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---
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language:
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- en
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license: apache-2.0
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tags:
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- generated_from_trainer
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- dataset_size:50000
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- loss:CachedGISTEmbedLoss
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base_model: microsoft/mpnet-base
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widget:
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- source_sentence: who ordered the charge of the light brigade
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sentences:
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- Charge of the Light Brigade The Charge of the Light Brigade was a charge of British
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light cavalry led by Lord Cardigan against Russian forces during the Battle of
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Balaclava on 25 October 1854 in the Crimean War. Lord Raglan, overall commander
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of the British forces, had intended to send the Light Brigade to prevent the Russians
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from removing captured guns from overrun Turkish positions, a task well-suited
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to light cavalry.
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- UNICEF The United Nations International Children's Emergency Fund was created
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by the United Nations General Assembly on 11 December 1946, to provide emergency
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food and healthcare to children in countries that had been devastated by World
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War II. The Polish physician Ludwik Rajchman is widely regarded as the founder
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of UNICEF and served as its first chairman from 1946. On Rajchman's suggestion,
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the American Maurice Pate was appointed its first executive director, serving
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from 1947 until his death in 1965.[5][6] In 1950, UNICEF's mandate was extended
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to address the long-term needs of children and women in developing countries everywhere.
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In 1953 it became a permanent part of the United Nations System, and the words
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"international" and "emergency" were dropped from the organization's name, making
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it simply the United Nations Children's Fund, retaining the original acronym,
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"UNICEF".[3]
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- Marcus Jordan Marcus James Jordan (born December 24, 1990) is an American former
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college basketball player who played for the UCF Knights men's basketball team
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of Conference USA.[1] He is the son of retired Hall of Fame basketball player
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Michael Jordan.
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- source_sentence: what part of the cow is the rib roast
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sentences:
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- Standing rib roast A standing rib roast, also known as prime rib, is a cut of
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beef from the primal rib, one of the nine primal cuts of beef. While the entire
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rib section comprises ribs six through 12, a standing rib roast may contain anywhere
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from two to seven ribs.
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- Blaine Anderson Kurt begins to mend their relationship in "Thanksgiving", just
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before New Directions loses at Sectionals to the Warblers, and they spend Christmas
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together in New York City.[29][30] Though he and Kurt continue to be on good terms,
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Blaine finds himself developing a crush on his best friend, Sam, which he knows
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will come to nothing as he knows Sam is not gay; the two of them team up to find
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evidence that the Warblers cheated at Sectionals, which means New Directions will
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be competing at Regionals. He ends up going to the Sadie Hawkins dance with Tina
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Cohen-Chang (Jenna Ushkowitz), who has developed a crush on him, but as friends
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only.[31] When Kurt comes to Lima for the wedding of glee club director Will (Matthew
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Morrison) and Emma (Jayma Mays)—which Emma flees—he and Blaine make out beforehand,
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and sleep together afterward, though they do not resume a permanent relationship.[32]
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- 'Soviet Union The Soviet Union (Russian: Сове́тский Сою́з, tr. Sovétsky Soyúz,
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IPA: [sɐˈvʲɛt͡skʲɪj sɐˈjus] ( listen)), officially the Union of Soviet Socialist
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Republics (Russian: Сою́з Сове́тских Социалисти́ческих Респу́блик, tr. Soyúz Sovétskikh
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Sotsialistícheskikh Respúblik, IPA: [sɐˈjus sɐˈvʲɛtskʲɪx sətsɨəlʲɪsˈtʲitɕɪskʲɪx
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rʲɪˈspublʲɪk] ( listen)), abbreviated as the USSR (Russian: СССР, tr. SSSR), was
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a socialist state in Eurasia that existed from 1922 to 1991. Nominally a union
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of multiple national Soviet republics,[a] its government and economy were highly
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centralized. The country was a one-party state, governed by the Communist Party
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with Moscow as its capital in its largest republic, the Russian Soviet Federative
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Socialist Republic. The Russian nation had constitutionally equal status among
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the many nations of the union but exerted de facto dominance in various respects.[7]
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Other major urban centres were Leningrad, Kiev, Minsk, Alma-Ata and Novosibirsk.
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The Soviet Union was one of the five recognized nuclear weapons states and possessed
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the largest stockpile of weapons of mass destruction.[8] It was a founding permanent
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member of the United Nations Security Council, as well as a member of the Organization
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for Security and Co-operation in Europe (OSCE) and the leading member of the Council
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for Mutual Economic Assistance (CMEA) and the Warsaw Pact.'
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- source_sentence: what is the current big bang theory season
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sentences:
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- Byzantine army From the seventh to the 12th centuries, the Byzantine army was
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among the most powerful and effective military forces in the world – neither
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Middle Ages Europe nor (following its early successes) the fracturing Caliphate
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could match the strategies and the efficiency of the Byzantine army. Restricted
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to a largely defensive role in the 7th to mid-9th centuries, the Byzantines developed
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the theme-system to counter the more powerful Caliphate. From the mid-9th century,
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however, they gradually went on the offensive, culminating in the great conquests
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of the 10th century under a series of soldier-emperors such as Nikephoros II Phokas,
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John Tzimiskes and Basil II. The army they led was less reliant on the militia
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of the themes; it was by now a largely professional force, with a strong and well-drilled
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infantry at its core and augmented by a revived heavy cavalry arm. With one of
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the most powerful economies in the world at the time, the Empire had the resources
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to put to the field a powerful host when needed, in order to reclaim its long-lost
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territories.
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- The Big Bang Theory The Big Bang Theory is an American television sitcom created
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by Chuck Lorre and Bill Prady, both of whom serve as executive producers on the
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series, along with Steven Molaro. All three also serve as head writers. The show
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premiered on CBS on September 24, 2007.[3] The series' tenth season premiered
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on September 19, 2016.[4] In March 2017, the series was renewed for two additional
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seasons, bringing its total to twelve, and running through the 2018–19 television
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season. The eleventh season is set to premiere on September 25, 2017.[5]
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- 2016 NCAA Division I Softball Tournament The 2016 NCAA Division I Softball Tournament
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was held from May 20 through June 8, 2016 as the final part of the 2016 NCAA Division
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I softball season. The 64 NCAA Division I college softball teams were to be selected
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out of an eligible 293 teams on May 15, 2016. Thirty-two teams were awarded an
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automatic bid as champions of their conference, and thirty-two teams were selected
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at-large by the NCAA Division I softball selection committee. The tournament culminated
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with eight teams playing in the 2016 Women's College World Series at ASA Hall
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of Fame Stadium in Oklahoma City in which the Oklahoma Sooners were crowned the
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champions.
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- source_sentence: what happened to tates mom on days of our lives
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sentences:
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- 'Paige O''Hara Donna Paige Helmintoller, better known as Paige O''Hara (born May
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10, 1956),[1] is an American actress, voice actress, singer and painter. O''Hara
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began her career as a Broadway actress in 1983 when she portrayed Ellie May Chipley
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in the musical Showboat. In 1991, she made her motion picture debut in Disney''s
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Beauty and the Beast, in which she voiced the film''s heroine, Belle. Following
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the critical and commercial success of Beauty and the Beast, O''Hara reprised
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her role as Belle in the film''s two direct-to-video follow-ups, Beauty and the
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Beast: The Enchanted Christmas and Belle''s Magical World.'
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- M. Shadows Matthew Charles Sanders (born July 31, 1981), better known as M. Shadows,
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is an American singer, songwriter, and musician. He is best known as the lead
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vocalist, songwriter, and a founding member of the American heavy metal band Avenged
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Sevenfold. In 2017, he was voted 3rd in the list of Top 25 Greatest Modern Frontmen
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by Ultimate Guitar.[1]
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- Theresa Donovan In July 2013, Jeannie returns to Salem, this time going by her
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middle name, Theresa. Initially, she strikes up a connection with resident bad
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boy JJ Deveraux (Casey Moss) while trying to secure some pot.[28] During a confrontation
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with JJ and his mother Jennifer Horton (Melissa Reeves) in her office, her aunt
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Kayla confirms that Theresa is in fact Jeannie and that Jen promised to hire her
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as her assistant, a promise she reluctantly agrees to. Kayla reminds Theresa it
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is her last chance at a fresh start.[29] Theresa also strikes up a bad first impression
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with Jennifer's daughter Abigail Deveraux (Kate Mansi) when Abigail smells pot
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on Theresa in her mother's office.[30] To continue to battle against Jennifer,
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she teams up with Anne Milbauer (Meredith Scott Lynn) in hopes of exacting her
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perfect revenge. In a ploy, Theresa reveals her intentions to hopefully woo Dr.
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Daniel Jonas (Shawn Christian). After sleeping with JJ, Theresa overdoses on marijuana
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and GHB. Upon hearing of their daughter's overdose and continuing problems, Shane
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and Kimberly return to town in the hopes of handling their daughter's problem,
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together. After believing that Theresa has a handle on her addictions, Shane and
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Kimberly leave town together. Theresa then teams up with hospital co-worker Anne
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Milbauer (Meredith Scott Lynn) to conspire against Jennifer, using Daniel as a
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way to hurt their relationship. In early 2014, following a Narcotics Anonymous
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(NA) meeting, she begins a sexual and drugged-fused relationship with Brady Black
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(Eric Martsolf). In 2015, after it is found that Kristen DiMera (Eileen Davidson)
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stole Theresa's embryo and carried it to term, Brady and Melanie Jonas return
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her son, Christopher, to her and Brady, and the pair rename him Tate. When Theresa
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moves into the Kiriakis mansion, tensions arise between her and Victor. She eventually
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expresses her interest in purchasing Basic Black and running it as her own fashion
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company, with financial backing from Maggie Horton (Suzanne Rogers). In the hopes
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of finding the right partner, she teams up with Kate Roberts (Lauren Koslow) and
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Nicole Walker (Arianne Zucker) to achieve the goal of purchasing Basic Black,
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with Kate and Nicole's business background and her own interest in fashion design.
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As she and Brady share several instances of rekindling their romance, she is kicked
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out of the mansion by Victor; as a result, Brady quits Titan and moves in with
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Theresa and Tate, in their own penthouse.
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- source_sentence: where does the last name francisco come from
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sentences:
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- Francisco Francisco is the Spanish and Portuguese form of the masculine given
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name Franciscus (corresponding to English Francis).
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- 'Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah),
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is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the
|
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Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls
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(Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia,
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born as Hadassah but known as Esther, who becomes queen of Persia and thwarts
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a genocide of her people. The story forms the core of the Jewish festival of Purim,
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during which it is read aloud twice: once in the evening and again the following
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morning. The books of Esther and Song of Songs are the only books in the Hebrew
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Bible that do not explicitly mention God.[2]'
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- Times Square Times Square is a major commercial intersection, tourist destination,
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entertainment center and neighborhood in the Midtown Manhattan section of New
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York City at the junction of Broadway and Seventh Avenue. It stretches from West
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42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements,
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Times Square is sometimes referred to as "The Crossroads of the World",[2] "The
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Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the
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"heart of the world".[7] One of the world's busiest pedestrian areas,[8] it is
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also the hub of the Broadway Theater District[9] and a major center of the world's
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entertainment industry.[10] Times Square is one of the world's most visited tourist
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attractions, drawing an estimated 50 million visitors annually.[11] Approximately
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330,000 people pass through Times Square daily,[12] many of them tourists,[13]
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while over 460,000 pedestrians walk through Times Square on its busiest days.[7]
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datasets:
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- sentence-transformers/natural-questions
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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metrics:
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- cosine_accuracy@1
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- cosine_accuracy@3
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- cosine_accuracy@5
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- cosine_accuracy@10
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- cosine_precision@1
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- cosine_precision@3
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- cosine_precision@5
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- cosine_precision@10
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- cosine_recall@1
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- cosine_recall@3
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- cosine_recall@5
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- cosine_recall@10
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- cosine_ndcg@10
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- cosine_mrr@10
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- cosine_map@100
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co2_eq_emissions:
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emissions: 59.31009589078217
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energy_consumed: 0.15258500314066348
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source: codecarbon
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training_type: fine-tuning
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on_cloud: false
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cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
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ram_total_size: 31.777088165283203
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hours_used: 0.396
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hardware_used: 1 x NVIDIA GeForce RTX 3090
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model-index:
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- name: MPNet base trained on Natural Questions pairs
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results:
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- task:
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type: information-retrieval
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name: Information Retrieval
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dataset:
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name: NanoClimateFEVER
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type: NanoClimateFEVER
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metrics:
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- type: cosine_accuracy@1
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value: 0.16
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name: Cosine Accuracy@1
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- type: cosine_accuracy@3
|
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value: 0.34
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name: Cosine Accuracy@3
|
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- type: cosine_accuracy@5
|
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value: 0.56
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
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value: 0.64
|
|
name: Cosine Accuracy@10
|
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- type: cosine_precision@1
|
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value: 0.16
|
|
name: Cosine Precision@1
|
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- type: cosine_precision@3
|
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value: 0.12
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name: Cosine Precision@3
|
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- type: cosine_precision@5
|
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value: 0.128
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name: Cosine Precision@5
|
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- type: cosine_precision@10
|
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value: 0.08199999999999999
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name: Cosine Precision@10
|
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- type: cosine_recall@1
|
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value: 0.06
|
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name: Cosine Recall@1
|
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- type: cosine_recall@3
|
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value: 0.12166666666666666
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name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.24833333333333332
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.31566666666666665
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.22803817515986124
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.30941269841269836
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.1655130902515993
|
|
name: Cosine Map@100
|
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- task:
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type: information-retrieval
|
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name: Information Retrieval
|
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dataset:
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name: NanoDBPedia
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type: NanoDBPedia
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metrics:
|
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- type: cosine_accuracy@1
|
|
value: 0.52
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.62
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.7
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.78
|
|
name: Cosine Accuracy@10
|
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- type: cosine_precision@1
|
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value: 0.52
|
|
name: Cosine Precision@1
|
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- type: cosine_precision@3
|
|
value: 0.36
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.364
|
|
name: Cosine Precision@5
|
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- type: cosine_precision@10
|
|
value: 0.322
|
|
name: Cosine Precision@10
|
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- type: cosine_recall@1
|
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value: 0.0336711515516074
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.06005334302891617
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.1119370784549358
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.1974683849453542
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.37302114460618035
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.5887222222222221
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.2524550843440785
|
|
name: Cosine Map@100
|
|
- task:
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type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
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name: NanoFEVER
|
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type: NanoFEVER
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|
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: 0.52
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.62
|
|
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: 0.10800000000000001
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.064
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.28
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.48
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.51
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.6
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.4358687601068153
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.38569047619047614
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.3903171462871314
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoFiQA2018
|
|
type: NanoFiQA2018
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.14
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.32
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.36
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.46
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.14
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.1333333333333333
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.1
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.07
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.06933333333333333
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.20319047619047617
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.2276904761904762
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.32354761904761903
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.2271808224609275
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.23985714285714288
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.18355553344945122
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoHotpotQA
|
|
type: NanoHotpotQA
|
|
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: 0.48
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.58
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.32
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.1733333333333333
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.11600000000000002
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.068
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.16
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.26
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.29
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.34
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.30497689087635044
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.39905555555555544
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.26301906759091515
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoMSMARCO
|
|
type: NanoMSMARCO
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.14
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.28
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.34
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.44
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.14
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.09333333333333332
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.068
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.044000000000000004
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.14
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.28
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.34
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.44
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.27595760463916813
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.22488095238095238
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.24656541883369498
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoNFCorpus
|
|
type: NanoNFCorpus
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.22
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.3
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.34
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.36
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.22
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.1533333333333333
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.124
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.096
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.007116944515649617
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.01288483574625764
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.02025290517580909
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.02555956272966021
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.11695533319556885
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.2651904761904762
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.030363746300173234
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoNQ
|
|
type: NanoNQ
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.14
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.24
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.32
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.48
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.14
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.07999999999999999
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.06400000000000002
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.05
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.13
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.22
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.29
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.46
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.2706566987839319
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.22174603174603175
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.22631004639318789
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoQuoraRetrieval
|
|
type: NanoQuoraRetrieval
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.78
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.88
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.9
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.94
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.78
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.35999999999999993
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.23999999999999994
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.13199999999999998
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.6806666666666666
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.8346666666666667
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.8793333333333334
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.9366666666666665
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.8528887039265185
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.8324126984126984
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.820234632034632
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoSCIDOCS
|
|
type: NanoSCIDOCS
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.28
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.42
|
|
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: 0.28
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.22666666666666668
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.2
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.12399999999999999
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.05866666666666667
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.14066666666666666
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.20566666666666666
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.25566666666666665
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.24909911706779386
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.38332539682539685
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.20162687946594338
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoArguAna
|
|
type: NanoArguAna
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.18
|
|
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: 0.88
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.18
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.17333333333333337
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.128
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.088
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.18
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.52
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.64
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.88
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.5102396499498778
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.3946269841269841
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.4001733643377607
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: information-retrieval
|
|
name: Information Retrieval
|
|
dataset:
|
|
name: NanoSciFact
|
|
type: NanoSciFact
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.3
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.34
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.42
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.5
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.3
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.11999999999999998
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.09200000000000001
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.055999999999999994
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.265
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.315
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.4
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.485
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.3688721552089384
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.3476666666666667
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.34115921547380024
|
|
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: 0.7346938775510204
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.8163265306122449
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.9387755102040817
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.4897959183673469
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.4013605442176871
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.3673469387755102
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.3102040816326531
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.036516156386696134
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.08582342270510718
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.12560656255524566
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.2064747763464094
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.3575303928348819
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.6281098153547133
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
|
value: 0.27828847509729454
|
|
name: Cosine Map@100
|
|
- task:
|
|
type: nano-beir
|
|
name: Nano BEIR
|
|
dataset:
|
|
name: NanoBEIR mean
|
|
type: NanoBEIR_mean
|
|
metrics:
|
|
- type: cosine_accuracy@1
|
|
value: 0.30383045525902674
|
|
name: Cosine Accuracy@1
|
|
- type: cosine_accuracy@3
|
|
value: 0.45651491365777075
|
|
name: Cosine Accuracy@3
|
|
- type: cosine_accuracy@5
|
|
value: 0.5320251177394035
|
|
name: Cosine Accuracy@5
|
|
- type: cosine_accuracy@10
|
|
value: 0.6337519623233908
|
|
name: Cosine Accuracy@10
|
|
- type: cosine_precision@1
|
|
value: 0.30383045525902674
|
|
name: Cosine Precision@1
|
|
- type: cosine_precision@3
|
|
value: 0.19702773417059127
|
|
name: Cosine Precision@3
|
|
- type: cosine_precision@5
|
|
value: 0.16148822605965465
|
|
name: Cosine Precision@5
|
|
- type: cosine_precision@10
|
|
value: 0.11586185243328104
|
|
name: Cosine Precision@10
|
|
- type: cosine_recall@1
|
|
value: 0.16161314762466306
|
|
name: Cosine Recall@1
|
|
- type: cosine_recall@3
|
|
value: 0.2718424675131352
|
|
name: Cosine Recall@3
|
|
- type: cosine_recall@5
|
|
value: 0.32990925813152305
|
|
name: Cosine Recall@5
|
|
- type: cosine_recall@10
|
|
value: 0.42046541100531104
|
|
name: Cosine Recall@10
|
|
- type: cosine_ndcg@10
|
|
value: 0.35163734221667803
|
|
name: Cosine Ndcg@10
|
|
- type: cosine_mrr@10
|
|
value: 0.4015920859186165
|
|
name: Cosine Mrr@10
|
|
- type: cosine_map@100
|
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value: 0.2922755153738202
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name: Cosine Map@100
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---
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# MPNet base trained on Natural Questions pairs
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This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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## Model Details
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### Model Description
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- **Model Type:** Sentence Transformer
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- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
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- **Maximum Sequence Length:** 512 tokens
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- **Output Dimensionality:** 768 dimensions
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- **Similarity Function:** Cosine Similarity
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- **Training Dataset:**
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- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
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- **Language:** en
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- **License:** apache-2.0
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
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### Full Model Architecture
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```
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
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(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})
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)
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```
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## Usage
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### Direct Usage (Sentence Transformers)
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First install the Sentence Transformers library:
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```bash
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pip install -U sentence-transformers
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```
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Then you can load this model and run inference.
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```python
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from sentence_transformers import SentenceTransformer
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# Download from the 🤗 Hub
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model = SentenceTransformer("tomaarsen/mpnet-base-nq-cgist-2-gte")
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# Run inference
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sentences = [
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'where does the last name francisco come from',
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'Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
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'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]',
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]
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embeddings = model.encode(sentences)
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print(embeddings.shape)
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# [3, 768]
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# Get the similarity scores for the embeddings
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similarities = model.similarity(embeddings, embeddings)
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print(similarities.shape)
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# [3, 3]
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```
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<!--
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### Direct Usage (Transformers)
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<details><summary>Click to see the direct usage in Transformers</summary>
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</details>
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-->
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<!--
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### Downstream Usage (Sentence Transformers)
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You can finetune this model on your own dataset.
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<details><summary>Click to expand</summary>
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</details>
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-->
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<!--
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### Out-of-Scope Use
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*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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-->
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## Evaluation
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### Metrics
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#### Information Retrieval
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* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
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| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
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|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
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| cosine_accuracy@1 | 0.16 | 0.52 | 0.28 | 0.14 | 0.32 | 0.14 | 0.22 | 0.14 | 0.78 | 0.28 | 0.18 | 0.3 | 0.4898 |
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| cosine_accuracy@3 | 0.34 | 0.62 | 0.5 | 0.32 | 0.44 | 0.28 | 0.3 | 0.24 | 0.88 | 0.42 | 0.52 | 0.34 | 0.7347 |
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| cosine_accuracy@5 | 0.56 | 0.7 | 0.52 | 0.36 | 0.48 | 0.34 | 0.34 | 0.32 | 0.9 | 0.52 | 0.64 | 0.42 | 0.8163 |
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| cosine_accuracy@10 | 0.64 | 0.78 | 0.62 | 0.46 | 0.58 | 0.44 | 0.36 | 0.48 | 0.94 | 0.62 | 0.88 | 0.5 | 0.9388 |
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| cosine_precision@1 | 0.16 | 0.52 | 0.28 | 0.14 | 0.32 | 0.14 | 0.22 | 0.14 | 0.78 | 0.28 | 0.18 | 0.3 | 0.4898 |
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| cosine_precision@3 | 0.12 | 0.36 | 0.1667 | 0.1333 | 0.1733 | 0.0933 | 0.1533 | 0.08 | 0.36 | 0.2267 | 0.1733 | 0.12 | 0.4014 |
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| cosine_precision@5 | 0.128 | 0.364 | 0.108 | 0.1 | 0.116 | 0.068 | 0.124 | 0.064 | 0.24 | 0.2 | 0.128 | 0.092 | 0.3673 |
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| cosine_precision@10 | 0.082 | 0.322 | 0.064 | 0.07 | 0.068 | 0.044 | 0.096 | 0.05 | 0.132 | 0.124 | 0.088 | 0.056 | 0.3102 |
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| cosine_recall@1 | 0.06 | 0.0337 | 0.28 | 0.0693 | 0.16 | 0.14 | 0.0071 | 0.13 | 0.6807 | 0.0587 | 0.18 | 0.265 | 0.0365 |
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| cosine_recall@3 | 0.1217 | 0.0601 | 0.48 | 0.2032 | 0.26 | 0.28 | 0.0129 | 0.22 | 0.8347 | 0.1407 | 0.52 | 0.315 | 0.0858 |
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| cosine_recall@5 | 0.2483 | 0.1119 | 0.51 | 0.2277 | 0.29 | 0.34 | 0.0203 | 0.29 | 0.8793 | 0.2057 | 0.64 | 0.4 | 0.1256 |
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| cosine_recall@10 | 0.3157 | 0.1975 | 0.6 | 0.3235 | 0.34 | 0.44 | 0.0256 | 0.46 | 0.9367 | 0.2557 | 0.88 | 0.485 | 0.2065 |
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| **cosine_ndcg@10** | **0.228** | **0.373** | **0.4359** | **0.2272** | **0.305** | **0.276** | **0.117** | **0.2707** | **0.8529** | **0.2491** | **0.5102** | **0.3689** | **0.3575** |
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| cosine_mrr@10 | 0.3094 | 0.5887 | 0.3857 | 0.2399 | 0.3991 | 0.2249 | 0.2652 | 0.2217 | 0.8324 | 0.3833 | 0.3946 | 0.3477 | 0.6281 |
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| cosine_map@100 | 0.1655 | 0.2525 | 0.3903 | 0.1836 | 0.263 | 0.2466 | 0.0304 | 0.2263 | 0.8202 | 0.2016 | 0.4002 | 0.3412 | 0.2783 |
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#### Nano BEIR
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* Dataset: `NanoBEIR_mean`
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* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
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| Metric | Value |
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|:--------------------|:-----------|
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| cosine_accuracy@1 | 0.3038 |
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| cosine_accuracy@3 | 0.4565 |
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| cosine_accuracy@5 | 0.532 |
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| cosine_accuracy@10 | 0.6338 |
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| cosine_precision@1 | 0.3038 |
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| cosine_precision@3 | 0.197 |
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| cosine_precision@5 | 0.1615 |
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| cosine_precision@10 | 0.1159 |
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| cosine_recall@1 | 0.1616 |
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| cosine_recall@3 | 0.2718 |
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| cosine_recall@5 | 0.3299 |
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| cosine_recall@10 | 0.4205 |
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| **cosine_ndcg@10** | **0.3516** |
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| cosine_mrr@10 | 0.4016 |
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| cosine_map@100 | 0.2923 |
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<!--
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## Bias, Risks and Limitations
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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-->
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<!--
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### Recommendations
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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-->
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## Training Details
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### Training Dataset
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#### natural-questions
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* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
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* Size: 50,000 training samples
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* Columns: <code>query</code> and <code>answer</code>
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* Approximate statistics based on the first 1000 samples:
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| | query | answer |
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|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
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| type | string | string |
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| 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> |
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* Samples:
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| query | answer |
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|:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| <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> |
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| <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> |
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| <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> |
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* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
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```json
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{'guide': SentenceTransformer(
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 384, '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})
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(2): Normalize()
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), 'temperature': 0.01}
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```
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### Evaluation Dataset
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#### natural-questions
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* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
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* Size: 100,231 evaluation samples
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* Columns: <code>query</code> and <code>answer</code>
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* Approximate statistics based on the first 1000 samples:
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| | query | answer |
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|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
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| type | string | string |
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| 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> |
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* Samples:
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| query | answer |
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|:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| <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> |
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| <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> |
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| <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> |
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* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
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```json
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{'guide': SentenceTransformer(
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(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 384, '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})
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(2): Normalize()
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), 'temperature': 0.01}
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```
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### Training Hyperparameters
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#### Non-Default Hyperparameters
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- `eval_strategy`: steps
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- `per_device_train_batch_size`: 2048
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- `per_device_eval_batch_size`: 2048
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- `learning_rate`: 2e-05
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- `num_train_epochs`: 1
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- `warmup_ratio`: 0.1
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- `seed`: 12
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- `bf16`: True
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#### All Hyperparameters
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<details><summary>Click to expand</summary>
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- `overwrite_output_dir`: False
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- `do_predict`: False
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- `eval_strategy`: steps
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- `prediction_loss_only`: True
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- `per_device_train_batch_size`: 2048
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- `per_device_eval_batch_size`: 2048
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- `per_gpu_train_batch_size`: None
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- `per_gpu_eval_batch_size`: None
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- `gradient_accumulation_steps`: 1
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- `eval_accumulation_steps`: None
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- `torch_empty_cache_steps`: None
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- `learning_rate`: 2e-05
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- `weight_decay`: 0.0
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- `adam_beta1`: 0.9
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- `adam_beta2`: 0.999
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- `adam_epsilon`: 1e-08
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- `max_grad_norm`: 1.0
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- `num_train_epochs`: 1
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- `max_steps`: -1
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- `lr_scheduler_type`: linear
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- `lr_scheduler_kwargs`: {}
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- `warmup_ratio`: 0.1
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- `warmup_steps`: 0
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- `log_level`: passive
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- `log_level_replica`: warning
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- `log_on_each_node`: True
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- `logging_nan_inf_filter`: True
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- `save_safetensors`: True
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- `save_on_each_node`: False
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- `save_only_model`: False
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- `restore_callback_states_from_checkpoint`: False
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- `no_cuda`: False
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- `use_cpu`: False
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- `use_mps_device`: False
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- `seed`: 12
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- `data_seed`: None
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- `jit_mode_eval`: False
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- `use_ipex`: False
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- `bf16`: True
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- `fp16`: False
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- `fp16_opt_level`: O1
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- `half_precision_backend`: auto
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- `bf16_full_eval`: False
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- `fp16_full_eval`: False
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- `tf32`: None
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- `local_rank`: 0
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- `ddp_backend`: None
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- `tpu_num_cores`: None
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- `tpu_metrics_debug`: False
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- `debug`: []
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- `dataloader_drop_last`: False
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- `dataloader_num_workers`: 0
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- `dataloader_prefetch_factor`: None
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- `past_index`: -1
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- `disable_tqdm`: False
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- `remove_unused_columns`: True
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- `label_names`: None
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- `load_best_model_at_end`: False
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- `ignore_data_skip`: False
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- `fsdp`: []
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- `fsdp_min_num_params`: 0
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
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- `fsdp_transformer_layer_cls_to_wrap`: None
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
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- `deepspeed`: None
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- `label_smoothing_factor`: 0.0
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- `optim`: adamw_torch
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- `optim_args`: None
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- `adafactor`: False
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- `group_by_length`: False
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- `length_column_name`: length
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- `ddp_find_unused_parameters`: None
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- `ddp_bucket_cap_mb`: None
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- `ddp_broadcast_buffers`: False
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- `dataloader_pin_memory`: True
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- `dataloader_persistent_workers`: False
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- `skip_memory_metrics`: True
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- `use_legacy_prediction_loop`: False
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- `push_to_hub`: False
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- `resume_from_checkpoint`: None
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- `hub_model_id`: None
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- `hub_strategy`: every_save
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- `hub_private_repo`: False
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- `hub_always_push`: False
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- `gradient_checkpointing`: False
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- `gradient_checkpointing_kwargs`: None
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- `include_inputs_for_metrics`: False
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- `include_for_metrics`: []
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- `eval_do_concat_batches`: True
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- `fp16_backend`: auto
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- `push_to_hub_model_id`: None
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- `push_to_hub_organization`: None
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- `mp_parameters`:
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- `auto_find_batch_size`: False
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- `full_determinism`: False
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- `torchdynamo`: None
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- `ray_scope`: last
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- `ddp_timeout`: 1800
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- `torch_compile`: False
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- `torch_compile_backend`: None
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|
- `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
|
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- `batch_sampler`: batch_sampler
|
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- `multi_dataset_batch_sampler`: proportional
|
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|
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</details>
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|
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### 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 |
|
|
|:-----:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
|
|
| 0.04 | 1 | 15.537 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
|
|
| 0.2 | 5 | 11.6576 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
|
|
| 0.4 | 10 | 7.1392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
|
|
| 0.6 | 15 | 5.0005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
|
|
| 0.8 | 20 | 4.0541 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
|
|
| 1.0 | 25 | 3.4117 | 2.3797 | 0.2280 | 0.3730 | 0.4359 | 0.2272 | 0.3050 | 0.2760 | 0.1170 | 0.2707 | 0.8529 | 0.2491 | 0.5102 | 0.3689 | 0.3575 | 0.3516 |
|
|
|
|
|
|
### Environmental Impact
|
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
|
|
- **Energy Consumed**: 0.153 kWh
|
|
- **Carbon Emitted**: 0.059 kg of CO2
|
|
- **Hours Used**: 0.396 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: 3.4.0.dev0
|
|
- Transformers: 4.46.2
|
|
- PyTorch: 2.5.0+cu121
|
|
- Accelerate: 0.35.0.dev0
|
|
- Datasets: 2.20.0
|
|
- Tokenizers: 0.20.3
|
|
|
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## Citation
|
|
|
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### BibTeX
|
|
|
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#### 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",
|
|
}
|
|
```
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