metadata

language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:100231
  - loss:CachedMultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
widget:
  - source_sentence: 'query: who ordered the charge of the light brigade'
    sentences:
      - >-
        document: Charge of the Light Brigade The Charge of the Light Brigade
        was a charge of British light cavalry led by Lord Cardigan against
        Russian forces during the Battle of Balaclava on 25 October 1854 in the
        Crimean War. Lord Raglan, overall commander of the British forces, had
        intended to send the Light Brigade to prevent the Russians from removing
        captured guns from overrun Turkish positions, a task well-suited to
        light cavalry.
      - >-
        document: UNICEF The United Nations International Children's Emergency
        Fund was created by the United Nations General Assembly on 11 December
        1946, to provide emergency food and healthcare to children in countries
        that had been devastated by World War II. The Polish physician Ludwik
        Rajchman is widely regarded as the founder of UNICEF and served as its
        first chairman from 1946. On Rajchman's suggestion, the American Maurice
        Pate was appointed its first executive director, serving from 1947 until
        his death in 1965.[5][6] In 1950, UNICEF's mandate was extended to
        address the long-term needs of children and women in developing
        countries everywhere. In 1953 it became a permanent part of the United
        Nations System, and the words "international" and "emergency" were
        dropped from the organization's name, making it simply the United
        Nations Children's Fund, retaining the original acronym, "UNICEF".[3]
      - >-
        document: Marcus Jordan Marcus James Jordan (born December 24, 1990) is
        an American former college basketball player who played for the UCF
        Knights men's basketball team of Conference USA.[1] He is the son of
        retired Hall of Fame basketball player Michael Jordan.
  - source_sentence: 'query: what part of the cow is the rib roast'
    sentences:
      - >-
        document: Standing rib roast A standing rib roast, also known as prime
        rib, is a cut of beef from the primal rib, one of the nine primal cuts
        of beef. While the entire rib section comprises ribs six through 12, a
        standing rib roast may contain anywhere from two to seven ribs.
      - >-
        document: Blaine Anderson Kurt begins to mend their relationship in
        "Thanksgiving", just before New Directions loses at Sectionals to the
        Warblers, and they spend Christmas together in New York City.[29][30]
        Though he and Kurt continue to be on good terms, Blaine finds himself
        developing a crush on his best friend, Sam, which he knows will come to
        nothing as he knows Sam is not gay; the two of them team up to find
        evidence that the Warblers cheated at Sectionals, which means New
        Directions will be competing at Regionals. He ends up going to the Sadie
        Hawkins dance with Tina Cohen-Chang (Jenna Ushkowitz), who has developed
        a crush on him, but as friends only.[31] When Kurt comes to Lima for the
        wedding of glee club director Will (Matthew Morrison) and Emma (Jayma
        Mays)—which Emma flees—he and Blaine make out beforehand, and sleep
        together afterward, though they do not resume a permanent
        relationship.[32]
      - "document: Soviet Union The Soviet Union (Russian: Сове́тский Сою́з, tr. Sovétsky Soyúz, IPA:\_[sɐˈvʲɛt͡skʲɪj sɐˈjus]\_(\_listen)), officially the Union of Soviet Socialist Republics (Russian: Сою́з Сове́тских Социалисти́ческих Респу́блик, tr. Soyúz Sovétskikh Sotsialistícheskikh Respúblik, IPA:\_[sɐˈjus sɐˈvʲɛtskʲɪx sətsɨəlʲɪsˈtʲitɕɪskʲɪx rʲɪˈspublʲɪk]\_(\_listen)), abbreviated as the USSR (Russian: СССР, tr. SSSR), was a socialist state in Eurasia that existed from 1922 to 1991. Nominally a union of multiple national Soviet republics,[a] its government and economy were highly centralized. The country was a one-party state, governed by the Communist Party with Moscow as its capital in its largest republic, the Russian Soviet Federative Socialist Republic. The Russian nation had constitutionally equal status among the many nations of the union but exerted de facto dominance in various respects.[7] Other major urban centres were Leningrad, Kiev, Minsk, Alma-Ata and Novosibirsk. The Soviet Union was one of the five recognized nuclear weapons states and possessed the largest stockpile of weapons of mass destruction.[8] It was a founding permanent member of the United Nations Security Council, as well as a member of the Organization for Security and Co-operation in Europe (OSCE) and the leading member of the Council for Mutual Economic Assistance (CMEA) and the Warsaw Pact."
  - source_sentence: 'query: what is the current big bang theory season'
    sentences:
      - >-
        document: Byzantine army From the seventh to the 12th centuries, the
        Byzantine army was among the most powerful and effective military forces
        in the world â€“ neither Middle Ages Europe nor (following its early
        successes) the fracturing Caliphate could match the strategies and the
        efficiency of the Byzantine army. Restricted to a largely defensive role
        in the 7th to mid-9th centuries, the Byzantines developed the
        theme-system to counter the more powerful Caliphate. From the mid-9th
        century, however, they gradually went on the offensive, culminating in
        the great conquests of the 10th century under a series of
        soldier-emperors such as Nikephoros II Phokas, John Tzimiskes and Basil
        II. The army they led was less reliant on the militia of the themes; it
        was by now a largely professional force, with a strong and well-drilled
        infantry at its core and augmented by a revived heavy cavalry arm. With
        one of the most powerful economies in the world at the time, the Empire
        had the resources to put to the field a powerful host when needed, in
        order to reclaim its long-lost territories.
      - >-
        document: The Big Bang Theory The Big Bang Theory is an American
        television sitcom created by Chuck Lorre and Bill Prady, both of whom
        serve as executive producers on the series, along with Steven Molaro.
        All three also serve as head writers. The show premiered on CBS on
        September 24, 2007.[3] The series' tenth season premiered on September
        19, 2016.[4] In March 2017, the series was renewed for two additional
        seasons, bringing its total to twelve, and running through the 2018â€“19
        television season. The eleventh season is set to premiere on September
        25, 2017.[5]
      - >-
        document: 2016 NCAA Division I Softball Tournament The 2016 NCAA
        Division I Softball Tournament was held from May 20 through June 8, 2016
        as the final part of the 2016 NCAA Division I softball season. The 64
        NCAA Division I college softball teams were to be selected out of an
        eligible 293 teams on May 15, 2016. Thirty-two teams were awarded an
        automatic bid as champions of their conference, and thirty-two teams
        were selected at-large by the NCAA Division I softball selection
        committee. The tournament culminated with eight teams playing in the
        2016 Women's College World Series at ASA Hall of Fame Stadium in
        Oklahoma City in which the Oklahoma Sooners were crowned the champions.
  - source_sentence: 'query: what happened to tates mom on days of our lives'
    sentences:
      - >-
        document: Paige O'Hara Donna Paige Helmintoller, better known as Paige
        O'Hara (born May 10, 1956),[1] is an American actress, voice actress,
        singer and painter. O'Hara began her career as a Broadway actress in
        1983 when she portrayed Ellie May Chipley in the musical Showboat. In
        1991, she made her motion picture debut in Disney's Beauty and the
        Beast, in which she voiced the film's heroine, Belle. Following the
        critical and commercial success of Beauty and the Beast, O'Hara reprised
        her role as Belle in the film's two direct-to-video follow-ups, Beauty
        and the Beast: The Enchanted Christmas and Belle's Magical World.
      - >-
        document: M. Shadows Matthew Charles Sanders (born July 31, 1981),
        better known as M. Shadows, is an American singer, songwriter, and
        musician. He is best known as the lead vocalist, songwriter, and a
        founding member of the American heavy metal band Avenged Sevenfold. In
        2017, he was voted 3rd in the list of Top 25 Greatest Modern Frontmen by
        Ultimate Guitar.[1]
      - >-
        document: Theresa Donovan In July 2013, Jeannie returns to Salem, this
        time going by her middle name, Theresa. Initially, she strikes up a
        connection with resident bad boy JJ Deveraux (Casey Moss) while trying
        to secure some pot.[28] During a confrontation with JJ and his mother
        Jennifer Horton (Melissa Reeves) in her office, her aunt Kayla confirms
        that Theresa is in fact Jeannie and that Jen promised to hire her as her
        assistant, a promise she reluctantly agrees to. Kayla reminds Theresa it
        is her last chance at a fresh start.[29] Theresa also strikes up a bad
        first impression with Jennifer's daughter Abigail Deveraux (Kate Mansi)
        when Abigail smells pot on Theresa in her mother's office.[30] To
        continue to battle against Jennifer, she teams up with Anne Milbauer
        (Meredith Scott Lynn) in hopes of exacting her perfect revenge. In a
        ploy, Theresa reveals her intentions to hopefully woo Dr. Daniel Jonas
        (Shawn Christian). After sleeping with JJ, Theresa overdoses on
        marijuana and GHB. Upon hearing of their daughter's overdose and
        continuing problems, Shane and Kimberly return to town in the hopes of
        handling their daughter's problem, together. After believing that
        Theresa has a handle on her addictions, Shane and Kimberly leave town
        together. Theresa then teams up with hospital co-worker Anne Milbauer
        (Meredith Scott Lynn) to conspire against Jennifer, using Daniel as a
        way to hurt their relationship. In early 2014, following a Narcotics
        Anonymous (NA) meeting, she begins a sexual and drugged-fused
        relationship with Brady Black (Eric Martsolf). In 2015, after it is
        found that Kristen DiMera (Eileen Davidson) stole Theresa's embryo and
        carried it to term, Brady and Melanie Jonas return her son, Christopher,
        to her and Brady, and the pair rename him Tate. When Theresa moves into
        the Kiriakis mansion, tensions arise between her and Victor. She
        eventually expresses her interest in purchasing Basic Black and running
        it as her own fashion company, with financial backing from Maggie Horton
        (Suzanne Rogers). In the hopes of finding the right partner, she teams
        up with Kate Roberts (Lauren Koslow) and Nicole Walker (Arianne Zucker)
        to achieve the goal of purchasing Basic Black, with Kate and Nicole's
        business background and her own interest in fashion design. As she and
        Brady share several instances of rekindling their romance, she is kicked
        out of the mansion by Victor; as a result, Brady quits Titan and moves
        in with Theresa and Tate, in their own penthouse.
  - source_sentence: 'query: where does the last name francisco come from'
    sentences:
      - >-
        document: Francisco Francisco is the Spanish and Portuguese form of the
        masculine given name Franciscus (corresponding to English Francis).
      - >-
        document: Book of Esther The Book of Esther, also known in Hebrew as
        "the Scroll" (Megillah), is a book in the third section (Ketuvim,
        "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian
        Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew
        Bible. It relates the story of a Hebrew woman in Persia, born as
        Hadassah but known as Esther, who becomes queen of Persia and thwarts a
        genocide of her people. The story forms the core of the Jewish festival
        of Purim, during which it is read aloud twice: once in the evening and
        again the following morning. The books of Esther and Song of Songs are
        the only books in the Hebrew Bible that do not explicitly mention
        God.[2]
      - >-
        document: Times Square Times Square is a major commercial intersection,
        tourist destination, entertainment center and neighborhood in the
        Midtown Manhattan section of New York City at the junction of Broadway
        and Seventh Avenue. It stretches from West 42nd to West 47th Streets.[1]
        Brightly adorned with billboards and advertisements, Times Square is
        sometimes referred to as "The Crossroads of the World",[2] "The Center
        of the Universe",[3] "the heart of The Great White Way",[4][5][6] and
        the "heart of the world".[7] One of the world's busiest pedestrian
        areas,[8] it is also the hub of the Broadway Theater District[9] and a
        major center of the world's entertainment industry.[10] Times Square is
        one of the world's most visited tourist attractions, drawing an
        estimated 50 million visitors annually.[11] Approximately 330,000 people
        pass through Times Square daily,[12] many of them tourists,[13] while
        over 460,000 pedestrians walk through Times Square on its busiest
        days.[7]
datasets:
  - sentence-transformers/natural-questions
pipeline_tag: sentence-similarity
library_name: sentence-transformers
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
model-index:
  - name: mpnet-base trained with prompts on NQ using AdamWScheduleFree
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.28
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.36
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.28
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.12666666666666665
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.11599999999999999
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09200000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.1433333333333333
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.19166666666666665
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.2533333333333333
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.37233333333333335
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2950161644833465
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3755158730158729
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.23243293517692906
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: cosine_accuracy@1
            value: 0.6
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.76
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.82
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.88
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.4599999999999999
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.41200000000000003
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.36400000000000005
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.0709499931778319
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.12400234300102314
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.16572122951213678
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.2519638237448877
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4596289476095236
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6925238095238094
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3443317205533522
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.48
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.64
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.68
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.78
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.48
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.21333333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14400000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08199999999999999
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.46
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.62
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.68
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.77
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6179814365809698
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5765238095238094
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.570903423312224
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        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: 0.54
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.64
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.34
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.22
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16399999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.10599999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.1459126984126984
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.3164365079365079
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.36612698412698413
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5014603174603175
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.37392634443044265
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.43504761904761907
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.29956628042057504
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: cosine_accuracy@1
            value: 0.6
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.62
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.64
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.74
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16799999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.106
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.3
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.39
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.42
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.53
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.494393163961926
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6252142857142857
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4320916637948489
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.28
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.5
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.66
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.74
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.28
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.16666666666666663
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.132
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07400000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.28
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.5
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.66
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.74
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.500937041308907
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.42516666666666664
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4367280525741788
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: cosine_accuracy@1
            value: 0.36
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.42
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.44
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.56
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.36
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2733333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.248
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.196
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.020994306917657503
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.03809164296283565
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.058898256149743984
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.07960989620862456
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.23742445427659972
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4061904761904762
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.08901776083294548
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.5
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.62
            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: 0.5
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.21333333333333332
            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: 0.49
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.6
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.68
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.76
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6237892768519717
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5864126984126984
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5850328492888432
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: cosine_accuracy@1
            value: 0.84
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.94
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.98
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.98
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.84
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.38666666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.24399999999999994
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.12799999999999997
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7406666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.912
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.9426666666666668
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.9626666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9059548101310628
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8956666666666666
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8850698051948052
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: cosine_accuracy@1
            value: 0.36
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.52
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.64
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.72
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.36
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.24
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.22
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.152
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.07666666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.14866666666666667
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.22666666666666668
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.31266666666666665
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.2985413535131396
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.47671428571428565
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2251189111673314
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: cosine_accuracy@1
            value: 0.24
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.62
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.82
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.92
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.24
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.20666666666666667
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16399999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.092
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.24
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.62
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.82
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.92
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5813694215083484
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4723015873015872
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4750760909588496
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: cosine_accuracy@1
            value: 0.42
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.54
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7
            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: 0.08
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.385
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.51
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.585
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.69
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.545928107796286
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5088809523809524
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.5029443319838057
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: cosine_accuracy@1
            value: 0.5306122448979592
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7755102040816326
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8979591836734694
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9795918367346939
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.5306122448979592
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.5102040816326531
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.4816326530612245
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.3979591836734694
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.041696751373871646
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.11562841103794785
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.17604238983621973
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.265743150970596
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4527613644282212
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6790816326530613
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3595842660520079
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.44850863422292
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.6027315541601256
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6875353218210362
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.7799686028257456
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.44850863422292
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.26693877551020406
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.21397174254317114
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.1503045525902669
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26117080127297887
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.39126863371320364
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.4641888866378271
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.5504956811577763
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.49135783745236505
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5504031048316762
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.41829985317774593
            name: Cosine Map@100

mpnet-base trained with prompts on NQ using AdamWScheduleFree

This is a sentence-transformers model finetuned from microsoft/mpnet-base on the natural-questions dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: microsoft/mpnet-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- natural-questions
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("din0s/mpnet-base-nq-prompts")
# Run inference
sentences = [
    'query: where does the last name francisco come from',
    'document: Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
    'document: Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Information Retrieval

Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.28	0.6	0.48	0.34	0.6	0.28	0.36	0.5	0.84	0.36	0.24	0.42	0.5306
cosine_accuracy@3	0.36	0.76	0.64	0.52	0.62	0.5	0.42	0.62	0.94	0.52	0.62	0.54	0.7755
cosine_accuracy@5	0.5	0.82	0.68	0.54	0.64	0.66	0.44	0.72	0.98	0.64	0.82	0.6	0.898
cosine_accuracy@10	0.7	0.88	0.78	0.64	0.74	0.74	0.56	0.8	0.98	0.72	0.92	0.7	0.9796
cosine_precision@1	0.28	0.6	0.48	0.34	0.6	0.28	0.36	0.5	0.84	0.36	0.24	0.42	0.5306
cosine_precision@3	0.1267	0.46	0.2133	0.22	0.26	0.1667	0.2733	0.2133	0.3867	0.24	0.2067	0.1933	0.5102
cosine_precision@5	0.116	0.412	0.144	0.164	0.168	0.132	0.248	0.152	0.244	0.22	0.164	0.136	0.4816
cosine_precision@10	0.092	0.364	0.082	0.106	0.106	0.074	0.196	0.084	0.128	0.152	0.092	0.08	0.398
cosine_recall@1	0.1433	0.0709	0.46	0.1459	0.3	0.28	0.021	0.49	0.7407	0.0767	0.24	0.385	0.0417
cosine_recall@3	0.1917	0.124	0.62	0.3164	0.39	0.5	0.0381	0.6	0.912	0.1487	0.62	0.51	0.1156
cosine_recall@5	0.2533	0.1657	0.68	0.3661	0.42	0.66	0.0589	0.68	0.9427	0.2267	0.82	0.585	0.176
cosine_recall@10	0.3723	0.252	0.77	0.5015	0.53	0.74	0.0796	0.76	0.9627	0.3127	0.92	0.69	0.2657
cosine_ndcg@10	0.295	0.4596	0.618	0.3739	0.4944	0.5009	0.2374	0.6238	0.906	0.2985	0.5814	0.5459	0.4528
cosine_mrr@10	0.3755	0.6925	0.5765	0.435	0.6252	0.4252	0.4062	0.5864	0.8957	0.4767	0.4723	0.5089	0.6791
cosine_map@100	0.2324	0.3443	0.5709	0.2996	0.4321	0.4367	0.089	0.585	0.8851	0.2251	0.4751	0.5029	0.3596

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.4485
cosine_accuracy@3	0.6027
cosine_accuracy@5	0.6875
cosine_accuracy@10	0.78
cosine_precision@1	0.4485
cosine_precision@3	0.2669
cosine_precision@5	0.214
cosine_precision@10	0.1503
cosine_recall@1	0.2612
cosine_recall@3	0.3913
cosine_recall@5	0.4642
cosine_recall@10	0.5505
cosine_ndcg@10	0.4914
cosine_mrr@10	0.5504
cosine_map@100	0.4183

Training Details

Training Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 100,231 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 12 tokens
mean: 13.74 tokens
max: 26 tokens

min: 17 tokens
mean: 139.2 tokens
max: 510 tokens

	query	answer
type	string	string
details	min: 12 tokens mean: 13.74 tokens max: 26 tokens	min: 17 tokens mean: 139.2 tokens max: 510 tokens

Samples:

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

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Evaluation Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 100,231 evaluation samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 12 tokens
mean: 13.78 tokens
max: 24 tokens

min: 13 tokens
mean: 137.63 tokens
max: 512 tokens

	query	answer
type	string	string
details	min: 12 tokens mean: 13.78 tokens max: 24 tokens	min: 13 tokens mean: 137.63 tokens max: 512 tokens

Samples:

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

Loss: CachedMultipleNegativesRankingLoss with these parameters:

{
    "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: 0.0001
num_train_epochs: 1
lr_scheduler_type: constant
seed: 12
bf16: True
prompts: {'query': 'query: ', 'answer': 'document: '}
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

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: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: constant
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 12
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: {'query': 'query: ', 'answer': 'document: '}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	Validation Loss	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
-1	-1	-	-	0.0442	0.0851	0.0326	0.0282	0.0625	0.0708	0.0262	0.0331	0.6747	0.0387	0.2764	0.0617	0.0721	0.1159
0.0026	1	5.0155	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0129	5	2.0912	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0258	10	0.4697	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0387	15	0.2656	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0515	20	0.2065	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0644	25	0.2006	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0773	30	0.2404	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0902	35	0.1468	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1031	40	0.1628	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1160	45	0.1597	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1289	50	0.1362	0.1357	0.3132	0.4550	0.6557	0.3637	0.4801	0.4989	0.2352	0.5084	0.8936	0.3248	0.5646	0.5511	0.4737	0.4860
0.1418	55	0.1532	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1546	60	0.1707	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1675	65	0.1267	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1804	70	0.1242	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1933	75	0.1181	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2062	80	0.1371	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2191	85	0.1386	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2320	90	0.1281	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2448	95	0.111	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2577	100	0.122	0.1126	0.3035	0.4551	0.6227	0.3226	0.5171	0.5206	0.2168	0.5701	0.8877	0.3288	0.5576	0.5301	0.4748	0.4852
0.2706	105	0.0884	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2835	110	0.1132	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2964	115	0.1278	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3093	120	0.1064	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3222	125	0.1141	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3351	130	0.1019	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3479	135	0.0946	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3608	140	0.1149	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3737	145	0.118	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3866	150	0.1103	0.0972	0.3099	0.4664	0.6229	0.3466	0.4920	0.5038	0.2256	0.5644	0.8732	0.3309	0.5702	0.5394	0.4786	0.4865
0.3995	155	0.0905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4124	160	0.1074	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4253	165	0.0916	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4381	170	0.1056	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4510	175	0.0872	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4639	180	0.0914	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4768	185	0.0926	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4897	190	0.0884	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5026	195	0.0924	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5155	200	0.0985	0.0895	0.3109	0.4779	0.6218	0.3756	0.5014	0.5039	0.2258	0.5940	0.9014	0.3164	0.5766	0.5624	0.4776	0.4958
0.5284	205	0.1024	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5412	210	0.0897	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5541	215	0.0945	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5670	220	0.1056	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5799	225	0.1022	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5928	230	0.085	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6057	235	0.0969	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6186	240	0.1056	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6314	245	0.0905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6443	250	0.0951	0.0848	0.3101	0.4916	0.6179	0.3684	0.4922	0.5053	0.2377	0.5853	0.8911	0.2987	0.5805	0.5418	0.4702	0.4916
0.6572	255	0.0938	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6701	260	0.0992	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6830	265	0.0924	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6959	270	0.0697	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7088	275	0.087	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7216	280	0.0853	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7345	285	0.0965	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7474	290	0.0884	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7603	295	0.104	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7732	300	0.0803	0.0805	0.3094	0.4823	0.6343	0.3797	0.4899	0.5118	0.2343	0.6076	0.9057	0.3006	0.5854	0.5404	0.4719	0.4964
0.7861	305	0.0724	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7990	310	0.0804	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8119	315	0.07	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8247	320	0.0913	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8376	325	0.0912	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8505	330	0.0826	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8634	335	0.0868	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8763	340	0.0916	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8892	345	0.0869	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9021	350	0.0716	0.0750	0.3093	0.4686	0.6419	0.3717	0.4960	0.5251	0.2360	0.6091	0.9123	0.3039	0.5895	0.5645	0.4716	0.5000
0.9149	355	0.0838	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9278	360	0.0684	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9407	365	0.0831	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9536	370	0.0647	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9665	375	0.0867	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9794	380	0.089	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9923	385	0.0715	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
-1	-1	-	-	0.2950	0.4596	0.6180	0.3739	0.4944	0.5009	0.2374	0.6238	0.9060	0.2985	0.5814	0.5459	0.4528	0.4914

Framework Versions

Python: 3.11.11
Sentence Transformers: 3.4.1
Transformers: 4.48.3
PyTorch: 2.5.1+cu124
Accelerate: 1.3.0
Datasets: 3.3.2
Tokenizers: 0.21.0

Citation

BibTeX

Sentence Transformers

@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

@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}
}