3156 lines
434 KiB
Markdown
3156 lines
434 KiB
Markdown
---
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- mteb
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- transformers.js
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model-index:
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- name: binarize_False
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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: None
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name: MTEB AmazonCounterfactualClassification (en)
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config: en
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split: test
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205
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metrics:
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- type: accuracy
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value: 78.13432835820896
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- type: ap
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value: 42.190424731303246
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- type: f1
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value: 72.34446401534811
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- task:
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type: Classification
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dataset:
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type: None
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name: MTEB AmazonPolarityClassification
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config: default
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split: test
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revision: e2d317d38cd51312af73b3d32a06d1a08b442046
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metrics:
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- type: accuracy
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value: 93.093825
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- type: ap
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value: 90.03727505544286
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- type: f1
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value: 93.0874055138833
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- task:
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type: Classification
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dataset:
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type: None
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name: MTEB AmazonReviewsClassification (en)
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config: en
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split: test
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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metrics:
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- type: accuracy
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value: 48.428000000000004
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- type: f1
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value: 47.74311520203536
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- task:
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type: Retrieval
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dataset:
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type: None
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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
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value: 23.898
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- type: map_at_10
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value: 39.775
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- type: map_at_100
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value: 40.827000000000005
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- type: map_at_1000
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value: 40.837
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- type: map_at_20
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value: 40.604
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- type: map_at_3
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value: 34.519
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- type: map_at_5
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value: 37.307
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- type: mrr_at_1
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value: 24.395
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- type: mrr_at_10
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value: 39.963
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- type: mrr_at_100
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value: 41.014
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- type: mrr_at_1000
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value: 41.024
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- type: mrr_at_20
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value: 40.791
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- type: mrr_at_3
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value: 34.732
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- type: mrr_at_5
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value: 37.480999999999995
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- type: ndcg_at_1
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value: 23.898
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- type: ndcg_at_10
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value: 48.962
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- type: ndcg_at_100
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value: 53.386
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- type: ndcg_at_1000
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value: 53.634
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- type: ndcg_at_20
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value: 51.898999999999994
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- type: ndcg_at_3
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value: 38.034
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- type: ndcg_at_5
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value: 43.036
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- type: precision_at_1
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value: 23.898
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- type: precision_at_10
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value: 7.852
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- type: precision_at_100
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value: 0.9769999999999999
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- type: precision_at_1000
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value: 0.1
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- type: precision_at_20
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value: 4.4990000000000006
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- type: precision_at_3
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value: 16.073999999999998
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- type: precision_at_5
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value: 12.063
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- type: recall_at_1
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value: 23.898
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- type: recall_at_10
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value: 78.521
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- type: recall_at_100
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value: 97.724
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- type: recall_at_1000
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value: 99.644
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- type: recall_at_20
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value: 89.972
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- type: recall_at_3
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value: 48.222
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- type: recall_at_5
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value: 60.313
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- task:
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type: Clustering
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dataset:
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type: None
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name: MTEB ArxivClusteringP2P
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config: default
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split: test
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revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
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metrics:
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- type: v_measure
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value: 47.69067314293749
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- type: v_measures
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|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB ArxivClusteringS2S
|
|
config: default
|
|
split: test
|
|
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
|
|
metrics:
|
|
- type: v_measure
|
|
value: 38.0916537995626
|
|
- type: v_measures
|
|
value: [0.37814352051854533, 0.39235658929084877, 0.3871170834588581, 0.4042678213739614, 0.3918486409557737, 0.38473003463452093, 0.35622070034791886, 0.3911472272128115, 0.3986923912337426, 0.39040109467533013, 0.4370949482641744, 0.4414023630938724, 0.4351473848532441, 0.4401176389499172, 0.4423731097742471, 0.438309696145818, 0.43410597641884624, 0.43900908630646696, 0.44081346534023286, 0.4386000014888906, 0.4047539306032343, 0.21697191913450847, 0.29241358200068185, 0.3390740154458194, 0.2793967439904601, 0.20383792346854981, 0.23904022437429004, 0.14733601126565044, 0.22946888289524586, 1.0, 0.19422067034794377, 0.37814352051854533, 0.39235658929084877, 0.3871170834588581, 0.4042678213739614, 0.3918486409557737, 0.38473003463452093, 0.35622070034791886, 0.3911472272128115, 0.3986923912337426, 0.39040109467533013, 0.4370949482641744, 0.4414023630938724, 0.4351473848532441, 0.4401176389499172, 0.4423731097742471, 0.438309696145818, 0.43410597641884624, 0.43900908630646696, 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|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: None
|
|
name: MTEB AskUbuntuDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
|
|
metrics:
|
|
- type: map
|
|
value: 62.33195643912506
|
|
- type: mrr
|
|
value: 76.43978366970057
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB BIOSSES
|
|
config: default
|
|
split: test
|
|
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 81.20285894915236
|
|
- type: cos_sim_spearman
|
|
value: 78.16322678527897
|
|
- type: euclidean_pearson
|
|
value: 80.6118408638417
|
|
- type: euclidean_spearman
|
|
value: 78.19033583671204
|
|
- type: manhattan_pearson
|
|
value: 80.41282660275819
|
|
- type: manhattan_spearman
|
|
value: 77.98611431591628
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB Banking77Classification
|
|
config: default
|
|
split: test
|
|
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
|
|
metrics:
|
|
- type: accuracy
|
|
value: 85.25324675324676
|
|
- type: f1
|
|
value: 85.19854235582687
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB BiorxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
|
|
metrics:
|
|
- type: v_measure
|
|
value: 39.65216461057432
|
|
- type: v_measures
|
|
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|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB BiorxivClusteringS2S
|
|
config: default
|
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split: test
|
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revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
|
|
metrics:
|
|
- type: v_measure
|
|
value: 33.28787287895752
|
|
- type: v_measures
|
|
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|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackAndroidRetrieval
|
|
config: default
|
|
split: test
|
|
revision: f46a197baaae43b4f621051089b82a364682dfeb
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 32.677
|
|
- type: map_at_10
|
|
value: 43.739
|
|
- type: map_at_100
|
|
value: 45.152
|
|
- type: map_at_1000
|
|
value: 45.279
|
|
- type: map_at_20
|
|
value: 44.553
|
|
- type: map_at_3
|
|
value: 40.321
|
|
- type: map_at_5
|
|
value: 42.201
|
|
- type: mrr_at_1
|
|
value: 40.2
|
|
- type: mrr_at_10
|
|
value: 49.755
|
|
- type: mrr_at_100
|
|
value: 50.468
|
|
- type: mrr_at_1000
|
|
value: 50.513
|
|
- type: mrr_at_20
|
|
value: 50.192
|
|
- type: mrr_at_3
|
|
value: 47.163
|
|
- type: mrr_at_5
|
|
value: 48.686
|
|
- type: ndcg_at_1
|
|
value: 40.2
|
|
- type: ndcg_at_10
|
|
value: 49.963
|
|
- type: ndcg_at_100
|
|
value: 54.978
|
|
- type: ndcg_at_1000
|
|
value: 56.979
|
|
- type: ndcg_at_20
|
|
value: 51.983000000000004
|
|
- type: ndcg_at_3
|
|
value: 45.086999999999996
|
|
- type: ndcg_at_5
|
|
value: 47.309
|
|
- type: precision_at_1
|
|
value: 40.2
|
|
- type: precision_at_10
|
|
value: 9.328
|
|
- type: precision_at_100
|
|
value: 1.443
|
|
- type: precision_at_1000
|
|
value: 0.19
|
|
- type: precision_at_20
|
|
value: 5.558
|
|
- type: precision_at_3
|
|
value: 21.364
|
|
- type: precision_at_5
|
|
value: 15.222
|
|
- type: recall_at_1
|
|
value: 32.677
|
|
- type: recall_at_10
|
|
value: 61.71
|
|
- type: recall_at_100
|
|
value: 82.431
|
|
- type: recall_at_1000
|
|
value: 94.896
|
|
- type: recall_at_20
|
|
value: 68.73700000000001
|
|
- type: recall_at_3
|
|
value: 47.431
|
|
- type: recall_at_5
|
|
value: 53.739000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackEnglishRetrieval
|
|
config: default
|
|
split: test
|
|
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 32.71
|
|
- type: map_at_10
|
|
value: 43.297000000000004
|
|
- type: map_at_100
|
|
value: 44.607
|
|
- type: map_at_1000
|
|
value: 44.729
|
|
- type: map_at_20
|
|
value: 44.013999999999996
|
|
- type: map_at_3
|
|
value: 40.213
|
|
- type: map_at_5
|
|
value: 42.004000000000005
|
|
- type: mrr_at_1
|
|
value: 40.892
|
|
- type: mrr_at_10
|
|
value: 49.394
|
|
- type: mrr_at_100
|
|
value: 50.005
|
|
- type: mrr_at_1000
|
|
value: 50.043000000000006
|
|
- type: mrr_at_20
|
|
value: 49.764
|
|
- type: mrr_at_3
|
|
value: 47.134
|
|
- type: mrr_at_5
|
|
value: 48.522
|
|
- type: ndcg_at_1
|
|
value: 40.892
|
|
- type: ndcg_at_10
|
|
value: 49.047000000000004
|
|
- type: ndcg_at_100
|
|
value: 53.266999999999996
|
|
- type: ndcg_at_1000
|
|
value: 55.096999999999994
|
|
- type: ndcg_at_20
|
|
value: 50.707
|
|
- type: ndcg_at_3
|
|
value: 44.896
|
|
- type: ndcg_at_5
|
|
value: 46.983000000000004
|
|
- type: precision_at_1
|
|
value: 40.892
|
|
- type: precision_at_10
|
|
value: 9.293
|
|
- type: precision_at_100
|
|
value: 1.473
|
|
- type: precision_at_1000
|
|
value: 0.192
|
|
- type: precision_at_20
|
|
value: 5.446
|
|
- type: precision_at_3
|
|
value: 21.592
|
|
- type: precision_at_5
|
|
value: 15.540999999999999
|
|
- type: recall_at_1
|
|
value: 32.71
|
|
- type: recall_at_10
|
|
value: 58.592999999999996
|
|
- type: recall_at_100
|
|
value: 76.242
|
|
- type: recall_at_1000
|
|
value: 87.717
|
|
- type: recall_at_20
|
|
value: 64.646
|
|
- type: recall_at_3
|
|
value: 46.253
|
|
- type: recall_at_5
|
|
value: 51.946999999999996
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackGamingRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 41.644999999999996
|
|
- type: map_at_10
|
|
value: 53.825
|
|
- type: map_at_100
|
|
value: 54.82
|
|
- type: map_at_1000
|
|
value: 54.87499999999999
|
|
- type: map_at_20
|
|
value: 54.43
|
|
- type: map_at_3
|
|
value: 50.705
|
|
- type: map_at_5
|
|
value: 52.501
|
|
- type: mrr_at_1
|
|
value: 47.524
|
|
- type: mrr_at_10
|
|
value: 57.260999999999996
|
|
- type: mrr_at_100
|
|
value: 57.902
|
|
- type: mrr_at_1000
|
|
value: 57.931999999999995
|
|
- type: mrr_at_20
|
|
value: 57.689
|
|
- type: mrr_at_3
|
|
value: 55.089
|
|
- type: mrr_at_5
|
|
value: 56.38999999999999
|
|
- type: ndcg_at_1
|
|
value: 47.524
|
|
- type: ndcg_at_10
|
|
value: 59.41499999999999
|
|
- type: ndcg_at_100
|
|
value: 63.258
|
|
- type: ndcg_at_1000
|
|
value: 64.376
|
|
- type: ndcg_at_20
|
|
value: 61.149
|
|
- type: ndcg_at_3
|
|
value: 54.381
|
|
- type: ndcg_at_5
|
|
value: 56.89999999999999
|
|
- type: precision_at_1
|
|
value: 47.524
|
|
- type: precision_at_10
|
|
value: 9.386
|
|
- type: precision_at_100
|
|
value: 1.221
|
|
- type: precision_at_1000
|
|
value: 0.136
|
|
- type: precision_at_20
|
|
value: 5.223
|
|
- type: precision_at_3
|
|
value: 24.096
|
|
- type: precision_at_5
|
|
value: 16.364
|
|
- type: recall_at_1
|
|
value: 41.644999999999996
|
|
- type: recall_at_10
|
|
value: 72.386
|
|
- type: recall_at_100
|
|
value: 88.794
|
|
- type: recall_at_1000
|
|
value: 96.75399999999999
|
|
- type: recall_at_20
|
|
value: 78.74
|
|
- type: recall_at_3
|
|
value: 59.028000000000006
|
|
- type: recall_at_5
|
|
value: 65.197
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackGisRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 5003b3064772da1887988e05400cf3806fe491f2
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 28.648
|
|
- type: map_at_10
|
|
value: 36.388999999999996
|
|
- type: map_at_100
|
|
value: 37.372
|
|
- type: map_at_1000
|
|
value: 37.457
|
|
- type: map_at_20
|
|
value: 36.912
|
|
- type: map_at_3
|
|
value: 34.076
|
|
- type: map_at_5
|
|
value: 35.415
|
|
- type: mrr_at_1
|
|
value: 30.508000000000003
|
|
- type: mrr_at_10
|
|
value: 38.132
|
|
- type: mrr_at_100
|
|
value: 39.04
|
|
- type: mrr_at_1000
|
|
value: 39.106
|
|
- type: mrr_at_20
|
|
value: 38.643
|
|
- type: mrr_at_3
|
|
value: 35.876000000000005
|
|
- type: mrr_at_5
|
|
value: 37.208999999999996
|
|
- type: ndcg_at_1
|
|
value: 30.508000000000003
|
|
- type: ndcg_at_10
|
|
value: 40.762
|
|
- type: ndcg_at_100
|
|
value: 45.732
|
|
- type: ndcg_at_1000
|
|
value: 47.799
|
|
- type: ndcg_at_20
|
|
value: 42.591
|
|
- type: ndcg_at_3
|
|
value: 36.266999999999996
|
|
- type: ndcg_at_5
|
|
value: 38.58
|
|
- type: precision_at_1
|
|
value: 30.508000000000003
|
|
- type: precision_at_10
|
|
value: 6.010999999999999
|
|
- type: precision_at_100
|
|
value: 0.897
|
|
- type: precision_at_1000
|
|
value: 0.11100000000000002
|
|
- type: precision_at_20
|
|
value: 3.412
|
|
- type: precision_at_3
|
|
value: 14.991
|
|
- type: precision_at_5
|
|
value: 10.328
|
|
- type: recall_at_1
|
|
value: 28.648
|
|
- type: recall_at_10
|
|
value: 52.342999999999996
|
|
- type: recall_at_100
|
|
value: 75.268
|
|
- type: recall_at_1000
|
|
value: 90.641
|
|
- type: recall_at_20
|
|
value: 59.303
|
|
- type: recall_at_3
|
|
value: 40.447
|
|
- type: recall_at_5
|
|
value: 46.117000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackMathematicaRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 18.476
|
|
- type: map_at_10
|
|
value: 27.148
|
|
- type: map_at_100
|
|
value: 28.317999999999998
|
|
- type: map_at_1000
|
|
value: 28.427999999999997
|
|
- type: map_at_20
|
|
value: 27.764
|
|
- type: map_at_3
|
|
value: 24.801000000000002
|
|
- type: map_at_5
|
|
value: 26.133
|
|
- type: mrr_at_1
|
|
value: 22.886
|
|
- type: mrr_at_10
|
|
value: 31.741000000000003
|
|
- type: mrr_at_100
|
|
value: 32.708
|
|
- type: mrr_at_1000
|
|
value: 32.769
|
|
- type: mrr_at_20
|
|
value: 32.296
|
|
- type: mrr_at_3
|
|
value: 29.498
|
|
- type: mrr_at_5
|
|
value: 30.773
|
|
- type: ndcg_at_1
|
|
value: 22.886
|
|
- type: ndcg_at_10
|
|
value: 32.265
|
|
- type: ndcg_at_100
|
|
value: 37.829
|
|
- type: ndcg_at_1000
|
|
value: 40.558
|
|
- type: ndcg_at_20
|
|
value: 34.372
|
|
- type: ndcg_at_3
|
|
value: 28.105000000000004
|
|
- type: ndcg_at_5
|
|
value: 30.04
|
|
- type: precision_at_1
|
|
value: 22.886
|
|
- type: precision_at_10
|
|
value: 5.808
|
|
- type: precision_at_100
|
|
value: 0.985
|
|
- type: precision_at_1000
|
|
value: 0.13699999999999998
|
|
- type: precision_at_20
|
|
value: 3.495
|
|
- type: precision_at_3
|
|
value: 13.639999999999999
|
|
- type: precision_at_5
|
|
value: 9.577
|
|
- type: recall_at_1
|
|
value: 18.476
|
|
- type: recall_at_10
|
|
value: 43.442
|
|
- type: recall_at_100
|
|
value: 67.376
|
|
- type: recall_at_1000
|
|
value: 86.874
|
|
- type: recall_at_20
|
|
value: 51.038
|
|
- type: recall_at_3
|
|
value: 31.785999999999998
|
|
- type: recall_at_5
|
|
value: 36.858999999999995
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackPhysicsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 29.098000000000003
|
|
- type: map_at_10
|
|
value: 38.97
|
|
- type: map_at_100
|
|
value: 40.293
|
|
- type: map_at_1000
|
|
value: 40.397
|
|
- type: map_at_20
|
|
value: 39.778999999999996
|
|
- type: map_at_3
|
|
value: 35.723
|
|
- type: map_at_5
|
|
value: 37.519999999999996
|
|
- type: mrr_at_1
|
|
value: 35.515
|
|
- type: mrr_at_10
|
|
value: 44.55
|
|
- type: mrr_at_100
|
|
value: 45.37
|
|
- type: mrr_at_1000
|
|
value: 45.412
|
|
- type: mrr_at_20
|
|
value: 45.054
|
|
- type: mrr_at_3
|
|
value: 41.835
|
|
- type: mrr_at_5
|
|
value: 43.356
|
|
- type: ndcg_at_1
|
|
value: 35.515
|
|
- type: ndcg_at_10
|
|
value: 44.91
|
|
- type: ndcg_at_100
|
|
value: 50.27700000000001
|
|
- type: ndcg_at_1000
|
|
value: 52.215
|
|
- type: ndcg_at_20
|
|
value: 47.235
|
|
- type: ndcg_at_3
|
|
value: 39.505
|
|
- type: ndcg_at_5
|
|
value: 42.016
|
|
- type: precision_at_1
|
|
value: 35.515
|
|
- type: precision_at_10
|
|
value: 8.152
|
|
- type: precision_at_100
|
|
value: 1.262
|
|
- type: precision_at_1000
|
|
value: 0.16
|
|
- type: precision_at_20
|
|
value: 4.851
|
|
- type: precision_at_3
|
|
value: 18.447
|
|
- type: precision_at_5
|
|
value: 13.321
|
|
- type: recall_at_1
|
|
value: 29.098000000000003
|
|
- type: recall_at_10
|
|
value: 57.115
|
|
- type: recall_at_100
|
|
value: 79.467
|
|
- type: recall_at_1000
|
|
value: 92.162
|
|
- type: recall_at_20
|
|
value: 65.161
|
|
- type: recall_at_3
|
|
value: 42.254000000000005
|
|
- type: recall_at_5
|
|
value: 48.415
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackProgrammersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.372000000000003
|
|
- type: map_at_10
|
|
value: 37.781
|
|
- type: map_at_100
|
|
value: 39.128
|
|
- type: map_at_1000
|
|
value: 39.238
|
|
- type: map_at_20
|
|
value: 38.592
|
|
- type: map_at_3
|
|
value: 34.782999999999994
|
|
- type: map_at_5
|
|
value: 36.466
|
|
- type: mrr_at_1
|
|
value: 33.904
|
|
- type: mrr_at_10
|
|
value: 43.15
|
|
- type: mrr_at_100
|
|
value: 44.049
|
|
- type: mrr_at_1000
|
|
value: 44.107
|
|
- type: mrr_at_20
|
|
value: 43.721
|
|
- type: mrr_at_3
|
|
value: 40.677
|
|
- type: mrr_at_5
|
|
value: 42.19
|
|
- type: ndcg_at_1
|
|
value: 33.904
|
|
- type: ndcg_at_10
|
|
value: 43.527
|
|
- type: ndcg_at_100
|
|
value: 49.004999999999995
|
|
- type: ndcg_at_1000
|
|
value: 51.276999999999994
|
|
- type: ndcg_at_20
|
|
value: 45.988
|
|
- type: ndcg_at_3
|
|
value: 38.824999999999996
|
|
- type: ndcg_at_5
|
|
value: 41.04
|
|
- type: precision_at_1
|
|
value: 33.904
|
|
- type: precision_at_10
|
|
value: 7.854
|
|
- type: precision_at_100
|
|
value: 1.2309999999999999
|
|
- type: precision_at_1000
|
|
value: 0.16
|
|
- type: precision_at_20
|
|
value: 4.692
|
|
- type: precision_at_3
|
|
value: 18.531
|
|
- type: precision_at_5
|
|
value: 13.150999999999998
|
|
- type: recall_at_1
|
|
value: 27.372000000000003
|
|
- type: recall_at_10
|
|
value: 55.245999999999995
|
|
- type: recall_at_100
|
|
value: 78.278
|
|
- type: recall_at_1000
|
|
value: 93.718
|
|
- type: recall_at_20
|
|
value: 64.095
|
|
- type: recall_at_3
|
|
value: 41.665
|
|
- type: recall_at_5
|
|
value: 47.632000000000005
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackRetrieval
|
|
config: default
|
|
split: test
|
|
revision: f46a197baaae43b4f621051089b82a364682dfeb
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 27.734166666666667
|
|
- type: map_at_10
|
|
value: 36.858
|
|
- type: map_at_100
|
|
value: 38.043833333333325
|
|
- type: map_at_1000
|
|
value: 38.15541666666667
|
|
- type: map_at_20
|
|
value: 37.521249999999995
|
|
- type: map_at_3
|
|
value: 34.07658333333333
|
|
- type: map_at_5
|
|
value: 35.62683333333333
|
|
- type: mrr_at_1
|
|
value: 32.676249999999996
|
|
- type: mrr_at_10
|
|
value: 40.999
|
|
- type: mrr_at_100
|
|
value: 41.835
|
|
- type: mrr_at_1000
|
|
value: 41.8895
|
|
- type: mrr_at_20
|
|
value: 41.4865
|
|
- type: mrr_at_3
|
|
value: 38.645
|
|
- type: mrr_at_5
|
|
value: 39.99725000000001
|
|
- type: ndcg_at_1
|
|
value: 32.676249999999996
|
|
- type: ndcg_at_10
|
|
value: 42.08016666666666
|
|
- type: ndcg_at_100
|
|
value: 47.082750000000004
|
|
- type: ndcg_at_1000
|
|
value: 49.276583333333335
|
|
- type: ndcg_at_20
|
|
value: 44.04808333333334
|
|
- type: ndcg_at_3
|
|
value: 37.43375
|
|
- type: ndcg_at_5
|
|
value: 39.623000000000005
|
|
- type: precision_at_1
|
|
value: 32.676249999999996
|
|
- type: precision_at_10
|
|
value: 7.271
|
|
- type: precision_at_100
|
|
value: 1.1458333333333333
|
|
- type: precision_at_1000
|
|
value: 0.152
|
|
- type: precision_at_20
|
|
value: 4.282916666666667
|
|
- type: precision_at_3
|
|
value: 17.061416666666666
|
|
- type: precision_at_5
|
|
value: 12.05466666666667
|
|
- type: recall_at_1
|
|
value: 27.734166666666667
|
|
- type: recall_at_10
|
|
value: 53.33574999999999
|
|
- type: recall_at_100
|
|
value: 75.16275
|
|
- type: recall_at_1000
|
|
value: 90.34891666666665
|
|
- type: recall_at_20
|
|
value: 60.4935
|
|
- type: recall_at_3
|
|
value: 40.377916666666664
|
|
- type: recall_at_5
|
|
value: 46.0195
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackStatsRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.653
|
|
- type: map_at_10
|
|
value: 32.151
|
|
- type: map_at_100
|
|
value: 33.152
|
|
- type: map_at_1000
|
|
value: 33.243
|
|
- type: map_at_20
|
|
value: 32.717
|
|
- type: map_at_3
|
|
value: 30.287
|
|
- type: map_at_5
|
|
value: 31.25
|
|
- type: mrr_at_1
|
|
value: 28.988000000000003
|
|
- type: mrr_at_10
|
|
value: 35.131
|
|
- type: mrr_at_100
|
|
value: 36.002
|
|
- type: mrr_at_1000
|
|
value: 36.069
|
|
- type: mrr_at_20
|
|
value: 35.61
|
|
- type: mrr_at_3
|
|
value: 33.308
|
|
- type: mrr_at_5
|
|
value: 34.259
|
|
- type: ndcg_at_1
|
|
value: 28.988000000000003
|
|
- type: ndcg_at_10
|
|
value: 35.988
|
|
- type: ndcg_at_100
|
|
value: 40.764
|
|
- type: ndcg_at_1000
|
|
value: 43.112
|
|
- type: ndcg_at_20
|
|
value: 37.852999999999994
|
|
- type: ndcg_at_3
|
|
value: 32.562000000000005
|
|
- type: ndcg_at_5
|
|
value: 33.983000000000004
|
|
- type: precision_at_1
|
|
value: 28.988000000000003
|
|
- type: precision_at_10
|
|
value: 5.475
|
|
- type: precision_at_100
|
|
value: 0.8500000000000001
|
|
- type: precision_at_1000
|
|
value: 0.11199999999999999
|
|
- type: precision_at_20
|
|
value: 3.229
|
|
- type: precision_at_3
|
|
value: 13.905999999999999
|
|
- type: precision_at_5
|
|
value: 9.386999999999999
|
|
- type: recall_at_1
|
|
value: 25.653
|
|
- type: recall_at_10
|
|
value: 44.962
|
|
- type: recall_at_100
|
|
value: 66.405
|
|
- type: recall_at_1000
|
|
value: 83.88799999999999
|
|
- type: recall_at_20
|
|
value: 51.79899999999999
|
|
- type: recall_at_3
|
|
value: 35.144999999999996
|
|
- type: recall_at_5
|
|
value: 38.814
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackTexRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 46989137a86843e03a6195de44b09deda022eec7
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 17.825
|
|
- type: map_at_10
|
|
value: 25.592
|
|
- type: map_at_100
|
|
value: 26.613999999999997
|
|
- type: map_at_1000
|
|
value: 26.734
|
|
- type: map_at_20
|
|
value: 26.115
|
|
- type: map_at_3
|
|
value: 23.119
|
|
- type: map_at_5
|
|
value: 24.54
|
|
- type: mrr_at_1
|
|
value: 21.335
|
|
- type: mrr_at_10
|
|
value: 29.165000000000003
|
|
- type: mrr_at_100
|
|
value: 30.049
|
|
- type: mrr_at_1000
|
|
value: 30.121
|
|
- type: mrr_at_20
|
|
value: 29.639
|
|
- type: mrr_at_3
|
|
value: 26.863999999999997
|
|
- type: mrr_at_5
|
|
value: 28.185
|
|
- type: ndcg_at_1
|
|
value: 21.335
|
|
- type: ndcg_at_10
|
|
value: 30.357
|
|
- type: ndcg_at_100
|
|
value: 35.410000000000004
|
|
- type: ndcg_at_1000
|
|
value: 38.24
|
|
- type: ndcg_at_20
|
|
value: 32.08
|
|
- type: ndcg_at_3
|
|
value: 25.95
|
|
- type: ndcg_at_5
|
|
value: 28.081
|
|
- type: precision_at_1
|
|
value: 21.335
|
|
- type: precision_at_10
|
|
value: 5.506
|
|
- type: precision_at_100
|
|
value: 0.928
|
|
- type: precision_at_1000
|
|
value: 0.135
|
|
- type: precision_at_20
|
|
value: 3.2550000000000003
|
|
- type: precision_at_3
|
|
value: 12.239
|
|
- type: precision_at_5
|
|
value: 8.885
|
|
- type: recall_at_1
|
|
value: 17.825
|
|
- type: recall_at_10
|
|
value: 41.105999999999995
|
|
- type: recall_at_100
|
|
value: 64.17
|
|
- type: recall_at_1000
|
|
value: 84.19200000000001
|
|
- type: recall_at_20
|
|
value: 47.497
|
|
- type: recall_at_3
|
|
value: 28.862
|
|
- type: recall_at_5
|
|
value: 34.348
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackUnixRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 29.435
|
|
- type: map_at_10
|
|
value: 38.261
|
|
- type: map_at_100
|
|
value: 39.242
|
|
- type: map_at_1000
|
|
value: 39.347
|
|
- type: map_at_20
|
|
value: 38.742
|
|
- type: map_at_3
|
|
value: 35.457
|
|
- type: map_at_5
|
|
value: 37.043
|
|
- type: mrr_at_1
|
|
value: 34.235
|
|
- type: mrr_at_10
|
|
value: 42.24
|
|
- type: mrr_at_100
|
|
value: 42.988
|
|
- type: mrr_at_1000
|
|
value: 43.043
|
|
- type: mrr_at_20
|
|
value: 42.613
|
|
- type: mrr_at_3
|
|
value: 39.832
|
|
- type: mrr_at_5
|
|
value: 41.227000000000004
|
|
- type: ndcg_at_1
|
|
value: 34.235
|
|
- type: ndcg_at_10
|
|
value: 43.384
|
|
- type: ndcg_at_100
|
|
value: 48.14
|
|
- type: ndcg_at_1000
|
|
value: 50.414
|
|
- type: ndcg_at_20
|
|
value: 44.913
|
|
- type: ndcg_at_3
|
|
value: 38.454
|
|
- type: ndcg_at_5
|
|
value: 40.776
|
|
- type: precision_at_1
|
|
value: 34.235
|
|
- type: precision_at_10
|
|
value: 7.164
|
|
- type: precision_at_100
|
|
value: 1.065
|
|
- type: precision_at_1000
|
|
value: 0.13699999999999998
|
|
- type: precision_at_20
|
|
value: 4.021
|
|
- type: precision_at_3
|
|
value: 17.226
|
|
- type: precision_at_5
|
|
value: 12.071
|
|
- type: recall_at_1
|
|
value: 29.435
|
|
- type: recall_at_10
|
|
value: 54.93900000000001
|
|
- type: recall_at_100
|
|
value: 76.176
|
|
- type: recall_at_1000
|
|
value: 91.989
|
|
- type: recall_at_20
|
|
value: 60.451
|
|
- type: recall_at_3
|
|
value: 41.332
|
|
- type: recall_at_5
|
|
value: 47.316
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWebmastersRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 25.605
|
|
- type: map_at_10
|
|
value: 34.162
|
|
- type: map_at_100
|
|
value: 35.827999999999996
|
|
- type: map_at_1000
|
|
value: 36.04
|
|
- type: map_at_20
|
|
value: 35.016000000000005
|
|
- type: map_at_3
|
|
value: 30.984
|
|
- type: map_at_5
|
|
value: 32.717
|
|
- type: mrr_at_1
|
|
value: 30.435000000000002
|
|
- type: mrr_at_10
|
|
value: 38.681
|
|
- type: mrr_at_100
|
|
value: 39.656000000000006
|
|
- type: mrr_at_1000
|
|
value: 39.71
|
|
- type: mrr_at_20
|
|
value: 39.208999999999996
|
|
- type: mrr_at_3
|
|
value: 35.903
|
|
- type: mrr_at_5
|
|
value: 37.454
|
|
- type: ndcg_at_1
|
|
value: 30.435000000000002
|
|
- type: ndcg_at_10
|
|
value: 39.916000000000004
|
|
- type: ndcg_at_100
|
|
value: 45.958
|
|
- type: ndcg_at_1000
|
|
value: 48.449999999999996
|
|
- type: ndcg_at_20
|
|
value: 42.085
|
|
- type: ndcg_at_3
|
|
value: 34.696
|
|
- type: ndcg_at_5
|
|
value: 37.147000000000006
|
|
- type: precision_at_1
|
|
value: 30.435000000000002
|
|
- type: precision_at_10
|
|
value: 7.767
|
|
- type: precision_at_100
|
|
value: 1.547
|
|
- type: precision_at_1000
|
|
value: 0.23800000000000002
|
|
- type: precision_at_20
|
|
value: 4.941
|
|
- type: precision_at_3
|
|
value: 16.073999999999998
|
|
- type: precision_at_5
|
|
value: 11.937000000000001
|
|
- type: recall_at_1
|
|
value: 25.605
|
|
- type: recall_at_10
|
|
value: 50.654999999999994
|
|
- type: recall_at_100
|
|
value: 77.609
|
|
- type: recall_at_1000
|
|
value: 93.518
|
|
- type: recall_at_20
|
|
value: 58.845000000000006
|
|
- type: recall_at_3
|
|
value: 36.272
|
|
- type: recall_at_5
|
|
value: 42.596000000000004
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: BeIR/cqadupstack
|
|
name: MTEB CQADupstackWordpressRetrieval
|
|
config: default
|
|
split: test
|
|
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 23.666
|
|
- type: map_at_10
|
|
value: 30.980999999999998
|
|
- type: map_at_100
|
|
value: 32.0
|
|
- type: map_at_1000
|
|
value: 32.098
|
|
- type: map_at_20
|
|
value: 31.621
|
|
- type: map_at_3
|
|
value: 28.449999999999996
|
|
- type: map_at_5
|
|
value: 29.731999999999996
|
|
- type: mrr_at_1
|
|
value: 25.692999999999998
|
|
- type: mrr_at_10
|
|
value: 32.788000000000004
|
|
- type: mrr_at_100
|
|
value: 33.783
|
|
- type: mrr_at_1000
|
|
value: 33.849000000000004
|
|
- type: mrr_at_20
|
|
value: 33.408
|
|
- type: mrr_at_3
|
|
value: 30.561
|
|
- type: mrr_at_5
|
|
value: 31.716
|
|
- type: ndcg_at_1
|
|
value: 25.692999999999998
|
|
- type: ndcg_at_10
|
|
value: 35.428
|
|
- type: ndcg_at_100
|
|
value: 40.375
|
|
- type: ndcg_at_1000
|
|
value: 42.802
|
|
- type: ndcg_at_20
|
|
value: 37.621
|
|
- type: ndcg_at_3
|
|
value: 30.476999999999997
|
|
- type: ndcg_at_5
|
|
value: 32.621
|
|
- type: precision_at_1
|
|
value: 25.692999999999998
|
|
- type: precision_at_10
|
|
value: 5.508
|
|
- type: precision_at_100
|
|
value: 0.848
|
|
- type: precision_at_1000
|
|
value: 0.116
|
|
- type: precision_at_20
|
|
value: 3.272
|
|
- type: precision_at_3
|
|
value: 12.631
|
|
- type: precision_at_5
|
|
value: 8.872
|
|
- type: recall_at_1
|
|
value: 23.666
|
|
- type: recall_at_10
|
|
value: 47.532000000000004
|
|
- type: recall_at_100
|
|
value: 69.73700000000001
|
|
- type: recall_at_1000
|
|
value: 87.83800000000001
|
|
- type: recall_at_20
|
|
value: 55.61000000000001
|
|
- type: recall_at_3
|
|
value: 34.06
|
|
- type: recall_at_5
|
|
value: 39.254
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB ClimateFEVER
|
|
config: default
|
|
split: test
|
|
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 16.337
|
|
- type: map_at_10
|
|
value: 26.488
|
|
- type: map_at_100
|
|
value: 28.415000000000003
|
|
- type: map_at_1000
|
|
value: 28.584
|
|
- type: map_at_20
|
|
value: 27.557
|
|
- type: map_at_3
|
|
value: 22.665
|
|
- type: map_at_5
|
|
value: 24.542
|
|
- type: mrr_at_1
|
|
value: 36.417
|
|
- type: mrr_at_10
|
|
value: 48.001
|
|
- type: mrr_at_100
|
|
value: 48.784
|
|
- type: mrr_at_1000
|
|
value: 48.809000000000005
|
|
- type: mrr_at_20
|
|
value: 48.507
|
|
- type: mrr_at_3
|
|
value: 45.103
|
|
- type: mrr_at_5
|
|
value: 46.843
|
|
- type: ndcg_at_1
|
|
value: 36.417
|
|
- type: ndcg_at_10
|
|
value: 35.67
|
|
- type: ndcg_at_100
|
|
value: 42.716
|
|
- type: ndcg_at_1000
|
|
value: 45.639
|
|
- type: ndcg_at_20
|
|
value: 38.471
|
|
- type: ndcg_at_3
|
|
value: 30.444
|
|
- type: ndcg_at_5
|
|
value: 32.004
|
|
- type: precision_at_1
|
|
value: 36.417
|
|
- type: precision_at_10
|
|
value: 10.73
|
|
- type: precision_at_100
|
|
value: 1.833
|
|
- type: precision_at_1000
|
|
value: 0.23800000000000002
|
|
- type: precision_at_20
|
|
value: 6.596
|
|
- type: precision_at_3
|
|
value: 22.302
|
|
- type: precision_at_5
|
|
value: 16.521
|
|
- type: recall_at_1
|
|
value: 16.337
|
|
- type: recall_at_10
|
|
value: 40.671
|
|
- type: recall_at_100
|
|
value: 64.55300000000001
|
|
- type: recall_at_1000
|
|
value: 80.934
|
|
- type: recall_at_20
|
|
value: 48.381
|
|
- type: recall_at_3
|
|
value: 27.279999999999998
|
|
- type: recall_at_5
|
|
value: 32.621
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB DBPedia
|
|
config: default
|
|
split: test
|
|
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 9.056000000000001
|
|
- type: map_at_10
|
|
value: 19.419
|
|
- type: map_at_100
|
|
value: 27.069
|
|
- type: map_at_1000
|
|
value: 28.666000000000004
|
|
- type: map_at_20
|
|
value: 22.434
|
|
- type: map_at_3
|
|
value: 13.895
|
|
- type: map_at_5
|
|
value: 16.121
|
|
- type: mrr_at_1
|
|
value: 69.0
|
|
- type: mrr_at_10
|
|
value: 75.804
|
|
- type: mrr_at_100
|
|
value: 76.117
|
|
- type: mrr_at_1000
|
|
value: 76.125
|
|
- type: mrr_at_20
|
|
value: 76.009
|
|
- type: mrr_at_3
|
|
value: 74.375
|
|
- type: mrr_at_5
|
|
value: 75.4
|
|
- type: ndcg_at_1
|
|
value: 57.49999999999999
|
|
- type: ndcg_at_10
|
|
value: 41.495
|
|
- type: ndcg_at_100
|
|
value: 45.208
|
|
- type: ndcg_at_1000
|
|
value: 52.221
|
|
- type: ndcg_at_20
|
|
value: 40.617999999999995
|
|
- type: ndcg_at_3
|
|
value: 46.592
|
|
- type: ndcg_at_5
|
|
value: 43.559
|
|
- type: precision_at_1
|
|
value: 69.0
|
|
- type: precision_at_10
|
|
value: 32.574999999999996
|
|
- type: precision_at_100
|
|
value: 10.205
|
|
- type: precision_at_1000
|
|
value: 2.036
|
|
- type: precision_at_20
|
|
value: 24.687
|
|
- type: precision_at_3
|
|
value: 49.75
|
|
- type: precision_at_5
|
|
value: 42.0
|
|
- type: recall_at_1
|
|
value: 9.056000000000001
|
|
- type: recall_at_10
|
|
value: 24.866
|
|
- type: recall_at_100
|
|
value: 50.097
|
|
- type: recall_at_1000
|
|
value: 72.038
|
|
- type: recall_at_20
|
|
value: 31.858999999999998
|
|
- type: recall_at_3
|
|
value: 15.096000000000002
|
|
- type: recall_at_5
|
|
value: 18.548000000000002
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB EmotionClassification
|
|
config: default
|
|
split: test
|
|
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
|
metrics:
|
|
- type: accuracy
|
|
value: 48.259999999999984
|
|
- type: f1
|
|
value: 43.1498589523159
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB FEVER
|
|
config: default
|
|
split: test
|
|
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 74.798
|
|
- type: map_at_10
|
|
value: 83.454
|
|
- type: map_at_100
|
|
value: 83.623
|
|
- type: map_at_1000
|
|
value: 83.635
|
|
- type: map_at_20
|
|
value: 83.55
|
|
- type: map_at_3
|
|
value: 82.392
|
|
- type: map_at_5
|
|
value: 83.167
|
|
- type: mrr_at_1
|
|
value: 80.708
|
|
- type: mrr_at_10
|
|
value: 88.377
|
|
- type: mrr_at_100
|
|
value: 88.411
|
|
- type: mrr_at_1000
|
|
value: 88.411
|
|
- type: mrr_at_20
|
|
value: 88.402
|
|
- type: mrr_at_3
|
|
value: 87.646
|
|
- type: mrr_at_5
|
|
value: 88.232
|
|
- type: ndcg_at_1
|
|
value: 80.708
|
|
- type: ndcg_at_10
|
|
value: 87.35199999999999
|
|
- type: ndcg_at_100
|
|
value: 87.91600000000001
|
|
- type: ndcg_at_1000
|
|
value: 88.12299999999999
|
|
- type: ndcg_at_20
|
|
value: 87.593
|
|
- type: ndcg_at_3
|
|
value: 85.738
|
|
- type: ndcg_at_5
|
|
value: 86.845
|
|
- type: precision_at_1
|
|
value: 80.708
|
|
- type: precision_at_10
|
|
value: 10.432
|
|
- type: precision_at_100
|
|
value: 1.091
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_20
|
|
value: 5.296
|
|
- type: precision_at_3
|
|
value: 32.778
|
|
- type: precision_at_5
|
|
value: 20.399
|
|
- type: recall_at_1
|
|
value: 74.798
|
|
- type: recall_at_10
|
|
value: 94.459
|
|
- type: recall_at_100
|
|
value: 96.614
|
|
- type: recall_at_1000
|
|
value: 97.868
|
|
- type: recall_at_20
|
|
value: 95.254
|
|
- type: recall_at_3
|
|
value: 90.144
|
|
- type: recall_at_5
|
|
value: 92.965
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB FiQA2018
|
|
config: default
|
|
split: test
|
|
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 20.008
|
|
- type: map_at_10
|
|
value: 32.731
|
|
- type: map_at_100
|
|
value: 34.467999999999996
|
|
- type: map_at_1000
|
|
value: 34.643
|
|
- type: map_at_20
|
|
value: 33.717000000000006
|
|
- type: map_at_3
|
|
value: 28.427999999999997
|
|
- type: map_at_5
|
|
value: 30.788
|
|
- type: mrr_at_1
|
|
value: 40.586
|
|
- type: mrr_at_10
|
|
value: 49.056
|
|
- type: mrr_at_100
|
|
value: 49.887
|
|
- type: mrr_at_1000
|
|
value: 49.929
|
|
- type: mrr_at_20
|
|
value: 49.552
|
|
- type: mrr_at_3
|
|
value: 46.785
|
|
- type: mrr_at_5
|
|
value: 48.004000000000005
|
|
- type: ndcg_at_1
|
|
value: 40.586
|
|
- type: ndcg_at_10
|
|
value: 40.589999999999996
|
|
- type: ndcg_at_100
|
|
value: 47.03
|
|
- type: ndcg_at_1000
|
|
value: 49.994
|
|
- type: ndcg_at_20
|
|
value: 43.229
|
|
- type: ndcg_at_3
|
|
value: 37.061
|
|
- type: ndcg_at_5
|
|
value: 37.992
|
|
- type: precision_at_1
|
|
value: 40.586
|
|
- type: precision_at_10
|
|
value: 11.219
|
|
- type: precision_at_100
|
|
value: 1.781
|
|
- type: precision_at_1000
|
|
value: 0.232
|
|
- type: precision_at_20
|
|
value: 6.705
|
|
- type: precision_at_3
|
|
value: 24.743000000000002
|
|
- type: precision_at_5
|
|
value: 18.086
|
|
- type: recall_at_1
|
|
value: 20.008
|
|
- type: recall_at_10
|
|
value: 47.412
|
|
- type: recall_at_100
|
|
value: 71.274
|
|
- type: recall_at_1000
|
|
value: 88.898
|
|
- type: recall_at_20
|
|
value: 55.706999999999994
|
|
- type: recall_at_3
|
|
value: 33.346
|
|
- type: recall_at_5
|
|
value: 39.112
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB HotpotQA
|
|
config: default
|
|
split: test
|
|
revision: ab518f4d6fcca38d87c25209f94beba119d02014
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 41.789
|
|
- type: map_at_10
|
|
value: 57.898
|
|
- type: map_at_100
|
|
value: 58.632
|
|
- type: map_at_1000
|
|
value: 58.693
|
|
- type: map_at_20
|
|
value: 58.314
|
|
- type: map_at_3
|
|
value: 55.236
|
|
- type: map_at_5
|
|
value: 56.852999999999994
|
|
- type: mrr_at_1
|
|
value: 83.57900000000001
|
|
- type: mrr_at_10
|
|
value: 87.631
|
|
- type: mrr_at_100
|
|
value: 87.764
|
|
- type: mrr_at_1000
|
|
value: 87.77000000000001
|
|
- type: mrr_at_20
|
|
value: 87.70700000000001
|
|
- type: mrr_at_3
|
|
value: 87.02499999999999
|
|
- type: mrr_at_5
|
|
value: 87.34100000000001
|
|
- type: ndcg_at_1
|
|
value: 83.57900000000001
|
|
- type: ndcg_at_10
|
|
value: 67.11399999999999
|
|
- type: ndcg_at_100
|
|
value: 69.686
|
|
- type: ndcg_at_1000
|
|
value: 70.926
|
|
- type: ndcg_at_20
|
|
value: 68.119
|
|
- type: ndcg_at_3
|
|
value: 63.402
|
|
- type: ndcg_at_5
|
|
value: 65.354
|
|
- type: precision_at_1
|
|
value: 83.57900000000001
|
|
- type: precision_at_10
|
|
value: 13.333
|
|
- type: precision_at_100
|
|
value: 1.537
|
|
- type: precision_at_1000
|
|
value: 0.16999999999999998
|
|
- type: precision_at_20
|
|
value: 6.988999999999999
|
|
- type: precision_at_3
|
|
value: 38.929
|
|
- type: precision_at_5
|
|
value: 24.897
|
|
- type: recall_at_1
|
|
value: 41.789
|
|
- type: recall_at_10
|
|
value: 66.664
|
|
- type: recall_at_100
|
|
value: 76.833
|
|
- type: recall_at_1000
|
|
value: 85.14500000000001
|
|
- type: recall_at_20
|
|
value: 69.892
|
|
- type: recall_at_3
|
|
value: 58.392999999999994
|
|
- type: recall_at_5
|
|
value: 62.242
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB ImdbClassification
|
|
config: default
|
|
split: test
|
|
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 86.6108
|
|
- type: ap
|
|
value: 81.63890253106925
|
|
- type: f1
|
|
value: 86.54585789538082
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB MSMARCO
|
|
config: default
|
|
split: dev
|
|
revision: c5a29a104738b98a9e76336939199e264163d4a0
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 22.407
|
|
- type: map_at_10
|
|
value: 34.603
|
|
- type: map_at_100
|
|
value: 35.808
|
|
- type: map_at_1000
|
|
value: 35.855
|
|
- type: map_at_20
|
|
value: 35.368
|
|
- type: map_at_3
|
|
value: 30.764000000000003
|
|
- type: map_at_5
|
|
value: 32.964
|
|
- type: mrr_at_1
|
|
value: 23.009
|
|
- type: mrr_at_10
|
|
value: 35.136
|
|
- type: mrr_at_100
|
|
value: 36.284
|
|
- type: mrr_at_1000
|
|
value: 36.325
|
|
- type: mrr_at_20
|
|
value: 35.869
|
|
- type: mrr_at_3
|
|
value: 31.351000000000003
|
|
- type: mrr_at_5
|
|
value: 33.54
|
|
- type: ndcg_at_1
|
|
value: 23.009
|
|
- type: ndcg_at_10
|
|
value: 41.471999999999994
|
|
- type: ndcg_at_100
|
|
value: 47.211999999999996
|
|
- type: ndcg_at_1000
|
|
value: 48.361
|
|
- type: ndcg_at_20
|
|
value: 44.169000000000004
|
|
- type: ndcg_at_3
|
|
value: 33.646
|
|
- type: ndcg_at_5
|
|
value: 37.580000000000005
|
|
- type: precision_at_1
|
|
value: 23.009
|
|
- type: precision_at_10
|
|
value: 6.54
|
|
- type: precision_at_100
|
|
value: 0.941
|
|
- type: precision_at_1000
|
|
value: 0.104
|
|
- type: precision_at_20
|
|
value: 3.832
|
|
- type: precision_at_3
|
|
value: 14.283999999999999
|
|
- type: precision_at_5
|
|
value: 10.564
|
|
- type: recall_at_1
|
|
value: 22.407
|
|
- type: recall_at_10
|
|
value: 62.678999999999995
|
|
- type: recall_at_100
|
|
value: 89.09700000000001
|
|
- type: recall_at_1000
|
|
value: 97.822
|
|
- type: recall_at_20
|
|
value: 73.116
|
|
- type: recall_at_3
|
|
value: 41.4
|
|
- type: recall_at_5
|
|
value: 50.855
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB MTOPDomainClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
|
metrics:
|
|
- type: accuracy
|
|
value: 92.94573643410853
|
|
- type: f1
|
|
value: 92.73148878666994
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB MTOPIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.86137710898313
|
|
- type: f1
|
|
value: 60.360562463738724
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB MassiveIntentClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
|
metrics:
|
|
- type: accuracy
|
|
value: 73.83322125084062
|
|
- type: f1
|
|
value: 71.61864304680206
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB MassiveScenarioClassification (en)
|
|
config: en
|
|
split: test
|
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
|
metrics:
|
|
- type: accuracy
|
|
value: 77.50504371217215
|
|
- type: f1
|
|
value: 77.52039268347185
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB MedrxivClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
|
|
metrics:
|
|
- type: v_measure
|
|
value: 34.346952648910225
|
|
- type: v_measures
|
|
value: [0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 0.36636134409497156, 0.3538734420417993, 0.3394557315590024, 0.3246964225451952, 0.33269208719245646, 0.3355911472371345, 0.32978655133380147, 0.3275090874657499, 0.3752583186941529, 0.3494711327267592, 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|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB MedrxivClusteringS2S
|
|
config: default
|
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split: test
|
|
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
|
|
metrics:
|
|
- type: v_measure
|
|
value: 32.19992734583148
|
|
- type: v_measures
|
|
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|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: None
|
|
name: MTEB MindSmallReranking
|
|
config: default
|
|
split: test
|
|
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
|
|
metrics:
|
|
- type: map
|
|
value: 30.62309561205373
|
|
- type: mrr
|
|
value: 31.707879717902554
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB NFCorpus
|
|
config: default
|
|
split: test
|
|
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 5.668
|
|
- type: map_at_10
|
|
value: 12.225999999999999
|
|
- type: map_at_100
|
|
value: 15.122
|
|
- type: map_at_1000
|
|
value: 16.422
|
|
- type: map_at_20
|
|
value: 13.361999999999998
|
|
- type: map_at_3
|
|
value: 9.083
|
|
- type: map_at_5
|
|
value: 10.5
|
|
- type: mrr_at_1
|
|
value: 46.44
|
|
- type: mrr_at_10
|
|
value: 53.553
|
|
- type: mrr_at_100
|
|
value: 54.15
|
|
- type: mrr_at_1000
|
|
value: 54.193000000000005
|
|
- type: mrr_at_20
|
|
value: 53.837
|
|
- type: mrr_at_3
|
|
value: 51.702999999999996
|
|
- type: mrr_at_5
|
|
value: 52.647
|
|
- type: ndcg_at_1
|
|
value: 44.272
|
|
- type: ndcg_at_10
|
|
value: 33.395
|
|
- type: ndcg_at_100
|
|
value: 29.976999999999997
|
|
- type: ndcg_at_1000
|
|
value: 38.388
|
|
- type: ndcg_at_20
|
|
value: 30.606
|
|
- type: ndcg_at_3
|
|
value: 39.212
|
|
- type: ndcg_at_5
|
|
value: 36.611
|
|
- type: precision_at_1
|
|
value: 46.129999999999995
|
|
- type: precision_at_10
|
|
value: 24.334
|
|
- type: precision_at_100
|
|
value: 7.553999999999999
|
|
- type: precision_at_1000
|
|
value: 1.994
|
|
- type: precision_at_20
|
|
value: 17.678
|
|
- type: precision_at_3
|
|
value: 36.326
|
|
- type: precision_at_5
|
|
value: 31.330999999999996
|
|
- type: recall_at_1
|
|
value: 5.668
|
|
- type: recall_at_10
|
|
value: 15.837000000000002
|
|
- type: recall_at_100
|
|
value: 29.845
|
|
- type: recall_at_1000
|
|
value: 60.563
|
|
- type: recall_at_20
|
|
value: 18.587999999999997
|
|
- type: recall_at_3
|
|
value: 10.096
|
|
- type: recall_at_5
|
|
value: 12.261
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB NQ
|
|
config: default
|
|
split: test
|
|
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 39.335
|
|
- type: map_at_10
|
|
value: 54.932
|
|
- type: map_at_100
|
|
value: 55.742000000000004
|
|
- type: map_at_1000
|
|
value: 55.766000000000005
|
|
- type: map_at_20
|
|
value: 55.504
|
|
- type: map_at_3
|
|
value: 50.904
|
|
- type: map_at_5
|
|
value: 53.388999999999996
|
|
- type: mrr_at_1
|
|
value: 44.003
|
|
- type: mrr_at_10
|
|
value: 57.419
|
|
- type: mrr_at_100
|
|
value: 57.963
|
|
- type: mrr_at_1000
|
|
value: 57.981
|
|
- type: mrr_at_20
|
|
value: 57.80499999999999
|
|
- type: mrr_at_3
|
|
value: 54.30199999999999
|
|
- type: mrr_at_5
|
|
value: 56.257000000000005
|
|
- type: ndcg_at_1
|
|
value: 43.974999999999994
|
|
- type: ndcg_at_10
|
|
value: 62.153999999999996
|
|
- type: ndcg_at_100
|
|
value: 65.326
|
|
- type: ndcg_at_1000
|
|
value: 65.862
|
|
- type: ndcg_at_20
|
|
value: 63.922999999999995
|
|
- type: ndcg_at_3
|
|
value: 54.834
|
|
- type: ndcg_at_5
|
|
value: 58.857000000000006
|
|
- type: precision_at_1
|
|
value: 43.974999999999994
|
|
- type: precision_at_10
|
|
value: 9.722
|
|
- type: precision_at_100
|
|
value: 1.153
|
|
- type: precision_at_1000
|
|
value: 0.12
|
|
- type: precision_at_20
|
|
value: 5.3
|
|
- type: precision_at_3
|
|
value: 24.392
|
|
- type: precision_at_5
|
|
value: 16.993
|
|
- type: recall_at_1
|
|
value: 39.335
|
|
- type: recall_at_10
|
|
value: 81.501
|
|
- type: recall_at_100
|
|
value: 94.851
|
|
- type: recall_at_1000
|
|
value: 98.817
|
|
- type: recall_at_20
|
|
value: 87.968
|
|
- type: recall_at_3
|
|
value: 62.795
|
|
- type: recall_at_5
|
|
value: 71.985
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB QuoraRetrieval
|
|
config: default
|
|
split: test
|
|
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 71.222
|
|
- type: map_at_10
|
|
value: 85.193
|
|
- type: map_at_100
|
|
value: 85.802
|
|
- type: map_at_1000
|
|
value: 85.81800000000001
|
|
- type: map_at_20
|
|
value: 85.587
|
|
- type: map_at_3
|
|
value: 82.253
|
|
- type: map_at_5
|
|
value: 84.142
|
|
- type: mrr_at_1
|
|
value: 82.04
|
|
- type: mrr_at_10
|
|
value: 88.101
|
|
- type: mrr_at_100
|
|
value: 88.196
|
|
- type: mrr_at_1000
|
|
value: 88.196
|
|
- type: mrr_at_20
|
|
value: 88.175
|
|
- type: mrr_at_3
|
|
value: 87.145
|
|
- type: mrr_at_5
|
|
value: 87.825
|
|
- type: ndcg_at_1
|
|
value: 82.04
|
|
- type: ndcg_at_10
|
|
value: 88.849
|
|
- type: ndcg_at_100
|
|
value: 89.992
|
|
- type: ndcg_at_1000
|
|
value: 90.089
|
|
- type: ndcg_at_20
|
|
value: 89.468
|
|
- type: ndcg_at_3
|
|
value: 86.06899999999999
|
|
- type: ndcg_at_5
|
|
value: 87.669
|
|
- type: precision_at_1
|
|
value: 82.04
|
|
- type: precision_at_10
|
|
value: 13.447000000000001
|
|
- type: precision_at_100
|
|
value: 1.528
|
|
- type: precision_at_1000
|
|
value: 0.157
|
|
- type: precision_at_20
|
|
value: 7.116
|
|
- type: precision_at_3
|
|
value: 37.617
|
|
- type: precision_at_5
|
|
value: 24.776
|
|
- type: recall_at_1
|
|
value: 71.222
|
|
- type: recall_at_10
|
|
value: 95.73899999999999
|
|
- type: recall_at_100
|
|
value: 99.572
|
|
- type: recall_at_1000
|
|
value: 99.988
|
|
- type: recall_at_20
|
|
value: 97.725
|
|
- type: recall_at_3
|
|
value: 87.742
|
|
- type: recall_at_5
|
|
value: 92.23400000000001
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB RedditClustering
|
|
config: default
|
|
split: test
|
|
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
|
|
metrics:
|
|
- type: v_measure
|
|
value: 56.502005725283524
|
|
- type: v_measures
|
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|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB RedditClusteringP2P
|
|
config: default
|
|
split: test
|
|
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
|
|
metrics:
|
|
- type: v_measure
|
|
value: 63.14989421688691
|
|
- type: v_measures
|
|
value: [0.673210410652684, 0.6825035243902045, 0.6275126414823813, 0.40001836573261074, 0.711458797825346, 0.6212317163461291, 0.4113635660304527, 0.7394060043565659, 0.6969073197749642, 0.7513770750973534, 0.673210410652684, 0.6825035243902045, 0.6275126414823813, 0.40001836573261074, 0.711458797825346, 0.6212317163461291, 0.4113635660304527, 0.7394060043565659, 0.6969073197749642, 0.7513770750973534, 0.673210410652684, 0.6825035243902045, 0.6275126414823813, 0.40001836573261074, 0.711458797825346, 0.6212317163461291, 0.4113635660304527, 0.7394060043565659, 0.6969073197749642, 0.7513770750973534, 0.673210410652684, 0.6825035243902045, 0.6275126414823813, 0.40001836573261074, 0.711458797825346, 0.6212317163461291, 0.4113635660304527, 0.7394060043565659, 0.6969073197749642, 0.7513770750973534, 0.673210410652684, 0.6825035243902045, 0.6275126414823813, 0.40001836573261074, 0.711458797825346, 0.6212317163461291, 0.4113635660304527, 0.7394060043565659, 0.6969073197749642, 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0.7513770750973534]
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB SCIDOCS
|
|
config: default
|
|
split: test
|
|
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 4.4830000000000005
|
|
- type: map_at_10
|
|
value: 11.04
|
|
- type: map_at_100
|
|
value: 12.764000000000001
|
|
- type: map_at_1000
|
|
value: 13.04
|
|
- type: map_at_20
|
|
value: 11.953
|
|
- type: map_at_3
|
|
value: 8.125
|
|
- type: map_at_5
|
|
value: 9.565999999999999
|
|
- type: mrr_at_1
|
|
value: 22.1
|
|
- type: mrr_at_10
|
|
value: 32.494
|
|
- type: mrr_at_100
|
|
value: 33.525
|
|
- type: mrr_at_1000
|
|
value: 33.596
|
|
- type: mrr_at_20
|
|
value: 33.089
|
|
- type: mrr_at_3
|
|
value: 29.416999999999998
|
|
- type: mrr_at_5
|
|
value: 31.267
|
|
- type: ndcg_at_1
|
|
value: 22.1
|
|
- type: ndcg_at_10
|
|
value: 18.587
|
|
- type: ndcg_at_100
|
|
value: 25.482
|
|
- type: ndcg_at_1000
|
|
value: 30.581999999999997
|
|
- type: ndcg_at_20
|
|
value: 21.077
|
|
- type: ndcg_at_3
|
|
value: 18.165
|
|
- type: ndcg_at_5
|
|
value: 15.676000000000002
|
|
- type: precision_at_1
|
|
value: 22.1
|
|
- type: precision_at_10
|
|
value: 9.48
|
|
- type: precision_at_100
|
|
value: 1.942
|
|
- type: precision_at_1000
|
|
value: 0.316
|
|
- type: precision_at_20
|
|
value: 6.175
|
|
- type: precision_at_3
|
|
value: 17.033
|
|
- type: precision_at_5
|
|
value: 13.719999999999999
|
|
- type: recall_at_1
|
|
value: 4.4830000000000005
|
|
- type: recall_at_10
|
|
value: 19.208
|
|
- type: recall_at_100
|
|
value: 39.417
|
|
- type: recall_at_1000
|
|
value: 64.235
|
|
- type: recall_at_20
|
|
value: 25.057000000000002
|
|
- type: recall_at_3
|
|
value: 10.348
|
|
- type: recall_at_5
|
|
value: 13.893
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB SICK-R
|
|
config: default
|
|
split: test
|
|
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.50181312649208
|
|
- type: cos_sim_spearman
|
|
value: 79.92900705478993
|
|
- type: euclidean_pearson
|
|
value: 81.13482128094503
|
|
- type: euclidean_spearman
|
|
value: 79.92732266864367
|
|
- type: manhattan_pearson
|
|
value: 81.06702121654993
|
|
- type: manhattan_spearman
|
|
value: 79.86983106619135
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB STS12
|
|
config: default
|
|
split: test
|
|
revision: a0d554a64d88156834ff5ae9920b964011b16384
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.85431681906961
|
|
- type: cos_sim_spearman
|
|
value: 77.61671419416626
|
|
- type: euclidean_pearson
|
|
value: 81.30538320520961
|
|
- type: euclidean_spearman
|
|
value: 77.62096481461272
|
|
- type: manhattan_pearson
|
|
value: 81.2306021173407
|
|
- type: manhattan_spearman
|
|
value: 77.58386300715222
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB STS13
|
|
config: default
|
|
split: test
|
|
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 84.98057702322754
|
|
- type: cos_sim_spearman
|
|
value: 86.13305071688859
|
|
- type: euclidean_pearson
|
|
value: 85.70903555966376
|
|
- type: euclidean_spearman
|
|
value: 86.13150222328171
|
|
- type: manhattan_pearson
|
|
value: 85.69380834788831
|
|
- type: manhattan_spearman
|
|
value: 86.10784739081191
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB STS14
|
|
config: default
|
|
split: test
|
|
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.43368314724589
|
|
- type: cos_sim_spearman
|
|
value: 81.26767916144169
|
|
- type: euclidean_pearson
|
|
value: 83.23234690932492
|
|
- type: euclidean_spearman
|
|
value: 81.2671726214706
|
|
- type: manhattan_pearson
|
|
value: 83.2381239261109
|
|
- type: manhattan_spearman
|
|
value: 81.27674961470714
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB STS15
|
|
config: default
|
|
split: test
|
|
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 86.8637546411748
|
|
- type: cos_sim_spearman
|
|
value: 88.25330888676139
|
|
- type: euclidean_pearson
|
|
value: 87.81194589390417
|
|
- type: euclidean_spearman
|
|
value: 88.25258669625579
|
|
- type: manhattan_pearson
|
|
value: 87.8131866998459
|
|
- type: manhattan_spearman
|
|
value: 88.26523268929576
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB STS16
|
|
config: default
|
|
split: test
|
|
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 83.83129743147286
|
|
- type: cos_sim_spearman
|
|
value: 85.73732687732624
|
|
- type: euclidean_pearson
|
|
value: 85.18051277328075
|
|
- type: euclidean_spearman
|
|
value: 85.73565846174445
|
|
- type: manhattan_pearson
|
|
value: 85.179029651079
|
|
- type: manhattan_spearman
|
|
value: 85.75709685404729
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB STS17 (en-en)
|
|
config: en-en
|
|
split: test
|
|
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 87.04715794253148
|
|
- type: cos_sim_spearman
|
|
value: 87.61577496386343
|
|
- type: euclidean_pearson
|
|
value: 88.34713614361046
|
|
- type: euclidean_spearman
|
|
value: 87.56541901567275
|
|
- type: manhattan_pearson
|
|
value: 88.26010824585985
|
|
- type: manhattan_spearman
|
|
value: 87.35211736948182
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB STS22 (en)
|
|
config: en
|
|
split: test
|
|
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 62.36160793264433
|
|
- type: cos_sim_spearman
|
|
value: 66.07767480051893
|
|
- type: euclidean_pearson
|
|
value: 66.4716471304865
|
|
- type: euclidean_spearman
|
|
value: 66.03999286501872
|
|
- type: manhattan_pearson
|
|
value: 66.46197824372902
|
|
- type: manhattan_spearman
|
|
value: 65.82936468127227
|
|
- task:
|
|
type: STS
|
|
dataset:
|
|
type: None
|
|
name: MTEB STSBenchmark
|
|
config: default
|
|
split: test
|
|
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 85.27768996785856
|
|
- type: cos_sim_spearman
|
|
value: 86.96704639052885
|
|
- type: euclidean_pearson
|
|
value: 86.48753189555983
|
|
- type: euclidean_spearman
|
|
value: 86.96981285751171
|
|
- type: manhattan_pearson
|
|
value: 86.49262465015401
|
|
- type: manhattan_spearman
|
|
value: 86.95378609580054
|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: None
|
|
name: MTEB SciDocsRR
|
|
config: default
|
|
split: test
|
|
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
|
|
metrics:
|
|
- type: map
|
|
value: 81.52012853393428
|
|
- type: mrr
|
|
value: 94.70817671798063
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB SciFact
|
|
config: default
|
|
split: test
|
|
revision: 0228b52cf27578f30900b9e5271d331663a030d7
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 55.344
|
|
- type: map_at_10
|
|
value: 64.82900000000001
|
|
- type: map_at_100
|
|
value: 65.42
|
|
- type: map_at_1000
|
|
value: 65.443
|
|
- type: map_at_20
|
|
value: 65.2
|
|
- type: map_at_3
|
|
value: 61.8
|
|
- type: map_at_5
|
|
value: 63.510999999999996
|
|
- type: mrr_at_1
|
|
value: 58.333
|
|
- type: mrr_at_10
|
|
value: 66.24600000000001
|
|
- type: mrr_at_100
|
|
value: 66.742
|
|
- type: mrr_at_1000
|
|
value: 66.762
|
|
- type: mrr_at_20
|
|
value: 66.549
|
|
- type: mrr_at_3
|
|
value: 64.056
|
|
- type: mrr_at_5
|
|
value: 65.372
|
|
- type: ndcg_at_1
|
|
value: 58.333
|
|
- type: ndcg_at_10
|
|
value: 69.626
|
|
- type: ndcg_at_100
|
|
value: 72.236
|
|
- type: ndcg_at_1000
|
|
value: 72.872
|
|
- type: ndcg_at_20
|
|
value: 70.864
|
|
- type: ndcg_at_3
|
|
value: 64.50399999999999
|
|
- type: ndcg_at_5
|
|
value: 67.07600000000001
|
|
- type: precision_at_1
|
|
value: 58.333
|
|
- type: precision_at_10
|
|
value: 9.4
|
|
- type: precision_at_100
|
|
value: 1.073
|
|
- type: precision_at_1000
|
|
value: 0.11299999999999999
|
|
- type: precision_at_20
|
|
value: 4.983
|
|
- type: precision_at_3
|
|
value: 25.222
|
|
- type: precision_at_5
|
|
value: 16.8
|
|
- type: recall_at_1
|
|
value: 55.344
|
|
- type: recall_at_10
|
|
value: 82.789
|
|
- type: recall_at_100
|
|
value: 94.6
|
|
- type: recall_at_1000
|
|
value: 99.667
|
|
- type: recall_at_20
|
|
value: 87.533
|
|
- type: recall_at_3
|
|
value: 69.18299999999999
|
|
- type: recall_at_5
|
|
value: 75.622
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: None
|
|
name: MTEB SprintDuplicateQuestions
|
|
config: default
|
|
split: test
|
|
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 99.69405940594059
|
|
- type: cos_sim_ap
|
|
value: 92.03642221694545
|
|
- type: cos_sim_f1
|
|
value: 84.06395048994327
|
|
- type: cos_sim_precision
|
|
value: 86.79446219382322
|
|
- type: cos_sim_recall
|
|
value: 81.5
|
|
- type: dot_accuracy
|
|
value: 99.6930693069307
|
|
- type: dot_ap
|
|
value: 91.9971441434875
|
|
- type: dot_f1
|
|
value: 83.8006230529595
|
|
- type: dot_precision
|
|
value: 87.14902807775377
|
|
- type: dot_recall
|
|
value: 80.7
|
|
- type: euclidean_accuracy
|
|
value: 99.69504950495049
|
|
- type: euclidean_ap
|
|
value: 92.03626548389335
|
|
- type: euclidean_f1
|
|
value: 84.10732714138285
|
|
- type: euclidean_precision
|
|
value: 86.88699360341151
|
|
- type: euclidean_recall
|
|
value: 81.5
|
|
- type: manhattan_accuracy
|
|
value: 99.69504950495049
|
|
- type: manhattan_ap
|
|
value: 92.02049659660081
|
|
- type: manhattan_f1
|
|
value: 84.34959349593495
|
|
- type: manhattan_precision
|
|
value: 85.74380165289256
|
|
- type: manhattan_recall
|
|
value: 83.0
|
|
- type: max_accuracy
|
|
value: 99.69504950495049
|
|
- type: max_ap
|
|
value: 92.03642221694545
|
|
- type: max_f1
|
|
value: 84.34959349593495
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB StackExchangeClustering
|
|
config: default
|
|
split: test
|
|
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
|
|
metrics:
|
|
- type: v_measure
|
|
value: 67.04916654680977
|
|
- type: v_measures
|
|
value: [0.707614120277991, 0.694974842783697, 0.5756359888519659, 0.6964499615297283, 0.6547764033608466, 0.6448470247319567, 0.6263766967145058, 0.7139286894225703, 0.6737195749489034, 0.6824504575459811, 0.7667603743275774, 0.7595788549615426, 0.7086156082505461, 0.6624140136843005, 0.6136884209896801, 0.6717953455355791, 0.6494834308652331, 0.6507885275711466, 0.6382769468968572, 0.6556052416453325, 0.6700496626301571, 0.6424264693175464, 0.6400679099051025, 0.7118398877792876, 0.6501271821744096, 0.707614120277991, 0.694974842783697, 0.5756359888519659, 0.6964499615297283, 0.6547764033608466, 0.6448470247319567, 0.6263766967145058, 0.7139286894225703, 0.6737195749489034, 0.6824504575459811, 0.7667603743275774, 0.7595788549615426, 0.7086156082505461, 0.6624140136843005, 0.6136884209896801, 0.6717953455355791, 0.6494834308652331, 0.6507885275711466, 0.6382769468968572, 0.6556052416453325, 0.6700496626301571, 0.6424264693175464, 0.6400679099051025, 0.7118398877792876, 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0.6501271821744096]
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB StackExchangeClusteringP2P
|
|
config: default
|
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split: test
|
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revision: 815ca46b2622cec33ccafc3735d572c266efdb44
|
|
metrics:
|
|
- type: v_measure
|
|
value: 33.36641413495258
|
|
- type: v_measures
|
|
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|
|
- task:
|
|
type: Reranking
|
|
dataset:
|
|
type: None
|
|
name: MTEB StackOverflowDupQuestions
|
|
config: default
|
|
split: test
|
|
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
|
|
metrics:
|
|
- type: map
|
|
value: 51.19282080158746
|
|
- type: mrr
|
|
value: 51.871100713012474
|
|
- task:
|
|
type: Summarization
|
|
dataset:
|
|
type: None
|
|
name: MTEB SummEval
|
|
config: default
|
|
split: test
|
|
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
|
|
metrics:
|
|
- type: cos_sim_pearson
|
|
value: 31.437664703708485
|
|
- type: cos_sim_spearman
|
|
value: 31.391119208581575
|
|
- type: dot_pearson
|
|
value: 31.19925970504054
|
|
- type: dot_spearman
|
|
value: 31.38087224016694
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB TRECCOVID
|
|
config: default
|
|
split: test
|
|
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 0.249
|
|
- type: map_at_10
|
|
value: 2.163
|
|
- type: map_at_100
|
|
value: 13.242999999999999
|
|
- type: map_at_1000
|
|
value: 30.866
|
|
- type: map_at_20
|
|
value: 3.9539999999999997
|
|
- type: map_at_3
|
|
value: 0.718
|
|
- type: map_at_5
|
|
value: 1.169
|
|
- type: mrr_at_1
|
|
value: 96.0
|
|
- type: mrr_at_10
|
|
value: 98.0
|
|
- type: mrr_at_100
|
|
value: 98.0
|
|
- type: mrr_at_1000
|
|
value: 98.0
|
|
- type: mrr_at_20
|
|
value: 98.0
|
|
- type: mrr_at_3
|
|
value: 98.0
|
|
- type: mrr_at_5
|
|
value: 98.0
|
|
- type: ndcg_at_1
|
|
value: 92.0
|
|
- type: ndcg_at_10
|
|
value: 84.147
|
|
- type: ndcg_at_100
|
|
value: 65.143
|
|
- type: ndcg_at_1000
|
|
value: 56.038
|
|
- type: ndcg_at_20
|
|
value: 80.869
|
|
- type: ndcg_at_3
|
|
value: 89.11200000000001
|
|
- type: ndcg_at_5
|
|
value: 87.199
|
|
- type: precision_at_1
|
|
value: 96.0
|
|
- type: precision_at_10
|
|
value: 87.8
|
|
- type: precision_at_100
|
|
value: 66.72
|
|
- type: precision_at_1000
|
|
value: 24.684
|
|
- type: precision_at_20
|
|
value: 84.3
|
|
- type: precision_at_3
|
|
value: 94.0
|
|
- type: precision_at_5
|
|
value: 91.2
|
|
- type: recall_at_1
|
|
value: 0.249
|
|
- type: recall_at_10
|
|
value: 2.284
|
|
- type: recall_at_100
|
|
value: 16.025
|
|
- type: recall_at_1000
|
|
value: 52.068999999999996
|
|
- type: recall_at_20
|
|
value: 4.3180000000000005
|
|
- type: recall_at_3
|
|
value: 0.738
|
|
- type: recall_at_5
|
|
value: 1.212
|
|
- task:
|
|
type: Retrieval
|
|
dataset:
|
|
type: None
|
|
name: MTEB Touche2020
|
|
config: default
|
|
split: test
|
|
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
|
|
metrics:
|
|
- type: map_at_1
|
|
value: 3.4520000000000004
|
|
- type: map_at_10
|
|
value: 13.045000000000002
|
|
- type: map_at_100
|
|
value: 19.442
|
|
- type: map_at_1000
|
|
value: 21.09
|
|
- type: map_at_20
|
|
value: 15.667
|
|
- type: map_at_3
|
|
value: 7.409000000000001
|
|
- type: map_at_5
|
|
value: 9.73
|
|
- type: mrr_at_1
|
|
value: 46.939
|
|
- type: mrr_at_10
|
|
value: 60.295
|
|
- type: mrr_at_100
|
|
value: 60.904
|
|
- type: mrr_at_1000
|
|
value: 60.919000000000004
|
|
- type: mrr_at_20
|
|
value: 60.77
|
|
- type: mrr_at_3
|
|
value: 58.50300000000001
|
|
- type: mrr_at_5
|
|
value: 59.014
|
|
- type: ndcg_at_1
|
|
value: 44.897999999999996
|
|
- type: ndcg_at_10
|
|
value: 31.911
|
|
- type: ndcg_at_100
|
|
value: 41.945
|
|
- type: ndcg_at_1000
|
|
value: 53.181999999999995
|
|
- type: ndcg_at_20
|
|
value: 31.505
|
|
- type: ndcg_at_3
|
|
value: 39.745000000000005
|
|
- type: ndcg_at_5
|
|
value: 35.528999999999996
|
|
- type: precision_at_1
|
|
value: 46.939
|
|
- type: precision_at_10
|
|
value: 26.531
|
|
- type: precision_at_100
|
|
value: 8.163
|
|
- type: precision_at_1000
|
|
value: 1.559
|
|
- type: precision_at_20
|
|
value: 19.387999999999998
|
|
- type: precision_at_3
|
|
value: 40.136
|
|
- type: precision_at_5
|
|
value: 33.878
|
|
- type: recall_at_1
|
|
value: 3.4520000000000004
|
|
- type: recall_at_10
|
|
value: 18.899
|
|
- type: recall_at_100
|
|
value: 50.207
|
|
- type: recall_at_1000
|
|
value: 83.871
|
|
- type: recall_at_20
|
|
value: 26.756999999999998
|
|
- type: recall_at_3
|
|
value: 8.729000000000001
|
|
- type: recall_at_5
|
|
value: 12.084999999999999
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB ToxicConversationsClassification
|
|
config: default
|
|
split: test
|
|
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
|
|
metrics:
|
|
- type: accuracy
|
|
value: 67.4560546875
|
|
- type: ap
|
|
value: 12.720403845355294
|
|
- type: f1
|
|
value: 51.76062666567839
|
|
- task:
|
|
type: Classification
|
|
dataset:
|
|
type: None
|
|
name: MTEB TweetSentimentExtractionClassification
|
|
config: default
|
|
split: test
|
|
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
|
metrics:
|
|
- type: accuracy
|
|
value: 62.36276174306734
|
|
- type: f1
|
|
value: 62.69956906934332
|
|
- task:
|
|
type: Clustering
|
|
dataset:
|
|
type: None
|
|
name: MTEB TwentyNewsgroupsClustering
|
|
config: default
|
|
split: test
|
|
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
|
|
metrics:
|
|
- type: v_measure
|
|
value: 49.473492910233965
|
|
- type: v_measures
|
|
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0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281, 0.48829262296803855, 0.49853262011854643, 0.48457750518082765, 0.5020774116970983, 0.5001897357021557, 0.4702417082210781, 0.4763216048226018, 0.49932879417585735, 0.5129628835129124, 0.514824404624281]
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: None
|
|
name: MTEB TwitterSemEval2015
|
|
config: default
|
|
split: test
|
|
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 85.75430649102938
|
|
- type: cos_sim_ap
|
|
value: 73.62842656477649
|
|
- type: cos_sim_f1
|
|
value: 67.76023680315738
|
|
- type: cos_sim_precision
|
|
value: 63.61741547012506
|
|
- type: cos_sim_recall
|
|
value: 72.4802110817942
|
|
- type: dot_accuracy
|
|
value: 85.7423854085951
|
|
- type: dot_ap
|
|
value: 73.59147637253723
|
|
- type: dot_f1
|
|
value: 67.69498693867396
|
|
- type: dot_precision
|
|
value: 64.03859731701577
|
|
- type: dot_recall
|
|
value: 71.79419525065963
|
|
- type: euclidean_accuracy
|
|
value: 85.7423854085951
|
|
- type: euclidean_ap
|
|
value: 73.6288990409654
|
|
- type: euclidean_f1
|
|
value: 67.80415430267064
|
|
- type: euclidean_precision
|
|
value: 63.79711493718009
|
|
- type: euclidean_recall
|
|
value: 72.34828496042216
|
|
- type: manhattan_accuracy
|
|
value: 85.69470107885796
|
|
- type: manhattan_ap
|
|
value: 73.49219614602531
|
|
- type: manhattan_f1
|
|
value: 67.60809797550613
|
|
- type: manhattan_precision
|
|
value: 64.22127255460589
|
|
- type: manhattan_recall
|
|
value: 71.37203166226914
|
|
- type: max_accuracy
|
|
value: 85.75430649102938
|
|
- type: max_ap
|
|
value: 73.6288990409654
|
|
- type: max_f1
|
|
value: 67.80415430267064
|
|
- task:
|
|
type: PairClassification
|
|
dataset:
|
|
type: None
|
|
name: MTEB TwitterURLCorpus
|
|
config: default
|
|
split: test
|
|
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
|
metrics:
|
|
- type: cos_sim_accuracy
|
|
value: 89.08293553770326
|
|
- type: cos_sim_ap
|
|
value: 86.21246419992926
|
|
- type: cos_sim_f1
|
|
value: 78.49922526377924
|
|
- type: cos_sim_precision
|
|
value: 75.35769939084857
|
|
- type: cos_sim_recall
|
|
value: 81.9140745303357
|
|
- type: dot_accuracy
|
|
value: 89.08681647067955
|
|
- type: dot_ap
|
|
value: 86.19733517196862
|
|
- type: dot_f1
|
|
value: 78.51132446157838
|
|
- type: dot_precision
|
|
value: 75.70233755093287
|
|
- type: dot_recall
|
|
value: 81.53680320295658
|
|
- type: euclidean_accuracy
|
|
value: 89.07517367175069
|
|
- type: euclidean_ap
|
|
value: 86.21198725320203
|
|
- type: euclidean_f1
|
|
value: 78.49867139061116
|
|
- type: euclidean_precision
|
|
value: 75.38276155372839
|
|
- type: euclidean_recall
|
|
value: 81.88327687095781
|
|
- type: manhattan_accuracy
|
|
value: 89.0538285403811
|
|
- type: manhattan_ap
|
|
value: 86.17785515765131
|
|
- type: manhattan_f1
|
|
value: 78.48184098593084
|
|
- type: manhattan_precision
|
|
value: 74.34396308285694
|
|
- type: manhattan_recall
|
|
value: 83.10748383122882
|
|
- type: max_accuracy
|
|
value: 89.08681647067955
|
|
- type: max_ap
|
|
value: 86.21246419992926
|
|
- type: max_f1
|
|
value: 78.51132446157838
|
|
license: apache-2.0
|
|
language:
|
|
- en
|
|
base_model:
|
|
- answerdotai/ModernBERT-base
|
|
- nomic-ai/modernbert-embed-unsupervised
|
|
---
|
|
|
|
# ModernBERT Embed
|
|
|
|
[](https://huggingface.co/nomic-ai/modernbert-embed-base)
|
|
|
|
ModernBERT Embed is an embedding model trained from [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), bringing the new advances of ModernBERT to embeddings!
|
|
|
|
Trained on the [Nomic Embed](https://arxiv.org/abs/2402.01613) weakly-supervised and supervised datasets, `modernbert-embed` also supports Matryoshka Representation Learning dimensions of 256, reducing memory by 3x with minimal performance loss.
|
|
|
|
## Performance
|
|
|
|
| Model | Dimensions | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) |
|
|
|-----------------------|------------|--------------|---------------------|-----------------|-------------------------|---------------|----------------|-----------|------------------|
|
|
| nomic-embed-text-v1 | 768 | 62.4 | 74.1 | 43.9 | **85.2** | 55.7 | 52.8 | 82.1 | 30.1 |
|
|
| nomic-embed-text-v1.5 | 768 | 62.28 | 73.55 | 43.93 | 84.61 | 55.78 | **53.01** | **81.94** | 30.4 |
|
|
| modernbert-embed-base | 768 | **62.62** | **74.31** | **44.98** | 83.96 | **56.42** | 52.89 | 81.78 | **31.39** |
|
|
| nomic-embed-text-v1.5 | 256 | 61.04 | 72.1 | 43.16 | 84.09 | 55.18 | 50.81 | 81.34 | 30.05 |
|
|
| modernbert-embed-base | 256 | 61.17 | 72.40 | 43.82 | 83.45 | 55.69 | 50.62 | 81.12 | 31.27 |
|
|
|
|
|
|
|
|
## Usage
|
|
|
|
You can use these models directly with the transformers library. Until the next transformers release, doing so requires installing `transformers` from `main`:
|
|
|
|
```bash
|
|
pip install git+https://github.com/huggingface/transformers.git
|
|
```
|
|
|
|
Reminder, this model is trained similarly to Nomic Embed and **REQUIRES** prefixes to be added to the input. For more information, see the instructions in [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5#task-instruction-prefixes).
|
|
|
|
Most use cases, adding `search_query: ` to the query and `search_document: ` to the documents will be sufficient.
|
|
|
|
### Sentence Transformers
|
|
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
model = SentenceTransformer("nomic-ai/modernbert-embed-base")
|
|
|
|
query_embeddings = model.encode([
|
|
"search_query: What is TSNE?",
|
|
"search_query: Who is Laurens van der Maaten?",
|
|
])
|
|
doc_embeddings = model.encode([
|
|
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
|
|
])
|
|
print(query_embeddings.shape, doc_embeddings.shape)
|
|
# (2, 768) (1, 768)
|
|
|
|
similarities = model.similarity(query_embeddings, doc_embeddings)
|
|
print(similarities)
|
|
# tensor([[0.7214],
|
|
# [0.3260]])
|
|
```
|
|
|
|
<details><summary>Click to see Sentence Transformers usage with Matryoshka Truncation</summary>
|
|
|
|
In Sentence Transformers, you can truncate embeddings to a smaller dimension by using the `truncate_dim` parameter when loading the `SentenceTransformer` model.
|
|
|
|
```python
|
|
from sentence_transformers import SentenceTransformer
|
|
|
|
model = SentenceTransformer("nomic-ai/modernbert-embed-base", truncate_dim=256)
|
|
|
|
query_embeddings = model.encode([
|
|
"search_query: What is TSNE?",
|
|
"search_query: Who is Laurens van der Maaten?",
|
|
])
|
|
doc_embeddings = model.encode([
|
|
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
|
|
])
|
|
print(query_embeddings.shape, doc_embeddings.shape)
|
|
# (2, 256) (1, 256)
|
|
|
|
similarities = model.similarity(query_embeddings, doc_embeddings)
|
|
print(similarities)
|
|
# tensor([[0.7759],
|
|
# [0.3419]])
|
|
```
|
|
|
|
Note the small differences compared to the full 768-dimensional similarities.
|
|
|
|
</details>
|
|
|
|
### Transformers
|
|
|
|
```python
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from transformers import AutoTokenizer, AutoModel
|
|
|
|
|
|
def mean_pooling(model_output, attention_mask):
|
|
token_embeddings = model_output[0]
|
|
input_mask_expanded = (
|
|
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
|
)
|
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
|
|
input_mask_expanded.sum(1), min=1e-9
|
|
)
|
|
|
|
|
|
queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
|
|
documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("nomic-ai/modernbert-embed-base")
|
|
model = AutoModel.from_pretrained("nomic-ai/modernbert-embed-base")
|
|
|
|
encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
|
|
encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
|
|
|
|
with torch.no_grad():
|
|
queries_outputs = model(**encoded_queries)
|
|
documents_outputs = model(**encoded_documents)
|
|
|
|
query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
|
|
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
|
|
doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
|
|
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
|
|
print(query_embeddings.shape, doc_embeddings.shape)
|
|
# torch.Size([2, 768]) torch.Size([1, 768])
|
|
|
|
similarities = query_embeddings @ doc_embeddings.T
|
|
print(similarities)
|
|
# tensor([[0.7214],
|
|
# [0.3260]])
|
|
```
|
|
|
|
<details><summary>Click to see Transformers usage with Matryoshka Truncation</summary>
|
|
|
|
In `transformers`, you can truncate embeddings to a smaller dimension by slicing the mean pooled embeddings, prior to normalization.
|
|
|
|
```python
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from transformers import AutoTokenizer, AutoModel
|
|
|
|
|
|
def mean_pooling(model_output, attention_mask):
|
|
token_embeddings = model_output[0]
|
|
input_mask_expanded = (
|
|
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
|
|
)
|
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
|
|
input_mask_expanded.sum(1), min=1e-9
|
|
)
|
|
|
|
|
|
queries = ["search_query: What is TSNE?", "search_query: Who is Laurens van der Maaten?"]
|
|
documents = ["search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten"]
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(".")
|
|
model = AutoModel.from_pretrained(".")
|
|
truncate_dim = 256
|
|
|
|
encoded_queries = tokenizer(queries, padding=True, truncation=True, return_tensors="pt")
|
|
encoded_documents = tokenizer(documents, padding=True, truncation=True, return_tensors="pt")
|
|
|
|
with torch.no_grad():
|
|
queries_outputs = model(**encoded_queries)
|
|
documents_outputs = model(**encoded_documents)
|
|
|
|
query_embeddings = mean_pooling(queries_outputs, encoded_queries["attention_mask"])
|
|
query_embeddings = query_embeddings[:, :truncate_dim]
|
|
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
|
|
doc_embeddings = mean_pooling(documents_outputs, encoded_documents["attention_mask"])
|
|
doc_embeddings = doc_embeddings[:, :truncate_dim]
|
|
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
|
|
print(query_embeddings.shape, doc_embeddings.shape)
|
|
# torch.Size([2, 256]) torch.Size([1, 256])
|
|
|
|
similarities = query_embeddings @ doc_embeddings.T
|
|
print(similarities)
|
|
# tensor([[0.7759],
|
|
# [0.3419]])
|
|
```
|
|
|
|
Note the small differences compared to the full 768-dimensional similarities.
|
|
|
|
</details>
|
|
|
|
### Transformers.js
|
|
|
|
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
|
|
```bash
|
|
npm i @huggingface/transformers
|
|
```
|
|
|
|
Then, you can compute embeddings as follows:
|
|
|
|
```javascript
|
|
import { pipeline, matmul } from '@huggingface/transformers';
|
|
|
|
// Create a feature extraction pipeline
|
|
const extractor = await pipeline(
|
|
"feature-extraction",
|
|
"nomic-ai/modernbert-embed-base",
|
|
{ dtype: "fp32" }, // Supported options: "fp32", "fp16", "q8", "q4", "q4f16"
|
|
);
|
|
|
|
// Embed queries and documents
|
|
const query_embeddings = await extractor([
|
|
"search_query: What is TSNE?",
|
|
"search_query: Who is Laurens van der Maaten?",
|
|
], { pooling: "mean", normalize: true },
|
|
);
|
|
const doc_embeddings = await extractor([
|
|
"search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten",
|
|
], { pooling: "mean", normalize: true },
|
|
);
|
|
|
|
// Compute similarity scores
|
|
const similarities = await matmul(query_embeddings, doc_embeddings.transpose(1, 0));
|
|
console.log(similarities.tolist()); // [[0.721383273601532], [0.3259955644607544]]
|
|
```
|
|
|
|
|
|
## Training
|
|
|
|
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
|
|
|
|
[](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
|
|
|
|
We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
|
|
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
|
|
|
|
In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
|
|
|
|
For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1).
|
|
|
|
Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
|
|
|
|
|
|
## Join the Nomic Community
|
|
|
|
- Nomic: [https://nomic.ai](https://nomic.ai)
|
|
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
|
|
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
|
|
|
|
## Citation
|
|
|
|
If you find the model, dataset, or training code useful, please cite our work
|
|
|
|
```bibtex
|
|
@misc{nussbaum2024nomic,
|
|
title={Nomic Embed: Training a Reproducible Long Context Text Embedder},
|
|
author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
|
|
year={2024},
|
|
eprint={2402.01613},
|
|
archivePrefix={arXiv},
|
|
primaryClass={cs.CL}
|
|
}
|
|
``` |