sentence-transformers safetensors bert 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:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index co2_eq_emissions autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: google-bert/bert-base-uncased
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: who ordered the charge of the light brigade
  sentences:
  - 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.
  - 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]
  - 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: what part of the cow is the rib roast
  sentences:
  - 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.
  - 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]
  - '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: what is the current big bang theory season
  sentences:
  - 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.
  - 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]
  - 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: what happened to tates mom on days of our lives
  sentences:
  - '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.'
  - 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]
  - 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: where does the last name francisco come from
  sentences:
  - Francisco Francisco is the Spanish and Portuguese form of the masculine given
    name Franciscus (corresponding to English Francis).
  - '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]'
  - 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]
co2_eq_emissions:
  emissions: 104.55706247798686
  energy_consumed: 0.2689902868469595
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.782
  hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: BERT base trained on Natural Questions pairs
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoClimateFEVER
      type: NanoClimateFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.22
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.44
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.52
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.62
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.22
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.136
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08199999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.09833333333333333
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.23566666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.27899999999999997
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.3256666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.272752213638476
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.35327777777777775
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22112124218381526
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoDBPedia
      type: NanoDBPedia
    metrics:
    - type: cosine_accuracy@1
      value: 0.64
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.74
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.84
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.64
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.46
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.44000000000000006
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.39000000000000007
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06726969384535866
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.10829208895108451
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.16535944626004745
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.2529406741716369
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4767334977280841
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7150238095238095
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.35379332004025005
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFEVER
      type: NanoFEVER
    metrics:
    - type: cosine_accuracy@1
      value: 0.52
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.72
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.74
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.52
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.24
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.15200000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08399999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.51
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.69
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.72
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.79
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.6577792985828184
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6226904761904761
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.6145173719999807
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoFiQA2018
      type: NanoFiQA2018
    metrics:
    - type: cosine_accuracy@1
      value: 0.2
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.36
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.42
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.11999999999999998
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.064
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08807936507936509
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.15074603174603174
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.20407936507936508
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.2524126984126984
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.20563274962284733
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.26849999999999996
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.17422019443090375
      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: 0.7
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.8
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.58
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19199999999999995
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.11399999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.29
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.45
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.48
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.57
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5242406054056967
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.643968253968254
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4580837397163866
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoMSMARCO
      type: NanoMSMARCO
    metrics:
    - type: cosine_accuracy@1
      value: 0.22
      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: 0.68
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.22
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.14666666666666664
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10000000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.068
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.22
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.44
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.68
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.43963932117536886
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3651269841269841
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3787220730332196
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNFCorpus
      type: NanoNFCorpus
    metrics:
    - type: cosine_accuracy@1
      value: 0.3
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.44
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.48
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.58
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.3
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2733333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.21600000000000003
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.17800000000000002
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.021273572702979568
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.05401978082802253
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.06417737946426297
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.09731580553141
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.22424563391154495
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3820476190476191
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.0898107385158962
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoNQ
      type: NanoNQ
    metrics:
    - type: cosine_accuracy@1
      value: 0.42
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.58
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.66
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.72
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.42
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.19333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.136
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07400000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.41
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.56
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.65
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.69
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.558255406320814
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5235
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.5201771704107404
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoQuoraRetrieval
      type: NanoQuoraRetrieval
    metrics:
    - type: cosine_accuracy@1
      value: 0.8
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.94
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.96
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.98
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.8
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3666666666666666
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.23999999999999996
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.13399999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.7040000000000001
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8786666666666667
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.9193333333333333
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.97
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8832492246317404
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8669999999999999
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8484825708061002
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSCIDOCS
      type: NanoSCIDOCS
    metrics:
    - type: cosine_accuracy@1
      value: 0.32
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.44
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.52
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6
      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: 0.192
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.136
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06766666666666668
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.1366666666666667
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.19766666666666666
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.2776666666666667
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2659892317280556
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.40594444444444444
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.2092360599636062
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoArguAna
      type: NanoArguAna
    metrics:
    - type: cosine_accuracy@1
      value: 0.2
      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: 0.8
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.14400000000000002
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.08
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.6
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.72
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.8
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.5055380364663926
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.40949999999999986
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4212792624042624
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: NanoSciFact
      type: NanoSciFact
    metrics:
    - type: cosine_accuracy@1
      value: 0.34
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.52
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.58
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.66
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.34
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.18
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.132
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.07600000000000001
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.305
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.485
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.565
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.65
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.48695696351260737
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.44471428571428573
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.4344481461926843
      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: 0.7755102040816326
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.9387755102040817
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9795918367346939
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4897959183673469
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.44897959183673464
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.4448979591836735
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.37755102040816335
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.038721005631019066
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.10614028485651965
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.16536659269196569
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.2595083655687104
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.418741764200365
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.6490038872691933
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.32735138614786596
      name: Cosine Map@100
  - task:
      type: nano-beir
      name: Nano BEIR
    dataset:
      name: NanoBEIR mean
      type: NanoBEIR_mean
    metrics:
    - type: cosine_accuracy@1
      value: 0.4038304552590267
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.5858084772370487
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.6552904238618524
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.7338147566718993
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4038304552590267
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2560753532182104
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.20191522762951336
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.14288854003139717
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.23233412594297867
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.37655370664474297
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.43307559873043383
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5088854520782915
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.4553656882249855
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.5115613490817572
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.38855717506505477
      name: Cosine Map@100
---

# BERT base trained on Natural Questions pairs

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) 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.

This model was trained using the script from the [Training with Prompts](https://sbert.net/examples/training/prompts/README.html) Sentence Transformers documentation.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **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: BertModel 
  (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("tomaarsen/bert-base-nq")
# Run inference
sentences = [
    'where does the last name francisco come from',
    'Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
    '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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### Downstream Usage (Sentence Transformers)

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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   | 0.22             | 0.64        | 0.52       | 0.2          | 0.58         | 0.22        | 0.3          | 0.42       | 0.8                | 0.32        | 0.2         | 0.34        | 0.4898         |
| cosine_accuracy@3   | 0.44             | 0.74        | 0.72       | 0.3          | 0.68         | 0.44        | 0.44         | 0.58       | 0.94               | 0.44        | 0.6         | 0.52        | 0.7755         |
| cosine_accuracy@5   | 0.52             | 0.84        | 0.74       | 0.36         | 0.7          | 0.5         | 0.48         | 0.66       | 0.96               | 0.52        | 0.72        | 0.58        | 0.9388         |
| cosine_accuracy@10  | 0.62             | 0.9         | 0.8        | 0.42         | 0.8          | 0.68        | 0.58         | 0.72       | 0.98               | 0.6         | 0.8         | 0.66        | 0.9796         |
| cosine_precision@1  | 0.22             | 0.64        | 0.52       | 0.2          | 0.58         | 0.22        | 0.3          | 0.42       | 0.8                | 0.32        | 0.2         | 0.34        | 0.4898         |
| cosine_precision@3  | 0.18             | 0.46        | 0.24       | 0.12         | 0.3          | 0.1467      | 0.2733       | 0.1933     | 0.3667             | 0.22        | 0.2         | 0.18        | 0.449          |
| cosine_precision@5  | 0.136            | 0.44        | 0.152      | 0.1          | 0.192        | 0.1         | 0.216        | 0.136      | 0.24               | 0.192       | 0.144       | 0.132       | 0.4449         |
| cosine_precision@10 | 0.082            | 0.39        | 0.084      | 0.064        | 0.114        | 0.068       | 0.178        | 0.074      | 0.134              | 0.136       | 0.08        | 0.076       | 0.3776         |
| cosine_recall@1     | 0.0983           | 0.0673      | 0.51       | 0.0881       | 0.29         | 0.22        | 0.0213       | 0.41       | 0.704              | 0.0677      | 0.2         | 0.305       | 0.0387         |
| cosine_recall@3     | 0.2357           | 0.1083      | 0.69       | 0.1507       | 0.45         | 0.44        | 0.054        | 0.56       | 0.8787             | 0.1367      | 0.6         | 0.485       | 0.1061         |
| cosine_recall@5     | 0.279            | 0.1654      | 0.72       | 0.2041       | 0.48         | 0.5         | 0.0642       | 0.65       | 0.9193             | 0.1977      | 0.72        | 0.565       | 0.1654         |
| cosine_recall@10    | 0.3257           | 0.2529      | 0.79       | 0.2524       | 0.57         | 0.68        | 0.0973       | 0.69       | 0.97               | 0.2777      | 0.8         | 0.65        | 0.2595         |
| **cosine_ndcg@10**  | **0.2728**       | **0.4767**  | **0.6578** | **0.2056**   | **0.5242**   | **0.4396**  | **0.2242**   | **0.5583** | **0.8832**         | **0.266**   | **0.5055**  | **0.487**   | **0.4187**     |
| cosine_mrr@10       | 0.3533           | 0.715       | 0.6227     | 0.2685       | 0.644        | 0.3651      | 0.382        | 0.5235     | 0.867              | 0.4059      | 0.4095      | 0.4447      | 0.649          |
| cosine_map@100      | 0.2211           | 0.3538      | 0.6145     | 0.1742       | 0.4581       | 0.3787      | 0.0898       | 0.5202     | 0.8485             | 0.2092      | 0.4213      | 0.4344      | 0.3274         |

#### 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   | 0.4038     |
| cosine_accuracy@3   | 0.5858     |
| cosine_accuracy@5   | 0.6553     |
| cosine_accuracy@10  | 0.7338     |
| cosine_precision@1  | 0.4038     |
| cosine_precision@3  | 0.2561     |
| cosine_precision@5  | 0.2019     |
| cosine_precision@10 | 0.1429     |
| cosine_recall@1     | 0.2323     |
| cosine_recall@3     | 0.3766     |
| cosine_recall@5     | 0.4331     |
| cosine_recall@10    | 0.5089     |
| **cosine_ndcg@10**  | **0.4554** |
| cosine_mrr@10       | 0.5116     |
| cosine_map@100      | 0.3886     |

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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: 10 tokens</li><li>mean: 11.74 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 137.2 tokens</li><li>max: 508 tokens</li></ul> |
* Samples:
  | query                                                                   | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   |
  |:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>who is required to report according to the hmda</code>            | <code>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>what is the definition of endoplasmic reticulum in biology</code> | <code>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 using elect...</code> |
  | <code>what does the ski mean in polish names</code>                     | <code>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: 10 tokens</li><li>mean: 11.78 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 135.64 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | query                                                             | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
  |:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>difference between russian blue and british blue cat</code> | <code>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>who played the little girl on mrs doubtfire</code>          | <code>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>what year did the movie the sound of music come out</code>  | <code>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
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `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`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | 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 |
|:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0      | 0    | -             | -               | 0.1046                          | 0.2182                     | 0.1573                   | 0.0575                      | 0.2597                      | 0.1602                     | 0.0521                      | 0.0493                | 0.7310                            | 0.1320                     | 0.2309                     | 0.1240                     | 0.0970                        | 0.1826                       |
| 0.0026 | 1    | 2.9906        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0129 | 5    | 3.0069        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0258 | 10   | 2.715         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0387 | 15   | 2.1234        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0515 | 20   | 1.5407        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0644 | 25   | 1.2437        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0773 | 30   | 0.9972        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.0902 | 35   | 0.7725        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1031 | 40   | 0.6277        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1160 | 45   | 0.5025        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1289 | 50   | 0.3382        | 0.3345          | 0.2316                          | 0.4851                     | 0.6465                   | 0.2296                      | 0.4966                      | 0.4263                     | 0.2129                      | 0.4993                | 0.8656                            | 0.2590                     | 0.4917                     | 0.4712                     | 0.4293                        | 0.4419                       |
| 0.1418 | 55   | 0.3324        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1546 | 60   | 0.2682        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1675 | 65   | 0.2473        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1804 | 70   | 0.1919        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.1933 | 75   | 0.2543        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2062 | 80   | 0.2179        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2191 | 85   | 0.2067        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2320 | 90   | 0.2065        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2448 | 95   | 0.2104        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2577 | 100  | 0.2068        | 0.1892          | 0.2690                          | 0.4640                     | 0.6510                   | 0.2334                      | 0.4844                      | 0.4357                     | 0.2162                      | 0.5012                | 0.8806                            | 0.2673                     | 0.5057                     | 0.4322                     | 0.4265                        | 0.4436                       |
| 0.2706 | 105  | 0.1864        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2835 | 110  | 0.1897        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.2964 | 115  | 0.1828        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3093 | 120  | 0.1715        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3222 | 125  | 0.1786        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3351 | 130  | 0.1505        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3479 | 135  | 0.1672        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3608 | 140  | 0.1742        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3737 | 145  | 0.1582        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.3866 | 150  | 0.1289        | 0.1575          | 0.2760                          | 0.4735                     | 0.6475                   | 0.2177                      | 0.5005                      | 0.4452                     | 0.2185                      | 0.5302                | 0.8873                            | 0.2687                     | 0.4949                     | 0.4475                     | 0.4220                        | 0.4484                       |
| 0.3995 | 155  | 0.1566        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4124 | 160  | 0.1502        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4253 | 165  | 0.143         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4381 | 170  | 0.1592        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4510 | 175  | 0.1398        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4639 | 180  | 0.1173        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4768 | 185  | 0.123         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.4897 | 190  | 0.1752        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5026 | 195  | 0.1433        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5155 | 200  | 0.1719        | 0.1433          | 0.2796                          | 0.4769                     | 0.6449                   | 0.2145                      | 0.5111                      | 0.4484                     | 0.2239                      | 0.5568                | 0.8901                            | 0.2709                     | 0.4841                     | 0.4647                     | 0.4184                        | 0.4526                       |
| 0.5284 | 205  | 0.1596        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5412 | 210  | 0.145         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5541 | 215  | 0.1654        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5670 | 220  | 0.1477        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5799 | 225  | 0.1559        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.5928 | 230  | 0.147         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6057 | 235  | 0.1645        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6186 | 240  | 0.1504        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6314 | 245  | 0.1648        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6443 | 250  | 0.1399        | 0.1345          | 0.2707                          | 0.4727                     | 0.6523                   | 0.2101                      | 0.5168                      | 0.4432                     | 0.2236                      | 0.5542                | 0.8904                            | 0.2637                     | 0.5034                     | 0.4737                     | 0.4207                        | 0.4535                       |
| 0.6572 | 255  | 0.1562        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6701 | 260  | 0.1532        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6830 | 265  | 0.1367        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.6959 | 270  | 0.1166        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7088 | 275  | 0.1307        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7216 | 280  | 0.1365        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7345 | 285  | 0.1279        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7474 | 290  | 0.1338        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7603 | 295  | 0.1448        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7732 | 300  | 0.1313        | 0.1278          | 0.2737                          | 0.4750                     | 0.6552                   | 0.2246                      | 0.5280                      | 0.4390                     | 0.2247                      | 0.5609                | 0.8832                            | 0.2661                     | 0.5092                     | 0.4865                     | 0.4224                        | 0.4576                       |
| 0.7861 | 305  | 0.1376        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.7990 | 310  | 0.106         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8119 | 315  | 0.1331        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8247 | 320  | 0.1376        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8376 | 325  | 0.1349        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8505 | 330  | 0.1392        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8634 | 335  | 0.1394        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8763 | 340  | 0.1276        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.8892 | 345  | 0.1228        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9021 | 350  | 0.112         | 0.1244          | 0.2807                          | 0.4747                     | 0.6564                   | 0.2063                      | 0.5249                      | 0.4373                     | 0.2238                      | 0.5576                | 0.8832                            | 0.2641                     | 0.5038                     | 0.5028                     | 0.4181                        | 0.4564                       |
| 0.9149 | 355  | 0.1258        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9278 | 360  | 0.1304        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9407 | 365  | 0.1372        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9536 | 370  | 0.145         | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9665 | 375  | 0.1423        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9794 | 380  | 0.1463        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 0.9923 | 385  | 0.1101        | -               | -                               | -                          | -                        | -                           | -                           | -                          | -                           | -                     | -                                 | -                          | -                          | -                          | -                             | -                            |
| 1.0    | 388  | -             | -               | 0.2728                          | 0.4767                     | 0.6578                   | 0.2056                      | 0.5242                      | 0.4396                     | 0.2242                      | 0.5583                | 0.8832                            | 0.2660                     | 0.5055                     | 0.4870                     | 0.4187                        | 0.4554                       |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.269 kWh
- **Carbon Emitted**: 0.105 kg of CO2
- **Hours Used**: 0.782 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.3.0.dev0
- Transformers: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.0.0
- Datasets: 2.20.0
- Tokenizers: 0.20.3

## 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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