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---
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datasets:
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- allenai/c4
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library_name: transformers
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tags:
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- sentence-transformers
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- gte
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- mteb
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- transformers.js
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- sentence-similarity
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license: apache-2.0
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language:
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- en
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model-index:
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- name: gte-large-en-v1.5
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results:
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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 (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: 73.01492537313432
|
|
- type: ap
|
|
value: 35.05341696659522
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|
- type: f1
|
|
value: 66.71270310883853
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- task:
|
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type: Classification
|
|
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:
|
|
- type: accuracy
|
|
value: 93.97189999999999
|
|
- type: ap
|
|
value: 90.5952493948908
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- type: f1
|
|
value: 93.95848137716877
|
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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 (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
|
|
value: 54.196
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|
- type: f1
|
|
value: 53.80122334012787
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- task:
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type: Retrieval
|
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dataset:
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type: mteb/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: c22ab2a51041ffd869aaddef7af8d8215647e41a
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metrics:
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|
- type: map_at_1
|
|
value: 47.297
|
|
- type: map_at_10
|
|
value: 64.303
|
|
- type: map_at_100
|
|
value: 64.541
|
|
- type: map_at_1000
|
|
value: 64.541
|
|
- type: map_at_3
|
|
value: 60.728
|
|
- type: map_at_5
|
|
value: 63.114000000000004
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|
- type: mrr_at_1
|
|
value: 48.435
|
|
- type: mrr_at_10
|
|
value: 64.657
|
|
- type: mrr_at_100
|
|
value: 64.901
|
|
- type: mrr_at_1000
|
|
value: 64.901
|
|
- type: mrr_at_3
|
|
value: 61.06
|
|
- type: mrr_at_5
|
|
value: 63.514
|
|
- type: ndcg_at_1
|
|
value: 47.297
|
|
- type: ndcg_at_10
|
|
value: 72.107
|
|
- type: ndcg_at_100
|
|
value: 72.963
|
|
- type: ndcg_at_1000
|
|
value: 72.963
|
|
- type: ndcg_at_3
|
|
value: 65.063
|
|
- type: ndcg_at_5
|
|
value: 69.352
|
|
- type: precision_at_1
|
|
value: 47.297
|
|
- type: precision_at_10
|
|
value: 9.623
|
|
- type: precision_at_100
|
|
value: 0.996
|
|
- type: precision_at_1000
|
|
value: 0.1
|
|
- type: precision_at_3
|
|
value: 25.865
|
|
- type: precision_at_5
|
|
value: 17.596
|
|
- type: recall_at_1
|
|
value: 47.297
|
|
- type: recall_at_10
|
|
value: 96.23
|
|
- type: recall_at_100
|
|
value: 99.644
|
|
- type: recall_at_1000
|
|
value: 99.644
|
|
- type: recall_at_3
|
|
value: 77.596
|
|
- type: recall_at_5
|
|
value: 87.98
|
|
- task:
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type: Clustering
|
|
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
|
|
metrics:
|
|
- type: v_measure
|
|
value: 48.467787861077475
|
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- task:
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type: Clustering
|
|
dataset:
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type: mteb/arxiv-clustering-s2s
|
|
name: MTEB ArxivClusteringS2S
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config: default
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split: test
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revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
|
metrics:
|
|
- type: v_measure
|
|
value: 43.39198391914257
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|
- task:
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type: Reranking
|
|
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
|
|
metrics:
|
|
- type: map
|
|
value: 63.12794820591384
|
|
- type: mrr
|
|
value: 75.9331442641692
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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: 87.85062993863319
|
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- type: cos_sim_spearman
|
|
value: 85.39049989733459
|
|
- type: euclidean_pearson
|
|
value: 86.00222680278333
|
|
- type: euclidean_spearman
|
|
value: 85.45556162077396
|
|
- type: manhattan_pearson
|
|
value: 85.88769871785621
|
|
- type: manhattan_spearman
|
|
value: 85.11760211290839
|
|
- task:
|
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type: Classification
|
|
dataset:
|
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type: mteb/banking77
|
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name: MTEB Banking77Classification
|
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config: default
|
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split: test
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revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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|
metrics:
|
|
- type: accuracy
|
|
value: 87.32792207792208
|
|
- type: f1
|
|
value: 87.29132945999555
|
|
- task:
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type: Clustering
|
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dataset:
|
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type: mteb/biorxiv-clustering-p2p
|
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name: MTEB BiorxivClusteringP2P
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config: default
|
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split: test
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revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
|
metrics:
|
|
- type: v_measure
|
|
value: 40.5779328301945
|
|
- task:
|
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type: Clustering
|
|
dataset:
|
|
type: mteb/biorxiv-clustering-s2s
|
|
name: MTEB BiorxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
|
metrics:
|
|
- type: v_measure
|
|
value: 37.94425623865118
|
|
- task:
|
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type: Retrieval
|
|
dataset:
|
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type: mteb/cqadupstack-android
|
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name: MTEB CQADupstackAndroidRetrieval
|
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config: default
|
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split: test
|
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revision: f46a197baaae43b4f621051089b82a364682dfeb
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 32.978
|
|
- type: map_at_10
|
|
value: 44.45
|
|
- type: map_at_100
|
|
value: 46.19
|
|
- type: map_at_1000
|
|
value: 46.303
|
|
- type: map_at_3
|
|
value: 40.849000000000004
|
|
- type: map_at_5
|
|
value: 42.55
|
|
- type: mrr_at_1
|
|
value: 40.629
|
|
- type: mrr_at_10
|
|
value: 50.848000000000006
|
|
- type: mrr_at_100
|
|
value: 51.669
|
|
- type: mrr_at_1000
|
|
value: 51.705
|
|
- type: mrr_at_3
|
|
value: 47.997
|
|
- type: mrr_at_5
|
|
value: 49.506
|
|
- type: ndcg_at_1
|
|
value: 40.629
|
|
- type: ndcg_at_10
|
|
value: 51.102000000000004
|
|
- type: ndcg_at_100
|
|
value: 57.159000000000006
|
|
- type: ndcg_at_1000
|
|
value: 58.669000000000004
|
|
- type: ndcg_at_3
|
|
value: 45.738
|
|
- type: ndcg_at_5
|
|
value: 47.632999999999996
|
|
- type: precision_at_1
|
|
value: 40.629
|
|
- type: precision_at_10
|
|
value: 9.700000000000001
|
|
- type: precision_at_100
|
|
value: 1.5970000000000002
|
|
- type: precision_at_1000
|
|
value: 0.202
|
|
- type: precision_at_3
|
|
value: 21.698
|
|
- type: precision_at_5
|
|
value: 15.393
|
|
- type: recall_at_1
|
|
value: 32.978
|
|
- type: recall_at_10
|
|
value: 63.711
|
|
- type: recall_at_100
|
|
value: 88.39399999999999
|
|
- type: recall_at_1000
|
|
value: 97.513
|
|
- type: recall_at_3
|
|
value: 48.025
|
|
- type: recall_at_5
|
|
value: 53.52
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-english
|
|
name: MTEB CQADupstackEnglishRetrieval
|
|
config: default
|
|
split: test
|
|
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 30.767
|
|
- type: map_at_10
|
|
value: 42.195
|
|
- type: map_at_100
|
|
value: 43.541999999999994
|
|
- type: map_at_1000
|
|
value: 43.673
|
|
- type: map_at_3
|
|
value: 38.561
|
|
- type: map_at_5
|
|
value: 40.532000000000004
|
|
- type: mrr_at_1
|
|
value: 38.79
|
|
- type: mrr_at_10
|
|
value: 48.021
|
|
- type: mrr_at_100
|
|
value: 48.735
|
|
- type: mrr_at_1000
|
|
value: 48.776
|
|
- type: mrr_at_3
|
|
value: 45.594
|
|
- type: mrr_at_5
|
|
value: 46.986
|
|
- type: ndcg_at_1
|
|
value: 38.79
|
|
- type: ndcg_at_10
|
|
value: 48.468
|
|
- type: ndcg_at_100
|
|
value: 53.037
|
|
- type: ndcg_at_1000
|
|
value: 55.001999999999995
|
|
- type: ndcg_at_3
|
|
value: 43.409
|
|
- type: ndcg_at_5
|
|
value: 45.654
|
|
- type: precision_at_1
|
|
value: 38.79
|
|
- type: precision_at_10
|
|
value: 9.452
|
|
- type: precision_at_100
|
|
value: 1.518
|
|
- type: precision_at_1000
|
|
value: 0.201
|
|
- type: precision_at_3
|
|
value: 21.21
|
|
- type: precision_at_5
|
|
value: 15.171999999999999
|
|
- type: recall_at_1
|
|
value: 30.767
|
|
- type: recall_at_10
|
|
value: 60.118
|
|
- type: recall_at_100
|
|
value: 79.271
|
|
- type: recall_at_1000
|
|
value: 91.43299999999999
|
|
- type: recall_at_3
|
|
value: 45.36
|
|
- type: recall_at_5
|
|
value: 51.705
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-gaming
|
|
name: MTEB CQADupstackGamingRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 40.007
|
|
- type: map_at_10
|
|
value: 53.529
|
|
- type: map_at_100
|
|
value: 54.602
|
|
- type: map_at_1000
|
|
value: 54.647
|
|
- type: map_at_3
|
|
value: 49.951
|
|
- type: map_at_5
|
|
value: 52.066
|
|
- type: mrr_at_1
|
|
value: 45.705
|
|
- type: mrr_at_10
|
|
value: 56.745000000000005
|
|
- type: mrr_at_100
|
|
value: 57.43899999999999
|
|
- type: mrr_at_1000
|
|
value: 57.462999999999994
|
|
- type: mrr_at_3
|
|
value: 54.25299999999999
|
|
- type: mrr_at_5
|
|
value: 55.842000000000006
|
|
- type: ndcg_at_1
|
|
value: 45.705
|
|
- type: ndcg_at_10
|
|
value: 59.809
|
|
- type: ndcg_at_100
|
|
value: 63.837999999999994
|
|
- type: ndcg_at_1000
|
|
value: 64.729
|
|
- type: ndcg_at_3
|
|
value: 53.994
|
|
- type: ndcg_at_5
|
|
value: 57.028
|
|
- type: precision_at_1
|
|
value: 45.705
|
|
- type: precision_at_10
|
|
value: 9.762
|
|
- type: precision_at_100
|
|
value: 1.275
|
|
- type: precision_at_1000
|
|
value: 0.13899999999999998
|
|
- type: precision_at_3
|
|
value: 24.368000000000002
|
|
- type: precision_at_5
|
|
value: 16.84
|
|
- type: recall_at_1
|
|
value: 40.007
|
|
- type: recall_at_10
|
|
value: 75.017
|
|
- type: recall_at_100
|
|
value: 91.99000000000001
|
|
- type: recall_at_1000
|
|
value: 98.265
|
|
- type: recall_at_3
|
|
value: 59.704
|
|
- type: recall_at_5
|
|
value: 67.109
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-gis
|
|
name: MTEB CQADupstackGisRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 5003b3064772da1887988e05400cf3806fe491f2
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.639000000000003
|
|
- type: map_at_10
|
|
value: 35.926
|
|
- type: map_at_100
|
|
value: 37.126999999999995
|
|
- type: map_at_1000
|
|
value: 37.202
|
|
- type: map_at_3
|
|
value: 32.989000000000004
|
|
- type: map_at_5
|
|
value: 34.465
|
|
- type: mrr_at_1
|
|
value: 28.475
|
|
- type: mrr_at_10
|
|
value: 37.7
|
|
- type: mrr_at_100
|
|
value: 38.753
|
|
- type: mrr_at_1000
|
|
value: 38.807
|
|
- type: mrr_at_3
|
|
value: 35.066
|
|
- type: mrr_at_5
|
|
value: 36.512
|
|
- type: ndcg_at_1
|
|
value: 28.475
|
|
- type: ndcg_at_10
|
|
value: 41.245
|
|
- type: ndcg_at_100
|
|
value: 46.814
|
|
- type: ndcg_at_1000
|
|
value: 48.571
|
|
- type: ndcg_at_3
|
|
value: 35.528999999999996
|
|
- type: ndcg_at_5
|
|
value: 38.066
|
|
- type: precision_at_1
|
|
value: 28.475
|
|
- type: precision_at_10
|
|
value: 6.497
|
|
- type: precision_at_100
|
|
value: 0.9650000000000001
|
|
- type: precision_at_1000
|
|
value: 0.11499999999999999
|
|
- type: precision_at_3
|
|
value: 15.065999999999999
|
|
- type: precision_at_5
|
|
value: 10.599
|
|
- type: recall_at_1
|
|
value: 26.639000000000003
|
|
- type: recall_at_10
|
|
value: 55.759
|
|
- type: recall_at_100
|
|
value: 80.913
|
|
- type: recall_at_1000
|
|
value: 93.929
|
|
- type: recall_at_3
|
|
value: 40.454
|
|
- type: recall_at_5
|
|
value: 46.439
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-mathematica
|
|
name: MTEB CQADupstackMathematicaRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 15.767999999999999
|
|
- type: map_at_10
|
|
value: 24.811
|
|
- type: map_at_100
|
|
value: 26.064999999999998
|
|
- type: map_at_1000
|
|
value: 26.186999999999998
|
|
- type: map_at_3
|
|
value: 21.736
|
|
- type: map_at_5
|
|
value: 23.283
|
|
- type: mrr_at_1
|
|
value: 19.527
|
|
- type: mrr_at_10
|
|
value: 29.179
|
|
- type: mrr_at_100
|
|
value: 30.153999999999996
|
|
- type: mrr_at_1000
|
|
value: 30.215999999999998
|
|
- type: mrr_at_3
|
|
value: 26.223000000000003
|
|
- type: mrr_at_5
|
|
value: 27.733999999999998
|
|
- type: ndcg_at_1
|
|
value: 19.527
|
|
- type: ndcg_at_10
|
|
value: 30.786
|
|
- type: ndcg_at_100
|
|
value: 36.644
|
|
- type: ndcg_at_1000
|
|
value: 39.440999999999995
|
|
- type: ndcg_at_3
|
|
value: 24.958
|
|
- type: ndcg_at_5
|
|
value: 27.392
|
|
- type: precision_at_1
|
|
value: 19.527
|
|
- type: precision_at_10
|
|
value: 5.995
|
|
- type: precision_at_100
|
|
value: 1.03
|
|
- type: precision_at_1000
|
|
value: 0.14100000000000001
|
|
- type: precision_at_3
|
|
value: 12.520999999999999
|
|
- type: precision_at_5
|
|
value: 9.129
|
|
- type: recall_at_1
|
|
value: 15.767999999999999
|
|
- type: recall_at_10
|
|
value: 44.824000000000005
|
|
- type: recall_at_100
|
|
value: 70.186
|
|
- type: recall_at_1000
|
|
value: 89.934
|
|
- type: recall_at_3
|
|
value: 28.607
|
|
- type: recall_at_5
|
|
value: 34.836
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-physics
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 31.952
|
|
- type: map_at_10
|
|
value: 44.438
|
|
- type: map_at_100
|
|
value: 45.778
|
|
- type: map_at_1000
|
|
value: 45.883
|
|
- type: map_at_3
|
|
value: 41.044000000000004
|
|
- type: map_at_5
|
|
value: 42.986000000000004
|
|
- type: mrr_at_1
|
|
value: 39.172000000000004
|
|
- type: mrr_at_10
|
|
value: 49.76
|
|
- type: mrr_at_100
|
|
value: 50.583999999999996
|
|
- type: mrr_at_1000
|
|
value: 50.621
|
|
- type: mrr_at_3
|
|
value: 47.353
|
|
- type: mrr_at_5
|
|
value: 48.739
|
|
- type: ndcg_at_1
|
|
value: 39.172000000000004
|
|
- type: ndcg_at_10
|
|
value: 50.760000000000005
|
|
- type: ndcg_at_100
|
|
value: 56.084
|
|
- type: ndcg_at_1000
|
|
value: 57.865
|
|
- type: ndcg_at_3
|
|
value: 45.663
|
|
- type: ndcg_at_5
|
|
value: 48.178
|
|
- type: precision_at_1
|
|
value: 39.172000000000004
|
|
- type: precision_at_10
|
|
value: 9.22
|
|
- type: precision_at_100
|
|
value: 1.387
|
|
- type: precision_at_1000
|
|
value: 0.17099999999999999
|
|
- type: precision_at_3
|
|
value: 21.976000000000003
|
|
- type: precision_at_5
|
|
value: 15.457
|
|
- type: recall_at_1
|
|
value: 31.952
|
|
- type: recall_at_10
|
|
value: 63.900999999999996
|
|
- type: recall_at_100
|
|
value: 85.676
|
|
- type: recall_at_1000
|
|
value: 97.03699999999999
|
|
- type: recall_at_3
|
|
value: 49.781
|
|
- type: recall_at_5
|
|
value: 56.330000000000005
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-programmers
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.332
|
|
- type: map_at_10
|
|
value: 36.874
|
|
- type: map_at_100
|
|
value: 38.340999999999994
|
|
- type: map_at_1000
|
|
value: 38.452
|
|
- type: map_at_3
|
|
value: 33.068
|
|
- type: map_at_5
|
|
value: 35.324
|
|
- type: mrr_at_1
|
|
value: 30.822
|
|
- type: mrr_at_10
|
|
value: 41.641
|
|
- type: mrr_at_100
|
|
value: 42.519
|
|
- type: mrr_at_1000
|
|
value: 42.573
|
|
- type: mrr_at_3
|
|
value: 38.413000000000004
|
|
- type: mrr_at_5
|
|
value: 40.542
|
|
- type: ndcg_at_1
|
|
value: 30.822
|
|
- type: ndcg_at_10
|
|
value: 43.414
|
|
- type: ndcg_at_100
|
|
value: 49.196
|
|
- type: ndcg_at_1000
|
|
value: 51.237
|
|
- type: ndcg_at_3
|
|
value: 37.230000000000004
|
|
- type: ndcg_at_5
|
|
value: 40.405
|
|
- type: precision_at_1
|
|
value: 30.822
|
|
- type: precision_at_10
|
|
value: 8.379
|
|
- type: precision_at_100
|
|
value: 1.315
|
|
- type: precision_at_1000
|
|
value: 0.168
|
|
- type: precision_at_3
|
|
value: 18.417
|
|
- type: precision_at_5
|
|
value: 13.744
|
|
- type: recall_at_1
|
|
value: 25.332
|
|
- type: recall_at_10
|
|
value: 57.774
|
|
- type: recall_at_100
|
|
value: 82.071
|
|
- type: recall_at_1000
|
|
value: 95.60600000000001
|
|
- type: recall_at_3
|
|
value: 40.722
|
|
- type: recall_at_5
|
|
value: 48.754999999999995
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.91033333333334
|
|
- type: map_at_10
|
|
value: 36.23225000000001
|
|
- type: map_at_100
|
|
value: 37.55766666666667
|
|
- type: map_at_1000
|
|
value: 37.672583333333336
|
|
- type: map_at_3
|
|
value: 32.95666666666667
|
|
- type: map_at_5
|
|
value: 34.73375
|
|
- type: mrr_at_1
|
|
value: 30.634
|
|
- type: mrr_at_10
|
|
value: 40.19449999999999
|
|
- type: mrr_at_100
|
|
value: 41.099250000000005
|
|
- type: mrr_at_1000
|
|
value: 41.15091666666667
|
|
- type: mrr_at_3
|
|
value: 37.4615
|
|
- type: mrr_at_5
|
|
value: 39.00216666666667
|
|
- type: ndcg_at_1
|
|
value: 30.634
|
|
- type: ndcg_at_10
|
|
value: 42.162166666666664
|
|
- type: ndcg_at_100
|
|
value: 47.60708333333333
|
|
- type: ndcg_at_1000
|
|
value: 49.68616666666666
|
|
- type: ndcg_at_3
|
|
value: 36.60316666666666
|
|
- type: ndcg_at_5
|
|
value: 39.15616666666668
|
|
- type: precision_at_1
|
|
value: 30.634
|
|
- type: precision_at_10
|
|
value: 7.6193333333333335
|
|
- type: precision_at_100
|
|
value: 1.2198333333333333
|
|
- type: precision_at_1000
|
|
value: 0.15975000000000003
|
|
- type: precision_at_3
|
|
value: 17.087
|
|
- type: precision_at_5
|
|
value: 12.298333333333334
|
|
- type: recall_at_1
|
|
value: 25.91033333333334
|
|
- type: recall_at_10
|
|
value: 55.67300000000001
|
|
- type: recall_at_100
|
|
value: 79.20608333333334
|
|
- type: recall_at_1000
|
|
value: 93.34866666666667
|
|
- type: recall_at_3
|
|
value: 40.34858333333333
|
|
- type: recall_at_5
|
|
value: 46.834083333333325
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-stats
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.006
|
|
- type: map_at_10
|
|
value: 32.177
|
|
- type: map_at_100
|
|
value: 33.324999999999996
|
|
- type: map_at_1000
|
|
value: 33.419
|
|
- type: map_at_3
|
|
value: 29.952
|
|
- type: map_at_5
|
|
value: 31.095
|
|
- type: mrr_at_1
|
|
value: 28.066999999999997
|
|
- type: mrr_at_10
|
|
value: 34.995
|
|
- type: mrr_at_100
|
|
value: 35.978
|
|
- type: mrr_at_1000
|
|
value: 36.042
|
|
- type: mrr_at_3
|
|
value: 33.103
|
|
- type: mrr_at_5
|
|
value: 34.001
|
|
- type: ndcg_at_1
|
|
value: 28.066999999999997
|
|
- type: ndcg_at_10
|
|
value: 36.481
|
|
- type: ndcg_at_100
|
|
value: 42.022999999999996
|
|
- type: ndcg_at_1000
|
|
value: 44.377
|
|
- type: ndcg_at_3
|
|
value: 32.394
|
|
- type: ndcg_at_5
|
|
value: 34.108
|
|
- type: precision_at_1
|
|
value: 28.066999999999997
|
|
- type: precision_at_10
|
|
value: 5.736
|
|
- type: precision_at_100
|
|
value: 0.9259999999999999
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_3
|
|
value: 13.804
|
|
- type: precision_at_5
|
|
value: 9.508999999999999
|
|
- type: recall_at_1
|
|
value: 25.006
|
|
- type: recall_at_10
|
|
value: 46.972
|
|
- type: recall_at_100
|
|
value: 72.138
|
|
- type: recall_at_1000
|
|
value: 89.479
|
|
- type: recall_at_3
|
|
value: 35.793
|
|
- type: recall_at_5
|
|
value: 39.947
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-tex
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 46989137a86843e03a6195de44b09deda022eec7
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 16.07
|
|
- type: map_at_10
|
|
value: 24.447
|
|
- type: map_at_100
|
|
value: 25.685999999999996
|
|
- type: map_at_1000
|
|
value: 25.813999999999997
|
|
- type: map_at_3
|
|
value: 21.634
|
|
- type: map_at_5
|
|
value: 23.133
|
|
- type: mrr_at_1
|
|
value: 19.580000000000002
|
|
- type: mrr_at_10
|
|
value: 28.127999999999997
|
|
- type: mrr_at_100
|
|
value: 29.119
|
|
- type: mrr_at_1000
|
|
value: 29.192
|
|
- type: mrr_at_3
|
|
value: 25.509999999999998
|
|
- type: mrr_at_5
|
|
value: 26.878
|
|
- type: ndcg_at_1
|
|
value: 19.580000000000002
|
|
- type: ndcg_at_10
|
|
value: 29.804000000000002
|
|
- type: ndcg_at_100
|
|
value: 35.555
|
|
- type: ndcg_at_1000
|
|
value: 38.421
|
|
- type: ndcg_at_3
|
|
value: 24.654999999999998
|
|
- type: ndcg_at_5
|
|
value: 26.881
|
|
- type: precision_at_1
|
|
value: 19.580000000000002
|
|
- type: precision_at_10
|
|
value: 5.736
|
|
- type: precision_at_100
|
|
value: 1.005
|
|
- type: precision_at_1000
|
|
value: 0.145
|
|
- type: precision_at_3
|
|
value: 12.033000000000001
|
|
- type: precision_at_5
|
|
value: 8.871
|
|
- type: recall_at_1
|
|
value: 16.07
|
|
- type: recall_at_10
|
|
value: 42.364000000000004
|
|
- type: recall_at_100
|
|
value: 68.01899999999999
|
|
- type: recall_at_1000
|
|
value: 88.122
|
|
- type: recall_at_3
|
|
value: 27.846
|
|
- type: recall_at_5
|
|
value: 33.638
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-unix
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 26.365
|
|
- type: map_at_10
|
|
value: 36.591
|
|
- type: map_at_100
|
|
value: 37.730000000000004
|
|
- type: map_at_1000
|
|
value: 37.84
|
|
- type: map_at_3
|
|
value: 33.403
|
|
- type: map_at_5
|
|
value: 35.272999999999996
|
|
- type: mrr_at_1
|
|
value: 30.503999999999998
|
|
- type: mrr_at_10
|
|
value: 39.940999999999995
|
|
- type: mrr_at_100
|
|
value: 40.818
|
|
- type: mrr_at_1000
|
|
value: 40.876000000000005
|
|
- type: mrr_at_3
|
|
value: 37.065
|
|
- type: mrr_at_5
|
|
value: 38.814
|
|
- type: ndcg_at_1
|
|
value: 30.503999999999998
|
|
- type: ndcg_at_10
|
|
value: 42.185
|
|
- type: ndcg_at_100
|
|
value: 47.416000000000004
|
|
- type: ndcg_at_1000
|
|
value: 49.705
|
|
- type: ndcg_at_3
|
|
value: 36.568
|
|
- type: ndcg_at_5
|
|
value: 39.416000000000004
|
|
- type: precision_at_1
|
|
value: 30.503999999999998
|
|
- type: precision_at_10
|
|
value: 7.276000000000001
|
|
- type: precision_at_100
|
|
value: 1.118
|
|
- type: precision_at_1000
|
|
value: 0.14300000000000002
|
|
- type: precision_at_3
|
|
value: 16.729
|
|
- type: precision_at_5
|
|
value: 12.107999999999999
|
|
- type: recall_at_1
|
|
value: 26.365
|
|
- type: recall_at_10
|
|
value: 55.616
|
|
- type: recall_at_100
|
|
value: 78.129
|
|
- type: recall_at_1000
|
|
value: 93.95599999999999
|
|
- type: recall_at_3
|
|
value: 40.686
|
|
- type: recall_at_5
|
|
value: 47.668
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-webmasters
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.750999999999998
|
|
- type: map_at_10
|
|
value: 33.446
|
|
- type: map_at_100
|
|
value: 35.235
|
|
- type: map_at_1000
|
|
value: 35.478
|
|
- type: map_at_3
|
|
value: 29.358
|
|
- type: map_at_5
|
|
value: 31.525
|
|
- type: mrr_at_1
|
|
value: 27.668
|
|
- type: mrr_at_10
|
|
value: 37.694
|
|
- type: mrr_at_100
|
|
value: 38.732
|
|
- type: mrr_at_1000
|
|
value: 38.779
|
|
- type: mrr_at_3
|
|
value: 34.223
|
|
- type: mrr_at_5
|
|
value: 36.08
|
|
- type: ndcg_at_1
|
|
value: 27.668
|
|
- type: ndcg_at_10
|
|
value: 40.557
|
|
- type: ndcg_at_100
|
|
value: 46.605999999999995
|
|
- type: ndcg_at_1000
|
|
value: 48.917
|
|
- type: ndcg_at_3
|
|
value: 33.677
|
|
- type: ndcg_at_5
|
|
value: 36.85
|
|
- type: precision_at_1
|
|
value: 27.668
|
|
- type: precision_at_10
|
|
value: 8.3
|
|
- type: precision_at_100
|
|
value: 1.6260000000000001
|
|
- type: precision_at_1000
|
|
value: 0.253
|
|
- type: precision_at_3
|
|
value: 16.008
|
|
- type: precision_at_5
|
|
value: 12.292
|
|
- type: recall_at_1
|
|
value: 22.750999999999998
|
|
- type: recall_at_10
|
|
value: 55.643
|
|
- type: recall_at_100
|
|
value: 82.151
|
|
- type: recall_at_1000
|
|
value: 95.963
|
|
- type: recall_at_3
|
|
value: 36.623
|
|
- type: recall_at_5
|
|
value: 44.708
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/cqadupstack-wordpress
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.288999999999998
|
|
- type: map_at_10
|
|
value: 25.903
|
|
- type: map_at_100
|
|
value: 27.071
|
|
- type: map_at_1000
|
|
value: 27.173000000000002
|
|
- type: map_at_3
|
|
value: 22.935
|
|
- type: map_at_5
|
|
value: 24.573
|
|
- type: mrr_at_1
|
|
value: 18.669
|
|
- type: mrr_at_10
|
|
value: 27.682000000000002
|
|
- type: mrr_at_100
|
|
value: 28.691
|
|
- type: mrr_at_1000
|
|
value: 28.761
|
|
- type: mrr_at_3
|
|
value: 24.738
|
|
- type: mrr_at_5
|
|
value: 26.392
|
|
- type: ndcg_at_1
|
|
value: 18.669
|
|
- type: ndcg_at_10
|
|
value: 31.335
|
|
- type: ndcg_at_100
|
|
value: 36.913000000000004
|
|
- type: ndcg_at_1000
|
|
value: 39.300000000000004
|
|
- type: ndcg_at_3
|
|
value: 25.423000000000002
|
|
- type: ndcg_at_5
|
|
value: 28.262999999999998
|
|
- type: precision_at_1
|
|
value: 18.669
|
|
- type: precision_at_10
|
|
value: 5.379
|
|
- type: precision_at_100
|
|
value: 0.876
|
|
- type: precision_at_1000
|
|
value: 0.11900000000000001
|
|
- type: precision_at_3
|
|
value: 11.214
|
|
- type: precision_at_5
|
|
value: 8.466
|
|
- type: recall_at_1
|
|
value: 17.288999999999998
|
|
- type: recall_at_10
|
|
value: 46.377
|
|
- type: recall_at_100
|
|
value: 71.53500000000001
|
|
- type: recall_at_1000
|
|
value: 88.947
|
|
- type: recall_at_3
|
|
value: 30.581999999999997
|
|
- type: recall_at_5
|
|
value: 37.354
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/climate-fever
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 21.795
|
|
- type: map_at_10
|
|
value: 37.614999999999995
|
|
- type: map_at_100
|
|
value: 40.037
|
|
- type: map_at_1000
|
|
value: 40.184999999999995
|
|
- type: map_at_3
|
|
value: 32.221
|
|
- type: map_at_5
|
|
value: 35.154999999999994
|
|
- type: mrr_at_1
|
|
value: 50.358000000000004
|
|
- type: mrr_at_10
|
|
value: 62.129
|
|
- type: mrr_at_100
|
|
value: 62.613
|
|
- type: mrr_at_1000
|
|
value: 62.62
|
|
- type: mrr_at_3
|
|
value: 59.272999999999996
|
|
- type: mrr_at_5
|
|
value: 61.138999999999996
|
|
- type: ndcg_at_1
|
|
value: 50.358000000000004
|
|
- type: ndcg_at_10
|
|
value: 48.362
|
|
- type: ndcg_at_100
|
|
value: 55.932
|
|
- type: ndcg_at_1000
|
|
value: 58.062999999999995
|
|
- type: ndcg_at_3
|
|
value: 42.111
|
|
- type: ndcg_at_5
|
|
value: 44.063
|
|
- type: precision_at_1
|
|
value: 50.358000000000004
|
|
- type: precision_at_10
|
|
value: 14.677999999999999
|
|
- type: precision_at_100
|
|
value: 2.2950000000000004
|
|
- type: precision_at_1000
|
|
value: 0.271
|
|
- type: precision_at_3
|
|
value: 31.77
|
|
- type: precision_at_5
|
|
value: 23.375
|
|
- type: recall_at_1
|
|
value: 21.795
|
|
- type: recall_at_10
|
|
value: 53.846000000000004
|
|
- type: recall_at_100
|
|
value: 78.952
|
|
- type: recall_at_1000
|
|
value: 90.41900000000001
|
|
- type: recall_at_3
|
|
value: 37.257
|
|
- type: recall_at_5
|
|
value: 44.661
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/dbpedia
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 9.728
|
|
- type: map_at_10
|
|
value: 22.691
|
|
- type: map_at_100
|
|
value: 31.734
|
|
- type: map_at_1000
|
|
value: 33.464
|
|
- type: map_at_3
|
|
value: 16.273
|
|
- type: map_at_5
|
|
value: 19.016
|
|
- type: mrr_at_1
|
|
value: 73.25
|
|
- type: mrr_at_10
|
|
value: 80.782
|
|
- type: mrr_at_100
|
|
value: 81.01899999999999
|
|
- type: mrr_at_1000
|
|
value: 81.021
|
|
- type: mrr_at_3
|
|
value: 79.583
|
|
- type: mrr_at_5
|
|
value: 80.146
|
|
- type: ndcg_at_1
|
|
value: 59.62499999999999
|
|
- type: ndcg_at_10
|
|
value: 46.304
|
|
- type: ndcg_at_100
|
|
value: 51.23
|
|
- type: ndcg_at_1000
|
|
value: 58.048
|
|
- type: ndcg_at_3
|
|
value: 51.541000000000004
|
|
- type: ndcg_at_5
|
|
value: 48.635
|
|
- type: precision_at_1
|
|
value: 73.25
|
|
- type: precision_at_10
|
|
value: 36.375
|
|
- type: precision_at_100
|
|
value: 11.53
|
|
- type: precision_at_1000
|
|
value: 2.23
|
|
- type: precision_at_3
|
|
value: 55.583000000000006
|
|
- type: precision_at_5
|
|
value: 47.15
|
|
- type: recall_at_1
|
|
value: 9.728
|
|
- type: recall_at_10
|
|
value: 28.793999999999997
|
|
- type: recall_at_100
|
|
value: 57.885
|
|
- type: recall_at_1000
|
|
value: 78.759
|
|
- type: recall_at_3
|
|
value: 17.79
|
|
- type: recall_at_5
|
|
value: 21.733
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/emotion
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 46.775
|
|
- type: f1
|
|
value: 41.89794273264891
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/fever
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 85.378
|
|
- type: map_at_10
|
|
value: 91.51
|
|
- type: map_at_100
|
|
value: 91.666
|
|
- type: map_at_1000
|
|
value: 91.676
|
|
- type: map_at_3
|
|
value: 90.757
|
|
- type: map_at_5
|
|
value: 91.277
|
|
- type: mrr_at_1
|
|
value: 91.839
|
|
- type: mrr_at_10
|
|
value: 95.49
|
|
- type: mrr_at_100
|
|
value: 95.493
|
|
- type: mrr_at_1000
|
|
value: 95.493
|
|
- type: mrr_at_3
|
|
value: 95.345
|
|
- type: mrr_at_5
|
|
value: 95.47200000000001
|
|
- type: ndcg_at_1
|
|
value: 91.839
|
|
- type: ndcg_at_10
|
|
value: 93.806
|
|
- type: ndcg_at_100
|
|
value: 94.255
|
|
- type: ndcg_at_1000
|
|
value: 94.399
|
|
- type: ndcg_at_3
|
|
value: 93.027
|
|
- type: ndcg_at_5
|
|
value: 93.51
|
|
- type: precision_at_1
|
|
value: 91.839
|
|
- type: precision_at_10
|
|
value: 10.93
|
|
- type: precision_at_100
|
|
value: 1.1400000000000001
|
|
- type: precision_at_1000
|
|
value: 0.117
|
|
- type: precision_at_3
|
|
value: 34.873
|
|
- type: precision_at_5
|
|
value: 21.44
|
|
- type: recall_at_1
|
|
value: 85.378
|
|
- type: recall_at_10
|
|
value: 96.814
|
|
- type: recall_at_100
|
|
value: 98.386
|
|
- type: recall_at_1000
|
|
value: 99.21600000000001
|
|
- type: recall_at_3
|
|
value: 94.643
|
|
- type: recall_at_5
|
|
value: 95.976
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/fiqa
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 32.190000000000005
|
|
- type: map_at_10
|
|
value: 53.605000000000004
|
|
- type: map_at_100
|
|
value: 55.550999999999995
|
|
- type: map_at_1000
|
|
value: 55.665
|
|
- type: map_at_3
|
|
value: 46.62
|
|
- type: map_at_5
|
|
value: 50.517999999999994
|
|
- type: mrr_at_1
|
|
value: 60.34
|
|
- type: mrr_at_10
|
|
value: 70.775
|
|
- type: mrr_at_100
|
|
value: 71.238
|
|
- type: mrr_at_1000
|
|
value: 71.244
|
|
- type: mrr_at_3
|
|
value: 68.72399999999999
|
|
- type: mrr_at_5
|
|
value: 69.959
|
|
- type: ndcg_at_1
|
|
value: 60.34
|
|
- type: ndcg_at_10
|
|
value: 63.226000000000006
|
|
- type: ndcg_at_100
|
|
value: 68.60300000000001
|
|
- type: ndcg_at_1000
|
|
value: 69.901
|
|
- type: ndcg_at_3
|
|
value: 58.048
|
|
- type: ndcg_at_5
|
|
value: 59.789
|
|
- type: precision_at_1
|
|
value: 60.34
|
|
- type: precision_at_10
|
|
value: 17.130000000000003
|
|
- type: precision_at_100
|
|
value: 2.29
|
|
- type: precision_at_1000
|
|
value: 0.256
|
|
- type: precision_at_3
|
|
value: 38.323
|
|
- type: precision_at_5
|
|
value: 27.87
|
|
- type: recall_at_1
|
|
value: 32.190000000000005
|
|
- type: recall_at_10
|
|
value: 73.041
|
|
- type: recall_at_100
|
|
value: 91.31
|
|
- type: recall_at_1000
|
|
value: 98.104
|
|
- type: recall_at_3
|
|
value: 53.70399999999999
|
|
- type: recall_at_5
|
|
value: 62.358999999999995
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/hotpotqa
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: ab518f4d6fcca38d87c25209f94beba119d02014
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 43.511
|
|
- type: map_at_10
|
|
value: 58.15
|
|
- type: map_at_100
|
|
value: 58.95399999999999
|
|
- type: map_at_1000
|
|
value: 59.018
|
|
- type: map_at_3
|
|
value: 55.31700000000001
|
|
- type: map_at_5
|
|
value: 57.04900000000001
|
|
- type: mrr_at_1
|
|
value: 87.022
|
|
- type: mrr_at_10
|
|
value: 91.32000000000001
|
|
- type: mrr_at_100
|
|
value: 91.401
|
|
- type: mrr_at_1000
|
|
value: 91.403
|
|
- type: mrr_at_3
|
|
value: 90.77
|
|
- type: mrr_at_5
|
|
value: 91.156
|
|
- type: ndcg_at_1
|
|
value: 87.022
|
|
- type: ndcg_at_10
|
|
value: 68.183
|
|
- type: ndcg_at_100
|
|
value: 70.781
|
|
- type: ndcg_at_1000
|
|
value: 72.009
|
|
- type: ndcg_at_3
|
|
value: 64.334
|
|
- type: ndcg_at_5
|
|
value: 66.449
|
|
- type: precision_at_1
|
|
value: 87.022
|
|
- type: precision_at_10
|
|
value: 13.406
|
|
- type: precision_at_100
|
|
value: 1.542
|
|
- type: precision_at_1000
|
|
value: 0.17099999999999999
|
|
- type: precision_at_3
|
|
value: 39.023
|
|
- type: precision_at_5
|
|
value: 25.080000000000002
|
|
- type: recall_at_1
|
|
value: 43.511
|
|
- type: recall_at_10
|
|
value: 67.02900000000001
|
|
- type: recall_at_100
|
|
value: 77.11
|
|
- type: recall_at_1000
|
|
value: 85.294
|
|
- type: recall_at_3
|
|
value: 58.535000000000004
|
|
- type: recall_at_5
|
|
value: 62.70099999999999
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/imdb
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.0996
|
|
- type: ap
|
|
value: 87.86206089096373
|
|
- type: f1
|
|
value: 92.07554547510763
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/msmarco
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: c5a29a104738b98a9e76336939199e264163d4a0
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.179
|
|
- type: map_at_10
|
|
value: 35.86
|
|
- type: map_at_100
|
|
value: 37.025999999999996
|
|
- type: map_at_1000
|
|
value: 37.068
|
|
- type: map_at_3
|
|
value: 31.921
|
|
- type: map_at_5
|
|
value: 34.172000000000004
|
|
- type: mrr_at_1
|
|
value: 23.926
|
|
- type: mrr_at_10
|
|
value: 36.525999999999996
|
|
- type: mrr_at_100
|
|
value: 37.627
|
|
- type: mrr_at_1000
|
|
value: 37.665
|
|
- type: mrr_at_3
|
|
value: 32.653
|
|
- type: mrr_at_5
|
|
value: 34.897
|
|
- type: ndcg_at_1
|
|
value: 23.910999999999998
|
|
- type: ndcg_at_10
|
|
value: 42.927
|
|
- type: ndcg_at_100
|
|
value: 48.464
|
|
- type: ndcg_at_1000
|
|
value: 49.533
|
|
- type: ndcg_at_3
|
|
value: 34.910000000000004
|
|
- type: ndcg_at_5
|
|
value: 38.937
|
|
- type: precision_at_1
|
|
value: 23.910999999999998
|
|
- type: precision_at_10
|
|
value: 6.758
|
|
- type: precision_at_100
|
|
value: 0.9520000000000001
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_3
|
|
value: 14.838000000000001
|
|
- type: precision_at_5
|
|
value: 10.934000000000001
|
|
- type: recall_at_1
|
|
value: 23.179
|
|
- type: recall_at_10
|
|
value: 64.622
|
|
- type: recall_at_100
|
|
value: 90.135
|
|
- type: recall_at_1000
|
|
value: 98.301
|
|
- type: recall_at_3
|
|
value: 42.836999999999996
|
|
- type: recall_at_5
|
|
value: 52.512
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_domain
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 96.59598723210215
|
|
- type: f1
|
|
value: 96.41913500001952
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/mtop_intent
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 82.89557683538533
|
|
- type: f1
|
|
value: 63.379319722356264
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_intent
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 78.93745796906524
|
|
- type: f1
|
|
value: 75.71616541785902
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/amazon_massive_scenario
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 81.41223940820443
|
|
- type: f1
|
|
value: 81.2877893719078
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-p2p
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 35.03682528325662
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/medrxiv-clustering-s2s
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 32.942529406124
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/mind_small
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 31.459949660460317
|
|
- type: mrr
|
|
value: 32.70509582031616
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/nfcorpus
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 6.497
|
|
- type: map_at_10
|
|
value: 13.843
|
|
- type: map_at_100
|
|
value: 17.713
|
|
- type: map_at_1000
|
|
value: 19.241
|
|
- type: map_at_3
|
|
value: 10.096
|
|
- type: map_at_5
|
|
value: 11.85
|
|
- type: mrr_at_1
|
|
value: 48.916
|
|
- type: mrr_at_10
|
|
value: 57.764
|
|
- type: mrr_at_100
|
|
value: 58.251
|
|
- type: mrr_at_1000
|
|
value: 58.282999999999994
|
|
- type: mrr_at_3
|
|
value: 55.623999999999995
|
|
- type: mrr_at_5
|
|
value: 57.018
|
|
- type: ndcg_at_1
|
|
value: 46.594
|
|
- type: ndcg_at_10
|
|
value: 36.945
|
|
- type: ndcg_at_100
|
|
value: 34.06
|
|
- type: ndcg_at_1000
|
|
value: 43.05
|
|
- type: ndcg_at_3
|
|
value: 41.738
|
|
- type: ndcg_at_5
|
|
value: 39.330999999999996
|
|
- type: precision_at_1
|
|
value: 48.916
|
|
- type: precision_at_10
|
|
value: 27.43
|
|
- type: precision_at_100
|
|
value: 8.616
|
|
- type: precision_at_1000
|
|
value: 2.155
|
|
- type: precision_at_3
|
|
value: 39.112
|
|
- type: precision_at_5
|
|
value: 33.808
|
|
- type: recall_at_1
|
|
value: 6.497
|
|
- type: recall_at_10
|
|
value: 18.163
|
|
- type: recall_at_100
|
|
value: 34.566
|
|
- type: recall_at_1000
|
|
value: 67.15
|
|
- type: recall_at_3
|
|
value: 11.100999999999999
|
|
- type: recall_at_5
|
|
value: 14.205000000000002
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/nq
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 31.916
|
|
- type: map_at_10
|
|
value: 48.123
|
|
- type: map_at_100
|
|
value: 49.103
|
|
- type: map_at_1000
|
|
value: 49.131
|
|
- type: map_at_3
|
|
value: 43.711
|
|
- type: map_at_5
|
|
value: 46.323
|
|
- type: mrr_at_1
|
|
value: 36.181999999999995
|
|
- type: mrr_at_10
|
|
value: 50.617999999999995
|
|
- type: mrr_at_100
|
|
value: 51.329
|
|
- type: mrr_at_1000
|
|
value: 51.348000000000006
|
|
- type: mrr_at_3
|
|
value: 47.010999999999996
|
|
- type: mrr_at_5
|
|
value: 49.175000000000004
|
|
- type: ndcg_at_1
|
|
value: 36.181999999999995
|
|
- type: ndcg_at_10
|
|
value: 56.077999999999996
|
|
- type: ndcg_at_100
|
|
value: 60.037
|
|
- type: ndcg_at_1000
|
|
value: 60.63499999999999
|
|
- type: ndcg_at_3
|
|
value: 47.859
|
|
- type: ndcg_at_5
|
|
value: 52.178999999999995
|
|
- type: precision_at_1
|
|
value: 36.181999999999995
|
|
- type: precision_at_10
|
|
value: 9.284
|
|
- type: precision_at_100
|
|
value: 1.149
|
|
- type: precision_at_1000
|
|
value: 0.121
|
|
- type: precision_at_3
|
|
value: 22.006999999999998
|
|
- type: precision_at_5
|
|
value: 15.695
|
|
- type: recall_at_1
|
|
value: 31.916
|
|
- type: recall_at_10
|
|
value: 77.771
|
|
- type: recall_at_100
|
|
value: 94.602
|
|
- type: recall_at_1000
|
|
value: 98.967
|
|
- type: recall_at_3
|
|
value: 56.528
|
|
- type: recall_at_5
|
|
value: 66.527
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/quora
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 71.486
|
|
- type: map_at_10
|
|
value: 85.978
|
|
- type: map_at_100
|
|
value: 86.587
|
|
- type: map_at_1000
|
|
value: 86.598
|
|
- type: map_at_3
|
|
value: 83.04899999999999
|
|
- type: map_at_5
|
|
value: 84.857
|
|
- type: mrr_at_1
|
|
value: 82.32000000000001
|
|
- type: mrr_at_10
|
|
value: 88.64
|
|
- type: mrr_at_100
|
|
value: 88.702
|
|
- type: mrr_at_1000
|
|
value: 88.702
|
|
- type: mrr_at_3
|
|
value: 87.735
|
|
- type: mrr_at_5
|
|
value: 88.36
|
|
- type: ndcg_at_1
|
|
value: 82.34
|
|
- type: ndcg_at_10
|
|
value: 89.67
|
|
- type: ndcg_at_100
|
|
value: 90.642
|
|
- type: ndcg_at_1000
|
|
value: 90.688
|
|
- type: ndcg_at_3
|
|
value: 86.932
|
|
- type: ndcg_at_5
|
|
value: 88.408
|
|
- type: precision_at_1
|
|
value: 82.34
|
|
- type: precision_at_10
|
|
value: 13.675999999999998
|
|
- type: precision_at_100
|
|
value: 1.544
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_3
|
|
value: 38.24
|
|
- type: precision_at_5
|
|
value: 25.068
|
|
- type: recall_at_1
|
|
value: 71.486
|
|
- type: recall_at_10
|
|
value: 96.844
|
|
- type: recall_at_100
|
|
value: 99.843
|
|
- type: recall_at_1000
|
|
value: 99.996
|
|
- type: recall_at_3
|
|
value: 88.92099999999999
|
|
- type: recall_at_5
|
|
value: 93.215
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 59.75758437908334
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/reddit-clustering-p2p
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 282350215ef01743dc01b456c7f5241fa8937f16
|
|
metrics:
|
|
- type: v_measure
|
|
value: 68.03497914092789
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/scidocs
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.808
|
|
- type: map_at_10
|
|
value: 16.059
|
|
- type: map_at_100
|
|
value: 19.048000000000002
|
|
- type: map_at_1000
|
|
value: 19.43
|
|
- type: map_at_3
|
|
value: 10.953
|
|
- type: map_at_5
|
|
value: 13.363
|
|
- type: mrr_at_1
|
|
value: 28.7
|
|
- type: mrr_at_10
|
|
value: 42.436
|
|
- type: mrr_at_100
|
|
value: 43.599
|
|
- type: mrr_at_1000
|
|
value: 43.62
|
|
- type: mrr_at_3
|
|
value: 38.45
|
|
- type: mrr_at_5
|
|
value: 40.89
|
|
- type: ndcg_at_1
|
|
value: 28.7
|
|
- type: ndcg_at_10
|
|
value: 26.346000000000004
|
|
- type: ndcg_at_100
|
|
value: 36.758
|
|
- type: ndcg_at_1000
|
|
value: 42.113
|
|
- type: ndcg_at_3
|
|
value: 24.254
|
|
- type: ndcg_at_5
|
|
value: 21.506
|
|
- type: precision_at_1
|
|
value: 28.7
|
|
- type: precision_at_10
|
|
value: 13.969999999999999
|
|
- type: precision_at_100
|
|
value: 2.881
|
|
- type: precision_at_1000
|
|
value: 0.414
|
|
- type: precision_at_3
|
|
value: 22.933
|
|
- type: precision_at_5
|
|
value: 19.220000000000002
|
|
- type: recall_at_1
|
|
value: 5.808
|
|
- type: recall_at_10
|
|
value: 28.310000000000002
|
|
- type: recall_at_100
|
|
value: 58.475
|
|
- type: recall_at_1000
|
|
value: 84.072
|
|
- type: recall_at_3
|
|
value: 13.957
|
|
- type: recall_at_5
|
|
value: 19.515
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sickr-sts
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.39274129958557
|
|
- type: cos_sim_spearman
|
|
value: 79.78021235170053
|
|
- type: euclidean_pearson
|
|
value: 79.35335401300166
|
|
- type: euclidean_spearman
|
|
value: 79.7271870968275
|
|
- type: manhattan_pearson
|
|
value: 79.35256263340601
|
|
- type: manhattan_spearman
|
|
value: 79.76036386976321
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts12-sts
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.99130429246708
|
|
- type: cos_sim_spearman
|
|
value: 73.88322811171203
|
|
- type: euclidean_pearson
|
|
value: 80.7569419170376
|
|
- type: euclidean_spearman
|
|
value: 73.82542155409597
|
|
- type: manhattan_pearson
|
|
value: 80.79468183847625
|
|
- type: manhattan_spearman
|
|
value: 73.87027144047784
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts13-sts
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.88548789489907
|
|
- type: cos_sim_spearman
|
|
value: 85.07535893847255
|
|
- type: euclidean_pearson
|
|
value: 84.6637222061494
|
|
- type: euclidean_spearman
|
|
value: 85.14200626702456
|
|
- type: manhattan_pearson
|
|
value: 84.75327892344734
|
|
- type: manhattan_spearman
|
|
value: 85.24406181838596
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts14-sts
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 82.88140039325008
|
|
- type: cos_sim_spearman
|
|
value: 79.61211268112362
|
|
- type: euclidean_pearson
|
|
value: 81.29639728816458
|
|
- type: euclidean_spearman
|
|
value: 79.51284578041442
|
|
- type: manhattan_pearson
|
|
value: 81.3381797137111
|
|
- type: manhattan_spearman
|
|
value: 79.55683684039808
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts15-sts
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.16716737270485
|
|
- type: cos_sim_spearman
|
|
value: 86.14823841857738
|
|
- type: euclidean_pearson
|
|
value: 85.36325733440725
|
|
- type: euclidean_spearman
|
|
value: 86.04919691402029
|
|
- type: manhattan_pearson
|
|
value: 85.3147511385052
|
|
- type: manhattan_spearman
|
|
value: 86.00676205857764
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts16-sts
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 80.34266645861588
|
|
- type: cos_sim_spearman
|
|
value: 81.59914035005882
|
|
- type: euclidean_pearson
|
|
value: 81.15053076245988
|
|
- type: euclidean_spearman
|
|
value: 81.52776915798489
|
|
- type: manhattan_pearson
|
|
value: 81.1819647418673
|
|
- type: manhattan_spearman
|
|
value: 81.57479527353556
|
|
- 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: 89.38263326821439
|
|
- type: cos_sim_spearman
|
|
value: 89.10946308202642
|
|
- type: euclidean_pearson
|
|
value: 88.87831312540068
|
|
- type: euclidean_spearman
|
|
value: 89.03615865973664
|
|
- type: manhattan_pearson
|
|
value: 88.79835539970384
|
|
- type: manhattan_spearman
|
|
value: 88.9766156339753
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/sts22-crosslingual-sts
|
|
name: MTEB STS22 (en)
|
|
config: en
|
|
split: test
|
|
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 70.1574915581685
|
|
- type: cos_sim_spearman
|
|
value: 70.59144980004054
|
|
- type: euclidean_pearson
|
|
value: 71.43246306918755
|
|
- type: euclidean_spearman
|
|
value: 70.5544189562984
|
|
- type: manhattan_pearson
|
|
value: 71.4071414609503
|
|
- type: manhattan_spearman
|
|
value: 70.31799126163712
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: mteb/stsbenchmark-sts
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.36215796635351
|
|
- type: cos_sim_spearman
|
|
value: 83.07276756467208
|
|
- type: euclidean_pearson
|
|
value: 83.06690453635584
|
|
- type: euclidean_spearman
|
|
value: 82.9635366303289
|
|
- type: manhattan_pearson
|
|
value: 83.04994049700815
|
|
- type: manhattan_spearman
|
|
value: 82.98120125356036
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/scidocs-reranking
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 86.92530011616722
|
|
- type: mrr
|
|
value: 96.21826793395421
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/scifact
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: 0228b52cf27578f30900b9e5271d331663a030d7
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 65.75
|
|
- type: map_at_10
|
|
value: 77.701
|
|
- type: map_at_100
|
|
value: 78.005
|
|
- type: map_at_1000
|
|
value: 78.006
|
|
- type: map_at_3
|
|
value: 75.48
|
|
- type: map_at_5
|
|
value: 76.927
|
|
- type: mrr_at_1
|
|
value: 68.333
|
|
- type: mrr_at_10
|
|
value: 78.511
|
|
- type: mrr_at_100
|
|
value: 78.704
|
|
- type: mrr_at_1000
|
|
value: 78.704
|
|
- type: mrr_at_3
|
|
value: 77
|
|
- type: mrr_at_5
|
|
value: 78.083
|
|
- type: ndcg_at_1
|
|
value: 68.333
|
|
- type: ndcg_at_10
|
|
value: 82.42699999999999
|
|
- type: ndcg_at_100
|
|
value: 83.486
|
|
- type: ndcg_at_1000
|
|
value: 83.511
|
|
- type: ndcg_at_3
|
|
value: 78.96300000000001
|
|
- type: ndcg_at_5
|
|
value: 81.028
|
|
- type: precision_at_1
|
|
value: 68.333
|
|
- type: precision_at_10
|
|
value: 10.667
|
|
- type: precision_at_100
|
|
value: 1.127
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_3
|
|
value: 31.333
|
|
- type: precision_at_5
|
|
value: 20.133000000000003
|
|
- type: recall_at_1
|
|
value: 65.75
|
|
- type: recall_at_10
|
|
value: 95.578
|
|
- type: recall_at_100
|
|
value: 99.833
|
|
- type: recall_at_1000
|
|
value: 100
|
|
- type: recall_at_3
|
|
value: 86.506
|
|
- type: recall_at_5
|
|
value: 91.75
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/sprintduplicatequestions-pairclassification
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.75247524752476
|
|
- type: cos_sim_ap
|
|
value: 94.16065078045173
|
|
- type: cos_sim_f1
|
|
value: 87.22986247544205
|
|
- type: cos_sim_precision
|
|
value: 85.71428571428571
|
|
- type: cos_sim_recall
|
|
value: 88.8
|
|
- type: dot_accuracy
|
|
value: 99.74554455445545
|
|
- type: dot_ap
|
|
value: 93.90633887037264
|
|
- type: dot_f1
|
|
value: 86.9873417721519
|
|
- type: dot_precision
|
|
value: 88.1025641025641
|
|
- type: dot_recall
|
|
value: 85.9
|
|
- type: euclidean_accuracy
|
|
value: 99.75247524752476
|
|
- type: euclidean_ap
|
|
value: 94.17466319018055
|
|
- type: euclidean_f1
|
|
value: 87.3405299313052
|
|
- type: euclidean_precision
|
|
value: 85.74181117533719
|
|
- type: euclidean_recall
|
|
value: 89
|
|
- type: manhattan_accuracy
|
|
value: 99.75445544554455
|
|
- type: manhattan_ap
|
|
value: 94.27688371923577
|
|
- type: manhattan_f1
|
|
value: 87.74002954209749
|
|
- type: manhattan_precision
|
|
value: 86.42095053346266
|
|
- type: manhattan_recall
|
|
value: 89.1
|
|
- type: max_accuracy
|
|
value: 99.75445544554455
|
|
- type: max_ap
|
|
value: 94.27688371923577
|
|
- type: max_f1
|
|
value: 87.74002954209749
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 71.26500637517056
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/stackexchange-clustering-p2p
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 39.17507906280528
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: mteb/stackoverflowdupquestions-reranking
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 52.4848744828509
|
|
- type: mrr
|
|
value: 53.33678168236992
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: mteb/summeval
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 30.599864323827887
|
|
- type: cos_sim_spearman
|
|
value: 30.91116204665598
|
|
- type: dot_pearson
|
|
value: 30.82637894269936
|
|
- type: dot_spearman
|
|
value: 30.957573868416066
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/trec-covid
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: None
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.23600000000000002
|
|
- type: map_at_10
|
|
value: 1.892
|
|
- type: map_at_100
|
|
value: 11.586
|
|
- type: map_at_1000
|
|
value: 27.761999999999997
|
|
- type: map_at_3
|
|
value: 0.653
|
|
- type: map_at_5
|
|
value: 1.028
|
|
- type: mrr_at_1
|
|
value: 88
|
|
- type: mrr_at_10
|
|
value: 94
|
|
- type: mrr_at_100
|
|
value: 94
|
|
- type: mrr_at_1000
|
|
value: 94
|
|
- type: mrr_at_3
|
|
value: 94
|
|
- type: mrr_at_5
|
|
value: 94
|
|
- type: ndcg_at_1
|
|
value: 82
|
|
- type: ndcg_at_10
|
|
value: 77.48899999999999
|
|
- type: ndcg_at_100
|
|
value: 60.141
|
|
- type: ndcg_at_1000
|
|
value: 54.228
|
|
- type: ndcg_at_3
|
|
value: 82.358
|
|
- type: ndcg_at_5
|
|
value: 80.449
|
|
- type: precision_at_1
|
|
value: 88
|
|
- type: precision_at_10
|
|
value: 82.19999999999999
|
|
- type: precision_at_100
|
|
value: 61.760000000000005
|
|
- type: precision_at_1000
|
|
value: 23.684
|
|
- type: precision_at_3
|
|
value: 88
|
|
- type: precision_at_5
|
|
value: 85.6
|
|
- type: recall_at_1
|
|
value: 0.23600000000000002
|
|
- type: recall_at_10
|
|
value: 2.117
|
|
- type: recall_at_100
|
|
value: 14.985000000000001
|
|
- type: recall_at_1000
|
|
value: 51.107
|
|
- type: recall_at_3
|
|
value: 0.688
|
|
- type: recall_at_5
|
|
value: 1.1039999999999999
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: mteb/touche2020
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 2.3040000000000003
|
|
- type: map_at_10
|
|
value: 9.025
|
|
- type: map_at_100
|
|
value: 15.312999999999999
|
|
- type: map_at_1000
|
|
value: 16.954
|
|
- type: map_at_3
|
|
value: 4.981
|
|
- type: map_at_5
|
|
value: 6.32
|
|
- type: mrr_at_1
|
|
value: 24.490000000000002
|
|
- type: mrr_at_10
|
|
value: 39.835
|
|
- type: mrr_at_100
|
|
value: 40.8
|
|
- type: mrr_at_1000
|
|
value: 40.8
|
|
- type: mrr_at_3
|
|
value: 35.034
|
|
- type: mrr_at_5
|
|
value: 37.687
|
|
- type: ndcg_at_1
|
|
value: 22.448999999999998
|
|
- type: ndcg_at_10
|
|
value: 22.545
|
|
- type: ndcg_at_100
|
|
value: 35.931999999999995
|
|
- type: ndcg_at_1000
|
|
value: 47.665
|
|
- type: ndcg_at_3
|
|
value: 23.311
|
|
- type: ndcg_at_5
|
|
value: 22.421
|
|
- type: precision_at_1
|
|
value: 24.490000000000002
|
|
- type: precision_at_10
|
|
value: 20.408
|
|
- type: precision_at_100
|
|
value: 7.815999999999999
|
|
- type: precision_at_1000
|
|
value: 1.553
|
|
- type: precision_at_3
|
|
value: 25.169999999999998
|
|
- type: precision_at_5
|
|
value: 23.265
|
|
- type: recall_at_1
|
|
value: 2.3040000000000003
|
|
- type: recall_at_10
|
|
value: 15.693999999999999
|
|
- type: recall_at_100
|
|
value: 48.917
|
|
- type: recall_at_1000
|
|
value: 84.964
|
|
- type: recall_at_3
|
|
value: 6.026
|
|
- type: recall_at_5
|
|
value: 9.066
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/toxic_conversations_50k
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
|
metrics:
|
|
- type: accuracy
|
|
value: 82.6074
|
|
- type: ap
|
|
value: 23.187467098602013
|
|
- type: f1
|
|
value: 65.36829506379657
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: mteb/tweet_sentiment_extraction
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 63.16355404640635
|
|
- type: f1
|
|
value: 63.534725639863346
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: mteb/twentynewsgroups-clustering
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 50.91004094411276
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twittersemeval2015-pairclassification
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 86.55301901412649
|
|
- type: cos_sim_ap
|
|
value: 75.25312618556728
|
|
- type: cos_sim_f1
|
|
value: 68.76561719140429
|
|
- type: cos_sim_precision
|
|
value: 65.3061224489796
|
|
- type: cos_sim_recall
|
|
value: 72.61213720316623
|
|
- type: dot_accuracy
|
|
value: 86.29671574178936
|
|
- type: dot_ap
|
|
value: 75.11910195501207
|
|
- type: dot_f1
|
|
value: 68.44048376830045
|
|
- type: dot_precision
|
|
value: 66.12546125461255
|
|
- type: dot_recall
|
|
value: 70.92348284960423
|
|
- type: euclidean_accuracy
|
|
value: 86.5828217202122
|
|
- type: euclidean_ap
|
|
value: 75.22986344900924
|
|
- type: euclidean_f1
|
|
value: 68.81267797449549
|
|
- type: euclidean_precision
|
|
value: 64.8238861674831
|
|
- type: euclidean_recall
|
|
value: 73.3245382585752
|
|
- type: manhattan_accuracy
|
|
value: 86.61262442629791
|
|
- type: manhattan_ap
|
|
value: 75.24401608557328
|
|
- type: manhattan_f1
|
|
value: 68.80473982483257
|
|
- type: manhattan_precision
|
|
value: 67.21187720181177
|
|
- type: manhattan_recall
|
|
value: 70.47493403693932
|
|
- type: max_accuracy
|
|
value: 86.61262442629791
|
|
- type: max_ap
|
|
value: 75.25312618556728
|
|
- type: max_f1
|
|
value: 68.81267797449549
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: mteb/twitterurlcorpus-pairclassification
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 88.10688089416696
|
|
- type: cos_sim_ap
|
|
value: 84.17862178779863
|
|
- type: cos_sim_f1
|
|
value: 76.17305208781748
|
|
- type: cos_sim_precision
|
|
value: 71.31246641590543
|
|
- type: cos_sim_recall
|
|
value: 81.74468740375731
|
|
- type: dot_accuracy
|
|
value: 88.1844995536927
|
|
- type: dot_ap
|
|
value: 84.33816725235876
|
|
- type: dot_f1
|
|
value: 76.43554032918746
|
|
- type: dot_precision
|
|
value: 74.01557767200346
|
|
- type: dot_recall
|
|
value: 79.0190945488143
|
|
- type: euclidean_accuracy
|
|
value: 88.07001203089223
|
|
- type: euclidean_ap
|
|
value: 84.12267000814985
|
|
- type: euclidean_f1
|
|
value: 76.12232600180778
|
|
- type: euclidean_precision
|
|
value: 74.50604541433205
|
|
- type: euclidean_recall
|
|
value: 77.81028641823221
|
|
- type: manhattan_accuracy
|
|
value: 88.06419063142779
|
|
- type: manhattan_ap
|
|
value: 84.11648917164187
|
|
- type: manhattan_f1
|
|
value: 76.20579953925474
|
|
- type: manhattan_precision
|
|
value: 72.56772755762935
|
|
- type: manhattan_recall
|
|
value: 80.22790267939637
|
|
- type: max_accuracy
|
|
value: 88.1844995536927
|
|
- type: max_ap
|
|
value: 84.33816725235876
|
|
- type: max_f1
|
|
value: 76.43554032918746
|
|
---
|
|
|
|
<!-- **English** | [中文](./README_zh.md) -->
|
|
|
|
# gte-large-en-v1.5
|
|
|
|
We introduce `gte-v1.5` series, upgraded `gte` embeddings that support the context length of up to **8192**, while further enhancing model performance.
|
|
The models are built upon the `transformer++` encoder [backbone](https://huggingface.co/Alibaba-NLP/new-impl) (BERT + RoPE + GLU).
|
|
|
|
The `gte-v1.5` series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to [Evaluation](#evaluation)).
|
|
|
|
We also present the [`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct),
|
|
a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.
|
|
|
|
<!-- Provide a longer summary of what this model is. -->
|
|
|
|
- **Developed by:** Institute for Intelligent Computing, Alibaba Group
|
|
- **Model type:** Text Embeddings
|
|
- **Paper:** [mGTE: Generalized Long-Context Text Representation and Reranking
|
|
Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669)
|
|
|
|
<!-- - **Demo [optional]:** [More Information Needed] -->
|
|
|
|
### Model list
|
|
|
|
| Models | Language | Model Size | Max Seq. Length | Dimension | MTEB-en | LoCo |
|
|
|:-----: | :-----: |:-----: |:-----: |:-----: | :-----: | :-----: |
|
|
|[`gte-Qwen1.5-7B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| Multiple | 7720 | 32768 | 4096 | 67.34 | 87.57 |
|
|
|[`gte-large-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | English | 434 | 8192 | 1024 | 65.39 | 86.71 |
|
|
|[`gte-base-en-v1.5`](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | English | 137 | 8192 | 768 | 64.11 | 87.44 |
|
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|
|
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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# Requires transformers>=4.36.0
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import torch.nn.functional as F
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from transformers import AutoModel, AutoTokenizer
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input_texts = [
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"what is the capital of China?",
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"how to implement quick sort in python?",
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"Beijing",
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|
"sorting algorithms"
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]
|
|
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model_path = 'Alibaba-NLP/gte-large-en-v1.5'
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
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|
|
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# Tokenize the input texts
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batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')
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|
|
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outputs = model(**batch_dict)
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embeddings = outputs.last_hidden_state[:, 0]
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|
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# (Optionally) normalize embeddings
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embeddings = F.normalize(embeddings, p=2, dim=1)
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scores = (embeddings[:1] @ embeddings[1:].T) * 100
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print(scores.tolist())
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```
|
|
|
|
**It is recommended to install xformers and enable unpadding for acceleration, refer to [enable-unpadding-and-xformers](https://huggingface.co/Alibaba-NLP/new-impl#recommendation-enable-unpadding-and-acceleration-with-xformers).**
|
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|
|
|
|
Use with sentence-transformers:
|
|
|
|
```python
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# Requires sentence_transformers>=2.7.0
|
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|
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from sentence_transformers import SentenceTransformer
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from sentence_transformers.util import cos_sim
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|
|
sentences = ['That is a happy person', 'That is a very happy person']
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|
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model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
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embeddings = model.encode(sentences)
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print(cos_sim(embeddings[0], embeddings[1]))
|
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```
|
|
|
|
Use with `transformers.js`:
|
|
|
|
```js
|
|
// npm i @xenova/transformers
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|
import { pipeline, dot } from '@xenova/transformers';
|
|
|
|
// Create feature extraction pipeline
|
|
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-large-en-v1.5', {
|
|
quantized: false, // Comment out this line to use the quantized version
|
|
});
|
|
|
|
// Generate sentence embeddings
|
|
const sentences = [
|
|
"what is the capital of China?",
|
|
"how to implement quick sort in python?",
|
|
"Beijing",
|
|
"sorting algorithms"
|
|
]
|
|
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
|
|
|
|
// Compute similarity scores
|
|
const [source_embeddings, ...document_embeddings ] = output.tolist();
|
|
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
|
|
console.log(similarities); // [41.86354093370361, 77.07076371259589, 37.02981979677899]
|
|
```
|
|
|
|
## Training Details
|
|
|
|
### Training Data
|
|
|
|
- Masked language modeling (MLM): `c4-en`
|
|
- Weak-supervised contrastive pre-training (CPT): [GTE](https://arxiv.org/pdf/2308.03281.pdf) pre-training data
|
|
- Supervised contrastive fine-tuning: [GTE](https://arxiv.org/pdf/2308.03281.pdf) fine-tuning data
|
|
|
|
### Training Procedure
|
|
|
|
To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy.
|
|
The model first undergoes preliminary MLM pre-training on shorter lengths.
|
|
And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.
|
|
|
|
The entire training process is as follows:
|
|
- MLM-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000
|
|
- MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000
|
|
- [MLM-8192](https://huggingface.co/Alibaba-NLP/gte-en-mlm-large): lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000
|
|
- CPT: max_len 512, lr 5e-5, batch_size 28672, num_steps 100000
|
|
- Fine-tuning: TODO
|
|
|
|
|
|
## Evaluation
|
|
|
|
|
|
### MTEB
|
|
|
|
The results of other models are retrieved from [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
|
|
|
|
The gte evaluation setting: `mteb==1.2.0, fp16 auto mix precision, max_length=8192`, and set ntk scaling factor to 2 (equivalent to rope_base * 2).
|
|
|
|
| Model Name | Param Size (M) | Dimension | Sequence Length | Average (56) | Class. (12) | Clust. (11) | Pair Class. (3) | Reran. (4) | Retr. (15) | STS (10) | Summ. (1) |
|
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
|
| [**gte-large-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) | 409 | 1024 | 8192 | **65.39** | 77.75 | 47.95 | 84.63 | 58.50 | 57.91 | 81.43 | 30.91 |
|
|
| [mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) | 335 | 1024 | 512 | 64.68 | 75.64 | 46.71 | 87.2 | 60.11 | 54.39 | 85 | 32.71 |
|
|
| [multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) | 560 | 1024 | 514 | 64.41 | 77.56 | 47.1 | 86.19 | 58.58 | 52.47 | 84.78 | 30.39 |
|
|
| [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 335 | 1024 | 512 | 64.23 | 75.97 | 46.08 | 87.12 | 60.03 | 54.29 | 83.11 | 31.61 |
|
|
| [**gte-base-en-v1.5**](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) | 137 | 768 | 8192 | **64.11** | 77.17 | 46.82 | 85.33 | 57.66 | 54.09 | 81.97 | 31.17 |
|
|
| [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5)| 109 | 768 | 512 | 63.55 | 75.53 | 45.77 | 86.55 | 58.86 | 53.25 | 82.4 | 31.07 |
|
|
|
|
|
|
### LoCo
|
|
|
|
| Model Name | Dimension | Sequence Length | Average (5) | QsmsumRetrieval | SummScreenRetrieval | QasperAbastractRetrieval | QasperTitleRetrieval | GovReportRetrieval |
|
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
|
| [gte-qwen1.5-7b](https://huggingface.co/Alibaba-NLP/gte-qwen1.5-7b) | 4096 | 32768 | 87.57 | 49.37 | 93.10 | 99.67 | 97.54 | 98.21 |
|
|
| [gte-large-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-v1.5) |1024 | 8192 | 86.71 | 44.55 | 92.61 | 99.82 | 97.81 | 98.74 |
|
|
| [gte-base-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-v1.5) | 768 | 8192 | 87.44 | 49.91 | 91.78 | 99.82 | 97.13 | 98.58 |
|
|
|
|
|
|
|
|
## Citation
|
|
|
|
If you find our paper or models helpful, please consider citing them as follows:
|
|
|
|
```
|
|
@article{zhang2024mgte,
|
|
title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
|
|
author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
|
|
journal={arXiv preprint arXiv:2407.19669},
|
|
year={2024}
|
|
}
|
|
|
|
@article{li2023towards,
|
|
title={Towards general text embeddings with multi-stage contrastive learning},
|
|
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
|
|
journal={arXiv preprint arXiv:2308.03281},
|
|
year={2023}
|
|
}
|
|
``` |
