49166666666667
|
|
- type: recall
|
|
value: 94.1
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: webis-touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 3.2910000000000004
|
|
- type: map_at_10
|
|
value: 10.373000000000001
|
|
- type: map_at_100
|
|
value: 15.612
|
|
- type: map_at_1000
|
|
value: 17.06
|
|
- type: map_at_3
|
|
value: 6.119
|
|
- type: map_at_5
|
|
value: 7.917000000000001
|
|
- type: mrr_at_1
|
|
value: 44.897999999999996
|
|
- type: mrr_at_10
|
|
value: 56.054
|
|
- type: mrr_at_100
|
|
value: 56.82000000000001
|
|
- type: mrr_at_1000
|
|
value: 56.82000000000001
|
|
- type: mrr_at_3
|
|
value: 52.381
|
|
- type: mrr_at_5
|
|
value: 53.81
|
|
- type: ndcg_at_1
|
|
value: 42.857
|
|
- type: ndcg_at_10
|
|
value: 27.249000000000002
|
|
- type: ndcg_at_100
|
|
value: 36.529
|
|
- type: ndcg_at_1000
|
|
value: 48.136
|
|
- type: ndcg_at_3
|
|
value: 33.938
|
|
- type: ndcg_at_5
|
|
value: 29.951
|
|
- type: precision_at_1
|
|
value: 44.897999999999996
|
|
- type: precision_at_10
|
|
value: 22.653000000000002
|
|
- type: precision_at_100
|
|
value: 7.000000000000001
|
|
- type: precision_at_1000
|
|
value: 1.48
|
|
- type: precision_at_3
|
|
value: 32.653
|
|
- type: precision_at_5
|
|
value: 27.755000000000003
|
|
- type: recall_at_1
|
|
value: 3.2910000000000004
|
|
- type: recall_at_10
|
|
value: 16.16
|
|
- type: recall_at_100
|
|
value: 43.908
|
|
- type: recall_at_1000
|
|
value: 79.823
|
|
- type: recall_at_3
|
|
value: 7.156
|
|
- type: recall_at_5
|
|
value: 10.204
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 71.05879999999999
|
|
- type: ap
|
|
value: 14.609748142799111
|
|
- type: f1
|
|
value: 54.878956295843096
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 64.61799660441426
|
|
- type: f1
|
|
value: 64.8698191961434
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 51.32860036611885
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 88.34714192048638
|
|
- type: cos_sim_ap
|
|
value: 80.26732975975634
|
|
- type: cos_sim_f1
|
|
value: 73.53415148134374
|
|
- type: cos_sim_precision
|
|
value: 69.34767360299276
|
|
- type: cos_sim_recall
|
|
value: 78.25857519788919
|
|
- type: dot_accuracy
|
|
value: 88.34714192048638
|
|
- type: dot_ap
|
|
value: 80.26733698491206
|
|
- type: dot_f1
|
|
value: 73.53415148134374
|
|
- type: dot_precision
|
|
value: 69.34767360299276
|
|
- type: dot_recall
|
|
value: 78.25857519788919
|
|
- type: euclidean_accuracy
|
|
value: 88.34714192048638
|
|
- type: euclidean_ap
|
|
value: 80.26734337771738
|
|
- type: euclidean_f1
|
|
value: 73.53415148134374
|
|
- type: euclidean_precision
|
|
value: 69.34767360299276
|
|
- type: euclidean_recall
|
|
value: 78.25857519788919
|
|
- type: manhattan_accuracy
|
|
value: 88.30541813196639
|
|
- type: manhattan_ap
|
|
value: 80.19415808104145
|
|
- type: manhattan_f1
|
|
value: 73.55143870713441
|
|
- type: manhattan_precision
|
|
value: 73.25307511122743
|
|
- type: manhattan_recall
|
|
value: 73.85224274406332
|
|
- type: max_accuracy
|
|
value: 88.34714192048638
|
|
- type: max_ap
|
|
value: 80.26734337771738
|
|
- type: max_f1
|
|
value: 73.55143870713441
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 89.81061047075717
|
|
- type: cos_sim_ap
|
|
value: 87.11747055081017
|
|
- type: cos_sim_f1
|
|
value: 80.04355498817256
|
|
- type: cos_sim_precision
|
|
value: 78.1165262000733
|
|
- type: cos_sim_recall
|
|
value: 82.06806282722513
|
|
- type: dot_accuracy
|
|
value: 89.81061047075717
|
|
- type: dot_ap
|
|
value: 87.11746902745236
|
|
- type: dot_f1
|
|
value: 80.04355498817256
|
|
- type: dot_precision
|
|
value: 78.1165262000733
|
|
- type: dot_recall
|
|
value: 82.06806282722513
|
|
- type: euclidean_accuracy
|
|
value: 89.81061047075717
|
|
- type: euclidean_ap
|
|
value: 87.11746919324248
|
|
- type: euclidean_f1
|
|
value: 80.04355498817256
|
|
- type: euclidean_precision
|
|
value: 78.1165262000733
|
|
- type: euclidean_recall
|
|
value: 82.06806282722513
|
|
- type: manhattan_accuracy
|
|
value: 89.79508673885202
|
|
- type: manhattan_ap
|
|
value: 87.11074390832218
|
|
- type: manhattan_f1
|
|
value: 80.13002540726349
|
|
- type: manhattan_precision
|
|
value: 77.83826945412311
|
|
- type: manhattan_recall
|
|
value: 82.56082537727133
|
|
- type: max_accuracy
|
|
value: 89.81061047075717
|
|
- type: max_ap
|
|
value: 87.11747055081017
|
|
- type: max_f1
|
|
value: 80.13002540726349
|
|
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
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- so
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- sq
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- sr
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- su
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- sv
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- sw
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- ta
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- te
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- tl
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- tr
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- ug
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- uk
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- xh
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- yi
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license: mit
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---
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## Multilingual-E5-large-instruct
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[Multilingual E5 Text Embeddings: A Technical Report](https://arxiv.org/pdf/2402.05672).
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Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
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This model has 24 layers and the embedding size is 1024.
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## Usage
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Below are examples to encode queries and passages from the MS-MARCO passage ranking dataset.
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### Transformers
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```python
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import torch.nn.functional as F
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from torch import Tensor
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from transformers import AutoTokenizer, AutoModel
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def average_pool(last_hidden_states: Tensor,
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attention_mask: Tensor) -> Tensor:
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last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
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return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
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def get_detailed_instruct(task_description: str, query: str) -> str:
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return f'Instruct: {task_description}\nQuery: {query}'
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# Each query must come with a one-sentence instruction that describes the task
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task = 'Given a web search query, retrieve relevant passages that answer the query'
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queries = [
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get_detailed_instruct(task, 'how much protein should a female eat'),
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get_detailed_instruct(task, '南瓜的家常做法')
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]
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# No need to add instruction for retrieval documents
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documents = [
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"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.",
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"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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input_texts = queries + documents
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tokenizer = AutoTokenizer.from_pretrained('intfloat/multilingual-e5-large-instruct')
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model = AutoModel.from_pretrained('intfloat/multilingual-e5-large-instruct')
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# Tokenize the input texts
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batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
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outputs = model(**batch_dict)
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embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
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# normalize embeddings
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embeddings = F.normalize(embeddings, p=2, dim=1)
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scores = (embeddings[:2] @ embeddings[2:].T) * 100
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print(scores.tolist())
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# => [[91.92852783203125, 67.580322265625], [70.3814468383789, 92.1330795288086]]
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```
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### Sentence Transformers
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```python
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from sentence_transformers import SentenceTransformer
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def get_detailed_instruct(task_description: str, query: str) -> str:
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return f'Instruct: {task_description}\nQuery: {query}'
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# Each query must come with a one-sentence instruction that describes the task
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task = 'Given a web search query, retrieve relevant passages that answer the query'
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queries = [
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get_detailed_instruct(task, 'how much protein should a female eat'),
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get_detailed_instruct(task, '南瓜的家常做法')
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]
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# No need to add instruction for retrieval documents
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documents = [
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"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.",
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"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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input_texts = queries + documents
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model = SentenceTransformer('intfloat/multilingual-e5-large-instruct')
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embeddings = model.encode(input_texts, convert_to_tensor=True, normalize_embeddings=True)
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scores = (embeddings[:2] @ embeddings[2:].T) * 100
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print(scores.tolist())
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# [[91.92853546142578, 67.5802993774414], [70.38143157958984, 92.13307189941406]]
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```
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## Supported Languages
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This model is initialized from [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
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and continually trained on a mixture of multilingual datasets.
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It supports 100 languages from xlm-roberta,
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but low-resource languages may see performance degradation.
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## Training Details
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**Initialization**: [xlm-roberta-large](https://huggingface.co/xlm-roberta-large)
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**First stage**: contrastive pre-training with 1 billion weakly supervised text pairs.
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**Second stage**: fine-tuning on datasets from the [E5-mistral](https://arxiv.org/abs/2401.00368) paper.
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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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## FAQ
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**1. Do I need to add instructions to the query?**
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Yes, this is how the model is trained, otherwise you will see a performance degradation.
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The task definition should be a one-sentence instruction that describes the task.
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This is a way to customize text embeddings for different scenarios through natural language instructions.
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Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation.
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On the other hand, there is no need to add instructions to the document side.
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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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```
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## Limitations
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Long texts will be truncated to at most 512 tokens.
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