---
base_model: answerdotai/ModernBERT-base
datasets:
- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
language:
- en
library_name: sentence-transformers
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11662655
- loss:CachedMultipleNegativesRankingLoss
widget:
- source_sentence: what county is lyndhurst, ohio in
  sentences:
  - This article is about the song written by Kenneth Gamble, Leon Huff and Cary Gilbert.
    For the Tina Turner song, see Don't Leave Me This Way (Tina Turner song). Don't
    Leave Me This Way is a song written by Kenneth Gamble, Leon Huff and Cary Gilbert.
    First charting as a hit for Harold Melvin & the Blue Notes featuring Teddy Pendergrass,
    an act on Gamble & Huff's Philadelphia International label in 1975, Don't Leave
    Me This Way was later a huge disco hit for Motown artist Thelma Houston in 1977.
  - "Lyndhurst is a city in Cuyahoga County, Ohio, United States. The population was\
    \ 14,001 at the 2010 census. Lyndhurst is located in northeastern Ohio, and is\
    \ a suburb of Cleveland. A small part of Lyndhurst was originally part of Mayfield\
    \ Township. It used to be called Euclidville before Lyndhurst was chosen. Lyndhurst\
    \ is located at 41°31â\x80²17â\x80³N 81°29â\x80²25â\x80³W / 41.52139°N 81.49028°W\
    \ / 41.52139; -81.49028 (41.521352, -81.490141)."
  - Welcome to Trumbull County... Trumbull County, the county seat, located in Warren,
    Ohio, consists of a combination of both urban and rural communities situated in
    the northeast corner of Ohio. It is situated roughly between the Youngstown, Cleveland
    and Akron corridors.
- source_sentence: who founded the american graphophone company
  sentences:
  - In 1886, Graham Bell and Charles Sumner Tainter founded the American Graphophone
    Company to distribute and sell graphophones in the US and Canada under license
    from the Volta Graphophone Company. In 1890, the American Graphophone Company
    stopped production of new phonographs due to sagging orders.
  - ShelfGenie How much does a ShelfGenie franchise cost? ShelfGenie has a franchise
    fee of up to $45,000, with a total initial investment range of $70,100 to $107,750.
    Local ShelfGenie franchise opportunities. ShelfGenie is looking to grow in a number
    of cities around the country. To find out if there's a franchise opportunity in
    your city, unlock more information.
  - "A+E Networks. The technology that made the modern music business possible came\
    \ into existence in the New Jersey laboratory where Thomas Alva Edison created\
    \ the first device to both record sound and play it back. He was awarded U.S.\
    \ Patent No. 200,521 for his inventionâ\x80\x93the phonographâ\x80\x93on this\
    \ day in 1878."
- source_sentence: is housekeeping camp flooded?
  sentences:
  - 'What is the importance of housekeeping at work? A: Workplace housekeeping promotes
    sanitation, safety, organization and productivity. It also boosts morale. Daily
    housekeeping maintenance keeps the workplac... Full Answer >'
  - The back patio area of a cabin is partially submerged in flood water at Housekeeping
    Camp on Monday, Jan. 9, 2017, in Yosemite National Park. The Merced River, swollen
    with storm runoff, crested at 12.7 feet at 4 a.m. SILVIA FLORES sflores@fresnobee.com.
  - "1 Bake for 8 minutes, then rotate the pan and check the underside of the bagels.\
    \ 2  If theyâ\x80\x99re getting too dark, place another pan under the baking sheet.\
    \ ( 3 Doubling the pan will insulate the first baking sheet.) Bake for another\
    \ 8 to 12 minutes, until the bagels are a golden brown. 4  13."
- source_sentence: causes for infection in the nerve of tooth
  sentences:
  - If a cavity is causing the toothache, your dentist will fill the cavity or possibly
    extract the tooth, if necessary. A root canal might be needed if the cause of
    the toothache is determined to be an infection of the tooth's nerve. Bacteria
    that have worked their way into the inner aspects of the tooth cause such an infection.
    An antibiotic may be prescribed if there is fever or swelling of the jaw.
  - "According to Article III, Section 1 of the Constitution, judges and justices\
    \ of the Judicial Branch serve during good behavior.. This means they are appointed\
    \ for life, unles â\x80¦ s they are impeached and removed from office. + 50 others\
    \ found this useful.he term length for members of the House are two years and\
    \ a staggering six years for members of the Senate."
  - Inflamed or infected pulp (pulpitis) most often causes a toothache. To relieve
    the pain and prevent further complications, the tooth may be extracted (surgically
    removed) or saved by root canal treatment.
- source_sentence: what county is hayden in
  sentences:
  - Normally, the Lead Agency is the agency with general governmental powers such
    as a city or a county. Agencies with limited powers or districts that provide
    a public service/utility such as a recreation and park district will tend to be
    a Responsible Agency.
  - According to the United States Census Bureau, the city has a total area of 9.61
    square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01
    square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake,
    and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is
    located on U.S. Route 95 at the junction of Route 41. It is also four miles (6
    km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest
    of Hayden.
  - Hayden is a city in Kootenai County, Idaho, United States. Located in the northern
    portion of the state, just north of Coeur d'Alene, its population was 13,294 at
    the 2010 census.
model-index:
- name: SentenceTransformer based on answerdotai/ModernBERT-base
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: msmarco co condenser dev
      type: msmarco-co-condenser-dev
    metrics:
    - type: cosine_accuracy
      value: 0.984
      name: Cosine Accuracy
  - dataset:
      config: en-ext
      name: MTEB AmazonCounterfactualClassification (en-ext)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
    - type: accuracy
      value: 65.997
    - type: f1
      value: 53.595099999999995
    - type: f1_weighted
      value: 72.5304
    - type: ap
      value: 16.3093
    - type: ap_weighted
      value: 16.3093
    - type: main_score
      value: 65.997
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB AmazonCounterfactualClassification (en)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    metrics:
    - type: accuracy
      value: 64.19399999999999
    - type: f1
      value: 58.5969
    - type: f1_weighted
      value: 67.7482
    - type: ap
      value: 28.0748
    - type: ap_weighted
      value: 28.0748
    - type: main_score
      value: 64.19399999999999
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB AmazonPolarityClassification (default)
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
      split: test
      type: mteb/amazon_polarity
    metrics:
    - type: accuracy
      value: 67.1713
    - type: f1
      value: 66.9443
    - type: f1_weighted
      value: 66.9443
    - type: ap
      value: 61.7296
    - type: ap_weighted
      value: 61.7296
    - type: main_score
      value: 67.1713
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB AmazonReviewsClassification (en)
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
      split: test
      type: mteb/amazon_reviews_multi
    metrics:
    - type: accuracy
      value: 32.318000000000005
    - type: f1
      value: 31.9973
    - type: f1_weighted
      value: 31.9973
    - type: main_score
      value: 32.318000000000005
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
    metrics:
    - type: ndcg_at_1
      value: 24.609
    - type: ndcg_at_3
      value: 36.278
    - type: ndcg_at_5
      value: 40.903
    - type: ndcg_at_10
      value: 46.381
    - type: ndcg_at_20
      value: 49.571
    - type: ndcg_at_100
      value: 51.666000000000004
    - type: ndcg_at_1000
      value: 52.098
    - type: map_at_1
      value: 24.609
    - type: map_at_3
      value: 33.404
    - type: map_at_5
      value: 35.968
    - type: map_at_10
      value: 38.204
    - type: map_at_20
      value: 39.113
    - type: map_at_100
      value: 39.432
    - type: map_at_1000
      value: 39.45
    - type: recall_at_1
      value: 24.609
    - type: recall_at_3
      value: 44.595
    - type: recall_at_5
      value: 55.832
    - type: recall_at_10
      value: 72.902
    - type: recall_at_20
      value: 85.277
    - type: recall_at_100
      value: 96.15899999999999
    - type: recall_at_1000
      value: 99.431
    - type: precision_at_1
      value: 24.609
    - type: precision_at_3
      value: 14.865
    - type: precision_at_5
      value: 11.166
    - type: precision_at_10
      value: 7.290000000000001
    - type: precision_at_20
      value: 4.263999999999999
    - type: precision_at_100
      value: 0.962
    - type: precision_at_1000
      value: 0.099
    - type: mrr_at_1
      value: 25.1067
    - type: mrr_at_3
      value: 33.5941
    - type: mrr_at_5
      value: 36.154599999999995
    - type: mrr_at_10
      value: 38.3849
    - type: mrr_at_20
      value: 39.3004
    - type: mrr_at_100
      value: 39.618900000000004
    - type: mrr_at_1000
      value: 39.6374
    - type: nauc_ndcg_at_1_max
      value: -5.6006
    - type: nauc_ndcg_at_1_std
      value: -4.8049
    - type: nauc_ndcg_at_1_diff1
      value: 14.902999999999999
    - type: nauc_ndcg_at_3_max
      value: -0.40800000000000003
    - type: nauc_ndcg_at_3_std
      value: -1.9375
    - type: nauc_ndcg_at_3_diff1
      value: 12.1454
    - type: nauc_ndcg_at_5_max
      value: -0.46849999999999997
    - type: nauc_ndcg_at_5_std
      value: -2.0393000000000003
    - type: nauc_ndcg_at_5_diff1
      value: 10.7318
    - type: nauc_ndcg_at_10_max
      value: 3.0675
    - type: nauc_ndcg_at_10_std
      value: -0.3638
    - type: nauc_ndcg_at_10_diff1
      value: 11.3071
    - type: nauc_ndcg_at_20_max
      value: 2.5101
    - type: nauc_ndcg_at_20_std
      value: -0.3348
    - type: nauc_ndcg_at_20_diff1
      value: 11.5383
    - type: nauc_ndcg_at_100_max
      value: 1.0754
    - type: nauc_ndcg_at_100_std
      value: 0.1077
    - type: nauc_ndcg_at_100_diff1
      value: 11.8117
    - type: nauc_ndcg_at_1000_max
      value: 0.5791000000000001
    - type: nauc_ndcg_at_1000_std
      value: -0.6433
    - type: nauc_ndcg_at_1000_diff1
      value: 11.9586
    - type: nauc_map_at_1_max
      value: -5.6006
    - type: nauc_map_at_1_std
      value: -4.8049
    - type: nauc_map_at_1_diff1
      value: 14.902999999999999
    - type: nauc_map_at_3_max
      value: -1.5511000000000001
    - type: nauc_map_at_3_std
      value: -2.4433
    - type: nauc_map_at_3_diff1
      value: 12.6974
    - type: nauc_map_at_5_max
      value: -1.5512
    - type: nauc_map_at_5_std
      value: -2.4537
    - type: nauc_map_at_5_diff1
      value: 11.982
    - type: nauc_map_at_10_max
      value: -0.244
    - type: nauc_map_at_10_std
      value: -1.7697999999999998
    - type: nauc_map_at_10_diff1
      value: 12.253400000000001
    - type: nauc_map_at_20_max
      value: -0.477
    - type: nauc_map_at_20_std
      value: -1.8189
    - type: nauc_map_at_20_diff1
      value: 12.3218
    - type: nauc_map_at_100_max
      value: -0.6815
    - type: nauc_map_at_100_std
      value: -1.7488
    - type: nauc_map_at_100_diff1
      value: 12.3542
    - type: nauc_map_at_1000_max
      value: -0.6970999999999999
    - type: nauc_map_at_1000_std
      value: -1.7691999999999999
    - type: nauc_map_at_1000_diff1
      value: 12.359399999999999
    - type: nauc_recall_at_1_max
      value: -5.6006
    - type: nauc_recall_at_1_std
      value: -4.8049
    - type: nauc_recall_at_1_diff1
      value: 14.902999999999999
    - type: nauc_recall_at_3_max
      value: 2.6873
    - type: nauc_recall_at_3_std
      value: -0.6182
    - type: nauc_recall_at_3_diff1
      value: 10.6725
    - type: nauc_recall_at_5_max
      value: 2.5734
    - type: nauc_recall_at_5_std
      value: -0.9853000000000001
    - type: nauc_recall_at_5_diff1
      value: 6.8572
    - type: nauc_recall_at_10_max
      value: 17.8111
    - type: nauc_recall_at_10_std
      value: 5.651599999999999
    - type: nauc_recall_at_10_diff1
      value: 7.600999999999999
    - type: nauc_recall_at_20_max
      value: 23.985899999999997
    - type: nauc_recall_at_20_std
      value: 10.423499999999999
    - type: nauc_recall_at_20_diff1
      value: 7.0172
    - type: nauc_recall_at_100_max
      value: 30.766900000000003
    - type: nauc_recall_at_100_std
      value: 50.9552
    - type: nauc_recall_at_100_diff1
      value: 5.1657
    - type: nauc_recall_at_1000_max
      value: 25.4176
    - type: nauc_recall_at_1000_std
      value: 73.0444
    - type: nauc_recall_at_1000_diff1
      value: 16.6349
    - type: nauc_precision_at_1_max
      value: -5.6006
    - type: nauc_precision_at_1_std
      value: -4.8049
    - type: nauc_precision_at_1_diff1
      value: 14.902999999999999
    - type: nauc_precision_at_3_max
      value: 2.6873
    - type: nauc_precision_at_3_std
      value: -0.6182
    - type: nauc_precision_at_3_diff1
      value: 10.6725
    - type: nauc_precision_at_5_max
      value: 2.5734
    - type: nauc_precision_at_5_std
      value: -0.9853000000000001
    - type: nauc_precision_at_5_diff1
      value: 6.8572
    - type: nauc_precision_at_10_max
      value: 17.8111
    - type: nauc_precision_at_10_std
      value: 5.651599999999999
    - type: nauc_precision_at_10_diff1
      value: 7.600999999999999
    - type: nauc_precision_at_20_max
      value: 23.985899999999997
    - type: nauc_precision_at_20_std
      value: 10.423499999999999
    - type: nauc_precision_at_20_diff1
      value: 7.0172
    - type: nauc_precision_at_100_max
      value: 30.766900000000003
    - type: nauc_precision_at_100_std
      value: 50.9552
    - type: nauc_precision_at_100_diff1
      value: 5.1657
    - type: nauc_precision_at_1000_max
      value: 25.4176
    - type: nauc_precision_at_1000_std
      value: 73.0444
    - type: nauc_precision_at_1000_diff1
      value: 16.6349
    - type: nauc_mrr_at_1_max
      value: -5.1381000000000006
    - type: nauc_mrr_at_1_std
      value: -4.1855
    - type: nauc_mrr_at_1_diff1
      value: 13.2056
    - type: nauc_mrr_at_3_max
      value: -1.7795999999999998
    - type: nauc_mrr_at_3_std
      value: -2.3432
    - type: nauc_mrr_at_3_diff1
      value: 11.4369
    - type: nauc_mrr_at_5_max
      value: -1.7394
    - type: nauc_mrr_at_5_std
      value: -2.3168
    - type: nauc_mrr_at_5_diff1
      value: 10.7454
    - type: nauc_mrr_at_10_max
      value: -0.5075999999999999
    - type: nauc_mrr_at_10_std
      value: -1.6223
    - type: nauc_mrr_at_10_diff1
      value: 10.906699999999999
    - type: nauc_mrr_at_20_max
      value: -0.715
    - type: nauc_mrr_at_20_std
      value: -1.6808
    - type: nauc_mrr_at_20_diff1
      value: 10.9766
    - type: nauc_mrr_at_100_max
      value: -0.9223
    - type: nauc_mrr_at_100_std
      value: -1.6097000000000001
    - type: nauc_mrr_at_100_diff1
      value: 10.9945
    - type: nauc_mrr_at_1000_max
      value: -0.938
    - type: nauc_mrr_at_1000_std
      value: -1.63
    - type: nauc_mrr_at_1000_diff1
      value: 10.998800000000001
    - type: main_score
      value: 46.381
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ArxivClusteringP2P (default)
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
      split: test
      type: mteb/arxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 38.9781
    - type: v_measure_std
      value: 14.0064
    - type: main_score
      value: 38.9781
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB ArxivClusteringS2S (default)
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
      split: test
      type: mteb/arxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 28.964499999999997
    - type: v_measure_std
      value: 14.276900000000001
    - type: main_score
      value: 28.964499999999997
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB AskUbuntuDupQuestions (default)
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
      split: test
      type: mteb/askubuntudupquestions-reranking
    metrics:
    - type: map
      value: 57.9553
    - type: mrr
      value: 70.82929999999999
    - type: nAUC_map_max
      value: 23.2041
    - type: nAUC_map_std
      value: 21.0013
    - type: nAUC_map_diff1
      value: 6.7763
    - type: nAUC_mrr_max
      value: 33.143499999999996
    - type: nAUC_mrr_std
      value: 25.8722
    - type: nAUC_mrr_diff1
      value: 10.2834
    - type: main_score
      value: 57.9553
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    metrics:
    - type: pearson
      value: 85.0909
    - type: spearman
      value: 80.7386
    - type: cosine_pearson
      value: 85.0909
    - type: cosine_spearman
      value: 80.7386
    - type: manhattan_pearson
      value: 82.0327
    - type: manhattan_spearman
      value: 78.562
    - type: euclidean_pearson
      value: 82.6159
    - type: euclidean_spearman
      value: 79.35419999999999
    - type: main_score
      value: 80.7386
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB Banking77Classification (default)
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
      split: test
      type: mteb/banking77
    metrics:
    - type: accuracy
      value: 82.026
    - type: f1
      value: 81.9738
    - type: f1_weighted
      value: 81.9738
    - type: main_score
      value: 82.026
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB BiorxivClusteringP2P (default)
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
      split: test
      type: mteb/biorxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 30.787
    - type: v_measure_std
      value: 0.9286000000000001
    - type: main_score
      value: 30.787
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB BiorxivClusteringS2S (default)
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
      split: test
      type: mteb/biorxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 27.099800000000002
    - type: v_measure_std
      value: 0.7908000000000001
    - type: main_score
      value: 27.099800000000002
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB CQADupstackAndroidRetrieval (default)
      revision: f46a197baaae43b4f621051089b82a364682dfeb
      split: test
      type: mteb/cqadupstack-android
    metrics:
    - type: ndcg_at_1
      value: 29.757
    - type: ndcg_at_3
      value: 34.736
    - type: ndcg_at_5
      value: 36.529
    - type: ndcg_at_10
      value: 39.114
    - type: ndcg_at_20
      value: 41.05
    - type: ndcg_at_100
      value: 44.359
    - type: ndcg_at_1000
      value: 47.015
    - type: map_at_1
      value: 24.501
    - type: map_at_3
      value: 30.857
    - type: map_at_5
      value: 32.307
    - type: map_at_10
      value: 33.582
    - type: map_at_20
      value: 34.229
    - type: map_at_100
      value: 34.801
    - type: map_at_1000
      value: 34.936
    - type: recall_at_1
      value: 24.501
    - type: recall_at_3
      value: 37.393
    - type: recall_at_5
      value: 42.701
    - type: recall_at_10
      value: 50.470000000000006
    - type: recall_at_20
      value: 57.45099999999999
    - type: recall_at_100
      value: 72.912
    - type: recall_at_1000
      value: 90.643
    - type: precision_at_1
      value: 29.757
    - type: precision_at_3
      value: 16.738
    - type: precision_at_5
      value: 11.788
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  - dataset:
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  - dataset:
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    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MTOPIntentClassification (en)
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
      split: test
      type: mteb/mtop_intent
    metrics:
    - type: accuracy
      value: 65.64750000000001
    - type: f1
      value: 49.0948
    - type: f1_weighted
      value: 69.3994
    - type: main_score
      value: 65.64750000000001
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveIntentClassification (en)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: test
      type: mteb/amazon_massive_intent
    metrics:
    - type: accuracy
      value: 65.2354
    - type: f1
      value: 63.5945
    - type: f1_weighted
      value: 65.03949999999999
    - type: main_score
      value: 65.2354
    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveScenarioClassification (en)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: test
      type: mteb/amazon_massive_scenario
    metrics:
    - type: accuracy
      value: 72.6126
    - type: f1
      value: 72.4117
    - type: f1_weighted
      value: 72.6333
    - type: main_score
      value: 72.6126
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB MedrxivClusteringP2P (default)
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
      split: test
      type: mteb/medrxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 28.95
    - type: v_measure_std
      value: 1.3911
    - type: main_score
      value: 28.95
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MedrxivClusteringS2S (default)
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
      split: test
      type: mteb/medrxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 27.076
    - type: v_measure_std
      value: 1.7323000000000002
    - type: main_score
      value: 27.076
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MindSmallReranking (default)
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
      split: test
      type: mteb/mind_small
    metrics:
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    - type: mrr
      value: 31.6768
    - type: nAUC_map_max
      value: -18.2556
    - type: nAUC_map_std
      value: -1.6764999999999999
    - type: nAUC_map_diff1
      value: 13.488900000000001
    - type: nAUC_mrr_max
      value: -12.8279
    - type: nAUC_mrr_std
      value: 0.3295
    - type: nAUC_mrr_diff1
      value: 12.8104
    - type: main_score
      value: 30.643700000000003
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB NFCorpus (default)
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
      split: test
      type: mteb/nfcorpus
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      value: 31.048
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      value: 29.451
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      value: 26.63
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      value: 24.319
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      value: 8.085
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      value: 9.278
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      value: 10.11
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      value: 11.312
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      value: 12.461
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      value: 4.3229999999999995
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      value: 7.573
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      value: 10.248
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      value: 15.931000000000001
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      value: 25.573
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      value: 19.505
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      value: 6.037
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      value: 36.2229
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      value: 42.7245
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      value: 44.3189
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      value: 30.4815
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    - type: nauc_precision_at_1_max
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      value: 30.629
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      value: 50.1486
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      value: 43.7322
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      value: 42.4984
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      value: 36.3724
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      value: 45.470699999999994
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      value: 21.3989
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      value: 45.2484
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      value: 6.2987
    - type: nauc_precision_at_1000_std
      value: 31.5722
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      value: -6.3268
    - type: nauc_mrr_at_1_max
      value: 49.3121
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      value: 43.9682
    - type: nauc_mrr_at_3_max
      value: 50.2532
    - type: nauc_mrr_at_3_std
      value: 24.9172
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      value: 41.0622
    - type: nauc_mrr_at_5_max
      value: 51.5072
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      value: 25.0577
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      value: 40.9294
    - type: nauc_mrr_at_10_max
      value: 51.0025
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      value: 25.546999999999997
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      value: 40.4908
    - type: nauc_mrr_at_20_max
      value: 51.031400000000005
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      value: 25.822699999999998
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      value: 40.4249
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      value: 51.1729
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      value: 25.8183
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      value: 51.1509
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      value: 40.502700000000004
    - type: main_score
      value: 26.63
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB NQ (default)
      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
      split: test
      type: mteb/nq
    metrics:
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      value: 28.708
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      value: 38.322
    - type: ndcg_at_5
      value: 42.087
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      value: 45.32
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      value: 47.42
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      value: 49.995
    - type: ndcg_at_1000
      value: 51.263000000000005
    - type: map_at_1
      value: 25.702
    - type: map_at_3
      value: 34.910999999999994
    - type: map_at_5
      value: 37.137
    - type: map_at_10
      value: 38.586999999999996
    - type: map_at_20
      value: 39.226
    - type: map_at_100
      value: 39.638
    - type: map_at_1000
      value: 39.689
    - type: recall_at_1
      value: 25.702
    - type: recall_at_3
      value: 45.394
    - type: recall_at_5
      value: 54.089
    - type: recall_at_10
      value: 63.504000000000005
    - type: recall_at_20
      value: 71.32300000000001
    - type: recall_at_100
      value: 84.154
    - type: recall_at_1000
      value: 93.687
    - type: precision_at_1
      value: 28.708
    - type: precision_at_3
      value: 17.468
    - type: precision_at_5
      value: 12.629999999999999
    - type: precision_at_10
      value: 7.5120000000000005
    - type: precision_at_20
      value: 4.25
    - type: precision_at_100
      value: 1.018
    - type: precision_at_1000
      value: 0.11399999999999999
    - type: mrr_at_1
      value: 28.708
    - type: mrr_at_3
      value: 37.7221
    - type: mrr_at_5
      value: 39.6833
    - type: mrr_at_10
      value: 40.9104
    - type: mrr_at_20
      value: 41.419
    - type: mrr_at_100
      value: 41.739599999999996
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      value: 41.7789
    - type: nauc_ndcg_at_1_max
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      value: 1.9234
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      value: 6.0516
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      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
      split: test
      type: mteb/quora
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    task:
      type: Retrieval
  - dataset:
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      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
      split: test
      type: mteb/reddit-clustering
    metrics:
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    task:
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  - dataset:
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      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
      split: test
      type: mteb/reddit-clustering-p2p
    metrics:
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    - type: main_score
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    task:
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  - dataset:
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      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
      split: test
      type: mteb/scidocs
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    task:
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  - dataset:
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      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
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    task:
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  - dataset:
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      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
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      value: 68.56259999999999
    - type: manhattan_spearman
      value: 66.8326
    - type: euclidean_pearson
      value: 68.4606
    - type: euclidean_spearman
      value: 66.8238
    - type: main_score
      value: 67.2036
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
    - type: pearson
      value: 77.3137
    - type: spearman
      value: 77.4032
    - type: cosine_pearson
      value: 77.3137
    - type: cosine_spearman
      value: 77.4032
    - type: manhattan_pearson
      value: 76.2551
    - type: manhattan_spearman
      value: 76.6366
    - type: euclidean_pearson
      value: 76.1261
    - type: euclidean_spearman
      value: 76.5129
    - type: main_score
      value: 77.4032
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
    - type: pearson
      value: 77.1255
    - type: spearman
      value: 72.3476
    - type: cosine_pearson
      value: 77.1255
    - type: cosine_spearman
      value: 72.3476
    - type: manhattan_pearson
      value: 74.1639
    - type: manhattan_spearman
      value: 72.1268
    - type: euclidean_pearson
      value: 74.118
    - type: euclidean_spearman
      value: 72.1061
    - type: main_score
      value: 72.3476
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    metrics:
    - type: pearson
      value: 81.992
    - type: spearman
      value: 82.4958
    - type: cosine_pearson
      value: 81.992
    - type: cosine_spearman
      value: 82.4958
    - type: manhattan_pearson
      value: 81.3437
    - type: manhattan_spearman
      value: 81.9727
    - type: euclidean_pearson
      value: 81.3543
    - type: euclidean_spearman
      value: 81.9557
    - type: main_score
      value: 82.4958
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    metrics:
    - type: pearson
      value: 78.755
    - type: spearman
      value: 79.408
    - type: cosine_pearson
      value: 78.755
    - type: cosine_spearman
      value: 79.408
    - type: manhattan_pearson
      value: 80.0336
    - type: manhattan_spearman
      value: 80.5912
    - type: euclidean_pearson
      value: 80.0315
    - type: euclidean_spearman
      value: 80.5413
    - type: main_score
      value: 79.408
    task:
      type: STS
  - dataset:
      config: fr-en
      name: MTEB STS17 (fr-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 41.280699999999996
    - type: spearman
      value: 39.9908
    - type: cosine_pearson
      value: 41.280699999999996
    - type: cosine_spearman
      value: 39.9908
    - type: manhattan_pearson
      value: 41.515
    - type: manhattan_spearman
      value: 39.6907
    - type: euclidean_pearson
      value: 41.5204
    - type: euclidean_spearman
      value: 39.6877
    - type: main_score
      value: 39.9908
    task:
      type: STS
  - dataset:
      config: en-en
      name: MTEB STS17 (en-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 84.3554
    - type: spearman
      value: 84.7607
    - type: cosine_pearson
      value: 84.3554
    - type: cosine_spearman
      value: 84.7607
    - type: manhattan_pearson
      value: 84.1083
    - type: manhattan_spearman
      value: 84.35979999999999
    - type: euclidean_pearson
      value: 84.1249
    - type: euclidean_spearman
      value: 84.33070000000001
    - type: main_score
      value: 84.7607
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS17 (es-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 36.045500000000004
    - type: spearman
      value: 36.9017
    - type: cosine_pearson
      value: 36.045500000000004
    - type: cosine_spearman
      value: 36.9017
    - type: manhattan_pearson
      value: 33.128099999999996
    - type: manhattan_spearman
      value: 33.9834
    - type: euclidean_pearson
      value: 33.434599999999996
    - type: euclidean_spearman
      value: 34.6078
    - type: main_score
      value: 36.9017
    task:
      type: STS
  - dataset:
      config: en-de
      name: MTEB STS17 (en-de)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 38.332899999999995
    - type: spearman
      value: 36.8844
    - type: cosine_pearson
      value: 38.333
    - type: cosine_spearman
      value: 36.8844
    - type: manhattan_pearson
      value: 34.725699999999996
    - type: manhattan_spearman
      value: 34.0722
    - type: euclidean_pearson
      value: 35.0828
    - type: euclidean_spearman
      value: 34.1548
    - type: main_score
      value: 36.8844
    task:
      type: STS
  - dataset:
      config: en-tr
      name: MTEB STS17 (en-tr)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 8.5032
    - type: spearman
      value: 5.6236
    - type: cosine_pearson
      value: 8.5032
    - type: cosine_spearman
      value: 5.6236
    - type: manhattan_pearson
      value: 11.7706
    - type: manhattan_spearman
      value: 8.512599999999999
    - type: euclidean_pearson
      value: 11.6449
    - type: euclidean_spearman
      value: 7.7363
    - type: main_score
      value: 5.6236
    task:
      type: STS
  - dataset:
      config: it-en
      name: MTEB STS17 (it-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 30.772899999999996
    - type: spearman
      value: 27.825899999999997
    - type: cosine_pearson
      value: 30.772899999999996
    - type: cosine_spearman
      value: 27.825899999999997
    - type: manhattan_pearson
      value: 34.1556
    - type: manhattan_spearman
      value: 32.852599999999995
    - type: euclidean_pearson
      value: 33.8333
    - type: euclidean_spearman
      value: 32.8262
    - type: main_score
      value: 27.825899999999997
    task:
      type: STS
  - dataset:
      config: nl-en
      name: MTEB STS17 (nl-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 37.4576
    - type: spearman
      value: 33.533
    - type: cosine_pearson
      value: 37.457499999999996
    - type: cosine_spearman
      value: 33.5406
    - type: manhattan_pearson
      value: 38.7747
    - type: manhattan_spearman
      value: 36.8791
    - type: euclidean_pearson
      value: 39.4756
    - type: euclidean_spearman
      value: 38.3307
    - type: main_score
      value: 33.5406
    task:
      type: STS
  - dataset:
      config: en-ar
      name: MTEB STS17 (en-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
    - type: pearson
      value: 12.8259
    - type: spearman
      value: 13.2374
    - type: cosine_pearson
      value: 12.8259
    - type: cosine_spearman
      value: 13.2374
    - type: manhattan_pearson
      value: 16.0609
    - type: manhattan_spearman
      value: 14.8943
    - type: euclidean_pearson
      value: 14.1566
    - type: euclidean_spearman
      value: 12.6913
    - type: main_score
      value: 13.2374
    task:
      type: STS
  - dataset:
      config: en
      name: MTEB STS22 (en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 66.1988
    - type: spearman
      value: 66.6842
    - type: cosine_pearson
      value: 66.1988
    - type: cosine_spearman
      value: 66.6842
    - type: manhattan_pearson
      value: 66.7457
    - type: manhattan_spearman
      value: 66.00200000000001
    - type: euclidean_pearson
      value: 66.9588
    - type: euclidean_spearman
      value: 66.16579999999999
    - type: main_score
      value: 66.6842
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS22 (es-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 55.525999999999996
    - type: spearman
      value: 58.790600000000005
    - type: cosine_pearson
      value: 55.525999999999996
    - type: cosine_spearman
      value: 58.790600000000005
    - type: manhattan_pearson
      value: 57.918499999999995
    - type: manhattan_spearman
      value: 60.4479
    - type: euclidean_pearson
      value: 58.0086
    - type: euclidean_spearman
      value: 60.5008
    - type: main_score
      value: 58.790600000000005
    task:
      type: STS
  - dataset:
      config: zh-en
      name: MTEB STS22 (zh-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 36.3507
    - type: spearman
      value: 39.9716
    - type: cosine_pearson
      value: 36.3506
    - type: cosine_spearman
      value: 39.9716
    - type: manhattan_pearson
      value: 25.3105
    - type: manhattan_spearman
      value: 28.190700000000003
    - type: euclidean_pearson
      value: 27.8303
    - type: euclidean_spearman
      value: 29.8408
    - type: main_score
      value: 39.9716
    task:
      type: STS
  - dataset:
      config: de-en
      name: MTEB STS22 (de-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 47.759800000000006
    - type: spearman
      value: 49.0005
    - type: cosine_pearson
      value: 47.759800000000006
    - type: cosine_spearman
      value: 49.0005
    - type: manhattan_pearson
      value: 46.2315
    - type: manhattan_spearman
      value: 49.9363
    - type: euclidean_pearson
      value: 46.818599999999996
    - type: euclidean_spearman
      value: 50.77779999999999
    - type: main_score
      value: 49.0005
    task:
      type: STS
  - dataset:
      config: pl-en
      name: MTEB STS22 (pl-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    metrics:
    - type: pearson
      value: 49.2874
    - type: spearman
      value: 43.5968
    - type: cosine_pearson
      value: 49.2874
    - type: cosine_spearman
      value: 43.5968
    - type: manhattan_pearson
      value: 56.0733
    - type: manhattan_spearman
      value: 51.0045
    - type: euclidean_pearson
      value: 56.356399999999994
    - type: euclidean_spearman
      value: 49.967800000000004
    - type: main_score
      value: 43.5968
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    metrics:
    - type: pearson
      value: 78.6387
    - type: spearman
      value: 77.48469999999999
    - type: cosine_pearson
      value: 78.6387
    - type: cosine_spearman
      value: 77.48469999999999
    - type: manhattan_pearson
      value: 77.51989999999999
    - type: manhattan_spearman
      value: 77.1479
    - type: euclidean_pearson
      value: 77.5843
    - type: euclidean_spearman
      value: 77.1979
    - type: main_score
      value: 77.48469999999999
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB SciDocsRR (default)
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
      split: test
      type: mteb/scidocs-reranking
    metrics:
    - type: map
      value: 72.4633
    - type: mrr
      value: 90.7647
    - type: nAUC_map_max
      value: 51.910999999999994
    - type: nAUC_map_std
      value: 61.063599999999994
    - type: nAUC_map_diff1
      value: 10.8873
    - type: nAUC_mrr_max
      value: 74.0887
    - type: nAUC_mrr_std
      value: 66.3654
    - type: nAUC_mrr_diff1
      value: 48.1591
    - type: main_score
      value: 72.4633
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SciFact (default)
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
      split: test
      type: mteb/scifact
    metrics:
    - type: ndcg_at_1
      value: 49.0
    - type: ndcg_at_3
      value: 55.969
    - type: ndcg_at_5
      value: 58.111000000000004
    - type: ndcg_at_10
      value: 60.427
    - type: ndcg_at_20
      value: 61.527
    - type: ndcg_at_100
      value: 63.535
    - type: ndcg_at_1000
      value: 64.773
    - type: map_at_1
      value: 46.344
    - type: map_at_3
      value: 53.188
    - type: map_at_5
      value: 54.567
    - type: map_at_10
      value: 55.704
    - type: map_at_20
      value: 56.055
    - type: map_at_100
      value: 56.35
    - type: map_at_1000
      value: 56.397
    - type: recall_at_1
      value: 46.344
    - type: recall_at_3
      value: 60.911
    - type: recall_at_5
      value: 66.294
    - type: recall_at_10
      value: 72.872
    - type: recall_at_20
      value: 77.033
    - type: recall_at_100
      value: 87.656
    - type: recall_at_1000
      value: 97.333
    - type: precision_at_1
      value: 49.0
    - type: precision_at_3
      value: 22.111
    - type: precision_at_5
      value: 14.6
    - type: precision_at_10
      value: 8.200000000000001
    - type: precision_at_20
      value: 4.383
    - type: precision_at_100
      value: 0.997
    - type: precision_at_1000
      value: 0.11
    - type: mrr_at_1
      value: 49.0
    - type: mrr_at_3
      value: 55.555600000000005
    - type: mrr_at_5
      value: 56.8056
    - type: mrr_at_10
      value: 57.5951
    - type: mrr_at_20
      value: 57.808800000000005
    - type: mrr_at_100
      value: 58.0653
    - type: mrr_at_1000
      value: 58.10530000000001
    - type: nauc_ndcg_at_1_max
      value: 49.4156
    - type: nauc_ndcg_at_1_std
      value: 17.982300000000002
    - type: nauc_ndcg_at_1_diff1
      value: 70.1234
    - type: nauc_ndcg_at_3_max
      value: 54.3891
    - type: nauc_ndcg_at_3_std
      value: 14.310400000000001
    - type: nauc_ndcg_at_3_diff1
      value: 67.3382
    - type: nauc_ndcg_at_5_max
      value: 52.9631
    - type: nauc_ndcg_at_5_std
      value: 11.7775
    - type: nauc_ndcg_at_5_diff1
      value: 66.2906
    - type: nauc_ndcg_at_10_max
      value: 54.9531
    - type: nauc_ndcg_at_10_std
      value: 13.3055
    - type: nauc_ndcg_at_10_diff1
      value: 66.4653
    - type: nauc_ndcg_at_20_max
      value: 54.7342
    - type: nauc_ndcg_at_20_std
      value: 14.571600000000002
    - type: nauc_ndcg_at_20_diff1
      value: 66.38929999999999
    - type: nauc_ndcg_at_100_max
      value: 54.2887
    - type: nauc_ndcg_at_100_std
      value: 16.3373
    - type: nauc_ndcg_at_100_diff1
      value: 66.2149
    - type: nauc_ndcg_at_1000_max
      value: 54.4544
    - type: nauc_ndcg_at_1000_std
      value: 15.9653
    - type: nauc_ndcg_at_1000_diff1
      value: 66.7584
    - type: nauc_map_at_1_max
      value: 48.4528
    - type: nauc_map_at_1_std
      value: 11.2364
    - type: nauc_map_at_1_diff1
      value: 71.3798
    - type: nauc_map_at_3_max
      value: 52.629000000000005
    - type: nauc_map_at_3_std
      value: 12.753900000000002
    - type: nauc_map_at_3_diff1
      value: 68.4559
    - type: nauc_map_at_5_max
      value: 52.1172
    - type: nauc_map_at_5_std
      value: 11.887
    - type: nauc_map_at_5_diff1
      value: 67.85940000000001
    - type: nauc_map_at_10_max
      value: 53.26
    - type: nauc_map_at_10_std
      value: 12.8623
    - type: nauc_map_at_10_diff1
      value: 67.9285
    - type: nauc_map_at_20_max
      value: 53.140100000000004
    - type: nauc_map_at_20_std
      value: 13.312299999999999
    - type: nauc_map_at_20_diff1
      value: 67.8706
    - type: nauc_map_at_100_max
      value: 53.07809999999999
    - type: nauc_map_at_100_std
      value: 13.570099999999998
    - type: nauc_map_at_100_diff1
      value: 67.8354
    - type: nauc_map_at_1000_max
      value: 53.0964
    - type: nauc_map_at_1000_std
      value: 13.5718
    - type: nauc_map_at_1000_diff1
      value: 67.8608
    - type: nauc_recall_at_1_max
      value: 48.4528
    - type: nauc_recall_at_1_std
      value: 11.2364
    - type: nauc_recall_at_1_diff1
      value: 71.3798
    - type: nauc_recall_at_3_max
      value: 55.2991
    - type: nauc_recall_at_3_std
      value: 10.4741
    - type: nauc_recall_at_3_diff1
      value: 63.9389
    - type: nauc_recall_at_5_max
      value: 52.3593
    - type: nauc_recall_at_5_std
      value: 5.2456000000000005
    - type: nauc_recall_at_5_diff1
      value: 59.9453
    - type: nauc_recall_at_10_max
      value: 58.082699999999996
    - type: nauc_recall_at_10_std
      value: 8.0142
    - type: nauc_recall_at_10_diff1
      value: 59.644600000000004
    - type: nauc_recall_at_20_max
      value: 58.9062
    - type: nauc_recall_at_20_std
      value: 13.9035
    - type: nauc_recall_at_20_diff1
      value: 59.78099999999999
    - type: nauc_recall_at_100_max
      value: 58.318999999999996
    - type: nauc_recall_at_100_std
      value: 34.7022
    - type: nauc_recall_at_100_diff1
      value: 55.9343
    - type: nauc_recall_at_1000_max
      value: 79.1958
    - type: nauc_recall_at_1000_std
      value: 63.7138
    - type: nauc_recall_at_1000_diff1
      value: 62.832600000000006
    - type: nauc_precision_at_1_max
      value: 49.4156
    - type: nauc_precision_at_1_std
      value: 17.982300000000002
    - type: nauc_precision_at_1_diff1
      value: 70.1234
    - type: nauc_precision_at_3_max
      value: 57.2273
    - type: nauc_precision_at_3_std
      value: 27.0677
    - type: nauc_precision_at_3_diff1
      value: 52.8038
    - type: nauc_precision_at_5_max
      value: 51.4554
    - type: nauc_precision_at_5_std
      value: 23.3615
    - type: nauc_precision_at_5_diff1
      value: 44.164500000000004
    - type: nauc_precision_at_10_max
      value: 50.428399999999996
    - type: nauc_precision_at_10_std
      value: 28.477200000000003
    - type: nauc_precision_at_10_diff1
      value: 31.4772
    - type: nauc_precision_at_20_max
      value: 42.5568
    - type: nauc_precision_at_20_std
      value: 32.6138
    - type: nauc_precision_at_20_diff1
      value: 23.4987
    - type: nauc_precision_at_100_max
      value: 33.7983
    - type: nauc_precision_at_100_std
      value: 44.8859
    - type: nauc_precision_at_100_diff1
      value: 7.9106
    - type: nauc_precision_at_1000_max
      value: 23.0914
    - type: nauc_precision_at_1000_std
      value: 46.0357
    - type: nauc_precision_at_1000_diff1
      value: -12.5412
    - type: nauc_mrr_at_1_max
      value: 49.4156
    - type: nauc_mrr_at_1_std
      value: 17.982300000000002
    - type: nauc_mrr_at_1_diff1
      value: 70.1234
    - type: nauc_mrr_at_3_max
      value: 53.5852
    - type: nauc_mrr_at_3_std
      value: 16.9294
    - type: nauc_mrr_at_3_diff1
      value: 67.91430000000001
    - type: nauc_mrr_at_5_max
      value: 52.8663
    - type: nauc_mrr_at_5_std
      value: 16.0492
    - type: nauc_mrr_at_5_diff1
      value: 67.1386
    - type: nauc_mrr_at_10_max
      value: 53.21320000000001
    - type: nauc_mrr_at_10_std
      value: 16.5224
    - type: nauc_mrr_at_10_diff1
      value: 67.1661
    - type: nauc_mrr_at_20_max
      value: 53.154500000000006
    - type: nauc_mrr_at_20_std
      value: 16.653599999999997
    - type: nauc_mrr_at_20_diff1
      value: 67.1882
    - type: nauc_mrr_at_100_max
      value: 53.0415
    - type: nauc_mrr_at_100_std
      value: 16.7896
    - type: nauc_mrr_at_100_diff1
      value: 67.1524
    - type: nauc_mrr_at_1000_max
      value: 53.04299999999999
    - type: nauc_mrr_at_1000_std
      value: 16.7823
    - type: nauc_mrr_at_1000_diff1
      value: 67.1834
    - type: main_score
      value: 60.427
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SprintDuplicateQuestions (default)
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
      split: test
      type: mteb/sprintduplicatequestions-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 99.802
    - type: similarity_accuracy_threshold
      value: 68.9054
    - type: similarity_f1
      value: 89.89439999999999
    - type: similarity_f1_threshold
      value: 68.1838
    - type: similarity_precision
      value: 90.3943
    - type: similarity_recall
      value: 89.4
    - type: similarity_ap
      value: 95.1114
    - type: cosine_accuracy
      value: 99.802
    - type: cosine_accuracy_threshold
      value: 68.9053
    - type: cosine_f1
      value: 89.89439999999999
    - type: cosine_f1_threshold
      value: 68.1838
    - type: cosine_precision
      value: 90.3943
    - type: cosine_recall
      value: 89.4
    - type: cosine_ap
      value: 95.1114
    - type: manhattan_accuracy
      value: 99.8
    - type: manhattan_accuracy_threshold
      value: 56569.586200000005
    - type: manhattan_f1
      value: 89.8899
    - type: manhattan_f1_threshold
      value: 56569.586200000005
    - type: manhattan_precision
      value: 89.98
    - type: manhattan_recall
      value: 89.8
    - type: manhattan_ap
      value: 94.9934
    - type: euclidean_accuracy
      value: 99.799
    - type: euclidean_accuracy_threshold
      value: 2539.7804
    - type: euclidean_f1
      value: 89.8348
    - type: euclidean_f1_threshold
      value: 2568.8804999999998
    - type: euclidean_precision
      value: 89.9699
    - type: euclidean_recall
      value: 89.7
    - type: euclidean_ap
      value: 95.06
    - type: dot_accuracy
      value: 99.7723
    - type: dot_accuracy_threshold
      value: 72398.77320000001
    - type: dot_f1
      value: 88.0829
    - type: dot_f1_threshold
      value: 72398.77320000001
    - type: dot_precision
      value: 91.39779999999999
    - type: dot_recall
      value: 85.0
    - type: dot_ap
      value: 93.74040000000001
    - type: max_accuracy
      value: 99.802
    - type: max_f1
      value: 89.89439999999999
    - type: max_precision
      value: 91.39779999999999
    - type: max_recall
      value: 89.8
    - type: max_ap
      value: 95.1114
    - type: main_score
      value: 95.1114
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB StackExchangeClustering (default)
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
      split: test
      type: mteb/stackexchange-clustering
    metrics:
    - type: v_measure
      value: 56.641
    - type: v_measure_std
      value: 4.6061
    - type: main_score
      value: 56.641
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackExchangeClusteringP2P (default)
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
      split: test
      type: mteb/stackexchange-clustering-p2p
    metrics:
    - type: v_measure
      value: 31.850099999999998
    - type: v_measure_std
      value: 1.3043
    - type: main_score
      value: 31.850099999999998
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackOverflowDupQuestions (default)
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
      split: test
      type: mteb/stackoverflowdupquestions-reranking
    metrics:
    - type: map
      value: 48.149300000000004
    - type: mrr
      value: 48.8603
    - type: nAUC_map_max
      value: 14.3434
    - type: nAUC_map_std
      value: 8.115400000000001
    - type: nAUC_map_diff1
      value: 35.017700000000005
    - type: nAUC_mrr_max
      value: 15.0698
    - type: nAUC_mrr_std
      value: 8.9674
    - type: nAUC_mrr_diff1
      value: 35.029199999999996
    - type: main_score
      value: 48.149300000000004
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SummEval (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
    metrics:
    - type: pearson
      value: 29.9904
    - type: spearman
      value: 31.8365
    - type: cosine_spearman
      value: 31.8365
    - type: cosine_pearson
      value: 29.9904
    - type: dot_spearman
      value: 29.713099999999997
    - type: dot_pearson
      value: 28.3169
    - type: main_score
      value: 31.8365
    task:
      type: Summarization
  - dataset:
      config: default
      name: MTEB TRECCOVID (default)
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
      split: test
      type: mteb/trec-covid
    metrics:
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      value: 81.0
    - type: ndcg_at_3
      value: 75.98899999999999
    - type: ndcg_at_5
      value: 75.205
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      value: 71.532
    - type: ndcg_at_20
      value: 68.284
    - type: ndcg_at_100
      value: 52.276
    - type: ndcg_at_1000
      value: 45.097
    - type: map_at_1
      value: 0.231
    - type: map_at_3
      value: 0.583
    - type: map_at_5
      value: 0.9369999999999999
    - type: map_at_10
      value: 1.718
    - type: map_at_20
      value: 3.005
    - type: map_at_100
      value: 9.005
    - type: map_at_1000
      value: 20.935000000000002
    - type: recall_at_1
      value: 0.231
    - type: recall_at_3
      value: 0.619
    - type: recall_at_5
      value: 1.024
    - type: recall_at_10
      value: 1.9609999999999999
    - type: recall_at_20
      value: 3.5319999999999996
    - type: recall_at_100
      value: 12.046
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      value: 41.831
    - type: precision_at_1
      value: 88.0
    - type: precision_at_3
      value: 80.0
    - type: precision_at_5
      value: 80.0
    - type: precision_at_10
      value: 75.8
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      value: 71.5
    - type: precision_at_100
      value: 53.059999999999995
    - type: precision_at_1000
      value: 20.355999999999998
    - type: mrr_at_1
      value: 88.0
    - type: mrr_at_3
      value: 92.6667
    - type: mrr_at_5
      value: 93.1667
    - type: mrr_at_10
      value: 93.1667
    - type: mrr_at_20
      value: 93.1667
    - type: mrr_at_100
      value: 93.1667
    - type: mrr_at_1000
      value: 93.1667
    - type: nauc_ndcg_at_1_max
      value: 23.5033
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      value: 42.5107
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      value: -16.6331
    - type: nauc_ndcg_at_3_max
      value: 45.7534
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      value: 49.449
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      value: -21.817700000000002
    - type: nauc_ndcg_at_5_max
      value: 52.3915
    - type: nauc_ndcg_at_5_std
      value: 54.0234
    - type: nauc_ndcg_at_5_diff1
      value: -22.4601
    - type: nauc_ndcg_at_10_max
      value: 48.9955
    - type: nauc_ndcg_at_10_std
      value: 57.918099999999995
    - type: nauc_ndcg_at_10_diff1
      value: -31.8485
    - type: nauc_ndcg_at_20_max
      value: 53.668800000000005
    - type: nauc_ndcg_at_20_std
      value: 69.0301
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      value: -35.4435
    - type: nauc_ndcg_at_100_max
      value: 51.8196
    - type: nauc_ndcg_at_100_std
      value: 82.2754
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      value: -39.7011
    - type: nauc_ndcg_at_1000_max
      value: 65.4034
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      value: 81.7756
    - type: nauc_ndcg_at_1000_diff1
      value: -30.1325
    - type: nauc_map_at_1_max
      value: 3.0979
    - type: nauc_map_at_1_std
      value: -11.2343
    - type: nauc_map_at_1_diff1
      value: 20.7618
    - type: nauc_map_at_3_max
      value: 28.6885
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      value: 2.9605
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      value: 11.3494
    - type: nauc_map_at_5_max
      value: 31.7008
    - type: nauc_map_at_5_std
      value: 5.6763
    - type: nauc_map_at_5_diff1
      value: 9.1052
    - type: nauc_map_at_10_max
      value: 29.9906
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      value: 6.5652
    - type: nauc_map_at_10_diff1
      value: 5.4684
    - type: nauc_map_at_20_max
      value: 38.6532
    - type: nauc_map_at_20_std
      value: 22.8509
    - type: nauc_map_at_20_diff1
      value: -2.6004
    - type: nauc_map_at_100_max
      value: 58.9551
    - type: nauc_map_at_100_std
      value: 67.3813
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      value: -23.7286
    - type: nauc_map_at_1000_max
      value: 66.5071
    - type: nauc_map_at_1000_std
      value: 87.6026
    - type: nauc_map_at_1000_diff1
      value: -34.2005
    - type: nauc_recall_at_1_max
      value: 3.0979
    - type: nauc_recall_at_1_std
      value: -11.2343
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      value: 20.7618
    - type: nauc_recall_at_3_max
      value: 22.5952
    - type: nauc_recall_at_3_std
      value: -5.0699000000000005
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      value: 12.1543
    - type: nauc_recall_at_5_max
      value: 23.1872
    - type: nauc_recall_at_5_std
      value: -4.1483
    - type: nauc_recall_at_5_diff1
      value: 11.938799999999999
    - type: nauc_recall_at_10_max
      value: 18.5173
    - type: nauc_recall_at_10_std
      value: -6.304800000000001
    - type: nauc_recall_at_10_diff1
      value: 8.7213
    - type: nauc_recall_at_20_max
      value: 27.1838
    - type: nauc_recall_at_20_std
      value: 9.6419
    - type: nauc_recall_at_20_diff1
      value: 3.6645999999999996
    - type: nauc_recall_at_100_max
      value: 51.14959999999999
    - type: nauc_recall_at_100_std
      value: 52.7999
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      value: -17.7583
    - type: nauc_recall_at_1000_max
      value: 64.6146
    - type: nauc_recall_at_1000_std
      value: 73.34049999999999
    - type: nauc_recall_at_1000_diff1
      value: -22.3217
    - type: nauc_precision_at_1_max
      value: 48.949999999999996
    - type: nauc_precision_at_1_std
      value: 75.8988
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      value: -3.4839
    - type: nauc_precision_at_3_max
      value: 70.9443
    - type: nauc_precision_at_3_std
      value: 67.53
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      value: -19.8922
    - type: nauc_precision_at_5_max
      value: 73.03150000000001
    - type: nauc_precision_at_5_std
      value: 70.8924
    - type: nauc_precision_at_5_diff1
      value: -24.3446
    - type: nauc_precision_at_10_max
      value: 55.359
    - type: nauc_precision_at_10_std
      value: 62.7347
    - type: nauc_precision_at_10_diff1
      value: -33.091
    - type: nauc_precision_at_20_max
      value: 59.1229
    - type: nauc_precision_at_20_std
      value: 76.0406
    - type: nauc_precision_at_20_diff1
      value: -35.061
    - type: nauc_precision_at_100_max
      value: 53.1281
    - type: nauc_precision_at_100_std
      value: 85.8325
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      value: -40.6393
    - type: nauc_precision_at_1000_max
      value: 41.3845
    - type: nauc_precision_at_1000_std
      value: 67.6367
    - type: nauc_precision_at_1000_diff1
      value: -33.204699999999995
    - type: nauc_mrr_at_1_max
      value: 48.949999999999996
    - type: nauc_mrr_at_1_std
      value: 75.8988
    - type: nauc_mrr_at_1_diff1
      value: -3.4839
    - type: nauc_mrr_at_3_max
      value: 54.543699999999994
    - type: nauc_mrr_at_3_std
      value: 71.44800000000001
    - type: nauc_mrr_at_3_diff1
      value: -10.0942
    - type: nauc_mrr_at_5_max
      value: 53.2249
    - type: nauc_mrr_at_5_std
      value: 74.1067
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      value: -6.6437
    - type: nauc_mrr_at_10_max
      value: 53.2249
    - type: nauc_mrr_at_10_std
      value: 74.1067
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      value: -6.6437
    - type: nauc_mrr_at_20_max
      value: 53.2249
    - type: nauc_mrr_at_20_std
      value: 74.1067
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      value: -6.6437
    - type: nauc_mrr_at_100_max
      value: 53.2249
    - type: nauc_mrr_at_100_std
      value: 74.1067
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      value: -6.6437
    - type: nauc_mrr_at_1000_max
      value: 53.2249
    - type: nauc_mrr_at_1000_std
      value: 74.1067
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      value: -6.6437
    - type: main_score
      value: 71.532
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB Touche2020 (default)
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
      split: test
      type: mteb/touche2020
    metrics:
    - type: ndcg_at_1
      value: 24.490000000000002
    - type: ndcg_at_3
      value: 24.729
    - type: ndcg_at_5
      value: 23.791
    - type: ndcg_at_10
      value: 22.788
    - type: ndcg_at_20
      value: 25.137999999999998
    - type: ndcg_at_100
      value: 35.506
    - type: ndcg_at_1000
      value: 47.535
    - type: map_at_1
      value: 2.094
    - type: map_at_3
      value: 5.037
    - type: map_at_5
      value: 6.666999999999999
    - type: map_at_10
      value: 9.567
    - type: map_at_20
      value: 12.238
    - type: map_at_100
      value: 15.456
    - type: map_at_1000
      value: 17.134
    - type: recall_at_1
      value: 2.094
    - type: recall_at_3
      value: 6.356000000000001
    - type: recall_at_5
      value: 9.517000000000001
    - type: recall_at_10
      value: 15.589
    - type: recall_at_20
      value: 24.322
    - type: recall_at_100
      value: 46.668
    - type: recall_at_1000
      value: 83.852
    - type: precision_at_1
      value: 26.531
    - type: precision_at_3
      value: 26.531
    - type: precision_at_5
      value: 24.490000000000002
    - type: precision_at_10
      value: 20.408
    - type: precision_at_20
      value: 17.143
    - type: precision_at_100
      value: 7.489999999999999
    - type: precision_at_1000
      value: 1.551
    - type: mrr_at_1
      value: 26.5306
    - type: mrr_at_3
      value: 38.0952
    - type: mrr_at_5
      value: 40.9524
    - type: mrr_at_10
      value: 42.150999999999996
    - type: mrr_at_20
      value: 43.148199999999996
    - type: mrr_at_100
      value: 43.3322
    - type: mrr_at_1000
      value: 43.3603
    - type: nauc_ndcg_at_1_max
      value: -21.184
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      value: 4.5078
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      value: -12.7358
    - type: nauc_ndcg_at_3_max
      value: -19.4252
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      value: -6.255199999999999
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      value: 1.3456
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      value: -21.515
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      value: -7.8812999999999995
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      value: -1.2183
    - type: nauc_ndcg_at_10_max
      value: -18.12
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      value: -5.5853
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      value: 1.2866
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      value: -5.8553
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      value: 12.623599999999998
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      value: 22.288800000000002
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    - type: nauc_map_at_1_max
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      value: -20.8214
    - type: nauc_recall_at_3_diff1
      value: -2.1295
    - type: nauc_recall_at_5_max
      value: -24.176000000000002
    - type: nauc_recall_at_5_std
      value: -21.3149
    - type: nauc_recall_at_5_diff1
      value: -6.895999999999999
    - type: nauc_recall_at_10_max
      value: -17.9446
    - type: nauc_recall_at_10_std
      value: -14.205799999999998
    - type: nauc_recall_at_10_diff1
      value: -2.2227
    - type: nauc_recall_at_20_max
      value: -30.1632
    - type: nauc_recall_at_20_std
      value: -11.9398
    - type: nauc_recall_at_20_diff1
      value: 4.2804
    - type: nauc_recall_at_100_max
      value: -21.802
    - type: nauc_recall_at_100_std
      value: 20.3004
    - type: nauc_recall_at_100_diff1
      value: 8.1152
    - type: nauc_recall_at_1000_max
      value: -15.484300000000001
    - type: nauc_recall_at_1000_std
      value: 61.0456
    - type: nauc_recall_at_1000_diff1
      value: -22.3118
    - type: nauc_precision_at_1_max
      value: -16.3555
    - type: nauc_precision_at_1_std
      value: 1.4887
    - type: nauc_precision_at_1_diff1
      value: -7.2894
    - type: nauc_precision_at_3_max
      value: -18.5059
    - type: nauc_precision_at_3_std
      value: -11.0688
    - type: nauc_precision_at_3_diff1
      value: 8.2707
    - type: nauc_precision_at_5_max
      value: -20.949
    - type: nauc_precision_at_5_std
      value: -13.556099999999999
    - type: nauc_precision_at_5_diff1
      value: 3.8719
    - type: nauc_precision_at_10_max
      value: -11.5299
    - type: nauc_precision_at_10_std
      value: -1.6832
    - type: nauc_precision_at_10_diff1
      value: 11.603299999999999
    - type: nauc_precision_at_20_max
      value: -27.1497
    - type: nauc_precision_at_20_std
      value: 5.7508
    - type: nauc_precision_at_20_diff1
      value: 20.0541
    - type: nauc_precision_at_100_max
      value: -2.628
    - type: nauc_precision_at_100_std
      value: 47.0634
    - type: nauc_precision_at_100_diff1
      value: 29.8468
    - type: nauc_precision_at_1000_max
      value: 30.866500000000002
    - type: nauc_precision_at_1000_std
      value: 33.8682
    - type: nauc_precision_at_1000_diff1
      value: 11.3792
    - type: nauc_mrr_at_1_max
      value: -16.3555
    - type: nauc_mrr_at_1_std
      value: 1.4887
    - type: nauc_mrr_at_1_diff1
      value: -7.2894
    - type: nauc_mrr_at_3_max
      value: -20.1747
    - type: nauc_mrr_at_3_std
      value: -9.8292
    - type: nauc_mrr_at_3_diff1
      value: 2.3817
    - type: nauc_mrr_at_5_max
      value: -20.2904
    - type: nauc_mrr_at_5_std
      value: -8.164299999999999
    - type: nauc_mrr_at_5_diff1
      value: -0.1708
    - type: nauc_mrr_at_10_max
      value: -19.431
    - type: nauc_mrr_at_10_std
      value: -4.6234
    - type: nauc_mrr_at_10_diff1
      value: -0.9554999999999999
    - type: nauc_mrr_at_20_max
      value: -19.7291
    - type: nauc_mrr_at_20_std
      value: -4.4182
    - type: nauc_mrr_at_20_diff1
      value: -0.7163
    - type: nauc_mrr_at_100_max
      value: -19.662
    - type: nauc_mrr_at_100_std
      value: -4.4295
    - type: nauc_mrr_at_100_diff1
      value: -0.9365999999999999
    - type: nauc_mrr_at_1000_max
      value: -19.6591
    - type: nauc_mrr_at_1000_std
      value: -4.5101
    - type: nauc_mrr_at_1000_diff1
      value: -0.9622
    - type: main_score
      value: 22.788
    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB ToxicConversationsClassification (default)
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
      split: test
      type: mteb/toxic_conversations_50k
    metrics:
    - type: accuracy
      value: 64.41409999999999
    - type: f1
      value: 48.512899999999995
    - type: f1_weighted
      value: 72.2769
    - type: ap
      value: 10.4382
    - type: ap_weighted
      value: 10.4382
    - type: main_score
      value: 64.41409999999999
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TweetSentimentExtractionClassification (default)
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
      split: test
      type: mteb/tweet_sentiment_extraction
    metrics:
    - type: accuracy
      value: 52.6316
    - type: f1
      value: 52.9212
    - type: f1_weighted
      value: 52.3632
    - type: main_score
      value: 52.6316
    task:
      type: Classification
  - dataset:
      config: default
      name: MTEB TwentyNewsgroupsClustering (default)
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
      split: test
      type: mteb/twentynewsgroups-clustering
    metrics:
    - type: v_measure
      value: 34.3169
    - type: v_measure_std
      value: 1.669
    - type: main_score
      value: 34.3169
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB TwitterSemEval2015 (default)
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
      split: test
      type: mteb/twittersemeval2015-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 83.45949999999999
    - type: similarity_accuracy_threshold
      value: 72.4461
    - type: similarity_f1
      value: 61.7155
    - type: similarity_f1_threshold
      value: 62.5381
    - type: similarity_precision
      value: 56.0681
    - type: similarity_recall
      value: 68.628
    - type: similarity_ap
      value: 65.0861
    - type: cosine_accuracy
      value: 83.45949999999999
    - type: cosine_accuracy_threshold
      value: 72.4461
    - type: cosine_f1
      value: 61.7155
    - type: cosine_f1_threshold
      value: 62.5381
    - type: cosine_precision
      value: 56.0681
    - type: cosine_recall
      value: 68.628
    - type: cosine_ap
      value: 65.0861
    - type: manhattan_accuracy
      value: 83.3999
    - type: manhattan_accuracy_threshold
      value: 53493.359399999994
    - type: manhattan_f1
      value: 61.0456
    - type: manhattan_f1_threshold
      value: 60751.0315
    - type: manhattan_precision
      value: 57.6589
    - type: manhattan_recall
      value: 64.8549
    - type: manhattan_ap
      value: 64.5486
    - type: euclidean_accuracy
      value: 83.4059
    - type: euclidean_accuracy_threshold
      value: 2422.2414
    - type: euclidean_f1
      value: 60.9938
    - type: euclidean_f1_threshold
      value: 2765.6849
    - type: euclidean_precision
      value: 57.6291
    - type: euclidean_recall
      value: 64.7757
    - type: euclidean_ap
      value: 64.60709999999999
    - type: dot_accuracy
      value: 82.60119999999999
    - type: dot_accuracy_threshold
      value: 77153.8452
    - type: dot_f1
      value: 59.933099999999996
    - type: dot_f1_threshold
      value: 67100.8545
    - type: dot_precision
      value: 54.785799999999995
    - type: dot_recall
      value: 66.1478
    - type: dot_ap
      value: 62.1271
    - type: max_accuracy
      value: 83.45949999999999
    - type: max_f1
      value: 61.7155
    - type: max_precision
      value: 57.6589
    - type: max_recall
      value: 68.628
    - type: max_ap
      value: 65.0861
    - type: main_score
      value: 65.0861
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB TwitterURLCorpus (default)
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
      split: test
      type: mteb/twitterurlcorpus-pairclassification
    metrics:
    - type: similarity_accuracy
      value: 88.1612
    - type: similarity_accuracy_threshold
      value: 63.8545
    - type: similarity_f1
      value: 76.3737
    - type: similarity_f1_threshold
      value: 60.4226
    - type: similarity_precision
      value: 74.1711
    - type: similarity_recall
      value: 78.7111
    - type: similarity_ap
      value: 83.5651
    - type: cosine_accuracy
      value: 88.1612
    - type: cosine_accuracy_threshold
      value: 63.8545
    - type: cosine_f1
      value: 76.3737
    - type: cosine_f1_threshold
      value: 60.4226
    - type: cosine_precision
      value: 74.1711
    - type: cosine_recall
      value: 78.7111
    - type: cosine_ap
      value: 83.5651
    - type: manhattan_accuracy
      value: 87.9652
    - type: manhattan_accuracy_threshold
      value: 57837.701400000005
    - type: manhattan_f1
      value: 76.1352
    - type: manhattan_f1_threshold
      value: 61746.6187
    - type: manhattan_precision
      value: 72.3335
    - type: manhattan_recall
      value: 80.3588
    - type: manhattan_ap
      value: 83.3006
    - type: euclidean_accuracy
      value: 88.00019999999999
    - type: euclidean_accuracy_threshold
      value: 2607.3253999999997
    - type: euclidean_f1
      value: 76.2589
    - type: euclidean_f1_threshold
      value: 2793.2058
    - type: euclidean_precision
      value: 73.1054
    - type: euclidean_recall
      value: 79.69659999999999
    - type: euclidean_ap
      value: 83.3352
    - type: dot_accuracy
      value: 87.69940000000001
    - type: dot_accuracy_threshold
      value: 62123.3459
    - type: dot_f1
      value: 75.509
    - type: dot_f1_threshold
      value: 56145.837400000004
    - type: dot_precision
      value: 70.9651
    - type: dot_recall
      value: 80.67450000000001
    - type: dot_ap
      value: 81.7367
    - type: max_accuracy
      value: 88.1612
    - type: max_f1
      value: 76.3737
    - type: max_precision
      value: 74.1711
    - type: max_recall
      value: 80.67450000000001
    - type: max_ap
      value: 83.5651
    - type: main_score
      value: 83.5651
    task:
      type: PairClassification
---

# SentenceTransformer based on answerdotai/ModernBERT-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

I finetune ModernBERT-base using script from offical repo [train_st.py](https://github.com/AnswerDotAI/ModernBERT/blob/main/examples/train_st.py) on a RTX 4090 GPU with batch size 128. Training for 1 epoch takes less than an hour.
With larger batch size, the finetuned model performs better than recorded in the paper.

Training logs can be found here: https://api.wandb.ai/links/joe32140/ekuauaao.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 5756c58a31a2478f9e62146021f48295a92c3da5 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
- **Language:** en
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("joe32140/ModernBERT-base-msmarco")
# Run inference
sentences = [
    'what county is hayden in',
    "Hayden is a city in Kootenai County, Idaho, United States. Located in the northern portion of the state, just north of Coeur d'Alene, its population was 13,294 at the 2010 census.",
    "According to the United States Census Bureau, the city has a total area of 9.61 square miles (24.89 km2), of which 9.60 square miles (24.86 km2) is land and 0.01 square miles (0.03 km2) is water. It lies at the southwestern end of Hayden Lake, and the elevation of the city is 2,287 feet (697 m) above sea level. Hayden is located on U.S. Route 95 at the junction of Route 41. It is also four miles (6 km) north of Interstate 90 and Coeur d'Alene. The Coeur d'Alene airport is northwest of Hayden.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

## Evaluation

### Metrics

#### Triplet

* Dataset: `msmarco-co-condenser-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| **cosine_accuracy** | **0.984** |

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1

* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
* Size: 11,662,655 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                            | positive                                                                            | negative                                                                            |
  |:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                           | string                                                                              | string                                                                              |
  | details | <ul><li>min: 4 tokens</li><li>mean: 9.26 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 79.14 tokens</li><li>max: 222 tokens</li></ul> | <ul><li>min: 24 tokens</li><li>mean: 80.09 tokens</li><li>max: 436 tokens</li></ul> |
* Samples:
  | query                                              | positive                                                                                                                                                                                                                                                                                                                               | negative                                                                                                                                                                                                                                                                                                                                                                                                                  |
  |:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what is the meaning of menu planning</code>  | <code>Menu planning is the selection of a menu for an event. Such as picking out the dinner for your wedding or even a meal at a Birthday Party. Menu planning is when you are preparing a calendar of meals and you have to sit down and decide what meat and veggies you want to serve on each certain day.</code>                   | <code>Menu Costs. In economics, a menu cost is the cost to a firm resulting from changing its prices. The name stems from the cost of restaurants literally printing new menus, but economists use it to refer to the costs of changing nominal prices in general.</code>                                                                                                                                                 |
  | <code>how old is brett butler</code>               | <code>Brett Butler is 59 years old. To be more precise (and nerdy), the current age as of right now is 21564 days or (even more geeky) 517536 hours. That's a lot of hours!</code>                                                                                                                                                     | <code>Passed in: St. John's, Newfoundland and Labrador, Canada. Passed on: 16/07/2016. Published in the St. John's Telegram. Passed away suddenly at the Health Sciences Centre surrounded by his loving family, on July 16, 2016 Robert (Bobby) Joseph Butler, age 52 years. Predeceased by his special aunt Geri Murrin and uncle Mike Mchugh; grandparents Joe and Margaret Murrin and Jack and Theresa Butler.</code> |
  | <code>when was the last navajo treaty sign?</code> | <code>In Executive Session, Senate of the United States, July 25, 1868. Resolved, (two-thirds of the senators present concurring,) That the Senate advise and consent to the ratification of the treaty between the United States and the Navajo Indians, concluded at Fort Sumner, New Mexico, on the first day of June, 1868.</code> | <code>Share Treaty of Greenville. The Treaty of Greenville was signed August 3, 1795, between the United States, represented by Gen. Anthony Wayne, and chiefs of the Indian tribes located in the Northwest Territory, including the Wyandots, Delawares, Shawnees, Ottawas, Miamis, and others.</code>                                                                                                                  |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1

* Dataset: [msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
* Size: 11,662,655 evaluation samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                           | positive                                                                            | negative                                                                            |
  |:--------|:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                          | string                                                                              | string                                                                              |
  | details | <ul><li>min: 4 tokens</li><li>mean: 9.2 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 80.44 tokens</li><li>max: 241 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 80.38 tokens</li><li>max: 239 tokens</li></ul> |
* Samples:
  | query                                                 | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  |:------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>what county is holly springs nc in</code>       | <code>Holly Springs, North Carolina. Holly Springs is a town in Wake County, North Carolina, United States. As of the 2010 census, the town population was 24,661, over 2½ times its population in 2000. Contents.</code>                                                                                                                                                                                                                                                                                                                                                 | <code>The Mt. Holly Springs Park & Resort. One of the numerous trolley routes that carried people around the county at the turn of the century was the Carlisle & Mt. Holly Railway Company. The “Holly Trolley” as it came to be known was put into service by Patricio Russo and made its first run on May 14, 1901.</code>                                                                                                                                                                                                                                                 |
  | <code>how long does nyquil stay in your system</code> | <code>In order to understand exactly how long Nyquil lasts, it is absolutely vital to learn about the various ingredients in the drug. One of the ingredients found in Nyquil is Doxylamine, which is an antihistamine. This specific medication has a biological half-life or 6 to 12 hours. With this in mind, it is possible for the drug to remain in the system for a period of 12 to 24 hours. It should be known that the specifics will depend on a wide variety of different factors, including your age and metabolism.</code>                                   | <code>I confirmed that NyQuil is about 10% alcohol, a higher content than most domestic beers. When I asked about the relatively high proof, I was told that the alcohol dilutes the active ingredients. The alcohol free version is there for customers with addiction issues.. also found that in that version there is twice the amount of DXM. When I asked if I could speak to a chemist or scientist, I was told they didn't have anyone who fit that description there. It’s been eight years since I kicked NyQuil. I've been sober from alcohol for four years.</code> |
  | <code>what are mineral water</code>                   | <code>1 Mineral water – water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2  It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source. Mineral water – water from a mineral spring that contains various minerals, such as salts and sulfur compounds. 2  It comes from a source tapped at one or more bore holes or spring, and originates from a geologically and physically protected underground water source.</code> | <code>Minerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.inerals for Your Body. Drinking mineral water is beneficial to health and well-being. But it is not only the amount of water you drink that is important-what the water contains is even more essential.</code>                                                                                                                                                    |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `learning_rate`: 8e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `bf16`: True
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 8e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | msmarco-co-condenser-dev_cosine_accuracy |
|:------:|:----:|:-------------:|:----------------------------------------:|
| 0      | 0    | -             | 0.606                                    |
| 0.2048 | 500  | 0.6405        | -                                        |
| 0.4095 | 1000 | 0.1473        | -                                        |
| 0.6143 | 1500 | 0.1091        | -                                        |
| 0.8190 | 2000 | 0.0907        | -                                        |
| 1.0    | 2442 | -             | 0.984                                    |


### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.4.0
- Accelerate: 1.2.1
- Datasets: 2.21.0
- Tokenizers: 0.21.0

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### CachedMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

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