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Alibaba-NLP_gte-large-en-v1.5

Alibaba-NLP · View on Hugging Face ↗

Large English general text embedding model (GTE v1.5) for high-quality retrieval and similarity.

✓ verified · rehash-vs-hf-metadata at 2026-08-24T07:55:26Z

apache-2.01.62 GB (1,737,604,631 bytes)transformersonnxsafetensorsnewfeature-extractionsentence-transformersgtemtebtransformers.jssentence-similaritycustom_codemodel-indextext-embeddings-inferenceendpoints_compatible1 language (en)paper: 2407.19669paper: 2308.03281

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datasets:

  • allenai/c4 library_name: transformers tags:
  • sentence-transformers
  • gte
  • mteb
  • transformers.js
  • sentence-similarity license: apache-2.0 language:
  • en model-index:
  • name: gte-large-en-v1.5 results:
    • task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
      • type: accuracy value: 73.01492537313432
      • type: ap value: 35.05341696659522
      • type: f1 value: 66.71270310883853
    • task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
      • type: accuracy value: 93.97189999999999
      • type: ap value: 90.5952493948908
      • type: f1 value: 93.95848137716877
    • task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
      • type: accuracy value: 54.196
      • type: f1 value: 53.80122334012787
    • task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics:
      • type: map_at_1 value: 47.297
      • type: map_at_10 value: 64.303
      • type: map_at_100 value: 64.541
      • type: map_at_1000 value: 64.541
      • type: map_at_3 value: 60.728
      • type: map_at_5 value: 63.114000000000004
      • type: mrr_at_1 value: 48.435
      • type: mrr_at_10 value: 64.657
      • type: mrr_at_100 value: 64.901
      • type: mrr_at_1000 value: 64.901
      • type: mrr_at_3 value: 61.06
      • type: mrr_at_5 value: 63.514
      • type: ndcg_at_1 value: 47.297
      • type: ndcg_at_10 value: 72.107
      • type: ndcg_at_100 value: 72.963
      • type: ndcg_at_1000 value: 72.963
      • type: ndcg_at_3 value: 65.063
      • type: ndcg_at_5 value: 69.352
      • type: precision_at_1 value: 47.297
      • type: precision_at_10 value: 9.623
      • type: precision_at_100 value: 0.996
      • type: precision_at_1000 value: 0.1
      • type: precision_at_3 value: 25.865
      • type: precision_at_5 value: 17.596
      • type: recall_at_1 value: 47.297
      • type: recall_at_10 value: 96.23
      • type: recall_at_100 value: 99.644
      • type: recall_at_1000 value: 99.644
      • type: recall_at_3 value: 77.596
      • type: recall_at_5 value: 87.98
    • task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
      • type: v_measure value: 48.467787861077475
    • task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
      • type: v_measure value: 43.39198391914257
    • task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
      • type: map value: 63.12794820591384
      • type: mrr value: 75.9331442641692
    • task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
      • type: cos_sim_pearson value: 87.85062993863319
      • type: cos_sim_spearman value: 85.39049989733459
      • type: euclidean_pearson value: 86.00222680278333
      • type: euclidean_spearman value: 85.45556162077396
      • type: manhattan_pearson value: 85.88769871785621
      • type: manhattan_spearman value: 85.11760211290839
    • task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
      • type: accuracy value: 87.32792207792208
      • type: f1 value: 87.29132945999555
    • task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
      • type: v_measure value: 40.5779328301945
    • task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
      • type: v_measure value: 37.94425623865118
    • task: type: Retrieval dataset: type: mteb/cqadupstack-android name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics:
      • type: map_at_1 value: 32.978
      • type: map_at_10 value: 44.45
      • type: map_at_100 value: 46.19
      • type: map_at_1000 value: 46.303
      • type: map_at_3 value: 40.849000000000004
      • type: map_at_5 value: 42.55
      • type: mrr_at_1 value: 40.629
      • type: mrr_at_10 value: 50.848000000000006
      • type: mrr_at_100 value: 51.669
      • type: mrr_at_1000 value: 51.705
      • type: mrr_at_3 value: 47.997
      • type: mrr_at_5 value: 49.506
      • type: ndcg_at_1 value: 40.629
      • type: ndcg_at_10 value: 51.102000000000004
      • type: ndcg_at_100 value: 57.159000000000006
      • type: ndcg_at_1000 value: 58.669000000000004
      • type: ndcg_at_3 value: 45.738
      • type: ndcg_at_5 value: 47.632999999999996
      • type: precision_at_1 value: 40.629
      • type: precision_at_10 value: 9.700000000000001
      • type: precision_at_100 value: 1.5970000000000002
      • type: precision_at_1000 value: 0.202
      • type: precision_at_3 value: 21.698
      • type: precision_at_5 value: 15.393
      • type: recall_at_1 value: 32.978
      • type: recall_at_10 value: 63.711
      • type: recall_at_100 value: 88.39399999999999
      • type: recall_at_1000 value: 97.513
      • type: recall_at_3 value: 48.025
      • type: recall_at_5 value: 53.52
    • task: type: Retrieval dataset: type: mteb/cqadupstack-english name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics:
      • type: map_at_1 value: 30.767
      • type: map_at_10 value: 42.195
      • type: map_at_100 value: 43.541999999999994
      • type: map_at_1000 value: 43.673
      • type: map_at_3 value: 38.561
      • type: map_at_5 value: 40.532000000000004
      • type: mrr_at_1 value: 38.79
      • type: mrr_at_10 value: 48.021
      • type: mrr_at_100 value: 48.735
      • type: mrr_at_1000 value: 48.776
      • type: mrr_at_3 value: 45.594
      • type: mrr_at_5 value: 46.986
      • type: ndcg_at_1 value: 38.79
      • type: ndcg_at_10 value: 48.468
      • type: ndcg_at_100 value: 53.037
      • type: ndcg_at_1000 value: 55.001999999999995
      • type: ndcg_at_3 value: 43.409
      • type: ndcg_at_5 value: 45.654
      • type: precision_at_1 value: 38.79
      • type: precision_at_10 value: 9.452
      • type: precision_at_100 value: 1.518
      • type: precision_at_1000 value: 0.201
      • type: precision_at_3 value: 21.21
      • type: precision_at_5 value: 15.171999999999999
      • type: recall_at_1 value: 30.767
      • type: recall_at_10 value: 60.118
      • type: recall_at_100 value: 79.271
      • type: recall_at_1000 value: 91.43299999999999
      • type: recall_at_3 value: 45.36
      • type: recall_at_5 value: 51.705
    • task: type: Retrieval dataset: type: mteb/cqadupstack-gaming name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics:
      • type: map_at_1 value: 40.007
      • type: map_at_10 value: 53.529
      • type: map_at_100 value: 54.602
      • type: map_at_1000 value: 54.647
      • type: map_at_3 value: 49.951
      • type: map_at_5 value: 52.066
      • type: mrr_at_1 value: 45.705
      • type: mrr_at_10 value: 56.745000000000005
      • type: mrr_at_100 value: 57.43899999999999
      • type: mrr_at_1000 value: 57.462999999999994
      • type: mrr_at_3 value: 54.25299999999999
      • type: mrr_at_5 value: 55.842000000000006
      • type: ndcg_at_1 value: 45.705
      • type: ndcg_at_10 value: 59.809
      • type: ndcg_at_100 value: 63.837999999999994
      • type: ndcg_at_1000 value: 64.729
      • type: ndcg_at_3 value: 53.994
      • type: ndcg_at_5 value: 57.028
      • type: precision_at_1 value: 45.705
      • type: precision_at_10 value: 9.762
      • type: precision_at_100 value: 1.275
      • type: precision_at_1000 value: 0.13899999999999998
      • type: precision_at_3 value: 24.368000000000002
      • type: precision_at_5 value: 16.84
      • type: recall_at_1 value: 40.007
      • type: recall_at_10 value: 75.017
      • type: recall_at_100 value: 91.99000000000001
      • type: recall_at_1000 value: 98.265
      • type: recall_at_3 value: 59.704
      • type: recall_at_5 value: 67.109
    • task: type: Retrieval dataset: type: mteb/cqadupstack-gis name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics:
      • type: map_at_1 value: 26.639000000000003
      • type: map_at_10 value: 35.926
      • type: map_at_100 value: 37.126999999999995
      • type: map_at_1000 value: 37.202
      • type: map_at_3 value: 32.989000000000004
      • type: map_at_5 value: 34.465
      • type: mrr_at_1 value: 28.475
      • type: mrr_at_10 value: 37.7
      • type: mrr_at_100 value: 38.753
      • type: mrr_at_1000 value: 38.807
      • type: mrr_at_3 value: 35.066
      • type: mrr_at_5 value: 36.512
      • type: ndcg_at_1 value: 28.475
      • type: ndcg_at_10 value: 41.245
      • type: ndcg_at_100 value: 46.814
      • type: ndcg_at_1000 value: 48.571
      • type: ndcg_at_3 value: 35.528999999999996
      • type: ndcg_at_5 value: 38.066
      • type: precision_at_1 value: 28.475
      • type: precision_at_10 value: 6.497
      • type: precision_at_100 value: 0.9650000000000001
      • type: precision_at_1000 value: 0.11499999999999999
      • type: precision_at_3 value: 15.065999999999999
      • type: precision_at_5 value: 10.599
      • type: recall_at_1 value: 26.639000000000003
      • type: recall_at_10 value: 55.759
      • type: recall_at_100 value: 80.913
      • type: recall_at_1000 value: 93.929
      • type: recall_at_3 value: 40.454
      • type: recall_at_5 value: 46.439
    • task: type: Retrieval dataset: type: mteb/cqadupstack-mathematica name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics:
      • type: map_at_1 value: 15.767999999999999
      • type: map_at_10 value: 24.811
      • type: map_at_100 value: 26.064999999999998
      • type: map_at_1000 value: 26.186999999999998
      • type: map_at_3 value: 21.736
      • type: map_at_5 value: 23.283
      • type: mrr_at_1 value: 19.527
      • type: mrr_at_10 value: 29.179
      • type: mrr_at_100 value: 30.153999999999996
      • type: mrr_at_1000 value: 30.215999999999998
      • type: mrr_at_3 value: 26.223000000000003
      • type: mrr_at_5 value: 27.733999999999998
      • type: ndcg_at_1 value: 19.527
      • type: ndcg_at_10 value: 30.786
      • type: ndcg_at_100 value: 36.644
      • type: ndcg_at_1000 value: 39.440999999999995
      • type: ndcg_at_3 value: 24.958
      • type: ndcg_at_5 value: 27.392
      • type: precision_at_1 value: 19.527
      • type: precision_at_10 value: 5.995
      • type: precision_at_100 value: 1.03
      • type: precision_at_1000 value: 0.14100000000000001
      • type: precision_at_3 value: 12.520999999999999
      • type: precision_at_5 value: 9.129
      • type: recall_at_1 value: 15.767999999999999
      • type: recall_at_10 value: 44.824000000000005
      • type: recall_at_100 value: 70.186
      • type: recall_at_1000 value: 89.934
      • type: recall_at_3 value: 28.607
      • type: recall_at_5 value: 34.836
    • task: type: Retrieval dataset: type: mteb/cqadupstack-physics name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics:
      • type: map_at_1 value: 31.952
      • type: map_at_10 value: 44.438
      • type: map_at_100 value: 45.778
      • type: map_at_1000 value: 45.883
      • type: map_at_3 value: 41.044000000000004
      • type: map_at_5 value: 42.986000000000004
      • type: mrr_at_1 value: 39.172000000000004
      • type: mrr_at_10 value: 49.76
      • type: mrr_at_100 value: 50.583999999999996
      • type: mrr_at_1000 value: 50.621
      • type: mrr_at_3 value: 47.353
      • type: mrr_at_5 value: 48.739
      • type: ndcg_at_1 value: 39.172000000000004
      • type: ndcg_at_10 value: 50.760000000000005
      • type: ndcg_at_100 value: 56.084
      • type: ndcg_at_1000 value: 57.865
      • type: ndcg_at_3 value: 45.663
      • type: ndcg_at_5 value: 48.178
      • type: precision_at_1 value: 39.172000000000004
      • type: precision_at_10 value: 9.22
      • type: precision_at_100 value: 1.387
      • type: precision_at_1000 value: 0.17099999999999999
      • type: precision_at_3 value: 21.976000000000003
      • type: precision_at_5 value: 15.457
      • type: recall_at_1 value: 31.952
      • type: recall_at_10 value: 63.900999999999996
      • type: recall_at_100 value: 85.676
      • type: recall_at_1000 value: 97.03699999999999
      • type: recall_at_3 value: 49.781
      • type: recall_at_5 value: 56.330000000000005
    • task: type: Retrieval dataset: type: mteb/cqadupstack-programmers name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics:
      • type: map_at_1 value: 25.332
      • type: map_at_10 value: 36.874
      • type: map_at_100 value: 38.340999999999994
      • type: map_at_1000 value: 38.452
      • type: map_at_3 value: 33.068
      • type: map_at_5 value: 35.324
      • type: mrr_at_1 value: 30.822
      • type: mrr_at_10 value: 41.641
      • type: mrr_at_100 value: 42.519
      • type: mrr_at_1000 value: 42.573
      • type: mrr_at_3 value: 38.413000000000004
      • type: mrr_at_5 value: 40.542
      • type: ndcg_at_1 value: 30.822
      • type: ndcg_at_10 value: 43.414
      • type: ndcg_at_100 value: 49.196
      • type: ndcg_at_1000 value: 51.237
      • type: ndcg_at_3 value: 37.230000000000004
      • type: ndcg_at_5 value: 40.405
      • type: precision_at_1 value: 30.822
      • type: precision_at_10 value: 8.379
      • type: precision_at_100 value: 1.315
      • type: precision_at_1000 value: 0.168
      • type: precision_at_3 value: 18.417
      • type: precision_at_5 value: 13.744
      • type: recall_at_1 value: 25.332
      • type: recall_at_10 value: 57.774
      • type: recall_at_100 value: 82.071
      • type: recall_at_1000 value: 95.60600000000001
      • type: recall_at_3 value: 40.722
      • type: recall_at_5 value: 48.754999999999995
    • task: type: Retrieval dataset: type: mteb/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
      • type: map_at_1 value: 25.91033333333334
      • type: map_at_10 value: 36.23225000000001
      • type: map_at_100 value: 37.55766666666667
      • type: map_at_1000 value: 37.672583333333336
      • type: map_at_3 value: 32.95666666666667
      • type: map_at_5 value: 34.73375
      • type: mrr_at_1 value: 30.634
      • type: mrr_at_10 value: 40.19449999999999
      • type: mrr_at_100 value: 41.099250000000005
      • type: mrr_at_1000 value: 41.15091666666667
      • type: mrr_at_3 value: 37.4615
      • type: mrr_at_5 value: 39.00216666666667
      • type: ndcg_at_1 value: 30.634
      • type: ndcg_at_10 value: 42.162166666666664
      • type: ndcg_at_100 value: 47.60708333333333
      • type: ndcg_at_1000 value: 49.68616666666666
      • type: ndcg_at_3 value: 36.60316666666666
      • type: ndcg_at_5 value: 39.15616666666668
      • type: precision_at_1 value: 30.634
      • type: precision_at_10 value: 7.6193333333333335
      • type: precision_at_100 value: 1.2198333333333333
      • type: precision_at_1000 value: 0.15975000000000003
      • type: precision_at_3 value: 17.087
      • type: precision_at_5 value: 12.298333333333334
      • type: recall_at_1 value: 25.91033333333334
      • type: recall_at_10 value: 55.67300000000001
      • type: recall_at_100 value: 79.20608333333334
      • type: recall_at_1000 value: 93.34866666666667
      • type: recall_at_3 value: 40.34858333333333
      • type: recall_at_5 value: 46.834083333333325
    • task: type: Retrieval dataset: type: mteb/cqadupstack-stats name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics:
      • type: map_at_1 value: 25.006
      • type: map_at_10 value: 32.177
      • type: map_at_100 value: 33.324999999999996
      • type: map_at_1000 value: 33.419
      • type: map_at_3 value: 29.952
      • type: map_at_5 value: 31.095
      • type: mrr_at_1 value: 28.066999999999997
      • type: mrr_at_10 value: 34.995
      • type: mrr_at_100 value: 35.978
      • type: mrr_at_1000 value: 36.042
      • type: mrr_at_3 value: 33.103
      • type: mrr_at_5 value: 34.001
      • type: ndcg_at_1 value: 28.066999999999997
      • type: ndcg_at_10 value: 36.481
      • type: ndcg_at_100 value: 42.022999999999996
      • type: ndcg_at_1000 value: 44.377
      • type: ndcg_at_3 value: 32.394
      • type: ndcg_at_5 value: 34.108
      • type: precision_at_1 value: 28.066999999999997
      • type: precision_at_10 value: 5.736
      • type: precision_at_100 value: 0.9259999999999999
      • type: precision_at_1000 value: 0.12
      • type: precision_at_3 value: 13.804
      • type: precision_at_5 value: 9.508999999999999
      • type: recall_at_1 value: 25.006
      • type: recall_at_10 value: 46.972
      • type: recall_at_100 value: 72.138
      • type: recall_at_1000 value: 89.479
      • type: recall_at_3 value: 35.793
      • type: recall_at_5 value: 39.947
    • task: type: Retrieval dataset: type: mteb/cqadupstack-tex name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics:
      • type: map_at_1 value: 16.07
      • type: map_at_10 value: 24.447
      • type: map_at_100 value: 25.685999999999996
      • type: map_at_1000 value: 25.813999999999997
      • type: map_at_3 value: 21.634
      • type: map_at_5 value: 23.133
      • type: mrr_at_1 value: 19.580000000000002
      • type: mrr_at_10 value: 28.127999999999997
      • type: mrr_at_100 value: 29.119
      • type: mrr_at_1000 value: 29.192
      • type: mrr_at_3 value: 25.509999999999998
      • type: mrr_at_5 value: 26.878
      • type: ndcg_at_1 value: 19.580000000000002
      • type: ndcg_at_10 value: 29.804000000000002
      • type: ndcg_at_100 value: 35.555
      • type: ndcg_at_1000 value: 38.421
      • type: ndcg_at_3 value: 24.654999999999998
      • type: ndcg_at_5 value: 26.881
      • type: precision_at_1 value: 19.580000000000002
      • type: precision_at_10 value: 5.736
      • type: precision_at_100 value: 1.005
      • type: precision_at_1000 value: 0.145
      • type: precision_at_3 value: 12.033000000000001
      • type: precision_at_5 value: 8.871
      • type: recall_at_1 value: 16.07
      • type: recall_at_10 value: 42.364000000000004
      • type: recall_at_100 value: 68.01899999999999
      • type: recall_at_1000 value: 88.122
      • type: recall_at_3 value: 27.846
      • type: recall_at_5 value: 33.638
    • task: type: Retrieval dataset: type: mteb/cqadupstack-unix name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics:
      • type: map_at_1 value: 26.365
      • type: map_at_10 value: 36.591
      • type: map_at_100 value: 37.730000000000004
      • type: map_at_1000 value: 37.84
      • type: map_at_3 value: 33.403
      • type: map_at_5 value: 35.272999999999996
      • type: mrr_at_1 value: 30.503999999999998
      • type: mrr_at_10 value: 39.940999999999995
      • type: mrr_at_100 value: 40.818
      • type: mrr_at_1000 value: 40.876000000000005
      • type: mrr_at_3 value: 37.065
      • type: mrr_at_5 value: 38.814
      • type: ndcg_at_1 value: 30.503999999999998
      • type: ndcg_at_10 value: 42.185
      • type: ndcg_at_100 value: 47.416000000000004
      • type: ndcg_at_1000 value: 49.705
      • type: ndcg_at_3 value: 36.568
      • type: ndcg_at_5 value: 39.416000000000004
      • type: precision_at_1 value: 30.503999999999998
      • type: precision_at_10 value: 7.276000000000001
      • type: precision_at_100 value: 1.118
      • type: precision_at_1000 value: 0.14300000000000002
      • type: precision_at_3 value: 16.729
      • type: precision_at_5 value: 12.107999999999999
      • type: recall_at_1 value: 26.365
      • type: recall_at_10 value: 55.616
      • type: recall_at_100 value: 78.129
      • type: recall_at_1000 value: 93.95599999999999
      • type: recall_at_3 value: 40.686
      • type: recall_at_5 value: 47.668
    • task: type: Retrieval dataset: type: mteb/cqadupstack-webmasters name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics:
      • type: map_at_1 value: 22.750999999999998
      • type: map_at_10 value: 33.446
      • type: map_at_100 value: 35.235
      • type: map_at_1000 value: 35.478
      • type: map_at_3 value: 29.358
      • type: map_at_5 value: 31.525
      • type: mrr_at_1 value: 27.668
      • type: mrr_at_10 value: 37.694
      • type: mrr_at_100 value: 38.732
      • type: mrr_at_1000 value: 38.779
      • type: mrr_at_3 value: 34.223
      • type: mrr_at_5 value: 36.08
      • type: ndcg_at_1 value: 27.668
      • type: ndcg_at_10 value: 40.557
      • type: ndcg_at_100 value: 46.605999999999995
      • type: ndcg_at_1000 value: 48.917
      • type: ndcg_at_3 value: 33.677
      • type: ndcg_at_5 value: 36.85
      • type: precision_at_1 value: 27.668
      • type: precision_at_10 value: 8.3
      • type: precision_at_100 value: 1.6260000000000001
      • type: precision_at_1000 value: 0.253
      • type: precision_at_3 value: 16.008
      • type: precision_at_5 value: 12.292
      • type: recall_at_1 value: 22.750999999999998
      • type: recall_at_10 value: 55.643
      • type: recall_at_100 value: 82.151
      • type: recall_at_1000 value: 95.963
      • type: recall_at_3 value: 36.623
      • type: recall_at_5 value: 44.708
    • task: type: Retrieval dataset: type: mteb/cqadupstack-wordpress name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
      • type: map_at_1 value: 17.288999999999998
      • type: map_at_10 value: 25.903
      • type: map_at_100 value: 27.071
      • type: map_at_1000 value: 27.173000000000002
      • type: map_at_3 value: 22.935
      • type: map_at_5 value: 24.573
      • type: mrr_at_1 value: 18.669
      • type: mrr_at_10 value: 27.682000000000002
      • type: mrr_at_100 value: 28.691
      • type: mrr_at_1000 value: 28.761
      • type: mrr_at_3 value: 24.738
      • type: mrr_at_5 value: 26.392
      • type: ndcg_at_1 value: 18.669
      • type: ndcg_at_10 value: 31.335
      • type: ndcg_at_100 value: 36.913000000000004
      • type: ndcg_at_1000 value: 39.300000000000004
      • type: ndcg_at_3 value: 25.423000000000002
      • type: ndcg_at_5 value: 28.262999999999998
      • type: precision_at_1 value: 18.669
      • type: precision_at_10 value: 5.379
      • type: precision_at_100 value: 0.876
      • type: precision_at_1000 value: 0.11900000000000001
      • type: precision_at_3 value: 11.214
      • type: precision_at_5 value: 8.466
      • type: recall_at_1 value: 17.288999999999998
      • type: recall_at_10 value: 46.377
      • type: recall_at_100 value: 71.53500000000001
      • type: recall_at_1000 value: 88.947
      • type: recall_at_3 value: 30.581999999999997
      • type: recall_at_5 value: 37.354
    • task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics:
      • type: map_at_1 value: 21.795
      • type: map_at_10 value: 37.614999999999995
      • type: map_at_100 value: 40.037
      • type: map_at_1000 value: 40.184999999999995
      • type: map_at_3 value: 32.221
      • type: map_at_5 value: 35.154999999999994
      • type: mrr_at_1 value: 50.358000000000004
      • type: mrr_at_10 value: 62.129
      • type: mrr_at_100 value: 62.613
      • type: mrr_at_1000 value: 62.62
      • type: mrr_at_3 value: 59.272999999999996
      • type: mrr_at_5 value: 61.138999999999996
      • type: ndcg_at_1 value: 50.358000000000004
      • type: ndcg_at_10 value: 48.362
      • type: ndcg_at_100 value: 55.932
      • type: ndcg_at_1000 value: 58.062999999999995
      • type: ndcg_at_3 value: 42.111
      • type: ndcg_at_5 value: 44.063
      • type: precision_at_1 value: 50.358000000000004
      • type: precision_at_10 value: 14.677999999999999
      • type: precision_at_100 value: 2.2950000000000004
      • type: precision_at_1000 value: 0.271
      • type: precision_at_3 value: 31.77
      • type: precision_at_5 value: 23.375
      • type: recall_at_1 value: 21.795
      • type: recall_at_10 value: 53.846000000000004
      • type: recall_at_100 value: 78.952
      • type: recall_at_1000 value: 90.41900000000001
      • type: recall_at_3 value: 37.257
      • type: recall_at_5 value: 44.661
    • task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics:
      • type: map_at_1 value: 9.728
      • type: map_at_10 value: 22.691
      • type: map_at_100 value: 31.734
      • type: map_at_1000 value: 33.464
      • type: map_at_3 value: 16.273
      • type: map_at_5 value: 19.016
      • type: mrr_at_1 value: 73.25
      • type: mrr_at_10 value: 80.782
      • type: mrr_at_100 value: 81.01899999999999
      • type: mrr_at_1000 value: 81.021
      • type: mrr_at_3 value: 79.583
      • type: mrr_at_5 value: 80.146
      • type: ndcg_at_1 value: 59.62499999999999
      • type: ndcg_at_10 value: 46.304
      • type: ndcg_at_100 value: 51.23
      • type: ndcg_at_1000 value: 58.048
      • type: ndcg_at_3 value: 51.541000000000004
      • type: ndcg_at_5 value: 48.635
      • type: precision_at_1 value: 73.25
      • type: precision_at_10 value: 36.375
      • type: precision_at_100 value: 11.53
      • type: precision_at_1000 value: 2.23
      • type: precision_at_3 value: 55.583000000000006
      • type: precision_at_5 value: 47.15
      • type: recall_at_1 value: 9.728
      • type: recall_at_10 value: 28.793999999999997
      • type: recall_at_100 value: 57.885
      • type: recall_at_1000 value: 78.759
      • type: recall_at_3 value: 17.79
      • type: recall_at_5 value: 21.733
    • task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
      • type: accuracy value: 46.775
      • type: f1 value: 41.89794273264891
    • task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics:
      • type: map_at_1 value: 85.378
      • type: map_at_10 value: 91.51
      • type: map_at_100 value: 91.666
      • type: map_at_1000 value: 91.676
      • type: map_at_3 value: 90.757
      • type: map_at_5 value: 91.277
      • type: mrr_at_1 value: 91.839
      • type: mrr_at_10 value: 95.49
      • type: mrr_at_100 value: 95.493
      • type: mrr_at_1000 value: 95.493
      • type: mrr_at_3 value: 95.345
      • type: mrr_at_5 value: 95.47200000000001
      • type: ndcg_at_1 value: 91.839
      • type: ndcg_at_10 value: 93.806
      • type: ndcg_at_100 value: 94.255
      • type: ndcg_at_1000 value: 94.399
      • type: ndcg_at_3 value: 93.027
      • type: ndcg_at_5 value: 93.51
      • type: precision_at_1 value: 91.839
      • type: precision_at_10 value: 10.93
      • type: precision_at_100 value: 1.1400000000000001
      • type: precision_at_1000 value: 0.117
      • type: precision_at_3 value: 34.873
      • type: precision_at_5 value: 21.44
      • type: recall_at_1 value: 85.378
      • type: recall_at_10 value: 96.814
      • type: recall_at_100 value: 98.386
      • type: recall_at_1000 value: 99.21600000000001
      • type: recall_at_3 value: 94.643
      • type: recall_at_5 value: 95.976
    • task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics:
      • type: map_at_1 value: 32.190000000000005
      • type: map_at_10 value: 53.605000000000004
      • type: map_at_100 value: 55.550999999999995
      • type: map_at_1000 value: 55.665
      • type: map_at_3 value: 46.62
      • type: map_at_5 value: 50.517999999999994
      • type: mrr_at_1 value: 60.34
      • type: mrr_at_10 value: 70.775
      • type: mrr_at_100 value: 71.238
      • type: mrr_at_1000 value: 71.244
      • type: mrr_at_3 value: 68.72399999999999
      • type: mrr_at_5 value: 69.959
      • type: ndcg_at_1 value: 60.34
      • type: ndcg_at_10 value: 63.226000000000006
      • type: ndcg_at_100 value: 68.60300000000001
      • type: ndcg_at_1000 value: 69.901
      • type: ndcg_at_3 value: 58.048
      • type: ndcg_at_5 value: 59.789
      • type: precision_at_1 value: 60.34
      • type: precision_at_10 value: 17.130000000000003
      • type: precision_at_100 value: 2.29
      • type: precision_at_1000 value: 0.256
      • type: precision_at_3 value: 38.323
      • type: precision_at_5 value: 27.87
      • type: recall_at_1 value: 32.190000000000005
      • type: recall_at_10 value: 73.041
      • type: recall_at_100 value: 91.31
      • type: recall_at_1000 value: 98.104
      • type: recall_at_3 value: 53.70399999999999
      • type: recall_at_5 value: 62.358999999999995
    • task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics:
      • type: map_at_1 value: 43.511
      • type: map_at_10 value: 58.15
      • type: map_at_100 value: 58.95399999999999
      • type: map_at_1000 value: 59.018
      • type: map_at_3 value: 55.31700000000001
      • type: map_at_5 value: 57.04900000000001
      • type: mrr_at_1 value: 87.022
      • type: mrr_at_10 value: 91.32000000000001
      • type: mrr_at_100 value: 91.401
      • type: mrr_at_1000 value: 91.403
      • type: mrr_at_3 value: 90.77
      • type: mrr_at_5 value: 91.156
      • type: ndcg_at_1 value: 87.022
      • type: ndcg_at_10 value: 68.183
      • type: ndcg_at_100 value: 70.781
      • type: ndcg_at_1000 value: 72.009
      • type: ndcg_at_3 value: 64.334
      • type: ndcg_at_5 value: 66.449
      • type: precision_at_1 value: 87.022
      • type: precision_at_10 value: 13.406
      • type: precision_at_100 value: 1.542
      • type: precision_at_1000 value: 0.17099999999999999
      • type: precision_at_3 value: 39.023
      • type: precision_at_5 value: 25.080000000000002
      • type: recall_at_1 value: 43.511
      • type: recall_at_10 value: 67.02900000000001
      • type: recall_at_100 value: 77.11
      • type: recall_at_1000 value: 85.294
      • type: recall_at_3 value: 58.535000000000004
      • type: recall_at_5 value: 62.70099999999999
    • task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
      • type: accuracy value: 92.0996
      • type: ap value: 87.86206089096373
      • type: f1 value: 92.07554547510763
    • task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics:
      • type: map_at_1 value: 23.179
      • type: map_at_10 value: 35.86
      • type: map_at_100 value: 37.025999999999996
      • type: map_at_1000 value: 37.068
      • type: map_at_3 value: 31.921
      • type: map_at_5 value: 34.172000000000004
      • type: mrr_at_1 value: 23.926
      • type: mrr_at_10 value: 36.525999999999996
      • type: mrr_at_100 value: 37.627
      • type: mrr_at_1000 value: 37.665
      • type: mrr_at_3 value: 32.653
      • type: mrr_at_5 value: 34.897
      • type: ndcg_at_1 value: 23.910999999999998
      • type: ndcg_at_10 value: 42.927
      • type: ndcg_at_100 value: 48.464
      • type: ndcg_at_1000 value: 49.533
      • type: ndcg_at_3 value: 34.910000000000004
      • type: ndcg_at_5 value: 38.937
      • type: precision_at_1 value: 23.910999999999998
      • type: precision_at_10 value: 6.758
      • type: precision_at_100 value: 0.9520000000000001
      • type: precision_at_1000 value: 0.104
      • type: precision_at_3 value: 14.838000000000001
      • type: precision_at_5 value: 10.934000000000001
      • type: recall_at_1 value: 23.179
      • type: recall_at_10 value: 64.622
      • type: recall_at_100 value: 90.135
      • type: recall_at_1000 value: 98.301
      • type: recall_at_3 value: 42.836999999999996
      • type: recall_at_5 value: 52.512
    • task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
      • type: accuracy value: 96.59598723210215
      • type: f1 value: 96.41913500001952
    • task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
      • type: accuracy value: 82.89557683538533
      • type: f1 value: 63.379319722356264
    • task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
      • type: accuracy value: 78.93745796906524
      • type: f1 value: 75.71616541785902
    • task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
      • type: accuracy value: 81.41223940820443
      • type: f1 value: 81.2877893719078
    • task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
      • type: v_measure value: 35.03682528325662
    • task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
      • type: v_measure value: 32.942529406124
    • task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
      • type: map value: 31.459949660460317
      • type: mrr value: 32.70509582031616
    • task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics:
      • type: map_at_1 value: 6.497
      • type: map_at_10 value: 13.843
      • type: map_at_100 value: 17.713
      • type: map_at_1000 value: 19.241
      • type: map_at_3 value: 10.096
      • type: map_at_5 value: 11.85
      • type: mrr_at_1 value: 48.916
      • type: mrr_at_10 value: 57.764
      • type: mrr_at_100 value: 58.251
      • type: mrr_at_1000 value: 58.282999999999994
      • type: mrr_at_3 value: 55.623999999999995
      • type: mrr_at_5 value: 57.018
      • type: ndcg_at_1 value: 46.594
      • type: ndcg_at_10 value: 36.945
      • type: ndcg_at_100 value: 34.06
      • type: ndcg_at_1000 value: 43.05
      • type: ndcg_at_3 value: 41.738
      • type: ndcg_at_5 value: 39.330999999999996
      • type: precision_at_1 value: 48.916
      • type: precision_at_10 value: 27.43
      • type: precision_at_100 value: 8.616
      • type: precision_at_1000 value: 2.155
      • type: precision_at_3 value: 39.112
      • type: precision_at_5 value: 33.808
      • type: recall_at_1 value: 6.497
      • type: recall_at_10 value: 18.163
      • type: recall_at_100 value: 34.566
      • type: recall_at_1000 value: 67.15
      • type: recall_at_3 value: 11.100999999999999
      • type: recall_at_5 value: 14.205000000000002
    • task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics:
      • type: map_at_1 value: 31.916
      • type: map_at_10 value: 48.123
      • type: map_at_100 value: 49.103
      • type: map_at_1000 value: 49.131
      • type: map_at_3 value: 43.711
      • type: map_at_5 value: 46.323
      • type: mrr_at_1 value: 36.181999999999995
      • type: mrr_at_10 value: 50.617999999999995
      • type: mrr_at_100 value: 51.329
      • type: mrr_at_1000 value: 51.348000000000006
      • type: mrr_at_3 value: 47.010999999999996
      • type: mrr_at_5 value: 49.175000000000004
      • type: ndcg_at_1 value: 36.181999999999995
      • type: ndcg_at_10 value: 56.077999999999996
      • type: ndcg_at_100 value: 60.037
      • type: ndcg_at_1000 value: 60.63499999999999
      • type: ndcg_at_3 value: 47.859
      • type: ndcg_at_5 value: 52.178999999999995
      • type: precision_at_1 value: 36.181999999999995
      • type: precision_at_10 value: 9.284
      • type: precision_at_100 value: 1.149
      • type: precision_at_1000 value: 0.121
      • type: precision_at_3 value: 22.006999999999998
      • type: precision_at_5 value: 15.695
      • type: recall_at_1 value: 31.916
      • type: recall_at_10 value: 77.771
      • type: recall_at_100 value: 94.602
      • type: recall_at_1000 value: 98.967
      • type: recall_at_3 value: 56.528
      • type: recall_at_5 value: 66.527
    • task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
      • type: map_at_1 value: 71.486
      • type: map_at_10 value: 85.978
      • type: map_at_100 value: 86.587
      • type: map_at_1000 value: 86.598
      • type: map_at_3 value: 83.04899999999999
      • type: map_at_5 value: 84.857
      • type: mrr_at_1 value: 82.32000000000001
      • type: mrr_at_10 value: 88.64
      • type: mrr_at_100 value: 88.702
      • type: mrr_at_1000 value: 88.702
      • type: mrr_at_3 value: 87.735
      • type: mrr_at_5 value: 88.36
      • type: ndcg_at_1 value: 82.34
      • type: ndcg_at_10 value: 89.67
      • type: ndcg_at_100 value: 90.642
      • type: ndcg_at_1000 value: 90.688
      • type: ndcg_at_3 value: 86.932
      • type: ndcg_at_5 value: 88.408
      • type: precision_at_1 value: 82.34
      • type: precision_at_10 value: 13.675999999999998
      • type: precision_at_100 value: 1.544
      • type: precision_at_1000 value: 0.157
      • type: precision_at_3 value: 38.24
      • type: precision_at_5 value: 25.068
      • type: recall_at_1 value: 71.486
      • type: recall_at_10 value: 96.844
      • type: recall_at_100 value: 99.843
      • type: recall_at_1000 value: 99.996
      • type: recall_at_3 value: 88.92099999999999
      • type: recall_at_5 value: 93.215
    • task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
      • type: v_measure value: 59.75758437908334
    • task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
      • type: v_measure value: 68.03497914092789
    • task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
      • type: map_at_1 value: 5.808
      • type: map_at_10 value: 16.059
      • type: map_at_100 value: 19.048000000000002
      • type: map_at_1000 value: 19.43
      • type: map_at_3 value: 10.953
      • type: map_at_5 value: 13.363
      • type: mrr_at_1 value: 28.7
      • type: mrr_at_10 value: 42.436
      • type: mrr_at_100 value: 43.599
      • type: mrr_at_1000 value: 43.62
      • type: mrr_at_3 value: 38.45
      • type: mrr_at_5 value: 40.89
      • type: ndcg_at_1 value: 28.7
      • type: ndcg_at_10 value: 26.346000000000004
      • type: ndcg_at_100 value: 36.758
      • type: ndcg_at_1000 value: 42.113
      • type: ndcg_at_3 value: 24.254
      • type: ndcg_at_5 value: 21.506
      • type: precision_at_1 value: 28.7
      • type: precision_at_10 value: 13.969999999999999
      • type: precision_at_100 value: 2.881
      • type: precision_at_1000 value: 0.414
      • type: precision_at_3 value: 22.933
      • type: precision_at_5 value: 19.220000000000002
      • type: recall_at_1 value: 5.808
      • type: recall_at_10 value: 28.310000000000002
      • type: recall_at_100 value: 58.475
      • type: recall_at_1000 value: 84.072
      • type: recall_at_3 value: 13.957
      • type: recall_at_5 value: 19.515
    • task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
      • type: cos_sim_pearson value: 82.39274129958557
      • type: cos_sim_spearman value: 79.78021235170053
      • type: euclidean_pearson value: 79.35335401300166
      • type: euclidean_spearman value: 79.7271870968275
      • type: manhattan_pearson value: 79.35256263340601
      • type: manhattan_spearman value: 79.76036386976321
    • task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
      • type: cos_sim_pearson value: 83.99130429246708
      • type: cos_sim_spearman value: 73.88322811171203
      • type: euclidean_pearson value: 80.7569419170376
      • type: euclidean_spearman value: 73.82542155409597
      • type: manhattan_pearson value: 80.79468183847625
      • type: manhattan_spearman value: 73.87027144047784
    • task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
      • type: cos_sim_pearson value: 84.88548789489907
      • type: cos_sim_spearman value: 85.07535893847255
      • type: euclidean_pearson value: 84.6637222061494
      • type: euclidean_spearman value: 85.14200626702456
      • type: manhattan_pearson value: 84.75327892344734
      • type: manhattan_spearman value: 85.24406181838596
    • task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
      • type: cos_sim_pearson value: 82.88140039325008
      • type: cos_sim_spearman value: 79.61211268112362
      • type: euclidean_pearson value: 81.29639728816458
      • type: euclidean_spearman value: 79.51284578041442
      • type: manhattan_pearson value: 81.3381797137111
      • type: manhattan_spearman value: 79.55683684039808
    • task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
      • type: cos_sim_pearson value: 85.16716737270485
      • type: cos_sim_spearman value: 86.14823841857738
      • type: euclidean_pearson value: 85.36325733440725
      • type: euclidean_spearman value: 86.04919691402029
      • type: manhattan_pearson value: 85.3147511385052
      • type: manhattan_spearman value: 86.00676205857764
    • task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
      • type: cos_sim_pearson value: 80.34266645861588
      • type: cos_sim_spearman value: 81.59914035005882
      • type: euclidean_pearson value: 81.15053076245988
      • type: euclidean_spearman value: 81.52776915798489
      • type: manhattan_pearson value: 81.1819647418673
      • type: manhattan_spearman value: 81.57479527353556
    • task: type: STS dataset: type: mteb/sts17-crosslingual-sts name: MTEB STS17 (en-en) config: en-en split: test revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d metrics:
      • type: cos_sim_pearson value: 89.38263326821439
      • type: cos_sim_spearman value: 89.10946308202642
      • type: euclidean_pearson value: 88.87831312540068
      • type: euclidean_spearman value: 89.03615865973664
      • type: manhattan_pearson value: 88.79835539970384
      • type: manhattan_spearman value: 88.9766156339753
    • task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics:
      • type: cos_sim_pearson value: 70.1574915581685
      • type: cos_sim_spearman value: 70.59144980004054
      • type: euclidean_pearson value: 71.43246306918755
      • type: euclidean_spearman value: 70.5544189562984
      • type: manhattan_pearson value: 71.4071414609503
      • type: manhattan_spearman value: 70.31799126163712
    • task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
      • type: cos_sim_pearson value: 83.36215796635351
      • type: cos_sim_spearman value: 83.07276756467208
      • type: euclidean_pearson value: 83.06690453635584
      • type: euclidean_spearman value: 82.9635366303289
      • type: manhattan_pearson value: 83.04994049700815
      • type: manhattan_spearman value: 82.98120125356036
    • task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
      • type: map value: 86.92530011616722
      • type: mrr value: 96.21826793395421
    • task: type: Retrieval dataset: type: mteb/scifact name: MTEB SciFact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics:
      • type: map_at_1 value: 65.75
      • type: map_at_10 value: 77.701
      • type: map_at_100 value: 78.005
      • type: map_at_1000 value: 78.006
      • type: map_at_3 value: 75.48
      • type: map_at_5 value: 76.927
      • type: mrr_at_1 value: 68.333
      • type: mrr_at_10 value: 78.511
      • type: mrr_at_100 value: 78.704
      • type: mrr_at_1000 value: 78.704
      • type: mrr_at_3 value: 77
      • type: mrr_at_5 value: 78.083
      • type: ndcg_at_1 value: 68.333
      • type: ndcg_at_10 value: 82.42699999999999
      • type: ndcg_at_100 value: 83.486
      • type: ndcg_at_1000 value: 83.511
      • type: ndcg_at_3 value: 78.96300000000001
      • type: ndcg_at_5 value: 81.028
      • type: precision_at_1 value: 68.333
      • type: precision_at_10 value: 10.667
      • type: precision_at_100 value: 1.127
      • type: precision_at_1000 value: 0.11299999999999999
      • type: precision_at_3 value: 31.333
      • type: precision_at_5 value: 20.133000000000003
      • type: recall_at_1 value: 65.75
      • type: recall_at_10 value: 95.578
      • type: recall_at_100 value: 99.833
      • type: recall_at_1000 value: 100
      • type: recall_at_3 value: 86.506
      • type: recall_at_5 value: 91.75
    • task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
      • type: cos_sim_accuracy value: 99.75247524752476
      • type: cos_sim_ap value: 94.16065078045173
      • type: cos_sim_f1 value: 87.22986247544205
      • type: cos_sim_precision value: 85.71428571428571
      • type: cos_sim_recall value: 88.8
      • type: dot_accuracy value: 99.74554455445545
      • type: dot_ap value: 93.90633887037264
      • type: dot_f1 value: 86.9873417721519
      • type: dot_precision value: 88.1025641025641
      • type: dot_recall value: 85.9
      • type: euclidean_accuracy value: 99.75247524752476
      • type: euclidean_ap value: 94.17466319018055
      • type: euclidean_f1 value: 87.3405299313052
      • type: euclidean_precision value: 85.74181117533719
      • type: euclidean_recall value: 89
      • type: manhattan_accuracy value: 99.75445544554455
      • type: manhattan_ap value: 94.27688371923577
      • type: manhattan_f1 value: 87.74002954209749
      • type: manhattan_precision value: 86.42095053346266
      • type: manhattan_recall value: 89.1
      • type: max_accuracy value: 99.75445544554455
      • type: max_ap value: 94.27688371923577
      • type: max_f1 value: 87.74002954209749
    • task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
      • type: v_measure value: 71.26500637517056
    • task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
      • type: v_measure value: 39.17507906280528
    • task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
      • type: map value: 52.4848744828509
      • type: mrr value: 53.33678168236992
    • task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
      • type: cos_sim_pearson value: 30.599864323827887
      • type: cos_sim_spearman value: 30.91116204665598
      • type: dot_pearson value: 30.82637894269936
      • type: dot_spearman value: 30.957573868416066
    • task: type: Retrieval dataset: type: mteb/trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics:
      • type: map_at_1 value: 0.23600000000000002
      • type: map_at_10 value: 1.892
      • type: map_at_100 value: 11.586
      • type: map_at_1000 value: 27.761999999999997
      • type: map_at_3 value: 0.653
      • type: map_at_5 value: 1.028
      • type: mrr_at_1 value: 88
      • type: mrr_at_10 value: 94
      • type: mrr_at_100 value: 94
      • type: mrr_at_1000 value: 94
      • type: mrr_at_3 value: 94
      • type: mrr_at_5 value: 94
      • type: ndcg_at_1 value: 82
      • type: ndcg_at_10 value: 77.48899999999999
      • type: ndcg_at_100 value: 60.141
      • type: ndcg_at_1000 value: 54.228
      • type: ndcg_at_3 value: 82.358
      • type: ndcg_at_5 value: 80.449
      • type: precision_at_1 value: 88
      • type: precision_at_10 value: 82.19999999999999
      • type: precision_at_100 value: 61.760000000000005
      • type: precision_at_1000 value: 23.684
      • type: precision_at_3 value: 88
      • type: precision_at_5 value: 85.6
      • type: recall_at_1 value: 0.23600000000000002
      • type: recall_at_10 value: 2.117
      • type: recall_at_100 value: 14.985000000000001
      • type: recall_at_1000 value: 51.107
      • type: recall_at_3 value: 0.688
      • type: recall_at_5 value: 1.1039999999999999
    • task: type: Retrieval dataset: type: mteb/touche2020 name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics:
      • type: map_at_1 value: 2.3040000000000003
      • type: map_at_10 value: 9.025
      • type: map_at_100 value: 15.312999999999999
      • type: map_at_1000 value: 16.954
      • type: map_at_3 value: 4.981
      • type: map_at_5 value: 6.32
      • type: mrr_at_1 value: 24.490000000000002
      • type: mrr_at_10 value: 39.835
      • type: mrr_at_100 value: 40.8
      • type: mrr_at_1000 value: 40.8
      • type: mrr_at_3 value: 35.034
      • type: mrr_at_5 value: 37.687
      • type: ndcg_at_1 value: 22.448999999999998
      • type: ndcg_at_10 value: 22.545
      • type: ndcg_at_100 value: 35.931999999999995
      • type: ndcg_at_1000 value: 47.665
      • type: ndcg_at_3 value: 23.311
      • type: ndcg_at_5 value: 22.421
      • type: precision_at_1 value: 24.490000000000002
      • type: precision_at_10 value: 20.408
      • type: precision_at_100 value: 7.815999999999999
      • type: precision_at_1000 value: 1.553
      • type: precision_at_3 value: 25.169999999999998
      • type: precision_at_5 value: 23.265
      • type: recall_at_1 value: 2.3040000000000003
      • type: recall_at_10 value: 15.693999999999999
      • type: recall_at_100 value: 48.917
      • type: recall_at_1000 value: 84.964
      • type: recall_at_3 value: 6.026
      • type: recall_at_5 value: 9.066
    • task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
      • type: accuracy value: 82.6074
      • type: ap value: 23.187467098602013
      • type: f1 value: 65.36829506379657
    • task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
      • type: accuracy value: 63.16355404640635
      • type: f1 value: 63.534725639863346
    • task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
      • type: v_measure value: 50.91004094411276
    • task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
      • type: cos_sim_accuracy value: 86.55301901412649
      • type: cos_sim_ap value: 75.25312618556728
      • type: cos_sim_f1 value: 68.76561719140429
      • type: cos_sim_precision value: 65.3061224489796
      • type: cos_sim_recall value: 72.61213720316623
      • type: dot_accuracy value: 86.29671574178936
      • type: dot_ap value: 75.11910195501207
      • type: dot_f1 value: 68.44048376830045
      • type: dot_precision value: 66.12546125461255
      • type: dot_recall value: 70.92348284960423
      • type: euclidean_accuracy value: 86.5828217202122
      • type: euclidean_ap value: 75.22986344900924
      • type: euclidean_f1 value: 68.81267797449549
      • type: euclidean_precision value: 64.8238861674831
      • type: euclidean_recall value: 73.3245382585752
      • type: manhattan_accuracy value: 86.61262442629791
      • type: manhattan_ap value: 75.24401608557328
      • type: manhattan_f1 value: 68.80473982483257
      • type: manhattan_precision value: 67.21187720181177
      • type: manhattan_recall value: 70.47493403693932
      • type: max_accuracy value: 86.61262442629791
      • type: max_ap value: 75.25312618556728
      • type: max_f1 value: 68.81267797449549
    • task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
      • type: cos_sim_accuracy value: 88.10688089416696
      • type: cos_sim_ap value: 84.17862178779863
      • type: cos_sim_f1 value: 76.17305208781748
      • type: cos_sim_precision value: 71.31246641590543
      • type: cos_sim_recall value: 81.74468740375731
      • type: dot_accuracy value: 88.1844995536927
      • type: dot_ap value: 84.33816725235876
      • type: dot_f1 value: 76.43554032918746
      • type: dot_precision value: 74.01557767200346
      • type: dot_recall value: 79.0190945488143
      • type: euclidean_accuracy value: 88.07001203089223
      • type: euclidean_ap value: 84.12267000814985
      • type: euclidean_f1 value: 76.12232600180778
      • type: euclidean_precision value: 74.50604541433205
      • type: euclidean_recall value: 77.81028641823221
      • type: manhattan_accuracy value: 88.06419063142779
      • type: manhattan_ap value: 84.11648917164187
      • type: manhattan_f1 value: 76.20579953925474
      • type: manhattan_precision value: 72.56772755762935
      • type: manhattan_recall value: 80.22790267939637
      • type: max_accuracy value: 88.1844995536927
      • type: max_ap value: 84.33816725235876
      • type: max_f1 value: 76.43554032918746

gte-large-en-v1.5

We introduce gte-v1.5 series, upgraded gte embeddings that support the context length of up to 8192, while further enhancing model performance. The models are built upon the transformer++ encoder backbone (BERT + RoPE + GLU).

The gte-v1.5 series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation).

We also present the gte-Qwen1.5-7B-instruct, a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.

  • Developed by: Institute for Intelligent Computing, Alibaba Group
  • Model type: Text Embeddings
  • Paper: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval

Model list

Models Language Model Size Max Seq. Length Dimension MTEB-en LoCo
gte-Qwen1.5-7B-instruct Multiple 7720 32768 4096 67.34 87.57
gte-large-en-v1.5 English 434 8192 1024 65.39 86.71
gte-base-en-v1.5 English 137 8192 768 64.11 87.44

How to Get Started with the Model

Use the code below to get started with the model.

# Requires transformers>=4.36.0

import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

input_texts = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "Beijing",
    "sorting algorithms"
]

model_path = 'Alibaba-NLP/gte-large-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
 
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.

Use with sentence-transformers:

# Requires sentence_transformers>=2.7.0

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = ['That is a happy person', 'That is a very happy person']

model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))

Use with transformers.js:

// npm i @xenova/transformers
import { pipeline, dot } from '@xenova/transformers';

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-large-en-v1.5', {
    quantized: false, // Comment out this line to use the quantized version
});

// Generate sentence embeddings
const sentences = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "Beijing",
    "sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });

// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [41.86354093370361, 77.07076371259589, 37.02981979677899]

Training Details

Training Data

  • Masked language modeling (MLM): c4-en
  • Weak-supervised contrastive pre-training (CPT): GTE pre-training data
  • Supervised contrastive fine-tuning: GTE fine-tuning data

Training Procedure

To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.

The entire training process is as follows:

  • MLM-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000
  • MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000
  • MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000
  • CPT: max_len 512, lr 5e-5, batch_size 28672, num_steps 100000
  • Fine-tuning: TODO

Evaluation

MTEB

The results of other models are retrieved from MTEB leaderboard.

The gte evaluation setting: mteb==1.2.0, fp16 auto mix precision, max_length=8192, and set ntk scaling factor to 2 (equivalent to rope_base * 2).

Model Name Param Size (M) Dimension Sequence Length Average (56) Class. (12) Clust. (11) Pair Class. (3) Reran. (4) Retr. (15) STS (10) Summ. (1)
gte-large-en-v1.5 409 1024 8192 65.39 77.75 47.95 84.63 58.50 57.91 81.43 30.91
mxbai-embed-large-v1 335 1024 512 64.68 75.64 46.71 87.2 60.11 54.39 85 32.71
multilingual-e5-large-instruct 560 1024 514 64.41 77.56 47.1 86.19 58.58 52.47 84.78 30.39
bge-large-en-v1.5 335 1024 512 64.23 75.97 46.08 87.12 60.03 54.29 83.11 31.61
gte-base-en-v1.5 137 768 8192 64.11 77.17 46.82 85.33 57.66 54.09 81.97 31.17
bge-base-en-v1.5 109 768 512 63.55 75.53 45.77 86.55 58.86 53.25 82.4 31.07

LoCo

Model Name Dimension Sequence Length Average (5) QsmsumRetrieval SummScreenRetrieval QasperAbastractRetrieval QasperTitleRetrieval GovReportRetrieval
gte-qwen1.5-7b 4096 32768 87.57 49.37 93.10 99.67 97.54 98.21
gte-large-v1.5 1024 8192 86.71 44.55 92.61 99.82 97.81 98.74
gte-base-v1.5 768 8192 87.44 49.91 91.78 99.82 97.13 98.58

Citation

If you find our paper or models helpful, please consider citing them as follows:

@article{zhang2024mgte,
  title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
  author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
  journal={arXiv preprint arXiv:2407.19669},
  year={2024}
}

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

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magnet:?xt=urn:btih:ece0c8f6d54bf80835e0710b527cefd2d569953c&dn=Alibaba-NLP_gte-large-en-v1.5

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Files & hashes

PathSizeMethodHash
1_Pooling/config.json297 B (297 B)sha1-git-blob553a16bda12e2a6d2bb35de78c6ea264b7856e6a
README.md70.1 KB (71,774 B)sha1-git-blob8b8ef01e0f0b3da21c81849b4a68a71e6958615f
config.json1.3 KB (1,349 B)sha1-git-blob9d775351bdd1f53cf7479ed8955eb429209261c1
model.safetensors1.62 GB (1,736,585,680 B)sha256-lfsfe6e4200b833d5332b7c61859d7f4ff204211b1583d732353efe1b7594176cf2
modules.json229 B (229 B)sha1-git-blobf7640f94e81bb7f4f04daf1668850b38763a13d9
sentence_bert_config.json54 B (54 B)sha1-git-blob0140ba1eac83a3c9b857d64baba91969d988624b
special_tokens_map.json695 B (695 B)sha1-git-blob9bbecc17cabbcbd3112c14d6982b51403b264bfa
tokenizer.json695.0 KB (711,661 B)sha1-git-blob8f778e7d950e055b18b53222c75d2c54c3732df5
tokenizer_config.json1.4 KB (1,384 B)sha1-git-blob633454d5fdd32d07ed2a1762e56485220ef20095
vocab.txt226.1 KB (231,508 B)sha1-git-blobfb140275c155a9c7c5a3b3e0e77a9e839594a938

Provenance

Upstream repositoryAlibaba-NLP/gte-large-en-v1.5
Revision (pinned)104333d6af6f97649377c2afbde10a7704870c7b
Fetched at2026-08-24T07:54:31Z
License at fetchapache-2.0
Snapshot toolhuggingface · seedbank 0.1.0

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