sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:100231 loss:CachedMultipleNegativesRankingLoss en dataset:sentence-transformers/natural-questions arxiv:1908.10084 arxiv:2101.06983 base_model:microsoft/mpnet-base base_model:finetune:microsoft/mpnet-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: microsoft/mpnet-base
datasets:
- sentence-transformers/natural-questions
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100231
- loss:CachedMultipleNegativesRankingLoss
widget:
- source_sentence: 'query: who ordered the charge of the light brigade'
  sentences:
  - 'document: Charge of the Light Brigade The Charge of the Light Brigade was a charge
    of British light cavalry led by Lord Cardigan against Russian forces during the
    Battle of Balaclava on 25 October 1854 in the Crimean War. Lord Raglan, overall
    commander of the British forces, had intended to send the Light Brigade to prevent
    the Russians from removing captured guns from overrun Turkish positions, a task
    well-suited to light cavalry.'
  - 'document: UNICEF The United Nations International Children''s Emergency Fund
    was created by the United Nations General Assembly on 11 December 1946, to provide
    emergency food and healthcare to children in countries that had been devastated
    by World War II. The Polish physician Ludwik Rajchman is widely regarded as the
    founder of UNICEF and served as its first chairman from 1946. On Rajchman''s suggestion,
    the American Maurice Pate was appointed its first executive director, serving
    from 1947 until his death in 1965.[5][6] In 1950, UNICEF''s mandate was extended
    to address the long-term needs of children and women in developing countries everywhere.
    In 1953 it became a permanent part of the United Nations System, and the words
    "international" and "emergency" were dropped from the organization''s name, making
    it simply the United Nations Children''s Fund, retaining the original acronym,
    "UNICEF".[3]'
  - 'document: Marcus Jordan Marcus James Jordan (born December 24, 1990) is an American
    former college basketball player who played for the UCF Knights men''s basketball
    team of Conference USA.[1] He is the son of retired Hall of Fame basketball player
    Michael Jordan.'
- source_sentence: 'query: what part of the cow is the rib roast'
  sentences:
  - 'document: Standing rib roast A standing rib roast, also known as prime rib, is
    a cut of beef from the primal rib, one of the nine primal cuts of beef. While
    the entire rib section comprises ribs six through 12, a standing rib roast may
    contain anywhere from two to seven ribs.'
  - 'document: Blaine Anderson Kurt begins to mend their relationship in "Thanksgiving",
    just before New Directions loses at Sectionals to the Warblers, and they spend
    Christmas together in New York City.[29][30] Though he and Kurt continue to be
    on good terms, Blaine finds himself developing a crush on his best friend, Sam,
    which he knows will come to nothing as he knows Sam is not gay; the two of them
    team up to find evidence that the Warblers cheated at Sectionals, which means
    New Directions will be competing at Regionals. He ends up going to the Sadie Hawkins
    dance with Tina Cohen-Chang (Jenna Ushkowitz), who has developed a crush on him,
    but as friends only.[31] When Kurt comes to Lima for the wedding of glee club
    director Will (Matthew Morrison) and Emma (Jayma Mays)—which Emma flees—he and
    Blaine make out beforehand, and sleep together afterward, though they do not resume
    a permanent relationship.[32]'
  - 'document: Soviet Union The Soviet Union (Russian: Сове́тский Сою́з, tr. Sovétsky
    Soyúz, IPA: [sɐˈvʲɛt͡skʲɪj sɐˈjus] ( listen)), officially the Union of Soviet
    Socialist Republics (Russian: Сою́з Сове́тских Социалисти́ческих Респу́блик, tr.
    Soyúz Sovétskikh Sotsialistícheskikh Respúblik, IPA: [sɐˈjus sɐˈvʲɛtskʲɪx sətsɨəlʲɪsˈtʲitɕɪskʲɪx
    rʲɪˈspublʲɪk] ( listen)), abbreviated as the USSR (Russian: СССР, tr. SSSR), was
    a socialist state in Eurasia that existed from 1922 to 1991. Nominally a union
    of multiple national Soviet republics,[a] its government and economy were highly
    centralized. The country was a one-party state, governed by the Communist Party
    with Moscow as its capital in its largest republic, the Russian Soviet Federative
    Socialist Republic. The Russian nation had constitutionally equal status among
    the many nations of the union but exerted de facto dominance in various respects.[7]
    Other major urban centres were Leningrad, Kiev, Minsk, Alma-Ata and Novosibirsk.
    The Soviet Union was one of the five recognized nuclear weapons states and possessed
    the largest stockpile of weapons of mass destruction.[8] It was a founding permanent
    member of the United Nations Security Council, as well as a member of the Organization
    for Security and Co-operation in Europe (OSCE) and the leading member of the Council
    for Mutual Economic Assistance (CMEA) and the Warsaw Pact.'
- source_sentence: 'query: what is the current big bang theory season'
  sentences:
  - 'document: Byzantine army From the seventh to the 12th centuries, the Byzantine
    army was among the most powerful and effective military forces in the world â€“
    neither Middle Ages Europe nor (following its early successes) the fracturing
    Caliphate could match the strategies and the efficiency of the Byzantine army.
    Restricted to a largely defensive role in the 7th to mid-9th centuries, the Byzantines
    developed the theme-system to counter the more powerful Caliphate. From the mid-9th
    century, however, they gradually went on the offensive, culminating in the great
    conquests of the 10th century under a series of soldier-emperors such as Nikephoros
    II Phokas, John Tzimiskes and Basil II. The army they led was less reliant on
    the militia of the themes; it was by now a largely professional force, with a
    strong and well-drilled infantry at its core and augmented by a revived heavy
    cavalry arm. With one of the most powerful economies in the world at the time,
    the Empire had the resources to put to the field a powerful host when needed,
    in order to reclaim its long-lost territories.'
  - 'document: The Big Bang Theory The Big Bang Theory is an American television sitcom
    created by Chuck Lorre and Bill Prady, both of whom serve as executive producers
    on the series, along with Steven Molaro. All three also serve as head writers.
    The show premiered on CBS on September 24, 2007.[3] The series'' tenth season
    premiered on September 19, 2016.[4] In March 2017, the series was renewed for
    two additional seasons, bringing its total to twelve, and running through the
    2018â€“19 television season. The eleventh season is set to premiere on September
    25, 2017.[5]'
  - 'document: 2016 NCAA Division I Softball Tournament The 2016 NCAA Division I Softball
    Tournament was held from May 20 through June 8, 2016 as the final part of the
    2016 NCAA Division I softball season. The 64 NCAA Division I college softball
    teams were to be selected out of an eligible 293 teams on May 15, 2016. Thirty-two
    teams were awarded an automatic bid as champions of their conference, and thirty-two
    teams were selected at-large by the NCAA Division I softball selection committee.
    The tournament culminated with eight teams playing in the 2016 Women''s College
    World Series at ASA Hall of Fame Stadium in Oklahoma City in which the Oklahoma
    Sooners were crowned the champions.'
- source_sentence: 'query: what happened to tates mom on days of our lives'
  sentences:
  - 'document: Paige O''Hara Donna Paige Helmintoller, better known as Paige O''Hara
    (born May 10, 1956),[1] is an American actress, voice actress, singer and painter.
    O''Hara began her career as a Broadway actress in 1983 when she portrayed Ellie
    May Chipley in the musical Showboat. In 1991, she made her motion picture debut
    in Disney''s Beauty and the Beast, in which she voiced the film''s heroine, Belle.
    Following the critical and commercial success of Beauty and the Beast, O''Hara
    reprised her role as Belle in the film''s two direct-to-video follow-ups, Beauty
    and the Beast: The Enchanted Christmas and Belle''s Magical World.'
  - 'document: M. Shadows Matthew Charles Sanders (born July 31, 1981), better known
    as M. Shadows, is an American singer, songwriter, and musician. He is best known
    as the lead vocalist, songwriter, and a founding member of the American heavy
    metal band Avenged Sevenfold. In 2017, he was voted 3rd in the list of Top 25
    Greatest Modern Frontmen by Ultimate Guitar.[1]'
  - 'document: Theresa Donovan In July 2013, Jeannie returns to Salem, this time going
    by her middle name, Theresa. Initially, she strikes up a connection with resident
    bad boy JJ Deveraux (Casey Moss) while trying to secure some pot.[28] During a
    confrontation with JJ and his mother Jennifer Horton (Melissa Reeves) in her office,
    her aunt Kayla confirms that Theresa is in fact Jeannie and that Jen promised
    to hire her as her assistant, a promise she reluctantly agrees to. Kayla reminds
    Theresa it is her last chance at a fresh start.[29] Theresa also strikes up a
    bad first impression with Jennifer''s daughter Abigail Deveraux (Kate Mansi) when
    Abigail smells pot on Theresa in her mother''s office.[30] To continue to battle
    against Jennifer, she teams up with Anne Milbauer (Meredith Scott Lynn) in hopes
    of exacting her perfect revenge. In a ploy, Theresa reveals her intentions to
    hopefully woo Dr. Daniel Jonas (Shawn Christian). After sleeping with JJ, Theresa
    overdoses on marijuana and GHB. Upon hearing of their daughter''s overdose and
    continuing problems, Shane and Kimberly return to town in the hopes of handling
    their daughter''s problem, together. After believing that Theresa has a handle
    on her addictions, Shane and Kimberly leave town together. Theresa then teams
    up with hospital co-worker Anne Milbauer (Meredith Scott Lynn) to conspire against
    Jennifer, using Daniel as a way to hurt their relationship. In early 2014, following
    a Narcotics Anonymous (NA) meeting, she begins a sexual and drugged-fused relationship
    with Brady Black (Eric Martsolf). In 2015, after it is found that Kristen DiMera
    (Eileen Davidson) stole Theresa''s embryo and carried it to term, Brady and Melanie
    Jonas return her son, Christopher, to her and Brady, and the pair rename him Tate.
    When Theresa moves into the Kiriakis mansion, tensions arise between her and Victor.
    She eventually expresses her interest in purchasing Basic Black and running it
    as her own fashion company, with financial backing from Maggie Horton (Suzanne
    Rogers). In the hopes of finding the right partner, she teams up with Kate Roberts
    (Lauren Koslow) and Nicole Walker (Arianne Zucker) to achieve the goal of purchasing
    Basic Black, with Kate and Nicole''s business background and her own interest
    in fashion design. As she and Brady share several instances of rekindling their
    romance, she is kicked out of the mansion by Victor; as a result, Brady quits
    Titan and moves in with Theresa and Tate, in their own penthouse.'
- source_sentence: 'query: where does the last name francisco come from'
  sentences:
  - 'document: Francisco Francisco is the Spanish and Portuguese form of the masculine
    given name Franciscus (corresponding to English Francis).'
  - 'document: Book of Esther The Book of Esther, also known in Hebrew as "the Scroll"
    (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish
    Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the
    five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew
    woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia
    and thwarts a genocide of her people. The story forms the core of the Jewish festival
    of Purim, during which it is read aloud twice: once in the evening and again the
    following morning. The books of Esther and Song of Songs are the only books in
    the Hebrew Bible that do not explicitly mention God.[2]'
  - 'document: Times Square Times Square is a major commercial intersection, tourist
    destination, entertainment center and neighborhood in the Midtown Manhattan section
    of New York City at the junction of Broadway and Seventh Avenue. It stretches
    from West 42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements,
    Times Square is sometimes referred to as "The Crossroads of the World",[2] "The
    Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the
    "heart of the world".[7] One of the world''s busiest pedestrian areas,[8] it is
    also the hub of the Broadway Theater District[9] and a major center of the world''s
    entertainment industry.[10] Times Square is one of the world''s most visited tourist
    attractions, drawing an estimated 50 million visitors annually.[11] Approximately
    330,000 people pass through Times Square daily,[12] many of them tourists,[13]
    while over 460,000 pedestrians walk through Times Square on its busiest days.[7]'
model-index:
- name: mpnet-base trained with prompts on NQ using AdamWScheduleFree
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoClimateFEVER
      type: NanoClimateFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.28
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.46
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.58
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.78
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.28
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1733333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.136
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10400000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.12833333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23333333333333336
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.284
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.42066666666666663
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3221543405383023
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.41043650793650793
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.24090500543743654
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoDBPedia
      type: NanoDBPedia
    metrics:
    - type: cosine_accuracy@1
      value: 0.58
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.82
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.88
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.92
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.58
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.48
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.452
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.39199999999999996
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06253108762399381
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.12063276522979356
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1751662362841334
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.28122474918448037
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.48431726610934717
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7072222222222223
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3514659991389771
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFEVER
      type: NanoFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.5
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.68
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.76
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.84
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15600000000000003
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.088
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.49
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.66
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.73
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.82
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6565997876414891
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.611547619047619
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6052584967270065
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFiQA2018
      type: NanoFiQA2018
    metrics:
    - type: cosine_accuracy@1
      value: 0.32
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.52
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.54
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.64
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.32
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.22
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.10199999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.16235714285714287
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.30493650793650795
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.35026984126984123
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.46793650793650793
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.36743198343231426
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4255714285714285
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.30678537828621655
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoHotpotQA
      type: NanoHotpotQA
    metrics:
    - type: cosine_accuracy@1
      value: 0.58
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.68
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.68
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.68
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.58
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.29333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.184
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.096
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.29
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.44
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.46
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.48
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4821721938161659
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6233333333333333
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4380356876037466
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoMSMARCO
      type: NanoMSMARCO
    metrics:
    - type: cosine_accuracy@1
      value: 0.26
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.56
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.66
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.26
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18666666666666668
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.132
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.26
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.56
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.66
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5170723155938319
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4283571428571428
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.43588111994720796
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNFCorpus
      type: NanoNFCorpus
    metrics:
    - type: cosine_accuracy@1
      value: 0.34
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.44
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.34
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2733333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.252
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.2
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.011883917506089279
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.040192439924163194
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.05309809187703614
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.07261841324595847
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.23206916265117422
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.40772222222222226
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.0805152236076489
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNQ
      type: NanoNQ
    metrics:
    - type: cosine_accuracy@1
      value: 0.44
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.72
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.74
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.44
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.20666666666666664
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15200000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07800000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.42
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.58
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.69
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.71
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5817200429581193
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5489999999999999
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5445049480115037
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoQuoraRetrieval
      type: NanoQuoraRetrieval
    metrics:
    - type: cosine_accuracy@1
      value: 0.86
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.94
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.96
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.86
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3733333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.23999999999999996
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.126
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7606666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.872
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9093333333333333
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.94
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8950032352824223
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.89
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8795381701631702
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSCIDOCS
      type: NanoSCIDOCS
    metrics:
    - type: cosine_accuracy@1
      value: 0.4
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.54
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.62
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.76
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.26666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.236
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.162
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08466666666666667
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.16566666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.2436666666666667
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3336666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3205651694402261
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.49392857142857133
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.25064568299580275
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoArguAna
      type: NanoArguAna
    metrics:
    - type: cosine_accuracy@1
      value: 0.22
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.62
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.94
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.22
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.20666666666666667
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.16
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09399999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.22
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.62
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.94
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5731240894567345
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4556587301587301
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.45808520074696546
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSciFact
      type: NanoSciFact
    metrics:
    - type: cosine_accuracy@1
      value: 0.42
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.72
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.76
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.42
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.21333333333333332
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.156
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08599999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.385
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.57
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.695
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.75
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5828417207414732
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5392222222222222
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5292752525252525
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoTouche2020
      type: NanoTouche2020
    metrics:
    - type: cosine_accuracy@1
      value: 0.4897959183673469
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8571428571428571
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9183673469387755
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9591836734693877
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4897959183673469
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.5306122448979591
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.49795918367346936
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.42040816326530617
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.036557626271543874
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.118296723617725
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.1844341323985051
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.2839509586565638
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4637991097478537
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6668610301263361
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.364459200173378
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.4376766091051805
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.6367032967032967
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.7167974882260597
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7983987441130298
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4376766091051805
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2813291470434327
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.22415070643642074
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.15603139717425435
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2547689569942644
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.40654295666986073
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4796129462945782
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5615433817197573
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4983746474930349
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5545277715481797
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4219504127203318
      name: Cosine Map@100
---

# mpnet-base trained with prompts on NQ using AdamWScheduleFree

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) 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.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0

### 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': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (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("din0s/mpnet-base-nq-prompts")
# Run inference
sentences = [
    'query: where does the last name francisco come from',
    'document: Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
    'document: Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]',
]
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]
```

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

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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Information Retrieval

* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | NanoClimateFEVER | NanoDBPedia | NanoFEVER  | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ     | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1   | 0.28             | 0.58        | 0.5        | 0.32         | 0.58         | 0.26        | 0.34         | 0.44       | 0.86               | 0.4         | 0.22        | 0.42        | 0.4898         |
| cosine_accuracy@3   | 0.46             | 0.82        | 0.68       | 0.52         | 0.68         | 0.56        | 0.44         | 0.6        | 0.9                | 0.54        | 0.62        | 0.6         | 0.8571         |
| cosine_accuracy@5   | 0.58             | 0.88        | 0.76       | 0.54         | 0.68         | 0.66        | 0.5          | 0.72       | 0.94               | 0.62        | 0.8         | 0.72        | 0.9184         |
| cosine_accuracy@10  | 0.78             | 0.92        | 0.84       | 0.64         | 0.68         | 0.8         | 0.6          | 0.74       | 0.96               | 0.76        | 0.94        | 0.76        | 0.9592         |
| cosine_precision@1  | 0.28             | 0.58        | 0.5        | 0.32         | 0.58         | 0.26        | 0.34         | 0.44       | 0.86               | 0.4         | 0.22        | 0.42        | 0.4898         |
| cosine_precision@3  | 0.1733           | 0.48        | 0.2333     | 0.22         | 0.2933       | 0.1867      | 0.2733       | 0.2067     | 0.3733             | 0.2667      | 0.2067      | 0.2133      | 0.5306         |
| cosine_precision@5  | 0.136            | 0.452       | 0.156      | 0.16         | 0.184        | 0.132       | 0.252        | 0.152      | 0.24               | 0.236       | 0.16        | 0.156       | 0.498          |
| cosine_precision@10 | 0.104            | 0.392       | 0.088      | 0.102        | 0.096        | 0.08        | 0.2          | 0.078      | 0.126              | 0.162       | 0.094       | 0.086       | 0.4204         |
| cosine_recall@1     | 0.1283           | 0.0625      | 0.49       | 0.1624       | 0.29         | 0.26        | 0.0119       | 0.42       | 0.7607             | 0.0847      | 0.22        | 0.385       | 0.0366         |
| cosine_recall@3     | 0.2333           | 0.1206      | 0.66       | 0.3049       | 0.44         | 0.56        | 0.0402       | 0.58       | 0.872              | 0.1657      | 0.62        | 0.57        | 0.1183         |
| cosine_recall@5     | 0.284            | 0.1752      | 0.73       | 0.3503       | 0.46         | 0.66        | 0.0531       | 0.69       | 0.9093             | 0.2437      | 0.8         | 0.695       | 0.1844         |
| cosine_recall@10    | 0.4207           | 0.2812      | 0.82       | 0.4679       | 0.48         | 0.8         | 0.0726       | 0.71       | 0.94               | 0.3337      | 0.94        | 0.75        | 0.284          |
| **cosine_ndcg@10**  | **0.3222**       | **0.4843**  | **0.6566** | **0.3674**   | **0.4822**   | **0.5171**  | **0.2321**   | **0.5817** | **0.895**          | **0.3206**  | **0.5731**  | **0.5828**  | **0.4638**     |
| cosine_mrr@10       | 0.4104           | 0.7072      | 0.6115     | 0.4256       | 0.6233       | 0.4284      | 0.4077       | 0.549      | 0.89               | 0.4939      | 0.4557      | 0.5392      | 0.6669         |
| cosine_map@100      | 0.2409           | 0.3515      | 0.6053     | 0.3068       | 0.438        | 0.4359      | 0.0805       | 0.5445     | 0.8795             | 0.2506      | 0.4581      | 0.5293      | 0.3645         |

#### Nano BEIR

* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.4377     |
| cosine_accuracy@3   | 0.6367     |
| cosine_accuracy@5   | 0.7168     |
| cosine_accuracy@10  | 0.7984     |
| cosine_precision@1  | 0.4377     |
| cosine_precision@3  | 0.2813     |
| cosine_precision@5  | 0.2242     |
| cosine_precision@10 | 0.156      |
| cosine_recall@1     | 0.2548     |
| cosine_recall@3     | 0.4065     |
| cosine_recall@5     | 0.4796     |
| cosine_recall@10    | 0.5615     |
| **cosine_ndcg@10**  | **0.4984** |
| cosine_mrr@10       | 0.5545     |
| cosine_map@100      | 0.422      |

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### Recommendations

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## Training Details

### Training Dataset

#### natural-questions

* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 100,231 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                              | answer                                                                              |
  |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                              |
  | details | <ul><li>min: 12 tokens</li><li>mean: 13.74 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 139.2 tokens</li><li>max: 510 tokens</li></ul> |
* Samples:
  | query                                                                          | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
  |:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>query: who is required to report according to the hmda</code>            | <code>document: Home Mortgage Disclosure Act US financial institutions must report HMDA data to their regulator if they meet certain criteria, such as having assets above a specific threshold. The criteria is different for depository and non-depository institutions and are available on the FFIEC website.[4] In 2012, there were 7,400 institutions that reported a total of 18.7 million HMDA records.[5]</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  | <code>query: what is the definition of endoplasmic reticulum in biology</code> | <code>document: Endoplasmic reticulum The endoplasmic reticulum (ER) is a type of organelle in eukaryotic cells that forms an interconnected network of flattened, membrane-enclosed sacs or tube-like structures known as cisternae. The membranes of the ER are continuous with the outer nuclear membrane. The endoplasmic reticulum occurs in most types of eukaryotic cells, but is absent from red blood cells and spermatozoa. There are two types of endoplasmic reticulum: rough and smooth. The outer (cytosolic) face of the rough endoplasmic reticulum is studded with ribosomes that are the sites of protein synthesis. The rough endoplasmic reticulum is especially prominent in cells such as hepatocytes. The smooth endoplasmic reticulum lacks ribosomes and functions in lipid manufacture and metabolism, the production of steroid hormones, and detoxification.[1] The smooth ER is especially abundant in mammalian liver and gonad cells. The lacy membranes of the endoplasmic reticulum were first seen in 1945 u...</code> |
  | <code>query: what does the ski mean in polish names</code>                     | <code>document: Polish name Since the High Middle Ages, Polish-sounding surnames ending with the masculine -ski suffix, including -cki and -dzki, and the corresponding feminine suffix -ska/-cka/-dzka were associated with the nobility (Polish szlachta), which alone, in the early years, had such suffix distinctions.[1] They are widely popular today.</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

#### natural-questions

* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 100,231 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
  |         | query                                                                              | answer                                                                               |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               |
  | details | <ul><li>min: 12 tokens</li><li>mean: 13.78 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 137.63 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | query                                                                    | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
  |:-------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>query: difference between russian blue and british blue cat</code> | <code>document: Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.</code> |
  | <code>query: who played the little girl on mrs doubtfire</code>          | <code>document: Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
  | <code>query: what year did the movie the sound of music come out</code>  | <code>document: The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.</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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `lr_scheduler_type`: constant
- `seed`: 12
- `bf16`: True
- `prompts`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates

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

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `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`: 2e-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`: constant
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: 12
- `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`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| -1     | -1   | -             | -               | 0.0442                          | 0.0851                     | 0.0326                   | 0.0282                      | 0.0625                      | 0.0708                     | 0.0262                      | 0.0331                | 0.6747                            | 0.0387                     | 0.2764                     | 0.0617                     | 0.0721                        | 0.1159                       |
| 0.0026 | 1    | 5.0155        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0129 | 5    | 4.0811        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0258 | 10   | 2.0749        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0387 | 15   | 1.0559        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0515 | 20   | 0.5994        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0644 | 25   | 0.4992        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0773 | 30   | 0.4846        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0902 | 35   | 0.323         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1031 | 40   | 0.3126        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1160 | 45   | 0.2803        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1289 | 50   | 0.2675        | 0.2596          | 0.2522                          | 0.4525                     | 0.6666                   | 0.3802                      | 0.4509                      | 0.4490                     | 0.1994                      | 0.4479                | 0.8526                            | 0.3377                     | 0.5264                     | 0.4983                     | 0.4682                        | 0.4601                       |
| 0.1418 | 55   | 0.2746        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1546 | 60   | 0.2594        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1675 | 65   | 0.2085        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1804 | 70   | 0.2017        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1933 | 75   | 0.1955        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2062 | 80   | 0.2121        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2191 | 85   | 0.2271        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2320 | 90   | 0.1999        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2448 | 95   | 0.185         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2577 | 100  | 0.1791        | 0.1811          | 0.2749                          | 0.4636                     | 0.6490                   | 0.3896                      | 0.4921                      | 0.5007                     | 0.2072                      | 0.4835                | 0.8805                            | 0.3291                     | 0.5372                     | 0.5321                     | 0.4807                        | 0.4785                       |
| 0.2706 | 105  | 0.1393        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2835 | 110  | 0.1771        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2964 | 115  | 0.1919        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3093 | 120  | 0.1556        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3222 | 125  | 0.1711        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3351 | 130  | 0.1596        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3479 | 135  | 0.1426        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3608 | 140  | 0.1659        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3737 | 145  | 0.1658        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3866 | 150  | 0.1516        | 0.1472          | 0.2956                          | 0.4696                     | 0.6508                   | 0.3576                      | 0.4990                      | 0.5186                     | 0.2206                      | 0.4970                | 0.8949                            | 0.3263                     | 0.5665                     | 0.5165                     | 0.4681                        | 0.4832                       |
| 0.3995 | 155  | 0.1464        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4124 | 160  | 0.147         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4253 | 165  | 0.1196        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4381 | 170  | 0.1364        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4510 | 175  | 0.1244        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4639 | 180  | 0.1294        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4768 | 185  | 0.1361        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4897 | 190  | 0.1298        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5026 | 195  | 0.1372        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5155 | 200  | 0.1348        | 0.1267          | 0.3000                          | 0.4713                     | 0.6426                   | 0.3673                      | 0.5020                      | 0.5296                     | 0.2315                      | 0.5166                | 0.8957                            | 0.3306                     | 0.5834                     | 0.5413                     | 0.4750                        | 0.4913                       |
| 0.5284 | 205  | 0.1499        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5412 | 210  | 0.1313        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5541 | 215  | 0.1412        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5670 | 220  | 0.1584        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5799 | 225  | 0.1336        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5928 | 230  | 0.1246        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6057 | 235  | 0.1191        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6186 | 240  | 0.1454        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6314 | 245  | 0.1257        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6443 | 250  | 0.1327        | 0.1155          | 0.3003                          | 0.4735                     | 0.6474                   | 0.3479                      | 0.4992                      | 0.5269                     | 0.2272                      | 0.5401                | 0.8953                            | 0.3383                     | 0.5867                     | 0.5667                     | 0.4775                        | 0.4944                       |
| 0.6572 | 255  | 0.1186        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6701 | 260  | 0.1441        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6830 | 265  | 0.1154        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6959 | 270  | 0.1058        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7088 | 275  | 0.1301        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7216 | 280  | 0.1166        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7345 | 285  | 0.1283        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7474 | 290  | 0.1219        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7603 | 295  | 0.1352        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7732 | 300  | 0.1123        | 0.1081          | 0.2978                          | 0.4616                     | 0.6497                   | 0.3555                      | 0.4940                      | 0.5116                     | 0.2358                      | 0.5703                | 0.9023                            | 0.3368                     | 0.5841                     | 0.5758                     | 0.4721                        | 0.4960                       |
| 0.7861 | 305  | 0.1045        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7990 | 310  | 0.1185        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8119 | 315  | 0.0997        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8247 | 320  | 0.1141        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8376 | 325  | 0.1241        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8505 | 330  | 0.1101        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8634 | 335  | 0.1214        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8763 | 340  | 0.1262        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8892 | 345  | 0.1246        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9021 | 350  | 0.0953        | 0.1023          | 0.3113                          | 0.4740                     | 0.6518                   | 0.3611                      | 0.4934                      | 0.5094                     | 0.2412                      | 0.5703                | 0.8971                            | 0.3293                     | 0.5808                     | 0.5869                     | 0.4655                        | 0.4979                       |
| 0.9149 | 355  | 0.1072        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9278 | 360  | 0.1134        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9407 | 365  | 0.1062        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9536 | 370  | 0.0886        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9665 | 375  | 0.1075        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9794 | 380  | 0.1122        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9923 | 385  | 0.1078        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| -1     | -1   | -             | -               | 0.3222                          | 0.4843                     | 0.6566                   | 0.3674                      | 0.4822                      | 0.5171                     | 0.2321                      | 0.5817                | 0.8950                            | 0.3206                     | 0.5731                     | 0.5828                     | 0.4638                        | 0.4984                       |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- 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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