Update README.md

0c73b2d verified 5 months ago

99.1 kB

	---
	tags:
	- mteb
	- sentence-transformers
	- transformers
	- Qwen2
	- sentence-similarity
	license: apache-2.0
	model-index:
	- name: gte-qwen2-7B-instruct
	results:
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_counterfactual
	name: MTEB AmazonCounterfactualClassification (en)
	config: en
	split: test
	revision: e8379541af4e31359cca9fbcf4b00f2671dba205
	metrics:
	- type: accuracy
	value: 91.31343283582089
	- type: ap
	value: 67.64251402604096
	- type: f1
	value: 87.53372530755692
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_polarity
	name: MTEB AmazonPolarityClassification
	config: default
	split: test
	revision: e2d317d38cd51312af73b3d32a06d1a08b442046
	metrics:
	- type: accuracy
	value: 97.497825
	- type: ap
	value: 96.30329547047529
	- type: f1
	value: 97.49769793778039
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (en)
	config: en
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 62.564
	- type: f1
	value: 60.975777935041066
	- task:
	type: Retrieval
	dataset:
	type: mteb/arguana
	name: MTEB ArguAna
	config: default
	split: test
	revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
	metrics:
	- type: map_at_1
	value: 36.486000000000004
	- type: map_at_10
	value: 54.842
	- type: map_at_100
	value: 55.206999999999994
	- type: map_at_1000
	value: 55.206999999999994
	- type: map_at_3
	value: 49.893
	- type: map_at_5
	value: 53.105000000000004
	- type: mrr_at_1
	value: 37.34
	- type: mrr_at_10
	value: 55.143
	- type: mrr_at_100
	value: 55.509
	- type: mrr_at_1000
	value: 55.509
	- type: mrr_at_3
	value: 50.212999999999994
	- type: mrr_at_5
	value: 53.432
	- type: ndcg_at_1
	value: 36.486000000000004
	- type: ndcg_at_10
	value: 64.273
	- type: ndcg_at_100
	value: 65.66199999999999
	- type: ndcg_at_1000
	value: 65.66199999999999
	- type: ndcg_at_3
	value: 54.352999999999994
	- type: ndcg_at_5
	value: 60.131
	- type: precision_at_1
	value: 36.486000000000004
	- type: precision_at_10
	value: 9.395000000000001
	- type: precision_at_100
	value: 0.996
	- type: precision_at_1000
	value: 0.1
	- type: precision_at_3
	value: 22.428
	- type: precision_at_5
	value: 16.259
	- type: recall_at_1
	value: 36.486000000000004
	- type: recall_at_10
	value: 93.95400000000001
	- type: recall_at_100
	value: 99.644
	- type: recall_at_1000
	value: 99.644
	- type: recall_at_3
	value: 67.283
	- type: recall_at_5
	value: 81.294
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-p2p
	name: MTEB ArxivClusteringP2P
	config: default
	split: test
	revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
	metrics:
	- type: v_measure
	value: 56.461169803700564
	- task:
	type: Clustering
	dataset:
	type: mteb/arxiv-clustering-s2s
	name: MTEB ArxivClusteringS2S
	config: default
	split: test
	revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
	metrics:
	- type: v_measure
	value: 51.73600434466286
	- task:
	type: Reranking
	dataset:
	type: mteb/askubuntudupquestions-reranking
	name: MTEB AskUbuntuDupQuestions
	config: default
	split: test
	revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
	metrics:
	- type: map
	value: 67.57827065898053
	- type: mrr
	value: 79.08136569493911
	- task:
	type: STS
	dataset:
	type: mteb/biosses-sts
	name: MTEB BIOSSES
	config: default
	split: test
	revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
	metrics:
	- type: cos_sim_pearson
	value: 83.53324575999243
	- type: cos_sim_spearman
	value: 81.37173362822374
	- type: euclidean_pearson
	value: 82.19243335103444
	- type: euclidean_spearman
	value: 81.33679307304334
	- type: manhattan_pearson
	value: 82.38752665975699
	- type: manhattan_spearman
	value: 81.31510583189689
	- task:
	type: Classification
	dataset:
	type: mteb/banking77
	name: MTEB Banking77Classification
	config: default
	split: test
	revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
	metrics:
	- type: accuracy
	value: 87.56818181818181
	- type: f1
	value: 87.25826722019875
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-p2p
	name: MTEB BiorxivClusteringP2P
	config: default
	split: test
	revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
	metrics:
	- type: v_measure
	value: 50.09239610327673
	- task:
	type: Clustering
	dataset:
	type: mteb/biorxiv-clustering-s2s
	name: MTEB BiorxivClusteringS2S
	config: default
	split: test
	revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
	metrics:
	- type: v_measure
	value: 46.64733054606282
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackAndroidRetrieval
	config: default
	split: test
	revision: f46a197baaae43b4f621051089b82a364682dfeb
	metrics:
	- type: map_at_1
	value: 33.997
	- type: map_at_10
	value: 48.176
	- type: map_at_100
	value: 49.82
	- type: map_at_1000
	value: 49.924
	- type: map_at_3
	value: 43.626
	- type: map_at_5
	value: 46.275
	- type: mrr_at_1
	value: 42.059999999999995
	- type: mrr_at_10
	value: 53.726
	- type: mrr_at_100
	value: 54.398
	- type: mrr_at_1000
	value: 54.416
	- type: mrr_at_3
	value: 50.714999999999996
	- type: mrr_at_5
	value: 52.639
	- type: ndcg_at_1
	value: 42.059999999999995
	- type: ndcg_at_10
	value: 55.574999999999996
	- type: ndcg_at_100
	value: 60.744
	- type: ndcg_at_1000
	value: 61.85699999999999
	- type: ndcg_at_3
	value: 49.363
	- type: ndcg_at_5
	value: 52.44
	- type: precision_at_1
	value: 42.059999999999995
	- type: precision_at_10
	value: 11.101999999999999
	- type: precision_at_100
	value: 1.73
	- type: precision_at_1000
	value: 0.218
	- type: precision_at_3
	value: 24.464
	- type: precision_at_5
	value: 18.026
	- type: recall_at_1
	value: 33.997
	- type: recall_at_10
	value: 70.35900000000001
	- type: recall_at_100
	value: 91.642
	- type: recall_at_1000
	value: 97.977
	- type: recall_at_3
	value: 52.76
	- type: recall_at_5
	value: 61.148
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackEnglishRetrieval
	config: default
	split: test
	revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
	metrics:
	- type: map_at_1
	value: 35.884
	- type: map_at_10
	value: 48.14
	- type: map_at_100
	value: 49.5
	- type: map_at_1000
	value: 49.63
	- type: map_at_3
	value: 44.646
	- type: map_at_5
	value: 46.617999999999995
	- type: mrr_at_1
	value: 44.458999999999996
	- type: mrr_at_10
	value: 53.751000000000005
	- type: mrr_at_100
	value: 54.37800000000001
	- type: mrr_at_1000
	value: 54.415
	- type: mrr_at_3
	value: 51.815
	- type: mrr_at_5
	value: 52.882
	- type: ndcg_at_1
	value: 44.458999999999996
	- type: ndcg_at_10
	value: 54.157
	- type: ndcg_at_100
	value: 58.362
	- type: ndcg_at_1000
	value: 60.178
	- type: ndcg_at_3
	value: 49.661
	- type: ndcg_at_5
	value: 51.74999999999999
	- type: precision_at_1
	value: 44.458999999999996
	- type: precision_at_10
	value: 10.248
	- type: precision_at_100
	value: 1.5890000000000002
	- type: precision_at_1000
	value: 0.207
	- type: precision_at_3
	value: 23.928
	- type: precision_at_5
	value: 16.878999999999998
	- type: recall_at_1
	value: 35.884
	- type: recall_at_10
	value: 64.798
	- type: recall_at_100
	value: 82.345
	- type: recall_at_1000
	value: 93.267
	- type: recall_at_3
	value: 51.847
	- type: recall_at_5
	value: 57.601
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGamingRetrieval
	config: default
	split: test
	revision: 4885aa143210c98657558c04aaf3dc47cfb54340
	metrics:
	- type: map_at_1
	value: 39.383
	- type: map_at_10
	value: 53.714
	- type: map_at_100
	value: 54.838
	- type: map_at_1000
	value: 54.87800000000001
	- type: map_at_3
	value: 50.114999999999995
	- type: map_at_5
	value: 52.153000000000006
	- type: mrr_at_1
	value: 45.016
	- type: mrr_at_10
	value: 56.732000000000006
	- type: mrr_at_100
	value: 57.411
	- type: mrr_at_1000
	value: 57.431
	- type: mrr_at_3
	value: 54.044000000000004
	- type: mrr_at_5
	value: 55.639
	- type: ndcg_at_1
	value: 45.016
	- type: ndcg_at_10
	value: 60.228
	- type: ndcg_at_100
	value: 64.277
	- type: ndcg_at_1000
	value: 65.07
	- type: ndcg_at_3
	value: 54.124
	- type: ndcg_at_5
	value: 57.147000000000006
	- type: precision_at_1
	value: 45.016
	- type: precision_at_10
	value: 9.937
	- type: precision_at_100
	value: 1.288
	- type: precision_at_1000
	value: 0.13899999999999998
	- type: precision_at_3
	value: 24.471999999999998
	- type: precision_at_5
	value: 16.991
	- type: recall_at_1
	value: 39.383
	- type: recall_at_10
	value: 76.175
	- type: recall_at_100
	value: 93.02
	- type: recall_at_1000
	value: 98.60900000000001
	- type: recall_at_3
	value: 60.265
	- type: recall_at_5
	value: 67.46600000000001
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackGisRetrieval
	config: default
	split: test
	revision: 5003b3064772da1887988e05400cf3806fe491f2
	metrics:
	- type: map_at_1
	value: 27.426000000000002
	- type: map_at_10
	value: 37.397000000000006
	- type: map_at_100
	value: 38.61
	- type: map_at_1000
	value: 38.678000000000004
	- type: map_at_3
	value: 34.150999999999996
	- type: map_at_5
	value: 36.137
	- type: mrr_at_1
	value: 29.944
	- type: mrr_at_10
	value: 39.654
	- type: mrr_at_100
	value: 40.638000000000005
	- type: mrr_at_1000
	value: 40.691
	- type: mrr_at_3
	value: 36.817
	- type: mrr_at_5
	value: 38.524
	- type: ndcg_at_1
	value: 29.944
	- type: ndcg_at_10
	value: 43.094
	- type: ndcg_at_100
	value: 48.789
	- type: ndcg_at_1000
	value: 50.339999999999996
	- type: ndcg_at_3
	value: 36.984
	- type: ndcg_at_5
	value: 40.248
	- type: precision_at_1
	value: 29.944
	- type: precision_at_10
	value: 6.78
	- type: precision_at_100
	value: 1.024
	- type: precision_at_1000
	value: 0.11800000000000001
	- type: precision_at_3
	value: 15.895000000000001
	- type: precision_at_5
	value: 11.39
	- type: recall_at_1
	value: 27.426000000000002
	- type: recall_at_10
	value: 58.464000000000006
	- type: recall_at_100
	value: 84.193
	- type: recall_at_1000
	value: 95.52000000000001
	- type: recall_at_3
	value: 42.172
	- type: recall_at_5
	value: 50.101
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackMathematicaRetrieval
	config: default
	split: test
	revision: 90fceea13679c63fe563ded68f3b6f06e50061de
	metrics:
	- type: map_at_1
	value: 19.721
	- type: map_at_10
	value: 31.604
	- type: map_at_100
	value: 32.972
	- type: map_at_1000
	value: 33.077
	- type: map_at_3
	value: 27.218999999999998
	- type: map_at_5
	value: 29.53
	- type: mrr_at_1
	value: 25.0
	- type: mrr_at_10
	value: 35.843
	- type: mrr_at_100
	value: 36.785000000000004
	- type: mrr_at_1000
	value: 36.842000000000006
	- type: mrr_at_3
	value: 32.193
	- type: mrr_at_5
	value: 34.264
	- type: ndcg_at_1
	value: 25.0
	- type: ndcg_at_10
	value: 38.606
	- type: ndcg_at_100
	value: 44.272
	- type: ndcg_at_1000
	value: 46.527
	- type: ndcg_at_3
	value: 30.985000000000003
	- type: ndcg_at_5
	value: 34.43
	- type: precision_at_1
	value: 25.0
	- type: precision_at_10
	value: 7.811
	- type: precision_at_100
	value: 1.203
	- type: precision_at_1000
	value: 0.15
	- type: precision_at_3
	value: 15.423
	- type: precision_at_5
	value: 11.791
	- type: recall_at_1
	value: 19.721
	- type: recall_at_10
	value: 55.625
	- type: recall_at_100
	value: 79.34400000000001
	- type: recall_at_1000
	value: 95.208
	- type: recall_at_3
	value: 35.19
	- type: recall_at_5
	value: 43.626
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackPhysicsRetrieval
	config: default
	split: test
	revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
	metrics:
	- type: map_at_1
	value: 33.784
	- type: map_at_10
	value: 47.522
	- type: map_at_100
	value: 48.949999999999996
	- type: map_at_1000
	value: 49.038
	- type: map_at_3
	value: 43.284
	- type: map_at_5
	value: 45.629
	- type: mrr_at_1
	value: 41.482
	- type: mrr_at_10
	value: 52.830999999999996
	- type: mrr_at_100
	value: 53.559999999999995
	- type: mrr_at_1000
	value: 53.588
	- type: mrr_at_3
	value: 50.016000000000005
	- type: mrr_at_5
	value: 51.614000000000004
	- type: ndcg_at_1
	value: 41.482
	- type: ndcg_at_10
	value: 54.569
	- type: ndcg_at_100
	value: 59.675999999999995
	- type: ndcg_at_1000
	value: 60.989000000000004
	- type: ndcg_at_3
	value: 48.187000000000005
	- type: ndcg_at_5
	value: 51.183
	- type: precision_at_1
	value: 41.482
	- type: precision_at_10
	value: 10.221
	- type: precision_at_100
	value: 1.486
	- type: precision_at_1000
	value: 0.17500000000000002
	- type: precision_at_3
	value: 23.548
	- type: precision_at_5
	value: 16.805
	- type: recall_at_1
	value: 33.784
	- type: recall_at_10
	value: 69.798
	- type: recall_at_100
	value: 90.098
	- type: recall_at_1000
	value: 98.176
	- type: recall_at_3
	value: 52.127
	- type: recall_at_5
	value: 59.861
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackProgrammersRetrieval
	config: default
	split: test
	revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
	metrics:
	- type: map_at_1
	value: 28.038999999999998
	- type: map_at_10
	value: 41.904
	- type: map_at_100
	value: 43.36
	- type: map_at_1000
	value: 43.453
	- type: map_at_3
	value: 37.785999999999994
	- type: map_at_5
	value: 40.105000000000004
	- type: mrr_at_1
	value: 35.046
	- type: mrr_at_10
	value: 46.926
	- type: mrr_at_100
	value: 47.815000000000005
	- type: mrr_at_1000
	value: 47.849000000000004
	- type: mrr_at_3
	value: 44.273
	- type: mrr_at_5
	value: 45.774
	- type: ndcg_at_1
	value: 35.046
	- type: ndcg_at_10
	value: 48.937000000000005
	- type: ndcg_at_100
	value: 54.544000000000004
	- type: ndcg_at_1000
	value: 56.069
	- type: ndcg_at_3
	value: 42.858000000000004
	- type: ndcg_at_5
	value: 45.644
	- type: precision_at_1
	value: 35.046
	- type: precision_at_10
	value: 9.452
	- type: precision_at_100
	value: 1.429
	- type: precision_at_1000
	value: 0.173
	- type: precision_at_3
	value: 21.346999999999998
	- type: precision_at_5
	value: 15.342
	- type: recall_at_1
	value: 28.038999999999998
	- type: recall_at_10
	value: 64.59700000000001
	- type: recall_at_100
	value: 87.735
	- type: recall_at_1000
	value: 97.41300000000001
	- type: recall_at_3
	value: 47.368
	- type: recall_at_5
	value: 54.93900000000001
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackRetrieval
	config: default
	split: test
	revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
	metrics:
	- type: map_at_1
	value: 28.17291666666667
	- type: map_at_10
	value: 40.025749999999995
	- type: map_at_100
	value: 41.39208333333333
	- type: map_at_1000
	value: 41.499249999999996
	- type: map_at_3
	value: 36.347
	- type: map_at_5
	value: 38.41391666666667
	- type: mrr_at_1
	value: 33.65925
	- type: mrr_at_10
	value: 44.085499999999996
	- type: mrr_at_100
	value: 44.94116666666667
	- type: mrr_at_1000
	value: 44.9855
	- type: mrr_at_3
	value: 41.2815
	- type: mrr_at_5
	value: 42.91491666666666
	- type: ndcg_at_1
	value: 33.65925
	- type: ndcg_at_10
	value: 46.430833333333325
	- type: ndcg_at_100
	value: 51.761
	- type: ndcg_at_1000
	value: 53.50899999999999
	- type: ndcg_at_3
	value: 40.45133333333333
	- type: ndcg_at_5
	value: 43.31483333333334
	- type: precision_at_1
	value: 33.65925
	- type: precision_at_10
	value: 8.4995
	- type: precision_at_100
	value: 1.3210000000000004
	- type: precision_at_1000
	value: 0.16591666666666666
	- type: precision_at_3
	value: 19.165083333333335
	- type: precision_at_5
	value: 13.81816666666667
	- type: recall_at_1
	value: 28.17291666666667
	- type: recall_at_10
	value: 61.12624999999999
	- type: recall_at_100
	value: 83.97266666666667
	- type: recall_at_1000
	value: 95.66550000000001
	- type: recall_at_3
	value: 44.661249999999995
	- type: recall_at_5
	value: 51.983333333333334
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackStatsRetrieval
	config: default
	split: test
	revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
	metrics:
	- type: map_at_1
	value: 24.681
	- type: map_at_10
	value: 34.892
	- type: map_at_100
	value: 35.996
	- type: map_at_1000
	value: 36.083
	- type: map_at_3
	value: 31.491999999999997
	- type: map_at_5
	value: 33.632
	- type: mrr_at_1
	value: 28.528
	- type: mrr_at_10
	value: 37.694
	- type: mrr_at_100
	value: 38.613
	- type: mrr_at_1000
	value: 38.668
	- type: mrr_at_3
	value: 34.714
	- type: mrr_at_5
	value: 36.616
	- type: ndcg_at_1
	value: 28.528
	- type: ndcg_at_10
	value: 40.703
	- type: ndcg_at_100
	value: 45.993
	- type: ndcg_at_1000
	value: 47.847
	- type: ndcg_at_3
	value: 34.622
	- type: ndcg_at_5
	value: 38.035999999999994
	- type: precision_at_1
	value: 28.528
	- type: precision_at_10
	value: 6.902
	- type: precision_at_100
	value: 1.0370000000000001
	- type: precision_at_1000
	value: 0.126
	- type: precision_at_3
	value: 15.798000000000002
	- type: precision_at_5
	value: 11.655999999999999
	- type: recall_at_1
	value: 24.681
	- type: recall_at_10
	value: 55.81
	- type: recall_at_100
	value: 79.785
	- type: recall_at_1000
	value: 92.959
	- type: recall_at_3
	value: 39.074
	- type: recall_at_5
	value: 47.568
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackTexRetrieval
	config: default
	split: test
	revision: 46989137a86843e03a6195de44b09deda022eec7
	metrics:
	- type: map_at_1
	value: 18.627
	- type: map_at_10
	value: 27.872000000000003
	- type: map_at_100
	value: 29.237999999999996
	- type: map_at_1000
	value: 29.363
	- type: map_at_3
	value: 24.751
	- type: map_at_5
	value: 26.521
	- type: mrr_at_1
	value: 23.021
	- type: mrr_at_10
	value: 31.924000000000003
	- type: mrr_at_100
	value: 32.922000000000004
	- type: mrr_at_1000
	value: 32.988
	- type: mrr_at_3
	value: 29.192
	- type: mrr_at_5
	value: 30.798
	- type: ndcg_at_1
	value: 23.021
	- type: ndcg_at_10
	value: 33.535
	- type: ndcg_at_100
	value: 39.732
	- type: ndcg_at_1000
	value: 42.201
	- type: ndcg_at_3
	value: 28.153
	- type: ndcg_at_5
	value: 30.746000000000002
	- type: precision_at_1
	value: 23.021
	- type: precision_at_10
	value: 6.459
	- type: precision_at_100
	value: 1.1320000000000001
	- type: precision_at_1000
	value: 0.153
	- type: precision_at_3
	value: 13.719000000000001
	- type: precision_at_5
	value: 10.193000000000001
	- type: recall_at_1
	value: 18.627
	- type: recall_at_10
	value: 46.463
	- type: recall_at_100
	value: 74.226
	- type: recall_at_1000
	value: 91.28500000000001
	- type: recall_at_3
	value: 31.357000000000003
	- type: recall_at_5
	value: 38.067
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackUnixRetrieval
	config: default
	split: test
	revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
	metrics:
	- type: map_at_1
	value: 31.457
	- type: map_at_10
	value: 42.888
	- type: map_at_100
	value: 44.24
	- type: map_at_1000
	value: 44.327
	- type: map_at_3
	value: 39.588
	- type: map_at_5
	value: 41.423
	- type: mrr_at_1
	value: 37.126999999999995
	- type: mrr_at_10
	value: 47.083000000000006
	- type: mrr_at_100
	value: 47.997
	- type: mrr_at_1000
	value: 48.044
	- type: mrr_at_3
	value: 44.574000000000005
	- type: mrr_at_5
	value: 46.202
	- type: ndcg_at_1
	value: 37.126999999999995
	- type: ndcg_at_10
	value: 48.833
	- type: ndcg_at_100
	value: 54.327000000000005
	- type: ndcg_at_1000
	value: 56.011
	- type: ndcg_at_3
	value: 43.541999999999994
	- type: ndcg_at_5
	value: 46.127
	- type: precision_at_1
	value: 37.126999999999995
	- type: precision_at_10
	value: 8.376999999999999
	- type: precision_at_100
	value: 1.2309999999999999
	- type: precision_at_1000
	value: 0.146
	- type: precision_at_3
	value: 20.211000000000002
	- type: precision_at_5
	value: 14.16
	- type: recall_at_1
	value: 31.457
	- type: recall_at_10
	value: 62.369
	- type: recall_at_100
	value: 85.444
	- type: recall_at_1000
	value: 96.65599999999999
	- type: recall_at_3
	value: 47.961
	- type: recall_at_5
	value: 54.676
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWebmastersRetrieval
	config: default
	split: test
	revision: 160c094312a0e1facb97e55eeddb698c0abe3571
	metrics:
	- type: map_at_1
	value: 27.139999999999997
	- type: map_at_10
	value: 38.801
	- type: map_at_100
	value: 40.549
	- type: map_at_1000
	value: 40.802
	- type: map_at_3
	value: 35.05
	- type: map_at_5
	value: 36.884
	- type: mrr_at_1
	value: 33.004
	- type: mrr_at_10
	value: 43.864
	- type: mrr_at_100
	value: 44.667
	- type: mrr_at_1000
	value: 44.717
	- type: mrr_at_3
	value: 40.777
	- type: mrr_at_5
	value: 42.319
	- type: ndcg_at_1
	value: 33.004
	- type: ndcg_at_10
	value: 46.022
	- type: ndcg_at_100
	value: 51.542
	- type: ndcg_at_1000
	value: 53.742000000000004
	- type: ndcg_at_3
	value: 39.795
	- type: ndcg_at_5
	value: 42.272
	- type: precision_at_1
	value: 33.004
	- type: precision_at_10
	value: 9.012
	- type: precision_at_100
	value: 1.7770000000000001
	- type: precision_at_1000
	value: 0.26
	- type: precision_at_3
	value: 19.038
	- type: precision_at_5
	value: 13.675999999999998
	- type: recall_at_1
	value: 27.139999999999997
	- type: recall_at_10
	value: 60.961
	- type: recall_at_100
	value: 84.451
	- type: recall_at_1000
	value: 98.113
	- type: recall_at_3
	value: 43.001
	- type: recall_at_5
	value: 49.896
	- task:
	type: Retrieval
	dataset:
	type: BeIR/cqadupstack
	name: MTEB CQADupstackWordpressRetrieval
	config: default
	split: test
	revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
	metrics:
	- type: map_at_1
	value: 17.936
	- type: map_at_10
	value: 27.399
	- type: map_at_100
	value: 28.632
	- type: map_at_1000
	value: 28.738000000000003
	- type: map_at_3
	value: 24.456
	- type: map_at_5
	value: 26.06
	- type: mrr_at_1
	value: 19.224
	- type: mrr_at_10
	value: 28.998
	- type: mrr_at_100
	value: 30.11
	- type: mrr_at_1000
	value: 30.177
	- type: mrr_at_3
	value: 26.247999999999998
	- type: mrr_at_5
	value: 27.708
	- type: ndcg_at_1
	value: 19.224
	- type: ndcg_at_10
	value: 32.911
	- type: ndcg_at_100
	value: 38.873999999999995
	- type: ndcg_at_1000
	value: 41.277
	- type: ndcg_at_3
	value: 27.142
	- type: ndcg_at_5
	value: 29.755
	- type: precision_at_1
	value: 19.224
	- type: precision_at_10
	value: 5.6930000000000005
	- type: precision_at_100
	value: 0.9259999999999999
	- type: precision_at_1000
	value: 0.126
	- type: precision_at_3
	value: 12.138
	- type: precision_at_5
	value: 8.909
	- type: recall_at_1
	value: 17.936
	- type: recall_at_10
	value: 48.096
	- type: recall_at_100
	value: 75.389
	- type: recall_at_1000
	value: 92.803
	- type: recall_at_3
	value: 32.812999999999995
	- type: recall_at_5
	value: 38.851
	- task:
	type: Retrieval
	dataset:
	type: mteb/climate-fever
	name: MTEB ClimateFEVER
	config: default
	split: test
	revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
	metrics:
	- type: map_at_1
	value: 22.076999999999998
	- type: map_at_10
	value: 35.44
	- type: map_at_100
	value: 37.651
	- type: map_at_1000
	value: 37.824999999999996
	- type: map_at_3
	value: 30.764999999999997
	- type: map_at_5
	value: 33.26
	- type: mrr_at_1
	value: 50.163000000000004
	- type: mrr_at_10
	value: 61.207
	- type: mrr_at_100
	value: 61.675000000000004
	- type: mrr_at_1000
	value: 61.692
	- type: mrr_at_3
	value: 58.60999999999999
	- type: mrr_at_5
	value: 60.307
	- type: ndcg_at_1
	value: 50.163000000000004
	- type: ndcg_at_10
	value: 45.882
	- type: ndcg_at_100
	value: 53.239999999999995
	- type: ndcg_at_1000
	value: 55.852000000000004
	- type: ndcg_at_3
	value: 40.514
	- type: ndcg_at_5
	value: 42.038
	- type: precision_at_1
	value: 50.163000000000004
	- type: precision_at_10
	value: 13.466000000000001
	- type: precision_at_100
	value: 2.164
	- type: precision_at_1000
	value: 0.266
	- type: precision_at_3
	value: 29.707
	- type: precision_at_5
	value: 21.694
	- type: recall_at_1
	value: 22.076999999999998
	- type: recall_at_10
	value: 50.193
	- type: recall_at_100
	value: 74.993
	- type: recall_at_1000
	value: 89.131
	- type: recall_at_3
	value: 35.472
	- type: recall_at_5
	value: 41.814
	- task:
	type: Retrieval
	dataset:
	type: mteb/dbpedia
	name: MTEB DBPedia
	config: default
	split: test
	revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
	metrics:
	- type: map_at_1
	value: 9.953
	- type: map_at_10
	value: 24.515
	- type: map_at_100
	value: 36.173
	- type: map_at_1000
	value: 38.351
	- type: map_at_3
	value: 16.592000000000002
	- type: map_at_5
	value: 20.036
	- type: mrr_at_1
	value: 74.25
	- type: mrr_at_10
	value: 81.813
	- type: mrr_at_100
	value: 82.006
	- type: mrr_at_1000
	value: 82.011
	- type: mrr_at_3
	value: 80.875
	- type: mrr_at_5
	value: 81.362
	- type: ndcg_at_1
	value: 62.5
	- type: ndcg_at_10
	value: 52.42
	- type: ndcg_at_100
	value: 56.808
	- type: ndcg_at_1000
	value: 63.532999999999994
	- type: ndcg_at_3
	value: 56.654
	- type: ndcg_at_5
	value: 54.18300000000001
	- type: precision_at_1
	value: 74.25
	- type: precision_at_10
	value: 42.699999999999996
	- type: precision_at_100
	value: 13.675
	- type: precision_at_1000
	value: 2.664
	- type: precision_at_3
	value: 60.5
	- type: precision_at_5
	value: 52.800000000000004
	- type: recall_at_1
	value: 9.953
	- type: recall_at_10
	value: 30.253999999999998
	- type: recall_at_100
	value: 62.516000000000005
	- type: recall_at_1000
	value: 84.163
	- type: recall_at_3
	value: 18.13
	- type: recall_at_5
	value: 22.771
	- task:
	type: Classification
	dataset:
	type: mteb/emotion
	name: MTEB EmotionClassification
	config: default
	split: test
	revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
	metrics:
	- type: accuracy
	value: 79.455
	- type: f1
	value: 74.16798697647569
	- task:
	type: Retrieval
	dataset:
	type: mteb/fever
	name: MTEB FEVER
	config: default
	split: test
	revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
	metrics:
	- type: map_at_1
	value: 87.531
	- type: map_at_10
	value: 93.16799999999999
	- type: map_at_100
	value: 93.341
	- type: map_at_1000
	value: 93.349
	- type: map_at_3
	value: 92.444
	- type: map_at_5
	value: 92.865
	- type: mrr_at_1
	value: 94.014
	- type: mrr_at_10
	value: 96.761
	- type: mrr_at_100
	value: 96.762
	- type: mrr_at_1000
	value: 96.762
	- type: mrr_at_3
	value: 96.672
	- type: mrr_at_5
	value: 96.736
	- type: ndcg_at_1
	value: 94.014
	- type: ndcg_at_10
	value: 95.112
	- type: ndcg_at_100
	value: 95.578
	- type: ndcg_at_1000
	value: 95.68900000000001
	- type: ndcg_at_3
	value: 94.392
	- type: ndcg_at_5
	value: 94.72500000000001
	- type: precision_at_1
	value: 94.014
	- type: precision_at_10
	value: 11.065
	- type: precision_at_100
	value: 1.157
	- type: precision_at_1000
	value: 0.11800000000000001
	- type: precision_at_3
	value: 35.259
	- type: precision_at_5
	value: 21.599
	- type: recall_at_1
	value: 87.531
	- type: recall_at_10
	value: 97.356
	- type: recall_at_100
	value: 98.965
	- type: recall_at_1000
	value: 99.607
	- type: recall_at_3
	value: 95.312
	- type: recall_at_5
	value: 96.295
	- task:
	type: Retrieval
	dataset:
	type: mteb/fiqa
	name: MTEB FiQA2018
	config: default
	split: test
	revision: 27a168819829fe9bcd655c2df245fb19452e8e06
	metrics:
	- type: map_at_1
	value: 32.055
	- type: map_at_10
	value: 53.114
	- type: map_at_100
	value: 55.235
	- type: map_at_1000
	value: 55.345
	- type: map_at_3
	value: 45.854
	- type: map_at_5
	value: 50.025
	- type: mrr_at_1
	value: 60.34
	- type: mrr_at_10
	value: 68.804
	- type: mrr_at_100
	value: 69.309
	- type: mrr_at_1000
	value: 69.32199999999999
	- type: mrr_at_3
	value: 66.40899999999999
	- type: mrr_at_5
	value: 67.976
	- type: ndcg_at_1
	value: 60.34
	- type: ndcg_at_10
	value: 62.031000000000006
	- type: ndcg_at_100
	value: 68.00500000000001
	- type: ndcg_at_1000
	value: 69.286
	- type: ndcg_at_3
	value: 56.355999999999995
	- type: ndcg_at_5
	value: 58.687
	- type: precision_at_1
	value: 60.34
	- type: precision_at_10
	value: 17.176
	- type: precision_at_100
	value: 2.36
	- type: precision_at_1000
	value: 0.259
	- type: precision_at_3
	value: 37.14
	- type: precision_at_5
	value: 27.809
	- type: recall_at_1
	value: 32.055
	- type: recall_at_10
	value: 70.91
	- type: recall_at_100
	value: 91.83
	- type: recall_at_1000
	value: 98.871
	- type: recall_at_3
	value: 51.202999999999996
	- type: recall_at_5
	value: 60.563
	- task:
	type: Retrieval
	dataset:
	type: mteb/hotpotqa
	name: MTEB HotpotQA
	config: default
	split: test
	revision: ab518f4d6fcca38d87c25209f94beba119d02014
	metrics:
	- type: map_at_1
	value: 43.68
	- type: map_at_10
	value: 64.389
	- type: map_at_100
	value: 65.24
	- type: map_at_1000
	value: 65.303
	- type: map_at_3
	value: 61.309000000000005
	- type: map_at_5
	value: 63.275999999999996
	- type: mrr_at_1
	value: 87.36
	- type: mrr_at_10
	value: 91.12
	- type: mrr_at_100
	value: 91.227
	- type: mrr_at_1000
	value: 91.229
	- type: mrr_at_3
	value: 90.57600000000001
	- type: mrr_at_5
	value: 90.912
	- type: ndcg_at_1
	value: 87.36
	- type: ndcg_at_10
	value: 73.076
	- type: ndcg_at_100
	value: 75.895
	- type: ndcg_at_1000
	value: 77.049
	- type: ndcg_at_3
	value: 68.929
	- type: ndcg_at_5
	value: 71.28
	- type: precision_at_1
	value: 87.36
	- type: precision_at_10
	value: 14.741000000000001
	- type: precision_at_100
	value: 1.694
	- type: precision_at_1000
	value: 0.185
	- type: precision_at_3
	value: 43.043
	- type: precision_at_5
	value: 27.681
	- type: recall_at_1
	value: 43.68
	- type: recall_at_10
	value: 73.707
	- type: recall_at_100
	value: 84.7
	- type: recall_at_1000
	value: 92.309
	- type: recall_at_3
	value: 64.564
	- type: recall_at_5
	value: 69.203
	- task:
	type: Classification
	dataset:
	type: mteb/imdb
	name: MTEB ImdbClassification
	config: default
	split: test
	revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
	metrics:
	- type: accuracy
	value: 96.75399999999999
	- type: ap
	value: 95.29389839242187
	- type: f1
	value: 96.75348377433475
	- task:
	type: Retrieval
	dataset:
	type: mteb/msmarco
	name: MTEB MSMARCO
	config: default
	split: dev
	revision: c5a29a104738b98a9e76336939199e264163d4a0
	metrics:
	- type: map_at_1
	value: 25.176
	- type: map_at_10
	value: 38.598
	- type: map_at_100
	value: 39.707
	- type: map_at_1000
	value: 39.744
	- type: map_at_3
	value: 34.566
	- type: map_at_5
	value: 36.863
	- type: mrr_at_1
	value: 25.874000000000002
	- type: mrr_at_10
	value: 39.214
	- type: mrr_at_100
	value: 40.251
	- type: mrr_at_1000
	value: 40.281
	- type: mrr_at_3
	value: 35.291
	- type: mrr_at_5
	value: 37.545
	- type: ndcg_at_1
	value: 25.874000000000002
	- type: ndcg_at_10
	value: 45.98
	- type: ndcg_at_100
	value: 51.197
	- type: ndcg_at_1000
	value: 52.073
	- type: ndcg_at_3
	value: 37.785999999999994
	- type: ndcg_at_5
	value: 41.870000000000005
	- type: precision_at_1
	value: 25.874000000000002
	- type: precision_at_10
	value: 7.181
	- type: precision_at_100
	value: 0.979
	- type: precision_at_1000
	value: 0.106
	- type: precision_at_3
	value: 16.051000000000002
	- type: precision_at_5
	value: 11.713
	- type: recall_at_1
	value: 25.176
	- type: recall_at_10
	value: 68.67699999999999
	- type: recall_at_100
	value: 92.55
	- type: recall_at_1000
	value: 99.164
	- type: recall_at_3
	value: 46.372
	- type: recall_at_5
	value: 56.16
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_domain
	name: MTEB MTOPDomainClassification (en)
	config: en
	split: test
	revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
	metrics:
	- type: accuracy
	value: 99.03784769721841
	- type: f1
	value: 98.97791641821495
	- task:
	type: Classification
	dataset:
	type: mteb/mtop_intent
	name: MTEB MTOPIntentClassification (en)
	config: en
	split: test
	revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
	metrics:
	- type: accuracy
	value: 91.88326493388054
	- type: f1
	value: 73.74809928034335
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (en)
	config: en
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 85.41358439811701
	- type: f1
	value: 83.503679460639
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (en)
	config: en
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 89.77135171486215
	- type: f1
	value: 88.89843747468366
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-p2p
	name: MTEB MedrxivClusteringP2P
	config: default
	split: test
	revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
	metrics:
	- type: v_measure
	value: 46.22695362087359
	- task:
	type: Clustering
	dataset:
	type: mteb/medrxiv-clustering-s2s
	name: MTEB MedrxivClusteringS2S
	config: default
	split: test
	revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
	metrics:
	- type: v_measure
	value: 44.132372165849425
	- task:
	type: Reranking
	dataset:
	type: mteb/mind_small
	name: MTEB MindSmallReranking
	config: default
	split: test
	revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
	metrics:
	- type: map
	value: 33.35680810650402
	- type: mrr
	value: 34.72625715637218
	- task:
	type: Retrieval
	dataset:
	type: mteb/nfcorpus
	name: MTEB NFCorpus
	config: default
	split: test
	revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
	metrics:
	- type: map_at_1
	value: 7.165000000000001
	- type: map_at_10
	value: 15.424
	- type: map_at_100
	value: 20.28
	- type: map_at_1000
	value: 22.065
	- type: map_at_3
	value: 11.236
	- type: map_at_5
	value: 13.025999999999998
	- type: mrr_at_1
	value: 51.702999999999996
	- type: mrr_at_10
	value: 59.965
	- type: mrr_at_100
	value: 60.667
	- type: mrr_at_1000
	value: 60.702999999999996
	- type: mrr_at_3
	value: 58.772000000000006
	- type: mrr_at_5
	value: 59.267
	- type: ndcg_at_1
	value: 49.536
	- type: ndcg_at_10
	value: 40.6
	- type: ndcg_at_100
	value: 37.848
	- type: ndcg_at_1000
	value: 46.657
	- type: ndcg_at_3
	value: 46.117999999999995
	- type: ndcg_at_5
	value: 43.619
	- type: precision_at_1
	value: 51.393
	- type: precision_at_10
	value: 30.31
	- type: precision_at_100
	value: 9.972
	- type: precision_at_1000
	value: 2.329
	- type: precision_at_3
	value: 43.137
	- type: precision_at_5
	value: 37.585
	- type: recall_at_1
	value: 7.165000000000001
	- type: recall_at_10
	value: 19.689999999999998
	- type: recall_at_100
	value: 39.237
	- type: recall_at_1000
	value: 71.417
	- type: recall_at_3
	value: 12.247
	- type: recall_at_5
	value: 14.902999999999999
	- task:
	type: Retrieval
	dataset:
	type: mteb/nq
	name: MTEB NQ
	config: default
	split: test
	revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
	metrics:
	- type: map_at_1
	value: 42.653999999999996
	- type: map_at_10
	value: 59.611999999999995
	- type: map_at_100
	value: 60.32300000000001
	- type: map_at_1000
	value: 60.336
	- type: map_at_3
	value: 55.584999999999994
	- type: map_at_5
	value: 58.19
	- type: mrr_at_1
	value: 47.683
	- type: mrr_at_10
	value: 62.06700000000001
	- type: mrr_at_100
	value: 62.537
	- type: mrr_at_1000
	value: 62.544999999999995
	- type: mrr_at_3
	value: 59.178
	- type: mrr_at_5
	value: 61.034
	- type: ndcg_at_1
	value: 47.654
	- type: ndcg_at_10
	value: 67.001
	- type: ndcg_at_100
	value: 69.73899999999999
	- type: ndcg_at_1000
	value: 69.986
	- type: ndcg_at_3
	value: 59.95700000000001
	- type: ndcg_at_5
	value: 64.025
	- type: precision_at_1
	value: 47.654
	- type: precision_at_10
	value: 10.367999999999999
	- type: precision_at_100
	value: 1.192
	- type: precision_at_1000
	value: 0.121
	- type: precision_at_3
	value: 26.651000000000003
	- type: precision_at_5
	value: 18.459
	- type: recall_at_1
	value: 42.653999999999996
	- type: recall_at_10
	value: 86.619
	- type: recall_at_100
	value: 98.04899999999999
	- type: recall_at_1000
	value: 99.812
	- type: recall_at_3
	value: 68.987
	- type: recall_at_5
	value: 78.158
	- task:
	type: Retrieval
	dataset:
	type: mteb/quora
	name: MTEB QuoraRetrieval
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 72.538
	- type: map_at_10
	value: 86.702
	- type: map_at_100
	value: 87.31
	- type: map_at_1000
	value: 87.323
	- type: map_at_3
	value: 83.87
	- type: map_at_5
	value: 85.682
	- type: mrr_at_1
	value: 83.31
	- type: mrr_at_10
	value: 89.225
	- type: mrr_at_100
	value: 89.30399999999999
	- type: mrr_at_1000
	value: 89.30399999999999
	- type: mrr_at_3
	value: 88.44300000000001
	- type: mrr_at_5
	value: 89.005
	- type: ndcg_at_1
	value: 83.32000000000001
	- type: ndcg_at_10
	value: 90.095
	- type: ndcg_at_100
	value: 91.12
	- type: ndcg_at_1000
	value: 91.179
	- type: ndcg_at_3
	value: 87.606
	- type: ndcg_at_5
	value: 89.031
	- type: precision_at_1
	value: 83.32000000000001
	- type: precision_at_10
	value: 13.641
	- type: precision_at_100
	value: 1.541
	- type: precision_at_1000
	value: 0.157
	- type: precision_at_3
	value: 38.377
	- type: precision_at_5
	value: 25.162000000000003
	- type: recall_at_1
	value: 72.538
	- type: recall_at_10
	value: 96.47200000000001
	- type: recall_at_100
	value: 99.785
	- type: recall_at_1000
	value: 99.99900000000001
	- type: recall_at_3
	value: 89.278
	- type: recall_at_5
	value: 93.367
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering
	name: MTEB RedditClustering
	config: default
	split: test
	revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
	metrics:
	- type: v_measure
	value: 73.55219145406065
	- task:
	type: Clustering
	dataset:
	type: mteb/reddit-clustering-p2p
	name: MTEB RedditClusteringP2P
	config: default
	split: test
	revision: 282350215ef01743dc01b456c7f5241fa8937f16
	metrics:
	- type: v_measure
	value: 74.13437105242755
	- task:
	type: Retrieval
	dataset:
	type: mteb/scidocs
	name: MTEB SCIDOCS
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 6.873
	- type: map_at_10
	value: 17.944
	- type: map_at_100
	value: 21.171
	- type: map_at_1000
	value: 21.528
	- type: map_at_3
	value: 12.415
	- type: map_at_5
	value: 15.187999999999999
	- type: mrr_at_1
	value: 33.800000000000004
	- type: mrr_at_10
	value: 46.455
	- type: mrr_at_100
	value: 47.378
	- type: mrr_at_1000
	value: 47.394999999999996
	- type: mrr_at_3
	value: 42.367
	- type: mrr_at_5
	value: 44.972
	- type: ndcg_at_1
	value: 33.800000000000004
	- type: ndcg_at_10
	value: 28.907
	- type: ndcg_at_100
	value: 39.695
	- type: ndcg_at_1000
	value: 44.582
	- type: ndcg_at_3
	value: 26.949
	- type: ndcg_at_5
	value: 23.988
	- type: precision_at_1
	value: 33.800000000000004
	- type: precision_at_10
	value: 15.079999999999998
	- type: precision_at_100
	value: 3.056
	- type: precision_at_1000
	value: 0.42100000000000004
	- type: precision_at_3
	value: 25.167
	- type: precision_at_5
	value: 21.26
	- type: recall_at_1
	value: 6.873
	- type: recall_at_10
	value: 30.568
	- type: recall_at_100
	value: 62.062
	- type: recall_at_1000
	value: 85.37700000000001
	- type: recall_at_3
	value: 15.312999999999999
	- type: recall_at_5
	value: 21.575
	- task:
	type: STS
	dataset:
	type: mteb/sickr-sts
	name: MTEB SICK-R
	config: default
	split: test
	revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
	metrics:
	- type: cos_sim_pearson
	value: 82.37009118256057
	- type: cos_sim_spearman
	value: 79.27986395671529
	- type: euclidean_pearson
	value: 79.18037715442115
	- type: euclidean_spearman
	value: 79.28004791561621
	- type: manhattan_pearson
	value: 79.34062972800541
	- type: manhattan_spearman
	value: 79.43106695543402
	- task:
	type: STS
	dataset:
	type: mteb/sts12-sts
	name: MTEB STS12
	config: default
	split: test
	revision: a0d554a64d88156834ff5ae9920b964011b16384
	metrics:
	- type: cos_sim_pearson
	value: 87.48474767383833
	- type: cos_sim_spearman
	value: 79.54505388752513
	- type: euclidean_pearson
	value: 83.43282704179565
	- type: euclidean_spearman
	value: 79.54579919925405
	- type: manhattan_pearson
	value: 83.77564492427952
	- type: manhattan_spearman
	value: 79.84558396989286
	- task:
	type: STS
	dataset:
	type: mteb/sts13-sts
	name: MTEB STS13
	config: default
	split: test
	revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
	metrics:
	- type: cos_sim_pearson
	value: 88.803698035802
	- type: cos_sim_spearman
	value: 88.83451367754881
	- type: euclidean_pearson
	value: 88.28939285711628
	- type: euclidean_spearman
	value: 88.83528996073112
	- type: manhattan_pearson
	value: 88.28017412671795
	- type: manhattan_spearman
	value: 88.9228828016344
	- task:
	type: STS
	dataset:
	type: mteb/sts14-sts
	name: MTEB STS14
	config: default
	split: test
	revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
	metrics:
	- type: cos_sim_pearson
	value: 85.27469288153428
	- type: cos_sim_spearman
	value: 83.87477064876288
	- type: euclidean_pearson
	value: 84.2601737035379
	- type: euclidean_spearman
	value: 83.87431082479074
	- type: manhattan_pearson
	value: 84.3621547772745
	- type: manhattan_spearman
	value: 84.12094375000423
	- task:
	type: STS
	dataset:
	type: mteb/sts15-sts
	name: MTEB STS15
	config: default
	split: test
	revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
	metrics:
	- type: cos_sim_pearson
	value: 88.12749863201587
	- type: cos_sim_spearman
	value: 88.54287568368565
	- type: euclidean_pearson
	value: 87.90429700607999
	- type: euclidean_spearman
	value: 88.5437689576261
	- type: manhattan_pearson
	value: 88.19276653356833
	- type: manhattan_spearman
	value: 88.99995393814679
	- task:
	type: STS
	dataset:
	type: mteb/sts16-sts
	name: MTEB STS16
	config: default
	split: test
	revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
	metrics:
	- type: cos_sim_pearson
	value: 85.68398747560902
	- type: cos_sim_spearman
	value: 86.48815303460574
	- type: euclidean_pearson
	value: 85.52356631237954
	- type: euclidean_spearman
	value: 86.486391949551
	- type: manhattan_pearson
	value: 85.67267981761788
	- type: manhattan_spearman
	value: 86.7073696332485
	- task:
	type: STS
	dataset:
	type: mteb/sts17-crosslingual-sts
	name: MTEB STS17 (en-en)
	config: en-en
	split: test
	revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
	metrics:
	- type: cos_sim_pearson
	value: 88.9057107443124
	- type: cos_sim_spearman
	value: 88.7312168757697
	- type: euclidean_pearson
	value: 88.72810439714794
	- type: euclidean_spearman
	value: 88.71976185854771
	- type: manhattan_pearson
	value: 88.50433745949111
	- type: manhattan_spearman
	value: 88.51726175544195
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (en)
	config: en
	split: test
	revision: eea2b4fe26a775864c896887d910b76a8098ad3f
	metrics:
	- type: cos_sim_pearson
	value: 67.59391795109886
	- type: cos_sim_spearman
	value: 66.87613008631367
	- type: euclidean_pearson
	value: 69.23198488262217
	- type: euclidean_spearman
	value: 66.85427723013692
	- type: manhattan_pearson
	value: 69.50730124841084
	- type: manhattan_spearman
	value: 67.10404669820792
	- task:
	type: STS
	dataset:
	type: mteb/stsbenchmark-sts
	name: MTEB STSBenchmark
	config: default
	split: test
	revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
	metrics:
	- type: cos_sim_pearson
	value: 87.0820605344619
	- type: cos_sim_spearman
	value: 86.8518089863434
	- type: euclidean_pearson
	value: 86.31087134689284
	- type: euclidean_spearman
	value: 86.8518520517941
	- type: manhattan_pearson
	value: 86.47203796160612
	- type: manhattan_spearman
	value: 87.1080149734421
	- task:
	type: Reranking
	dataset:
	type: mteb/scidocs-reranking
	name: MTEB SciDocsRR
	config: default
	split: test
	revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
	metrics:
	- type: map
	value: 89.09255369305481
	- type: mrr
	value: 97.10323445617563
	- task:
	type: Retrieval
	dataset:
	type: mteb/scifact
	name: MTEB SciFact
	config: default
	split: test
	revision: 0228b52cf27578f30900b9e5271d331663a030d7
	metrics:
	- type: map_at_1
	value: 61.260999999999996
	- type: map_at_10
	value: 74.043
	- type: map_at_100
	value: 74.37700000000001
	- type: map_at_1000
	value: 74.384
	- type: map_at_3
	value: 71.222
	- type: map_at_5
	value: 72.875
	- type: mrr_at_1
	value: 64.333
	- type: mrr_at_10
	value: 74.984
	- type: mrr_at_100
	value: 75.247
	- type: mrr_at_1000
	value: 75.25500000000001
	- type: mrr_at_3
	value: 73.167
	- type: mrr_at_5
	value: 74.35000000000001
	- type: ndcg_at_1
	value: 64.333
	- type: ndcg_at_10
	value: 79.06
	- type: ndcg_at_100
	value: 80.416
	- type: ndcg_at_1000
	value: 80.55600000000001
	- type: ndcg_at_3
	value: 74.753
	- type: ndcg_at_5
	value: 76.97500000000001
	- type: precision_at_1
	value: 64.333
	- type: precision_at_10
	value: 10.567
	- type: precision_at_100
	value: 1.1199999999999999
	- type: precision_at_1000
	value: 0.11299999999999999
	- type: precision_at_3
	value: 29.889
	- type: precision_at_5
	value: 19.533
	- type: recall_at_1
	value: 61.260999999999996
	- type: recall_at_10
	value: 93.167
	- type: recall_at_100
	value: 99.0
	- type: recall_at_1000
	value: 100.0
	- type: recall_at_3
	value: 81.667
	- type: recall_at_5
	value: 87.394
	- task:
	type: PairClassification
	dataset:
	type: mteb/sprintduplicatequestions-pairclassification
	name: MTEB SprintDuplicateQuestions
	config: default
	split: test
	revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
	metrics:
	- type: cos_sim_accuracy
	value: 99.71980198019801
	- type: cos_sim_ap
	value: 92.81616007802704
	- type: cos_sim_f1
	value: 85.17548454688318
	- type: cos_sim_precision
	value: 89.43894389438944
	- type: cos_sim_recall
	value: 81.3
	- type: dot_accuracy
	value: 99.71980198019801
	- type: dot_ap
	value: 92.81398760591358
	- type: dot_f1
	value: 85.17548454688318
	- type: dot_precision
	value: 89.43894389438944
	- type: dot_recall
	value: 81.3
	- type: euclidean_accuracy
	value: 99.71980198019801
	- type: euclidean_ap
	value: 92.81560637245072
	- type: euclidean_f1
	value: 85.17548454688318
	- type: euclidean_precision
	value: 89.43894389438944
	- type: euclidean_recall
	value: 81.3
	- type: manhattan_accuracy
	value: 99.73069306930694
	- type: manhattan_ap
	value: 93.14005487480794
	- type: manhattan_f1
	value: 85.56263269639068
	- type: manhattan_precision
	value: 91.17647058823529
	- type: manhattan_recall
	value: 80.60000000000001
	- type: max_accuracy
	value: 99.73069306930694
	- type: max_ap
	value: 93.14005487480794
	- type: max_f1
	value: 85.56263269639068
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering
	name: MTEB StackExchangeClustering
	config: default
	split: test
	revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
	metrics:
	- type: v_measure
	value: 79.86443362395185
	- task:
	type: Clustering
	dataset:
	type: mteb/stackexchange-clustering-p2p
	name: MTEB StackExchangeClusteringP2P
	config: default
	split: test
	revision: 815ca46b2622cec33ccafc3735d572c266efdb44
	metrics:
	- type: v_measure
	value: 49.40897096662564
	- task:
	type: Reranking
	dataset:
	type: mteb/stackoverflowdupquestions-reranking
	name: MTEB StackOverflowDupQuestions
	config: default
	split: test
	revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
	metrics:
	- type: map
	value: 55.66040806627947
	- type: mrr
	value: 56.58670475766064
	- task:
	type: Summarization
	dataset:
	type: mteb/summeval
	name: MTEB SummEval
	config: default
	split: test
	revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
	metrics:
	- type: cos_sim_pearson
	value: 31.51015090598575
	- type: cos_sim_spearman
	value: 31.35016454939226
	- type: dot_pearson
	value: 31.5150068731
	- type: dot_spearman
	value: 31.34790869023487
	- task:
	type: Retrieval
	dataset:
	type: mteb/trec-covid
	name: MTEB TRECCOVID
	config: default
	split: test
	revision: None
	metrics:
	- type: map_at_1
	value: 0.254
	- type: map_at_10
	value: 2.064
	- type: map_at_100
	value: 12.909
	- type: map_at_1000
	value: 31.761
	- type: map_at_3
	value: 0.738
	- type: map_at_5
	value: 1.155
	- 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_3
	value: 98.0
	- type: mrr_at_5
	value: 98.0
	- type: ndcg_at_1
	value: 93.0
	- type: ndcg_at_10
	value: 82.258
	- type: ndcg_at_100
	value: 64.34
	- type: ndcg_at_1000
	value: 57.912
	- type: ndcg_at_3
	value: 90.827
	- type: ndcg_at_5
	value: 86.79
	- type: precision_at_1
	value: 96.0
	- type: precision_at_10
	value: 84.8
	- type: precision_at_100
	value: 66.0
	- type: precision_at_1000
	value: 25.356
	- type: precision_at_3
	value: 94.667
	- type: precision_at_5
	value: 90.4
	- type: recall_at_1
	value: 0.254
	- type: recall_at_10
	value: 2.1950000000000003
	- type: recall_at_100
	value: 16.088
	- type: recall_at_1000
	value: 54.559000000000005
	- type: recall_at_3
	value: 0.75
	- type: recall_at_5
	value: 1.191
	- task:
	type: Retrieval
	dataset:
	type: mteb/touche2020
	name: MTEB Touche2020
	config: default
	split: test
	revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
	metrics:
	- type: map_at_1
	value: 2.976
	- type: map_at_10
	value: 11.389000000000001
	- type: map_at_100
	value: 18.429000000000002
	- type: map_at_1000
	value: 20.113
	- type: map_at_3
	value: 6.483
	- type: map_at_5
	value: 8.770999999999999
	- type: mrr_at_1
	value: 40.816
	- type: mrr_at_10
	value: 58.118
	- type: mrr_at_100
	value: 58.489999999999995
	- type: mrr_at_1000
	value: 58.489999999999995
	- type: mrr_at_3
	value: 53.061
	- type: mrr_at_5
	value: 57.041
	- type: ndcg_at_1
	value: 40.816
	- type: ndcg_at_10
	value: 30.567
	- type: ndcg_at_100
	value: 42.44
	- type: ndcg_at_1000
	value: 53.480000000000004
	- type: ndcg_at_3
	value: 36.016
	- type: ndcg_at_5
	value: 34.257
	- type: precision_at_1
	value: 42.857
	- type: precision_at_10
	value: 25.714
	- type: precision_at_100
	value: 8.429
	- type: precision_at_1000
	value: 1.5939999999999999
	- type: precision_at_3
	value: 36.735
	- type: precision_at_5
	value: 33.878
	- type: recall_at_1
	value: 2.976
	- type: recall_at_10
	value: 17.854999999999997
	- type: recall_at_100
	value: 51.833
	- type: recall_at_1000
	value: 86.223
	- type: recall_at_3
	value: 7.887
	- type: recall_at_5
	value: 12.026
	- task:
	type: Classification
	dataset:
	type: mteb/toxic_conversations_50k
	name: MTEB ToxicConversationsClassification
	config: default
	split: test
	revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
	metrics:
	- type: accuracy
	value: 85.1174
	- type: ap
	value: 30.169441069345748
	- type: f1
	value: 69.79254701873245
	- task:
	type: Classification
	dataset:
	type: mteb/tweet_sentiment_extraction
	name: MTEB TweetSentimentExtractionClassification
	config: default
	split: test
	revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
	metrics:
	- type: accuracy
	value: 72.58347481607245
	- type: f1
	value: 72.74877295564937
	- task:
	type: Clustering
	dataset:
	type: mteb/twentynewsgroups-clustering
	name: MTEB TwentyNewsgroupsClustering
	config: default
	split: test
	revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
	metrics:
	- type: v_measure
	value: 53.90586138221305
	- task:
	type: PairClassification
	dataset:
	type: mteb/twittersemeval2015-pairclassification
	name: MTEB TwitterSemEval2015
	config: default
	split: test
	revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
	metrics:
	- type: cos_sim_accuracy
	value: 87.35769207844072
	- type: cos_sim_ap
	value: 77.9645072410354
	- type: cos_sim_f1
	value: 71.32352941176471
	- type: cos_sim_precision
	value: 66.5903890160183
	- type: cos_sim_recall
	value: 76.78100263852242
	- type: dot_accuracy
	value: 87.37557370209214
	- type: dot_ap
	value: 77.96250046429908
	- type: dot_f1
	value: 71.28932757557064
	- type: dot_precision
	value: 66.95249130938586
	- type: dot_recall
	value: 76.22691292875989
	- type: euclidean_accuracy
	value: 87.35173153722357
	- type: euclidean_ap
	value: 77.96520460741593
	- type: euclidean_f1
	value: 71.32470733210104
	- type: euclidean_precision
	value: 66.91329479768785
	- type: euclidean_recall
	value: 76.35883905013192
	- type: manhattan_accuracy
	value: 87.25636287774931
	- type: manhattan_ap
	value: 77.77752485611796
	- type: manhattan_f1
	value: 71.18148599269183
	- type: manhattan_precision
	value: 66.10859728506787
	- type: manhattan_recall
	value: 77.0976253298153
	- type: max_accuracy
	value: 87.37557370209214
	- type: max_ap
	value: 77.96520460741593
	- type: max_f1
	value: 71.32470733210104
	- task:
	type: PairClassification
	dataset:
	type: mteb/twitterurlcorpus-pairclassification
	name: MTEB TwitterURLCorpus
	config: default
	split: test
	revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
	metrics:
	- type: cos_sim_accuracy
	value: 89.38176737687739
	- type: cos_sim_ap
	value: 86.58811861657401
	- type: cos_sim_f1
	value: 79.09430644097604
	- type: cos_sim_precision
	value: 75.45085977911366
	- type: cos_sim_recall
	value: 83.10748383122882
	- type: dot_accuracy
	value: 89.38370784336554
	- type: dot_ap
	value: 86.58840606004333
	- type: dot_f1
	value: 79.10179860068133
	- type: dot_precision
	value: 75.44546153308643
	- type: dot_recall
	value: 83.13058207576223
	- type: euclidean_accuracy
	value: 89.38564830985369
	- type: euclidean_ap
	value: 86.58820721061164
	- type: euclidean_f1
	value: 79.09070942235888
	- type: euclidean_precision
	value: 75.38729937194697
	- type: euclidean_recall
	value: 83.17677856482906
	- type: manhattan_accuracy
	value: 89.40699344122326
	- type: manhattan_ap
	value: 86.60631843011362
	- type: manhattan_f1
	value: 79.14949970570925
	- type: manhattan_precision
	value: 75.78191039729502
	- type: manhattan_recall
	value: 82.83030489682784
	- type: max_accuracy
	value: 89.40699344122326
	- type: max_ap
	value: 86.60631843011362
	- type: max_f1
	value: 79.14949970570925
	- task:
	type: STS
	dataset:
	type: C-MTEB/AFQMC
	name: MTEB AFQMC
	config: default
	split: validation
	revision: b44c3b011063adb25877c13823db83bb193913c4
	metrics:
	- type: cos_sim_pearson
	value: 65.58442135663871
	- type: cos_sim_spearman
	value: 72.2538631361313
	- type: euclidean_pearson
	value: 70.97255486607429
	- type: euclidean_spearman
	value: 72.25374250228647
	- type: manhattan_pearson
	value: 70.83250199989911
	- type: manhattan_spearman
	value: 72.14819496536272
	- task:
	type: STS
	dataset:
	type: C-MTEB/ATEC
	name: MTEB ATEC
	config: default
	split: test
	revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
	metrics:
	- type: cos_sim_pearson
	value: 59.99478404929932
	- type: cos_sim_spearman
	value: 62.61836216999812
	- type: euclidean_pearson
	value: 66.86429811933593
	- type: euclidean_spearman
	value: 62.6183520374191
	- type: manhattan_pearson
	value: 66.8063778911633
	- type: manhattan_spearman
	value: 62.569607573241115
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_reviews_multi
	name: MTEB AmazonReviewsClassification (zh)
	config: zh
	split: test
	revision: 1399c76144fd37290681b995c656ef9b2e06e26d
	metrics:
	- type: accuracy
	value: 53.98400000000001
	- type: f1
	value: 51.21447361350723
	- task:
	type: STS
	dataset:
	type: C-MTEB/BQ
	name: MTEB BQ
	config: default
	split: test
	revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
	metrics:
	- type: cos_sim_pearson
	value: 79.11941660686553
	- type: cos_sim_spearman
	value: 81.25029594540435
	- type: euclidean_pearson
	value: 82.06973504238826
	- type: euclidean_spearman
	value: 81.2501989488524
	- type: manhattan_pearson
	value: 82.10094630392753
	- type: manhattan_spearman
	value: 81.27987244392389
	- task:
	type: Clustering
	dataset:
	type: C-MTEB/CLSClusteringP2P
	name: MTEB CLSClusteringP2P
	config: default
	split: test
	revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
	metrics:
	- type: v_measure
	value: 47.07270168705156
	- task:
	type: Clustering
	dataset:
	type: C-MTEB/CLSClusteringS2S
	name: MTEB CLSClusteringS2S
	config: default
	split: test
	revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
	metrics:
	- type: v_measure
	value: 45.98511703185043
	- task:
	type: Reranking
	dataset:
	type: C-MTEB/CMedQAv1-reranking
	name: MTEB CMedQAv1
	config: default
	split: test
	revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
	metrics:
	- type: map
	value: 88.19895157194931
	- type: mrr
	value: 90.21424603174603
	- task:
	type: Reranking
	dataset:
	type: C-MTEB/CMedQAv2-reranking
	name: MTEB CMedQAv2
	config: default
	split: test
	revision: 23d186750531a14a0357ca22cd92d712fd512ea0
	metrics:
	- type: map
	value: 88.03317320980119
	- type: mrr
	value: 89.9461507936508
	- task:
	type: Retrieval
	dataset:
	type: C-MTEB/CmedqaRetrieval
	name: MTEB CmedqaRetrieval
	config: default
	split: dev
	revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
	metrics:
	- type: map_at_1
	value: 29.037000000000003
	- type: map_at_10
	value: 42.001
	- type: map_at_100
	value: 43.773
	- type: map_at_1000
	value: 43.878
	- type: map_at_3
	value: 37.637
	- type: map_at_5
	value: 40.034
	- type: mrr_at_1
	value: 43.136
	- type: mrr_at_10
	value: 51.158
	- type: mrr_at_100
	value: 52.083
	- type: mrr_at_1000
	value: 52.12
	- type: mrr_at_3
	value: 48.733
	- type: mrr_at_5
	value: 50.025
	- type: ndcg_at_1
	value: 43.136
	- type: ndcg_at_10
	value: 48.685
	- type: ndcg_at_100
	value: 55.513
	- type: ndcg_at_1000
	value: 57.242000000000004
	- type: ndcg_at_3
	value: 43.329
	- type: ndcg_at_5
	value: 45.438
	- type: precision_at_1
	value: 43.136
	- type: precision_at_10
	value: 10.56
	- type: precision_at_100
	value: 1.6129999999999998
	- type: precision_at_1000
	value: 0.184
	- type: precision_at_3
	value: 24.064
	- type: precision_at_5
	value: 17.269000000000002
	- type: recall_at_1
	value: 29.037000000000003
	- type: recall_at_10
	value: 59.245000000000005
	- type: recall_at_100
	value: 87.355
	- type: recall_at_1000
	value: 98.74000000000001
	- type: recall_at_3
	value: 42.99
	- type: recall_at_5
	value: 49.681999999999995
	- task:
	type: PairClassification
	dataset:
	type: C-MTEB/CMNLI
	name: MTEB Cmnli
	config: default
	split: validation
	revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
	metrics:
	- type: cos_sim_accuracy
	value: 82.68190018039687
	- type: cos_sim_ap
	value: 90.18017125327886
	- type: cos_sim_f1
	value: 83.64080906868193
	- type: cos_sim_precision
	value: 79.7076890489303
	- type: cos_sim_recall
	value: 87.98223053542202
	- type: dot_accuracy
	value: 82.68190018039687
	- type: dot_ap
	value: 90.18782350103646
	- type: dot_f1
	value: 83.64242087729039
	- type: dot_precision
	value: 79.65313028764805
	- type: dot_recall
	value: 88.05237315875614
	- type: euclidean_accuracy
	value: 82.68190018039687
	- type: euclidean_ap
	value: 90.1801957900632
	- type: euclidean_f1
	value: 83.63636363636364
	- type: euclidean_precision
	value: 79.52772506852203
	- type: euclidean_recall
	value: 88.19265840542437
	- type: manhattan_accuracy
	value: 82.14070956103427
	- type: manhattan_ap
	value: 89.96178420101427
	- type: manhattan_f1
	value: 83.21087838578791
	- type: manhattan_precision
	value: 78.35605121850475
	- type: manhattan_recall
	value: 88.70703764320785
	- type: max_accuracy
	value: 82.68190018039687
	- type: max_ap
	value: 90.18782350103646
	- type: max_f1
	value: 83.64242087729039
	- task:
	type: Retrieval
	dataset:
	type: C-MTEB/CovidRetrieval
	name: MTEB CovidRetrieval
	config: default
	split: dev
	revision: 1271c7809071a13532e05f25fb53511ffce77117
	metrics:
	- type: map_at_1
	value: 72.234
	- type: map_at_10
	value: 80.10000000000001
	- type: map_at_100
	value: 80.36
	- type: map_at_1000
	value: 80.363
	- type: map_at_3
	value: 78.315
	- type: map_at_5
	value: 79.607
	- type: mrr_at_1
	value: 72.392
	- type: mrr_at_10
	value: 80.117
	- type: mrr_at_100
	value: 80.36999999999999
	- type: mrr_at_1000
	value: 80.373
	- type: mrr_at_3
	value: 78.469
	- type: mrr_at_5
	value: 79.633
	- type: ndcg_at_1
	value: 72.392
	- type: ndcg_at_10
	value: 83.651
	- type: ndcg_at_100
	value: 84.749
	- type: ndcg_at_1000
	value: 84.83000000000001
	- type: ndcg_at_3
	value: 80.253
	- type: ndcg_at_5
	value: 82.485
	- type: precision_at_1
	value: 72.392
	- type: precision_at_10
	value: 9.557
	- type: precision_at_100
	value: 1.004
	- type: precision_at_1000
	value: 0.101
	- type: precision_at_3
	value: 28.732000000000003
	- type: precision_at_5
	value: 18.377
	- type: recall_at_1
	value: 72.234
	- type: recall_at_10
	value: 94.573
	- type: recall_at_100
	value: 99.368
	- type: recall_at_1000
	value: 100.0
	- type: recall_at_3
	value: 85.669
	- type: recall_at_5
	value: 91.01700000000001
	- task:
	type: Retrieval
	dataset:
	type: C-MTEB/DuRetrieval
	name: MTEB DuRetrieval
	config: default
	split: dev
	revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
	metrics:
	- type: map_at_1
	value: 26.173999999999996
	- type: map_at_10
	value: 80.04
	- type: map_at_100
	value: 82.94500000000001
	- type: map_at_1000
	value: 82.98100000000001
	- type: map_at_3
	value: 55.562999999999995
	- type: map_at_5
	value: 69.89800000000001
	- type: mrr_at_1
	value: 89.5
	- type: mrr_at_10
	value: 92.996
	- type: mrr_at_100
	value: 93.06400000000001
	- type: mrr_at_1000
	value: 93.065
	- type: mrr_at_3
	value: 92.658
	- type: mrr_at_5
	value: 92.84599999999999
	- type: ndcg_at_1
	value: 89.5
	- type: ndcg_at_10
	value: 87.443
	- type: ndcg_at_100
	value: 90.253
	- type: ndcg_at_1000
	value: 90.549
	- type: ndcg_at_3
	value: 85.874
	- type: ndcg_at_5
	value: 84.842
	- type: precision_at_1
	value: 89.5
	- type: precision_at_10
	value: 41.805
	- type: precision_at_100
	value: 4.827
	- type: precision_at_1000
	value: 0.49
	- type: precision_at_3
	value: 76.85
	- type: precision_at_5
	value: 64.8
	- type: recall_at_1
	value: 26.173999999999996
	- type: recall_at_10
	value: 89.101
	- type: recall_at_100
	value: 98.08099999999999
	- type: recall_at_1000
	value: 99.529
	- type: recall_at_3
	value: 57.902
	- type: recall_at_5
	value: 74.602
	- task:
	type: Retrieval
	dataset:
	type: C-MTEB/EcomRetrieval
	name: MTEB EcomRetrieval
	config: default
	split: dev
	revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
	metrics:
	- type: map_at_1
	value: 56.10000000000001
	- type: map_at_10
	value: 66.15299999999999
	- type: map_at_100
	value: 66.625
	- type: map_at_1000
	value: 66.636
	- type: map_at_3
	value: 63.632999999999996
	- type: map_at_5
	value: 65.293
	- type: mrr_at_1
	value: 56.10000000000001
	- type: mrr_at_10
	value: 66.15299999999999
	- type: mrr_at_100
	value: 66.625
	- type: mrr_at_1000
	value: 66.636
	- type: mrr_at_3
	value: 63.632999999999996
	- type: mrr_at_5
	value: 65.293
	- type: ndcg_at_1
	value: 56.10000000000001
	- type: ndcg_at_10
	value: 71.146
	- type: ndcg_at_100
	value: 73.27799999999999
	- type: ndcg_at_1000
	value: 73.529
	- type: ndcg_at_3
	value: 66.09
	- type: ndcg_at_5
	value: 69.08999999999999
	- type: precision_at_1
	value: 56.10000000000001
	- type: precision_at_10
	value: 8.68
	- type: precision_at_100
	value: 0.964
	- type: precision_at_1000
	value: 0.098
	- type: precision_at_3
	value: 24.4
	- type: precision_at_5
	value: 16.1
	- type: recall_at_1
	value: 56.10000000000001
	- type: recall_at_10
	value: 86.8
	- type: recall_at_100
	value: 96.39999999999999
	- type: recall_at_1000
	value: 98.3
	- type: recall_at_3
	value: 73.2
	- type: recall_at_5
	value: 80.5
	- task:
	type: Classification
	dataset:
	type: C-MTEB/IFlyTek-classification
	name: MTEB IFlyTek
	config: default
	split: validation
	revision: 421605374b29664c5fc098418fe20ada9bd55f8a
	metrics:
	- type: accuracy
	value: 54.52096960369373
	- type: f1
	value: 40.930845295808695
	- task:
	type: Classification
	dataset:
	type: C-MTEB/JDReview-classification
	name: MTEB JDReview
	config: default
	split: test
	revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
	metrics:
	- type: accuracy
	value: 86.51031894934334
	- type: ap
	value: 55.9516014323483
	- type: f1
	value: 81.54813679326381
	- task:
	type: STS
	dataset:
	type: C-MTEB/LCQMC
	name: MTEB LCQMC
	config: default
	split: test
	revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
	metrics:
	- type: cos_sim_pearson
	value: 69.67437838574276
	- type: cos_sim_spearman
	value: 73.81314174653045
	- type: euclidean_pearson
	value: 72.63430276680275
	- type: euclidean_spearman
	value: 73.81358736777001
	- type: manhattan_pearson
	value: 72.58743833842829
	- type: manhattan_spearman
	value: 73.7590419009179
	- task:
	type: Reranking
	dataset:
	type: C-MTEB/Mmarco-reranking
	name: MTEB MMarcoReranking
	config: default
	split: dev
	revision: None
	metrics:
	- type: map
	value: 31.648613483640254
	- type: mrr
	value: 30.37420634920635
	- task:
	type: Retrieval
	dataset:
	type: C-MTEB/MMarcoRetrieval
	name: MTEB MMarcoRetrieval
	config: default
	split: dev
	revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
	metrics:
	- type: map_at_1
	value: 73.28099999999999
	- type: map_at_10
	value: 81.977
	- type: map_at_100
	value: 82.222
	- type: map_at_1000
	value: 82.22699999999999
	- type: map_at_3
	value: 80.441
	- type: map_at_5
	value: 81.46600000000001
	- type: mrr_at_1
	value: 75.673
	- type: mrr_at_10
	value: 82.41000000000001
	- type: mrr_at_100
	value: 82.616
	- type: mrr_at_1000
	value: 82.621
	- type: mrr_at_3
	value: 81.094
	- type: mrr_at_5
	value: 81.962
	- type: ndcg_at_1
	value: 75.673
	- type: ndcg_at_10
	value: 85.15599999999999
	- type: ndcg_at_100
	value: 86.151
	- type: ndcg_at_1000
	value: 86.26899999999999
	- type: ndcg_at_3
	value: 82.304
	- type: ndcg_at_5
	value: 84.009
	- type: precision_at_1
	value: 75.673
	- type: precision_at_10
	value: 10.042
	- type: precision_at_100
	value: 1.052
	- type: precision_at_1000
	value: 0.106
	- type: precision_at_3
	value: 30.673000000000002
	- type: precision_at_5
	value: 19.326999999999998
	- type: recall_at_1
	value: 73.28099999999999
	- type: recall_at_10
	value: 94.446
	- type: recall_at_100
	value: 98.737
	- type: recall_at_1000
	value: 99.649
	- type: recall_at_3
	value: 86.984
	- type: recall_at_5
	value: 91.024
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_intent
	name: MTEB MassiveIntentClassification (zh-CN)
	config: zh-CN
	split: test
	revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
	metrics:
	- type: accuracy
	value: 81.08607935440484
	- type: f1
	value: 78.24879986066307
	- task:
	type: Classification
	dataset:
	type: mteb/amazon_massive_scenario
	name: MTEB MassiveScenarioClassification (zh-CN)
	config: zh-CN
	split: test
	revision: 7d571f92784cd94a019292a1f45445077d0ef634
	metrics:
	- type: accuracy
	value: 86.05917955615332
	- type: f1
	value: 85.05279279434997
	- task:
	type: Retrieval
	dataset:
	type: C-MTEB/MedicalRetrieval
	name: MTEB MedicalRetrieval
	config: default
	split: dev
	revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
	metrics:
	- type: map_at_1
	value: 56.2
	- type: map_at_10
	value: 62.57899999999999
	- type: map_at_100
	value: 63.154999999999994
	- type: map_at_1000
	value: 63.193
	- type: map_at_3
	value: 61.217
	- type: map_at_5
	value: 62.012
	- type: mrr_at_1
	value: 56.3
	- type: mrr_at_10
	value: 62.629000000000005
	- type: mrr_at_100
	value: 63.205999999999996
	- type: mrr_at_1000
	value: 63.244
	- type: mrr_at_3
	value: 61.267
	- type: mrr_at_5
	value: 62.062
	- type: ndcg_at_1
	value: 56.2
	- type: ndcg_at_10
	value: 65.592
	- type: ndcg_at_100
	value: 68.657
	- type: ndcg_at_1000
	value: 69.671
	- type: ndcg_at_3
	value: 62.808
	- type: ndcg_at_5
	value: 64.24499999999999
	- type: precision_at_1
	value: 56.2
	- type: precision_at_10
	value: 7.5
	- type: precision_at_100
	value: 0.899
	- type: precision_at_1000
	value: 0.098
	- type: precision_at_3
	value: 22.467000000000002
	- type: precision_at_5
	value: 14.180000000000001
	- type: recall_at_1
	value: 56.2
	- type: recall_at_10
	value: 75.0
	- type: recall_at_100
	value: 89.9
	- type: recall_at_1000
	value: 97.89999999999999
	- type: recall_at_3
	value: 67.4
	- type: recall_at_5
	value: 70.89999999999999
	- task:
	type: Classification
	dataset:
	type: C-MTEB/MultilingualSentiment-classification
	name: MTEB MultilingualSentiment
	config: default
	split: validation
	revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
	metrics:
	- type: accuracy
	value: 76.87666666666667
	- type: f1
	value: 76.7317686219665
	- task:
	type: PairClassification
	dataset:
	type: C-MTEB/OCNLI
	name: MTEB Ocnli
	config: default
	split: validation
	revision: 66e76a618a34d6d565d5538088562851e6daa7ec
	metrics:
	- type: cos_sim_accuracy
	value: 79.64266377910124
	- type: cos_sim_ap
	value: 84.78274442344829
	- type: cos_sim_f1
	value: 81.16947472745292
	- type: cos_sim_precision
	value: 76.47058823529412
	- type: cos_sim_recall
	value: 86.48363252375924
	- type: dot_accuracy
	value: 79.64266377910124
	- type: dot_ap
	value: 84.7851404063692
	- type: dot_f1
	value: 81.16947472745292
	- type: dot_precision
	value: 76.47058823529412
	- type: dot_recall
	value: 86.48363252375924
	- type: euclidean_accuracy
	value: 79.64266377910124
	- type: euclidean_ap
	value: 84.78068373762378
	- type: euclidean_f1
	value: 81.14794656110837
	- type: euclidean_precision
	value: 76.35009310986965
	- type: euclidean_recall
	value: 86.58922914466737
	- type: manhattan_accuracy
	value: 79.48023822414727
	- type: manhattan_ap
	value: 84.72928897427576
	- type: manhattan_f1
	value: 81.32084770823064
	- type: manhattan_precision
	value: 76.24768946395564
	- type: manhattan_recall
	value: 87.11721224920802
	- type: max_accuracy
	value: 79.64266377910124
	- type: max_ap
	value: 84.7851404063692
	- type: max_f1
	value: 81.32084770823064
	- task:
	type: Classification
	dataset:
	type: C-MTEB/OnlineShopping-classification
	name: MTEB OnlineShopping
	config: default
	split: test
	revision: e610f2ebd179a8fda30ae534c3878750a96db120
	metrics:
	- type: accuracy
	value: 94.3
	- type: ap
	value: 92.8664032274438
	- type: f1
	value: 94.29311102997727
	- task:
	type: STS
	dataset:
	type: C-MTEB/PAWSX
	name: MTEB PAWSX
	config: default
	split: test
	revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
	metrics:
	- type: cos_sim_pearson
	value: 48.51392279882909
	- type: cos_sim_spearman
	value: 54.06338895994974
	- type: euclidean_pearson
	value: 52.58480559573412
	- type: euclidean_spearman
	value: 54.06417276612201
	- type: manhattan_pearson
	value: 52.69525121721343
	- type: manhattan_spearman
	value: 54.048147455389675
	- task:
	type: STS
	dataset:
	type: C-MTEB/QBQTC
	name: MTEB QBQTC
	config: default
	split: test
	revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
	metrics:
	- type: cos_sim_pearson
	value: 29.728387290757325
	- type: cos_sim_spearman
	value: 31.366121633635284
	- type: euclidean_pearson
	value: 29.14588368552961
	- type: euclidean_spearman
	value: 31.36764411112844
	- type: manhattan_pearson
	value: 29.63517350523121
	- type: manhattan_spearman
	value: 31.94157020583762
	- task:
	type: STS
	dataset:
	type: mteb/sts22-crosslingual-sts
	name: MTEB STS22 (zh)
	config: zh
	split: test
	revision: eea2b4fe26a775864c896887d910b76a8098ad3f
	metrics:
	- type: cos_sim_pearson
	value: 63.64868296271406
	- type: cos_sim_spearman
	value: 66.12800618164744
	- type: euclidean_pearson
	value: 63.21405767340238
	- type: euclidean_spearman
	value: 66.12786567790748
	- type: manhattan_pearson
	value: 64.04300276525848
	- type: manhattan_spearman
	value: 66.5066857145652
	- task:
	type: STS
	dataset:
	type: C-MTEB/STSB
	name: MTEB STSB
	config: default
	split: test
	revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
	metrics:
	- type: cos_sim_pearson
	value: 81.2302623912794
	- type: cos_sim_spearman
	value: 81.16833673266562
	- type: euclidean_pearson
	value: 79.47647843876024
	- type: euclidean_spearman
	value: 81.16944349524972
	- type: manhattan_pearson
	value: 79.84947238492208
	- type: manhattan_spearman
	value: 81.64626599410026
	- task:
	type: Reranking
	dataset:
	type: C-MTEB/T2Reranking
	name: MTEB T2Reranking
	config: default
	split: dev
	revision: 76631901a18387f85eaa53e5450019b87ad58ef9
	metrics:
	- type: map
	value: 67.80129586475687
	- type: mrr
	value: 77.77402311635554
	- task:
	type: Retrieval
	dataset:
	type: C-MTEB/T2Retrieval
	name: MTEB T2Retrieval
	config: default
	split: dev
	revision: 8731a845f1bf500a4f111cf1070785c793d10e64
	metrics:
	- type: map_at_1
	value: 28.666999999999998
	- type: map_at_10
	value: 81.063
	- type: map_at_100
	value: 84.504
	- type: map_at_1000
	value: 84.552
	- type: map_at_3
	value: 56.897
	- type: map_at_5
	value: 70.073
	- type: mrr_at_1
	value: 92.087
	- type: mrr_at_10
	value: 94.132
	- type: mrr_at_100
	value: 94.19800000000001
	- type: mrr_at_1000
	value: 94.19999999999999
	- type: mrr_at_3
	value: 93.78999999999999
	- type: mrr_at_5
	value: 94.002
	- type: ndcg_at_1
	value: 92.087
	- type: ndcg_at_10
	value: 87.734
	- type: ndcg_at_100
	value: 90.736
	- type: ndcg_at_1000
	value: 91.184
	- type: ndcg_at_3
	value: 88.78
	- type: ndcg_at_5
	value: 87.676
	- type: precision_at_1
	value: 92.087
	- type: precision_at_10
	value: 43.46
	- type: precision_at_100
	value: 5.07
	- type: precision_at_1000
	value: 0.518
	- type: precision_at_3
	value: 77.49000000000001
	- type: precision_at_5
	value: 65.194
	- type: recall_at_1
	value: 28.666999999999998
	- type: recall_at_10
	value: 86.632
	- type: recall_at_100
	value: 96.646
	- type: recall_at_1000
	value: 98.917
	- type: recall_at_3
	value: 58.333999999999996
	- type: recall_at_5
	value: 72.974
	- task:
	type: Classification
	dataset:
	type: C-MTEB/TNews-classification
	name: MTEB TNews
	config: default
	split: validation
	revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
	metrics:
	- type: accuracy
	value: 52.971999999999994
	- type: f1
	value: 50.2898280984929
	- task:
	type: Clustering
	dataset:
	type: C-MTEB/ThuNewsClusteringP2P
	name: MTEB ThuNewsClusteringP2P
	config: default
	split: test
	revision: 5798586b105c0434e4f0fe5e767abe619442cf93
	metrics:
	- type: v_measure
	value: 86.0797948663824
	- task:
	type: Clustering
	dataset:
	type: C-MTEB/ThuNewsClusteringS2S
	name: MTEB ThuNewsClusteringS2S
	config: default
	split: test
	revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
	metrics:
	- type: v_measure
	value: 85.10759092255017
	- task:
	type: Retrieval
	dataset:
	type: C-MTEB/VideoRetrieval
	name: MTEB VideoRetrieval
	config: default
	split: dev
	revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
	metrics:
	- type: map_at_1
	value: 65.60000000000001
	- type: map_at_10
	value: 74.773
	- type: map_at_100
	value: 75.128
	- type: map_at_1000
	value: 75.136
	- type: map_at_3
	value: 73.05
	- type: map_at_5
	value: 74.13499999999999
	- type: mrr_at_1
	value: 65.60000000000001
	- type: mrr_at_10
	value: 74.773
	- type: mrr_at_100
	value: 75.128
	- type: mrr_at_1000
	value: 75.136
	- type: mrr_at_3
	value: 73.05
	- type: mrr_at_5
	value: 74.13499999999999
	- type: ndcg_at_1
	value: 65.60000000000001
	- type: ndcg_at_10
	value: 78.84299999999999
	- type: ndcg_at_100
	value: 80.40899999999999
	- type: ndcg_at_1000
	value: 80.57
	- type: ndcg_at_3
	value: 75.40599999999999
	- type: ndcg_at_5
	value: 77.351
	- type: precision_at_1
	value: 65.60000000000001
	- type: precision_at_10
	value: 9.139999999999999
	- type: precision_at_100
	value: 0.984
	- type: precision_at_1000
	value: 0.1
	- type: precision_at_3
	value: 27.400000000000002
	- type: precision_at_5
	value: 17.380000000000003
	- type: recall_at_1
	value: 65.60000000000001
	- type: recall_at_10
	value: 91.4
	- type: recall_at_100
	value: 98.4
	- type: recall_at_1000
	value: 99.6
	- type: recall_at_3
	value: 82.19999999999999
	- type: recall_at_5
	value: 86.9
	- task:
	type: Classification
	dataset:
	type: C-MTEB/waimai-classification
	name: MTEB Waimai
	config: default
	split: test
	revision: 339287def212450dcaa9df8c22bf93e9980c7023
	metrics:
	- type: accuracy
	value: 89.47
	- type: ap
	value: 75.59561751845389
	- type: f1
	value: 87.95207751382563
	---

	## gte-Qwen2-7B-instruct

	gte-Qwen2-7B-instruct is the latest model in the gte (General Text Embedding) model family that ranks No.1 in both English and Chinese evaluations on the Massive Text Embedding Benchmark [MTEB benchmark](https://huggingface.co./spaces/mteb/leaderboard) (as of June 16, 2024).

	Recently, the [Qwen team](https://huggingface.co./Qwen) released the Qwen2 series models, and we have trained the gte-Qwen2-7B-instruct model based on the [Qwen2-7B](https://huggingface.co./Qwen/Qwen2-7B) LLM model. Compared to the [gte-Qwen1.5-7B-instruct](https://huggingface.co./Alibaba-NLP/gte-Qwen1.5-7B-instruct) model, the gte-Qwen2-7B-instruct model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models.

	The model incorporates several key advancements:

	- Integration of bidirectional attention mechanisms, enriching its contextual understanding.
	- Instruction tuning, applied solely on the query side for streamlined efficiency
	- Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.


	## Model Information
	- Model Size: 7B
	- Embedding Dimension: 3584
	- Max Input Tokens: 32k

	## Requirements
	```
	transformers>=4.39.2
	flash_attn>=2.5.6
	```
	## Usage

	### Sentence Transformers

	```python
	from sentence_transformers import SentenceTransformer

	model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True)
	# In case you want to reduce the maximum length:
	model.max_seq_length = 8192

	queries = [
	"how much protein should a female eat",
	"summit define",
	]
	documents = [
	"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
	"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments.",
	]

	query_embeddings = model.encode(queries, prompt_name="query")
	document_embeddings = model.encode(documents)

	scores = (query_embeddings @ document_embeddings.T) * 100
	print(scores.tolist())
	```

	Observe the [config_sentence_transformers.json](config_sentence_transformers.json) to see all pre-built prompt names. Otherwise, you can use `model.encode(queries, prompt="Instruct: ...\nQuery: "` to use a custom prompt of your choice.

	### Transformers

	```python
	import torch
	import torch.nn.functional as F

	from torch import Tensor
	from transformers import AutoTokenizer, AutoModel


	def last_token_pool(last_hidden_states: Tensor,
	attention_mask: Tensor) -> Tensor:
	left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
	if left_padding:
	return last_hidden_states[:, -1]
	else:
	sequence_lengths = attention_mask.sum(dim=1) - 1
	batch_size = last_hidden_states.shape[0]
	return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]


	def get_detailed_instruct(task_description: str, query: str) -> str:
	return f'Instruct: {task_description}\nQuery: {query}'


	# Each query must come with a one-sentence instruction that describes the task
	task = 'Given a web search query, retrieve relevant passages that answer the query'
	queries = [
	get_detailed_instruct(task, 'how much protein should a female eat'),
	get_detailed_instruct(task, 'summit define')
	]
	# No need to add instruction for retrieval documents
	documents = [
	"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
	"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
	]
	input_texts = queries + documents

	tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)
	model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)

	max_length = 8192

	# Tokenize the input texts
	batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
	outputs = model(**batch_dict)
	embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

	# normalize embeddings
	embeddings = F.normalize(embeddings, p=2, dim=1)
	scores = (embeddings[:2] @ embeddings[2:].T) * 100
	print(scores.tolist())
	```

	## Evaluation

	### MTEB & C-MTEB

	You can use the [scripts/eval_mteb.py](https://huggingface.co./Alibaba-NLP/gte-Qwen2-7B-instruct/blob/main/scripts/eval_mteb.py) to reproduce the following result of gte-Qwen2-7B-instruct on MTEB(English)/C-MTEB(Chinese):

	\| Model Name \| MTEB(56) \| C-MTEB(35) \|
	\|:----:\|:---------:\|:----------:\|
	\| [bge-base-en-1.5](https://huggingface.co./BAAI/bge-base-en-v1.5) \| 64.23 \| - \|
	\| [bge-large-en-1.5](https://huggingface.co./BAAI/bge-large-en-v1.5) \| 63.55 \| - \|
	\| [gte-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5) \| 65.39 \| - \|
	\| [gte-base-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5) \| 64.11 \| - \|
	\| [mxbai-embed-large-v1](https://huggingface.co./mixedbread-ai/mxbai-embed-large-v1) \| 64.68 \| - \|
	\| [acge_text_embedding](https://huggingface.co./aspire/acge_text_embedding) \| - \| 69.07 \|
	\| [stella-mrl-large-zh-v3.5-1792d](https://huggingface.co./infgrad/stella-mrl-large-zh-v3.5-1792d) \| - \| 68.55 \|
	\| [gte-large-zh](https://huggingface.co./thenlper/gte-large-zh) \| - \| 66.72 \|
	\| [multilingual-e5-base](https://huggingface.co./intfloat/multilingual-e5-base) \| 59.45 \| 56.21 \|
	\| [multilingual-e5-large](https://huggingface.co./intfloat/multilingual-e5-large) \| 61.50 \| 58.81 \|
	\| [e5-mistral-7b-instruct](https://huggingface.co./intfloat/e5-mistral-7b-instruct) \| 66.63 \| 60.81 \|
	\| [gte-Qwen1.5-7B-instruct](https://huggingface.co./Alibaba-NLP/gte-Qwen1.5-7B-instruct) \| 67.34 \| 69.52 \|
	\| [NV-Embed-v1](https://huggingface.co./nvidia/NV-Embed-v1) \| 69.32 \| - \|
	\| [gte-Qwen2-7B-instruct](https://huggingface.co./Alibaba-NLP/gte-Qwen2-7B-instruct) \| 70.24 \| 72.05 \|

	### GTE Models

	The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).

	\| Models \| Language \| Max Sequence Length \| Dimension \| Model Size (Memory Usage, fp32) \|
	\|:-------------------------------------------------------------------------------------:\|:--------:\|:-----: \|:---------:\|:-------------------------------:\|
	\| [GTE-large-zh](https://huggingface.co./thenlper/gte-large-zh) \| Chinese \| 512 \| 1024 \| 1.25GB \|
	\| [GTE-base-zh](https://huggingface.co./thenlper/gte-base-zh) \| Chinese \| 512 \| 512 \| 0.41GB \|
	\| [GTE-small-zh](https://huggingface.co./thenlper/gte-small-zh) \| Chinese \| 512 \| 512 \| 0.12GB \|
	\| [GTE-large](https://huggingface.co./thenlper/gte-large) \| English \| 512 \| 1024 \| 1.25GB \|
	\| [GTE-base](https://huggingface.co./thenlper/gte-base) \| English \| 512 \| 512 \| 0.21GB \|
	\| [GTE-small](https://huggingface.co./thenlper/gte-small) \| English \| 512 \| 384 \| 0.10GB \|
	\| [GTE-large-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-large-en-v1.5) \| English \| 8192 \| 1024 \| 1.74GB \|
	\| [GTE-base-en-v1.5](https://huggingface.co./Alibaba-NLP/gte-base-en-v1.5) \| English \| 8192 \| 768 \| 0.51GB \|
	\| [GTE-Qwen1.5-7B-instruct](https://huggingface.co./Alibaba-NLP/gte-Qwen1.5-7B-instruct) \| Multilingual \| 32000 \| 4096 \| 26.45GB \|
	\| [GTE-Qwen2-7B-instruct](https://huggingface.co./Alibaba-NLP/gte-Qwen2-7B-instruct) \| Multilingual \| 32000 \| 3584 \| 26.45GB \|

	## Citation

	If you find our paper or models helpful, please consider cite:

	```
	@article{li2023towards,
	title={Towards general text embeddings with multi-stage contrastive learning},
	author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
	journal={arXiv preprint arXiv:2308.03281},
	year={2023}
	}
	```