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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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- dense |
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- generated_from_trainer |
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- dataset_size:99231 |
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- loss:CachedMultipleNegativesRankingLoss |
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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 |
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British light cavalry led by Lord Cardigan against Russian forces during the |
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Battle of Balaclava on 25 October 1854 in the Crimean War. Lord Raglan, |
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overall commander of the British forces, had intended to send the Light |
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Brigade to prevent the Russians from removing captured guns from overrun |
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Turkish positions, a task well-suited to light cavalry. |
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UNICEF The United Nations International Children's Emergency Fund was |
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created by the United Nations General Assembly on 11 December 1946, to |
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provide emergency food and healthcare to children in countries that had been |
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devastated by World War II. The Polish physician Ludwik Rajchman is widely |
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regarded as the founder of UNICEF and served as its first chairman from |
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1946. On Rajchman's suggestion, the American Maurice Pate was appointed its |
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first executive director, serving from 1947 until his death in 1965.[5][6] |
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In 1950, UNICEF's mandate was extended to address the long-term needs of |
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children and women in developing countries everywhere. In 1953 it became a |
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permanent part of the United Nations System, and the words "international" |
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and "emergency" were dropped from the organization's name, making it simply |
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the United Nations Children's Fund, retaining the original acronym, |
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"UNICEF".[3] |
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- >- |
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Marcus Jordan Marcus James Jordan (born December 24, 1990) is an American |
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former college basketball player who played for the UCF Knights men's |
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basketball team of Conference USA.[1] He is the son of retired Hall of Fame |
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basketball player 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 |
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of beef from the primal rib, one of the nine primal cuts of beef. While the |
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entire rib section comprises ribs six through 12, a standing rib roast may |
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contain anywhere from two to seven ribs. |
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- >- |
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Blaine Anderson Kurt begins to mend their relationship in "Thanksgiving", |
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just before New Directions loses at Sectionals to the Warblers, and they |
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spend Christmas together in New York City.[29][30] Though he and Kurt |
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continue to be on good terms, Blaine finds himself developing a crush on his |
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best friend, Sam, which he knows will come to nothing as he knows Sam is not |
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gay; the two of them team up to find evidence that the Warblers cheated at |
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Sectionals, which means New Directions will be competing at Regionals. He |
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ends up going to the Sadie Hawkins dance with Tina Cohen-Chang (Jenna |
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Ushkowitz), who has developed a crush on him, but as friends only.[31] When |
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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 |
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beforehand, and sleep together afterward, though they do not resume a |
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permanent relationship.[32] |
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- "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." |
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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 |
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was among the most powerful and effective military forces in the world – |
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neither Middle Ages Europe nor (following its early successes) the |
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fracturing Caliphate could match the strategies and the efficiency of the |
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Byzantine army. Restricted to a largely defensive role in the 7th to mid-9th |
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centuries, the Byzantines developed the theme-system to counter the more |
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powerful Caliphate. From the mid-9th century, however, they gradually went |
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on the offensive, culminating in the great conquests of the 10th century |
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under a series of soldier-emperors such as Nikephoros II Phokas, John |
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Tzimiskes and Basil II. The army they led was less reliant on the militia of |
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the themes; it was by now a largely professional force, with a strong and |
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well-drilled infantry at its core and augmented by a revived heavy cavalry |
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arm. With one of the most powerful economies in the world at the time, the |
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Empire had the resources to put to the field a powerful host when needed, in |
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order to reclaim its long-lost territories. |
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- >- |
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The Big Bang Theory The Big Bang Theory is an American television sitcom |
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created by Chuck Lorre and Bill Prady, both of whom serve as executive |
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producers on the series, along with Steven Molaro. All three also serve as |
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head writers. The show premiered on CBS on September 24, 2007.[3] The |
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series' tenth season premiered on September 19, 2016.[4] In March 2017, the |
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series was renewed for two additional seasons, bringing its total to twelve, |
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and running through the 2018–19 television season. The eleventh season is |
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set to premiere on September 25, 2017.[5] |
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- >- |
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2016 NCAA Division I Softball Tournament The 2016 NCAA Division I Softball |
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Tournament was held from May 20 through June 8, 2016 as the final part of |
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the 2016 NCAA Division I softball season. The 64 NCAA Division I college |
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softball teams were to be selected out of an eligible 293 teams on May 15, |
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2016. Thirty-two teams were awarded an automatic bid as champions of their |
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conference, and thirty-two teams were selected at-large by the NCAA Division |
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I softball selection committee. The tournament culminated with eight teams |
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playing in the 2016 Women's College World Series at ASA Hall of Fame Stadium |
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in Oklahoma City in which the Oklahoma Sooners were crowned the 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 |
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May 10, 1956),[1] is an American actress, voice actress, singer and painter. |
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O'Hara began her career as a Broadway actress in 1983 when she portrayed |
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Ellie May Chipley in the musical Showboat. In 1991, she made her motion |
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picture debut in Disney's Beauty and the Beast, in which she voiced the |
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film's heroine, Belle. Following the critical and commercial success of |
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Beauty and the Beast, O'Hara reprised her role as Belle in the film's two |
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direct-to-video follow-ups, Beauty and the Beast: The Enchanted Christmas |
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and Belle's Magical World. |
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- >- |
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M. Shadows Matthew Charles Sanders (born July 31, 1981), better known as M. |
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Shadows, is an American singer, songwriter, and musician. He is best known |
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as the lead vocalist, songwriter, and a founding member of the American |
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heavy metal band Avenged Sevenfold. In 2017, he was voted 3rd in the list of |
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Top 25 Greatest Modern Frontmen by Ultimate Guitar.[1] |
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- >- |
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Theresa Donovan In July 2013, Jeannie returns to Salem, this time going by |
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her middle name, Theresa. Initially, she strikes up a connection with |
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resident bad boy JJ Deveraux (Casey Moss) while trying to secure some |
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pot.[28] During a confrontation with JJ and his mother Jennifer Horton |
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(Melissa Reeves) in her office, her aunt Kayla confirms that Theresa is in |
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fact Jeannie and that Jen promised to hire her as her assistant, a promise |
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she reluctantly agrees to. Kayla reminds Theresa it is her last chance at a |
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fresh start.[29] Theresa also strikes up a bad first impression with |
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Jennifer's daughter Abigail Deveraux (Kate Mansi) when Abigail smells pot on |
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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 |
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her perfect revenge. In a ploy, Theresa reveals her intentions to hopefully |
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woo Dr. Daniel Jonas (Shawn Christian). After sleeping with JJ, Theresa |
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overdoses on marijuana and GHB. Upon hearing of their daughter's overdose |
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and continuing problems, Shane and Kimberly return to town in the hopes of |
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handling their daughter's problem, together. After believing that Theresa |
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has a handle on her addictions, Shane and Kimberly leave town together. |
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Theresa then teams up with hospital co-worker Anne Milbauer (Meredith Scott |
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Lynn) to conspire against Jennifer, using Daniel as a way to hurt their |
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relationship. In early 2014, following a Narcotics Anonymous (NA) meeting, |
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she begins a sexual and drugged-fused relationship with Brady Black (Eric |
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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 |
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return her son, Christopher, to her and Brady, and the pair rename him Tate. |
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When Theresa moves into the Kiriakis mansion, tensions arise between her and |
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Victor. She eventually expresses her interest in purchasing Basic Black and |
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running it as her own fashion company, with financial backing from Maggie |
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Horton (Suzanne Rogers). In the hopes of finding the right partner, she |
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teams up with Kate Roberts (Lauren Koslow) and Nicole Walker (Arianne |
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Zucker) to achieve the goal of purchasing Basic Black, with Kate and |
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Nicole's business background and her own interest in fashion design. As she |
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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 |
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with 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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- >- |
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Francisco Francisco is the Spanish and Portuguese form of the masculine |
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given name Franciscus (corresponding to English Francis). |
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- >- |
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Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" |
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(Megillah), is a book in the third section (Ketuvim, "Writings") of the |
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Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is |
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one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story |
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of a Hebrew woman in Persia, born as Hadassah but known as Esther, who |
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becomes queen of Persia and thwarts a genocide of her people. The story |
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forms the core of the Jewish festival of Purim, during which it is read |
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aloud twice: once in the evening and again the following morning. The books |
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of Esther and Song of Songs are the only books in the Hebrew Bible that do |
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not explicitly mention God.[2] |
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- >- |
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Times Square Times Square is a major commercial intersection, tourist |
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destination, entertainment center and neighborhood in the Midtown Manhattan |
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section of New York City at the junction of Broadway and Seventh Avenue. It |
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stretches from West 42nd to West 47th Streets.[1] Brightly adorned with |
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billboards and advertisements, Times Square is sometimes referred to as "The |
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Crossroads of the World",[2] "The Center of the Universe",[3] "the heart of |
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The Great White Way",[4][5][6] and the "heart of the world".[7] One of the |
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world's busiest pedestrian areas,[8] it is also the hub of the Broadway |
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Theater District[9] and a major center of the world's entertainment |
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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] |
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Approximately 330,000 people pass through Times Square daily,[12] many of |
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them tourists,[13] while over 460,000 pedestrians walk through Times Square |
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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: 405.46378000747745 |
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energy_consumed: 1.043122443433472 |
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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: 3.425 |
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hardware_used: 1 x NVIDIA GeForce RTX 3090 |
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model-index: |
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- name: LiquidAI/LFM2-350M 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: NanoMSMARCO |
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type: NanoMSMARCO |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.28 |
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name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.46 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
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value: 0.64 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
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value: 0.74 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.28 |
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.15333333333333332 |
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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: 0.07400000000000001 |
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name: Cosine Precision@10 |
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- type: cosine_recall@1 |
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value: 0.28 |
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|
name: Cosine Recall@1 |
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- type: cosine_recall@3 |
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value: 0.46 |
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name: Cosine Recall@3 |
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- type: cosine_recall@5 |
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value: 0.64 |
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name: Cosine Recall@5 |
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- type: cosine_recall@10 |
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value: 0.74 |
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name: Cosine Recall@10 |
|
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- type: cosine_ndcg@10 |
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value: 0.4909415698599729 |
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|
name: Cosine Ndcg@10 |
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- type: cosine_mrr@10 |
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value: 0.4130714285714285 |
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|
name: Cosine Mrr@10 |
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- type: cosine_map@100 |
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value: 0.42354966538209315 |
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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: NanoNFCorpus |
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type: NanoNFCorpus |
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metrics: |
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- type: cosine_accuracy@1 |
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value: 0.4 |
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|
name: Cosine Accuracy@1 |
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- type: cosine_accuracy@3 |
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value: 0.5 |
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name: Cosine Accuracy@3 |
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- type: cosine_accuracy@5 |
|
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value: 0.58 |
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name: Cosine Accuracy@5 |
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- type: cosine_accuracy@10 |
|
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value: 0.68 |
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name: Cosine Accuracy@10 |
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- type: cosine_precision@1 |
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value: 0.4 |
|
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name: Cosine Precision@1 |
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- type: cosine_precision@3 |
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value: 0.35999999999999993 |
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name: Cosine Precision@3 |
|
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- type: cosine_precision@5 |
|
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value: 0.324 |
|
|
name: Cosine Precision@5 |
|
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- type: cosine_precision@10 |
|
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value: 0.26599999999999996 |
|
|
name: Cosine Precision@10 |
|
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- type: cosine_recall@1 |
|
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value: 0.02298357366763854 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.061632366484571384 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.09750915762412557 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.13301219077618073 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.32361002047039217 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.47583333333333333 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.12539829347446158 |
|
|
name: Cosine Map@100 |
|
|
- task: |
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type: information-retrieval |
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name: Information Retrieval |
|
|
dataset: |
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name: NanoNQ |
|
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type: NanoNQ |
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metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.48 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.68 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.78 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.82 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.48 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.22666666666666666 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.15600000000000003 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.086 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.47 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.64 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.72 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.78 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.632163202477609 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.5983571428571428 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.5837963147038205 |
|
|
name: Cosine Map@100 |
|
|
- task: |
|
|
type: nano-beir |
|
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name: Nano BEIR |
|
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dataset: |
|
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name: NanoBEIR mean |
|
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type: NanoBEIR_mean |
|
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metrics: |
|
|
- type: cosine_accuracy@1 |
|
|
value: 0.3866666666666667 |
|
|
name: Cosine Accuracy@1 |
|
|
- type: cosine_accuracy@3 |
|
|
value: 0.5466666666666667 |
|
|
name: Cosine Accuracy@3 |
|
|
- type: cosine_accuracy@5 |
|
|
value: 0.6666666666666666 |
|
|
name: Cosine Accuracy@5 |
|
|
- type: cosine_accuracy@10 |
|
|
value: 0.7466666666666666 |
|
|
name: Cosine Accuracy@10 |
|
|
- type: cosine_precision@1 |
|
|
value: 0.3866666666666667 |
|
|
name: Cosine Precision@1 |
|
|
- type: cosine_precision@3 |
|
|
value: 0.24666666666666662 |
|
|
name: Cosine Precision@3 |
|
|
- type: cosine_precision@5 |
|
|
value: 0.2026666666666667 |
|
|
name: Cosine Precision@5 |
|
|
- type: cosine_precision@10 |
|
|
value: 0.142 |
|
|
name: Cosine Precision@10 |
|
|
- type: cosine_recall@1 |
|
|
value: 0.25766119122254616 |
|
|
name: Cosine Recall@1 |
|
|
- type: cosine_recall@3 |
|
|
value: 0.38721078882819054 |
|
|
name: Cosine Recall@3 |
|
|
- type: cosine_recall@5 |
|
|
value: 0.48583638587470857 |
|
|
name: Cosine Recall@5 |
|
|
- type: cosine_recall@10 |
|
|
value: 0.5510040635920602 |
|
|
name: Cosine Recall@10 |
|
|
- type: cosine_ndcg@10 |
|
|
value: 0.48223826426932465 |
|
|
name: Cosine Ndcg@10 |
|
|
- type: cosine_mrr@10 |
|
|
value: 0.4957539682539682 |
|
|
name: Cosine Mrr@10 |
|
|
- type: cosine_map@100 |
|
|
value: 0.37758142452012505 |
|
|
name: Cosine Map@100 |
|
|
base_model: |
|
|
- LiquidAI/LFM2-350M |
|
|
--- |
|
|
|
|
|
# LiquidAI/LFM2-350M trained on Natural Questions pairs |
|
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|
|
|
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset. It maps sentences & paragraphs to a 1024-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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|
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|
### Model Description |
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|
- **Model Type:** Sentence Transformer |
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|
- **Base model:** [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) |
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- **Maximum Sequence Length:** 128000 tokens |
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- **Output Dimensionality:** 1024 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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|
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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': 128000, 'do_lower_case': False, 'architecture': 'LFM2Model'}) |
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|
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True}) |
|
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) |
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``` |
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|
|
|
## Usage |
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|
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|
### Direct Usage (Sentence Transformers) |
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|
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|
First install the Sentence Transformers library: |
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|
|
|
```bash |
|
|
pip install -U sentence-transformers |
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|
``` |
|
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|
|
|
Then you can load this model and run inference. |
|
|
```python |
|
|
from sentence_transformers import SentenceTransformer |
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|
|
|
# Download from the 🤗 Hub |
|
|
model = SentenceTransformer("tomaarsen/LFM2-350M-nq-prompts") |
|
|
# Run inference |
|
|
queries = [ |
|
|
"where does the last name francisco come from", |
|
|
] |
|
|
documents = [ |
|
|
'Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).', |
|
|
'Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]', |
|
|
'Times Square Times Square is a major commercial intersection, tourist destination, entertainment center and neighborhood in the Midtown Manhattan section of New York City at the junction of Broadway and Seventh Avenue. It stretches from West 42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements, Times Square is sometimes referred to as "The Crossroads of the World",[2] "The Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the "heart of the world".[7] One of the world\'s busiest pedestrian areas,[8] it is also the hub of the Broadway Theater District[9] and a major center of the world\'s entertainment industry.[10] Times Square is one of the world\'s most visited tourist attractions, drawing an estimated 50 million visitors annually.[11] Approximately 330,000 people pass through Times Square daily,[12] many of them tourists,[13] while over 460,000 pedestrians walk through Times Square on its busiest days.[7]', |
|
|
] |
|
|
query_embeddings = model.encode_query(queries) |
|
|
document_embeddings = model.encode_document(documents) |
|
|
print(query_embeddings.shape, document_embeddings.shape) |
|
|
# [1, 1024] [3, 1024] |
|
|
|
|
|
# Get the similarity scores for the embeddings |
|
|
similarities = model.similarity(query_embeddings, document_embeddings) |
|
|
print(similarities) |
|
|
# tensor([[ 0.7825, -0.0811, -0.0414]]) |
|
|
``` |
|
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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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|
<!-- |
|
|
### 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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<!-- |
|
|
### 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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|
|
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## Evaluation |
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|
|
|
|
### Metrics |
|
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|
|
|
#### Information Retrieval |
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|
|
* Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ` |
|
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"query_prompt": "query: ", |
|
|
"corpus_prompt": "document: " |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | |
|
|
|:--------------------|:------------|:-------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.28 | 0.4 | 0.48 | |
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| cosine_accuracy@3 | 0.46 | 0.5 | 0.68 | |
|
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| cosine_accuracy@5 | 0.64 | 0.58 | 0.78 | |
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| cosine_accuracy@10 | 0.74 | 0.68 | 0.82 | |
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| cosine_precision@1 | 0.28 | 0.4 | 0.48 | |
|
|
| cosine_precision@3 | 0.1533 | 0.36 | 0.2267 | |
|
|
| cosine_precision@5 | 0.128 | 0.324 | 0.156 | |
|
|
| cosine_precision@10 | 0.074 | 0.266 | 0.086 | |
|
|
| cosine_recall@1 | 0.28 | 0.023 | 0.47 | |
|
|
| cosine_recall@3 | 0.46 | 0.0616 | 0.64 | |
|
|
| cosine_recall@5 | 0.64 | 0.0975 | 0.72 | |
|
|
| cosine_recall@10 | 0.74 | 0.133 | 0.78 | |
|
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| **cosine_ndcg@10** | **0.4909** | **0.3236** | **0.6322** | |
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|
| cosine_mrr@10 | 0.4131 | 0.4758 | 0.5984 | |
|
|
| cosine_map@100 | 0.4235 | 0.1254 | 0.5838 | |
|
|
|
|
|
#### Nano BEIR |
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|
|
|
* Dataset: `NanoBEIR_mean` |
|
|
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"dataset_names": [ |
|
|
"msmarco", |
|
|
"nfcorpus", |
|
|
"nq" |
|
|
], |
|
|
"query_prompts": { |
|
|
"msmarco": "query: ", |
|
|
"nfcorpus": "query: ", |
|
|
"nq": "query: " |
|
|
}, |
|
|
"corpus_prompts": { |
|
|
"msmarco": "document: ", |
|
|
"nfcorpus": "document: ", |
|
|
"nq": "document: " |
|
|
} |
|
|
} |
|
|
``` |
|
|
|
|
|
| Metric | Value | |
|
|
|:--------------------|:-----------| |
|
|
| cosine_accuracy@1 | 0.3867 | |
|
|
| cosine_accuracy@3 | 0.5467 | |
|
|
| cosine_accuracy@5 | 0.6667 | |
|
|
| cosine_accuracy@10 | 0.7467 | |
|
|
| cosine_precision@1 | 0.3867 | |
|
|
| cosine_precision@3 | 0.2467 | |
|
|
| cosine_precision@5 | 0.2027 | |
|
|
| cosine_precision@10 | 0.142 | |
|
|
| cosine_recall@1 | 0.2577 | |
|
|
| cosine_recall@3 | 0.3872 | |
|
|
| cosine_recall@5 | 0.4858 | |
|
|
| cosine_recall@10 | 0.551 | |
|
|
| **cosine_ndcg@10** | **0.4822** | |
|
|
| cosine_mrr@10 | 0.4958 | |
|
|
| cosine_map@100 | 0.3776 | |
|
|
|
|
|
<!-- |
|
|
## 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.* |
|
|
--> |
|
|
|
|
|
<!-- |
|
|
### Recommendations |
|
|
|
|
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
|
|
--> |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Dataset |
|
|
|
|
|
#### natural-questions |
|
|
|
|
|
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) |
|
|
* Size: 99,231 training samples |
|
|
* Columns: <code>query</code> and <code>answer</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | query | answer | |
|
|
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
|
|
| type | string | string | |
|
|
| details | <ul><li>min: 9 tokens</li><li>mean: 11.59 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 16 tokens</li><li>mean: 142.35 tokens</li><li>max: 559 tokens</li></ul> | |
|
|
* Samples: |
|
|
| query | answer | |
|
|
|:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>who is required to report according to the hmda</code> | <code>Home Mortgage Disclosure Act US financial institutions must report HMDA data to their regulator if they meet certain criteria, such as having assets above a specific threshold. The criteria is different for depository and non-depository institutions and are available on the FFIEC website.[4] In 2012, there were 7,400 institutions that reported a total of 18.7 million HMDA records.[5]</code> | |
|
|
| <code>what is the definition of endoplasmic reticulum in biology</code> | <code>Endoplasmic reticulum The endoplasmic reticulum (ER) is a type of organelle in eukaryotic cells that forms an interconnected network of flattened, membrane-enclosed sacs or tube-like structures known as cisternae. The membranes of the ER are continuous with the outer nuclear membrane. The endoplasmic reticulum occurs in most types of eukaryotic cells, but is absent from red blood cells and spermatozoa. There are two types of endoplasmic reticulum: rough and smooth. The outer (cytosolic) face of the rough endoplasmic reticulum is studded with ribosomes that are the sites of protein synthesis. The rough endoplasmic reticulum is especially prominent in cells such as hepatocytes. The smooth endoplasmic reticulum lacks ribosomes and functions in lipid manufacture and metabolism, the production of steroid hormones, and detoxification.[1] The smooth ER is especially abundant in mammalian liver and gonad cells. The lacy membranes of the endoplasmic reticulum were first seen in 1945 using elect...</code> | |
|
|
| <code>what does the ski mean in polish names</code> | <code>Polish name Since the High Middle Ages, Polish-sounding surnames ending with the masculine -ski suffix, including -cki and -dzki, and the corresponding feminine suffix -ska/-cka/-dzka were associated with the nobility (Polish szlachta), which alone, in the early years, had such suffix distinctions.[1] They are widely popular today.</code> | |
|
|
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"scale": 20.0, |
|
|
"similarity_fct": "cos_sim", |
|
|
"mini_batch_size": 4 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Evaluation Dataset |
|
|
|
|
|
#### natural-questions |
|
|
|
|
|
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) |
|
|
* Size: 1,000 evaluation samples |
|
|
* Columns: <code>query</code> and <code>answer</code> |
|
|
* Approximate statistics based on the first 1000 samples: |
|
|
| | query | answer | |
|
|
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
|
|
| type | string | string | |
|
|
| details | <ul><li>min: 9 tokens</li><li>mean: 11.62 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 141.66 tokens</li><li>max: 664 tokens</li></ul> | |
|
|
* Samples: |
|
|
| query | answer | |
|
|
|:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
|
| <code>difference between russian blue and british blue cat</code> | <code>Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.</code> | |
|
|
| <code>who played the little girl on mrs doubtfire</code> | <code>Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.</code> | |
|
|
| <code>what year did the movie the sound of music come out</code> | <code>The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.</code> | |
|
|
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
|
|
```json |
|
|
{ |
|
|
"scale": 20.0, |
|
|
"similarity_fct": "cos_sim", |
|
|
"mini_batch_size": 4 |
|
|
} |
|
|
``` |
|
|
|
|
|
### Training Hyperparameters |
|
|
#### Non-Default Hyperparameters |
|
|
|
|
|
- `eval_strategy`: steps |
|
|
- `per_device_train_batch_size`: 256 |
|
|
- `per_device_eval_batch_size`: 256 |
|
|
- `learning_rate`: 2e-05 |
|
|
- `num_train_epochs`: 1 |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `seed`: 12 |
|
|
- `bf16`: True |
|
|
- `prompts`: {'query': 'query: ', 'answer': 'document: '} |
|
|
- `batch_sampler`: no_duplicates |
|
|
|
|
|
#### All Hyperparameters |
|
|
<details><summary>Click to expand</summary> |
|
|
|
|
|
- `overwrite_output_dir`: False |
|
|
- `do_predict`: False |
|
|
- `eval_strategy`: steps |
|
|
- `prediction_loss_only`: True |
|
|
- `per_device_train_batch_size`: 256 |
|
|
- `per_device_eval_batch_size`: 256 |
|
|
- `per_gpu_train_batch_size`: None |
|
|
- `per_gpu_eval_batch_size`: None |
|
|
- `gradient_accumulation_steps`: 1 |
|
|
- `eval_accumulation_steps`: None |
|
|
- `torch_empty_cache_steps`: None |
|
|
- `learning_rate`: 2e-05 |
|
|
- `weight_decay`: 0.0 |
|
|
- `adam_beta1`: 0.9 |
|
|
- `adam_beta2`: 0.999 |
|
|
- `adam_epsilon`: 1e-08 |
|
|
- `max_grad_norm`: 1.0 |
|
|
- `num_train_epochs`: 1 |
|
|
- `max_steps`: -1 |
|
|
- `lr_scheduler_type`: linear |
|
|
- `lr_scheduler_kwargs`: {} |
|
|
- `warmup_ratio`: 0.1 |
|
|
- `warmup_steps`: 0 |
|
|
- `log_level`: passive |
|
|
- `log_level_replica`: warning |
|
|
- `log_on_each_node`: True |
|
|
- `logging_nan_inf_filter`: True |
|
|
- `save_safetensors`: True |
|
|
- `save_on_each_node`: False |
|
|
- `save_only_model`: False |
|
|
- `restore_callback_states_from_checkpoint`: False |
|
|
- `no_cuda`: False |
|
|
- `use_cpu`: False |
|
|
- `use_mps_device`: False |
|
|
- `seed`: 12 |
|
|
- `data_seed`: None |
|
|
- `jit_mode_eval`: False |
|
|
- `use_ipex`: False |
|
|
- `bf16`: True |
|
|
- `fp16`: False |
|
|
- `fp16_opt_level`: O1 |
|
|
- `half_precision_backend`: auto |
|
|
- `bf16_full_eval`: False |
|
|
- `fp16_full_eval`: False |
|
|
- `tf32`: None |
|
|
- `local_rank`: 0 |
|
|
- `ddp_backend`: None |
|
|
- `tpu_num_cores`: None |
|
|
- `tpu_metrics_debug`: False |
|
|
- `debug`: [] |
|
|
- `dataloader_drop_last`: False |
|
|
- `dataloader_num_workers`: 0 |
|
|
- `dataloader_prefetch_factor`: None |
|
|
- `past_index`: -1 |
|
|
- `disable_tqdm`: False |
|
|
- `remove_unused_columns`: True |
|
|
- `label_names`: None |
|
|
- `load_best_model_at_end`: False |
|
|
- `ignore_data_skip`: False |
|
|
- `fsdp`: [] |
|
|
- `fsdp_min_num_params`: 0 |
|
|
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
|
|
- `fsdp_transformer_layer_cls_to_wrap`: None |
|
|
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
|
|
- `deepspeed`: None |
|
|
- `label_smoothing_factor`: 0.0 |
|
|
- `optim`: adamw_torch |
|
|
- `optim_args`: None |
|
|
- `adafactor`: False |
|
|
- `group_by_length`: False |
|
|
- `length_column_name`: length |
|
|
- `ddp_find_unused_parameters`: None |
|
|
- `ddp_bucket_cap_mb`: None |
|
|
- `ddp_broadcast_buffers`: False |
|
|
- `dataloader_pin_memory`: True |
|
|
- `dataloader_persistent_workers`: False |
|
|
- `skip_memory_metrics`: True |
|
|
- `use_legacy_prediction_loop`: False |
|
|
- `push_to_hub`: False |
|
|
- `resume_from_checkpoint`: None |
|
|
- `hub_model_id`: None |
|
|
- `hub_strategy`: every_save |
|
|
- `hub_private_repo`: None |
|
|
- `hub_always_push`: False |
|
|
- `hub_revision`: None |
|
|
- `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 |
|
|
- `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 |
|
|
- `liger_kernel_config`: None |
|
|
- `eval_use_gather_object`: False |
|
|
- `average_tokens_across_devices`: False |
|
|
- `prompts`: {'query': 'query: ', 'answer': 'document: '} |
|
|
- `batch_sampler`: no_duplicates |
|
|
- `multi_dataset_batch_sampler`: proportional |
|
|
- `router_mapping`: {} |
|
|
- `learning_rate_mapping`: {} |
|
|
|
|
|
</details> |
|
|
|
|
|
### Training Logs |
|
|
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |
|
|
|:------:|:----:|:-------------:|:---------------:|:--------------------------:|:---------------------------:|:---------------------:|:----------------------------:| |
|
|
| -1 | -1 | - | - | 0.0086 | 0.0233 | 0.0063 | 0.0128 | |
|
|
| 0.0026 | 1 | 4.6189 | - | - | - | - | - | |
|
|
| 0.0129 | 5 | 4.1284 | - | - | - | - | - | |
|
|
| 0.0258 | 10 | 3.6638 | - | - | - | - | - | |
|
|
| 0.0387 | 15 | 2.3118 | - | - | - | - | - | |
|
|
| 0.0515 | 20 | 1.0986 | - | - | - | - | - | |
|
|
| 0.0644 | 25 | 0.5063 | - | - | - | - | - | |
|
|
| 0.0773 | 30 | 0.2891 | - | - | - | - | - | |
|
|
| 0.0902 | 35 | 0.2138 | - | - | - | - | - | |
|
|
| 0.1031 | 40 | 0.1967 | - | - | - | - | - | |
|
|
| 0.1160 | 45 | 0.1745 | - | - | - | - | - | |
|
|
| 0.1289 | 50 | 0.1479 | 0.1425 | 0.4927 | 0.3162 | 0.5375 | 0.4488 | |
|
|
| 0.1418 | 55 | 0.1257 | - | - | - | - | - | |
|
|
| 0.1546 | 60 | 0.1215 | - | - | - | - | - | |
|
|
| 0.1675 | 65 | 0.1475 | - | - | - | - | - | |
|
|
| 0.1804 | 70 | 0.1066 | - | - | - | - | - | |
|
|
| 0.1933 | 75 | 0.1056 | - | - | - | - | - | |
|
|
| 0.2062 | 80 | 0.1181 | - | - | - | - | - | |
|
|
| 0.2191 | 85 | 0.118 | - | - | - | - | - | |
|
|
| 0.2320 | 90 | 0.1031 | - | - | - | - | - | |
|
|
| 0.2448 | 95 | 0.0775 | - | - | - | - | - | |
|
|
| 0.2577 | 100 | 0.0906 | 0.1009 | 0.4791 | 0.3151 | 0.6007 | 0.4650 | |
|
|
| 0.2706 | 105 | 0.0921 | - | - | - | - | - | |
|
|
| 0.2835 | 110 | 0.1105 | - | - | - | - | - | |
|
|
| 0.2964 | 115 | 0.0906 | - | - | - | - | - | |
|
|
| 0.3093 | 120 | 0.1002 | - | - | - | - | - | |
|
|
| 0.3222 | 125 | 0.0952 | - | - | - | - | - | |
|
|
| 0.3351 | 130 | 0.0652 | - | - | - | - | - | |
|
|
| 0.3479 | 135 | 0.079 | - | - | - | - | - | |
|
|
| 0.3608 | 140 | 0.0951 | - | - | - | - | - | |
|
|
| 0.3737 | 145 | 0.0918 | - | - | - | - | - | |
|
|
| 0.3866 | 150 | 0.065 | 0.0772 | 0.5115 | 0.3070 | 0.6105 | 0.4763 | |
|
|
| 0.3995 | 155 | 0.1065 | - | - | - | - | - | |
|
|
| 0.4124 | 160 | 0.0871 | - | - | - | - | - | |
|
|
| 0.4253 | 165 | 0.0623 | - | - | - | - | - | |
|
|
| 0.4381 | 170 | 0.0771 | - | - | - | - | - | |
|
|
| 0.4510 | 175 | 0.0795 | - | - | - | - | - | |
|
|
| 0.4639 | 180 | 0.0814 | - | - | - | - | - | |
|
|
| 0.4768 | 185 | 0.0794 | - | - | - | - | - | |
|
|
| 0.4897 | 190 | 0.0744 | - | - | - | - | - | |
|
|
| 0.5026 | 195 | 0.0612 | - | - | - | - | - | |
|
|
| 0.5155 | 200 | 0.0684 | 0.0692 | 0.4818 | 0.3173 | 0.6161 | 0.4717 | |
|
|
| 0.5284 | 205 | 0.0635 | - | - | - | - | - | |
|
|
| 0.5412 | 210 | 0.0768 | - | - | - | - | - | |
|
|
| 0.5541 | 215 | 0.0544 | - | - | - | - | - | |
|
|
| 0.5670 | 220 | 0.0654 | - | - | - | - | - | |
|
|
| 0.5799 | 225 | 0.0729 | - | - | - | - | - | |
|
|
| 0.5928 | 230 | 0.0923 | - | - | - | - | - | |
|
|
| 0.6057 | 235 | 0.0763 | - | - | - | - | - | |
|
|
| 0.6186 | 240 | 0.0687 | - | - | - | - | - | |
|
|
| 0.6314 | 245 | 0.0657 | - | - | - | - | - | |
|
|
| 0.6443 | 250 | 0.0708 | 0.0643 | 0.4843 | 0.3152 | 0.6023 | 0.4673 | |
|
|
| 0.6572 | 255 | 0.0555 | - | - | - | - | - | |
|
|
| 0.6701 | 260 | 0.0792 | - | - | - | - | - | |
|
|
| 0.6830 | 265 | 0.0681 | - | - | - | - | - | |
|
|
| 0.6959 | 270 | 0.0855 | - | - | - | - | - | |
|
|
| 0.7088 | 275 | 0.0788 | - | - | - | - | - | |
|
|
| 0.7216 | 280 | 0.0631 | - | - | - | - | - | |
|
|
| 0.7345 | 285 | 0.0676 | - | - | - | - | - | |
|
|
| 0.7474 | 290 | 0.0536 | - | - | - | - | - | |
|
|
| 0.7603 | 295 | 0.0814 | - | - | - | - | - | |
|
|
| 0.7732 | 300 | 0.062 | 0.0606 | 0.4630 | 0.3235 | 0.6256 | 0.4707 | |
|
|
| 0.7861 | 305 | 0.0777 | - | - | - | - | - | |
|
|
| 0.7990 | 310 | 0.0801 | - | - | - | - | - | |
|
|
| 0.8119 | 315 | 0.0566 | - | - | - | - | - | |
|
|
| 0.8247 | 320 | 0.0711 | - | - | - | - | - | |
|
|
| 0.8376 | 325 | 0.0643 | - | - | - | - | - | |
|
|
| 0.8505 | 330 | 0.0422 | - | - | - | - | - | |
|
|
| 0.8634 | 335 | 0.0614 | - | - | - | - | - | |
|
|
| 0.8763 | 340 | 0.06 | - | - | - | - | - | |
|
|
| 0.8892 | 345 | 0.0584 | - | - | - | - | - | |
|
|
| 0.9021 | 350 | 0.0457 | 0.0583 | 0.4952 | 0.3214 | 0.6268 | 0.4811 | |
|
|
| 0.9149 | 355 | 0.0838 | - | - | - | - | - | |
|
|
| 0.9278 | 360 | 0.0657 | - | - | - | - | - | |
|
|
| 0.9407 | 365 | 0.0658 | - | - | - | - | - | |
|
|
| 0.9536 | 370 | 0.0757 | - | - | - | - | - | |
|
|
| 0.9665 | 375 | 0.0603 | - | - | - | - | - | |
|
|
| 0.9794 | 380 | 0.0647 | - | - | - | - | - | |
|
|
| 0.9923 | 385 | 0.0575 | - | - | - | - | - | |
|
|
| -1 | -1 | - | - | 0.4909 | 0.3236 | 0.6322 | 0.4822 | |
|
|
|
|
|
|
|
|
### Environmental Impact |
|
|
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). |
|
|
- **Energy Consumed**: 1.043 kWh |
|
|
- **Carbon Emitted**: 0.405 kg of CO2 |
|
|
- **Hours Used**: 3.425 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: 5.1.0.dev0 |
|
|
- Transformers: 4.53.0 |
|
|
- PyTorch: 2.7.1+cu126 |
|
|
- Accelerate: 1.5.1 |
|
|
- Datasets: 2.21.0 |
|
|
- Tokenizers: 0.21.1 |
|
|
|
|
|
## Citation |
|
|
|
|
|
### BibTeX |
|
|
|
|
|
#### Sentence Transformers |
|
|
```bibtex |
|
|
@inproceedings{reimers-2019-sentence-bert, |
|
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
|
month = "11", |
|
|
year = "2019", |
|
|
publisher = "Association for Computational Linguistics", |
|
|
url = "https://arxiv.org/abs/1908.10084", |
|
|
} |
|
|
``` |
|
|
|
|
|
#### CachedMultipleNegativesRankingLoss |
|
|
```bibtex |
|
|
@misc{gao2021scaling, |
|
|
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, |
|
|
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, |
|
|
year={2021}, |
|
|
eprint={2101.06983}, |
|
|
archivePrefix={arXiv}, |
|
|
primaryClass={cs.LG} |
|
|
} |
|
|
``` |
|
|
|
|
|
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