--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:100231 - loss:CachedMultipleNegativesRankingLoss base_model: microsoft/mpnet-base widget: - source_sentence: who ordered the charge of the light brigade sentences: - Charge of the Light Brigade The Charge of the Light Brigade was a charge of British light cavalry led by Lord Cardigan against Russian forces during the Battle of Balaclava on 25 October 1854 in the Crimean War. Lord Raglan, overall commander of the British forces, had intended to send the Light Brigade to prevent the Russians from removing captured guns from overrun Turkish positions, a task well-suited to light cavalry. - UNICEF The United Nations International Children's Emergency Fund was created by the United Nations General Assembly on 11 December 1946, to provide emergency food and healthcare to children in countries that had been devastated by World War II. The Polish physician Ludwik Rajchman is widely regarded as the founder of UNICEF and served as its first chairman from 1946. On Rajchman's suggestion, the American Maurice Pate was appointed its first executive director, serving from 1947 until his death in 1965.[5][6] In 1950, UNICEF's mandate was extended to address the long-term needs of children and women in developing countries everywhere. In 1953 it became a permanent part of the United Nations System, and the words "international" and "emergency" were dropped from the organization's name, making it simply the United Nations Children's Fund, retaining the original acronym, "UNICEF".[3] - Marcus Jordan Marcus James Jordan (born December 24, 1990) is an American former college basketball player who played for the UCF Knights men's basketball team of Conference USA.[1] He is the son of retired Hall of Fame basketball player Michael Jordan. - source_sentence: what part of the cow is the rib roast sentences: - Standing rib roast A standing rib roast, also known as prime rib, is a cut of beef from the primal rib, one of the nine primal cuts of beef. While the entire rib section comprises ribs six through 12, a standing rib roast may contain anywhere from two to seven ribs. - Blaine Anderson Kurt begins to mend their relationship in "Thanksgiving", just before New Directions loses at Sectionals to the Warblers, and they spend Christmas together in New York City.[29][30] Though he and Kurt continue to be on good terms, Blaine finds himself developing a crush on his best friend, Sam, which he knows will come to nothing as he knows Sam is not gay; the two of them team up to find evidence that the Warblers cheated at Sectionals, which means New Directions will be competing at Regionals. He ends up going to the Sadie Hawkins dance with Tina Cohen-Chang (Jenna Ushkowitz), who has developed a crush on him, but as friends only.[31] When Kurt comes to Lima for the wedding of glee club director Will (Matthew Morrison) and Emma (Jayma Mays)—which Emma flees—he and Blaine make out beforehand, and sleep together afterward, though they do not resume a permanent relationship.[32] - 'Soviet Union The Soviet Union (Russian: Сове́тский Сою́з, tr. Sovétsky Soyúz, IPA: [sɐˈvʲɛt͡skʲɪj sɐˈjus] ( listen)), officially the Union of Soviet Socialist Republics (Russian: Сою́з Сове́тских Социалисти́ческих Респу́блик, tr. Soyúz Sovétskikh Sotsialistícheskikh Respúblik, IPA: [sɐˈjus sɐˈvʲɛtskʲɪx sətsɨəlʲɪsˈtʲitɕɪskʲɪx rʲɪˈspublʲɪk] ( listen)), abbreviated as the USSR (Russian: СССР, tr. SSSR), was a socialist state in Eurasia that existed from 1922 to 1991. Nominally a union of multiple national Soviet republics,[a] its government and economy were highly centralized. The country was a one-party state, governed by the Communist Party with Moscow as its capital in its largest republic, the Russian Soviet Federative Socialist Republic. The Russian nation had constitutionally equal status among the many nations of the union but exerted de facto dominance in various respects.[7] Other major urban centres were Leningrad, Kiev, Minsk, Alma-Ata and Novosibirsk. The Soviet Union was one of the five recognized nuclear weapons states and possessed the largest stockpile of weapons of mass destruction.[8] It was a founding permanent member of the United Nations Security Council, as well as a member of the Organization for Security and Co-operation in Europe (OSCE) and the leading member of the Council for Mutual Economic Assistance (CMEA) and the Warsaw Pact.' - source_sentence: what is the current big bang theory season sentences: - Byzantine army From the seventh to the 12th centuries, the Byzantine army was among the most powerful and effective military forces in the world – neither Middle Ages Europe nor (following its early successes) the fracturing Caliphate could match the strategies and the efficiency of the Byzantine army. Restricted to a largely defensive role in the 7th to mid-9th centuries, the Byzantines developed the theme-system to counter the more powerful Caliphate. From the mid-9th century, however, they gradually went on the offensive, culminating in the great conquests of the 10th century under a series of soldier-emperors such as Nikephoros II Phokas, John Tzimiskes and Basil II. The army they led was less reliant on the militia of the themes; it was by now a largely professional force, with a strong and well-drilled infantry at its core and augmented by a revived heavy cavalry arm. With one of the most powerful economies in the world at the time, the Empire had the resources to put to the field a powerful host when needed, in order to reclaim its long-lost territories. - The Big Bang Theory The Big Bang Theory is an American television sitcom created by Chuck Lorre and Bill Prady, both of whom serve as executive producers on the series, along with Steven Molaro. All three also serve as head writers. The show premiered on CBS on September 24, 2007.[3] The series' tenth season premiered on September 19, 2016.[4] In March 2017, the series was renewed for two additional seasons, bringing its total to twelve, and running through the 2018–19 television season. The eleventh season is set to premiere on September 25, 2017.[5] - 2016 NCAA Division I Softball Tournament The 2016 NCAA Division I Softball Tournament was held from May 20 through June 8, 2016 as the final part of the 2016 NCAA Division I softball season. The 64 NCAA Division I college softball teams were to be selected out of an eligible 293 teams on May 15, 2016. Thirty-two teams were awarded an automatic bid as champions of their conference, and thirty-two teams were selected at-large by the NCAA Division I softball selection committee. The tournament culminated with eight teams playing in the 2016 Women's College World Series at ASA Hall of Fame Stadium in Oklahoma City in which the Oklahoma Sooners were crowned the champions. - source_sentence: what happened to tates mom on days of our lives sentences: - 'Paige O''Hara Donna Paige Helmintoller, better known as Paige O''Hara (born May 10, 1956),[1] is an American actress, voice actress, singer and painter. O''Hara began her career as a Broadway actress in 1983 when she portrayed Ellie May Chipley in the musical Showboat. In 1991, she made her motion picture debut in Disney''s Beauty and the Beast, in which she voiced the film''s heroine, Belle. Following the critical and commercial success of Beauty and the Beast, O''Hara reprised her role as Belle in the film''s two direct-to-video follow-ups, Beauty and the Beast: The Enchanted Christmas and Belle''s Magical World.' - M. Shadows Matthew Charles Sanders (born July 31, 1981), better known as M. Shadows, is an American singer, songwriter, and musician. He is best known as the lead vocalist, songwriter, and a founding member of the American heavy metal band Avenged Sevenfold. In 2017, he was voted 3rd in the list of Top 25 Greatest Modern Frontmen by Ultimate Guitar.[1] - Theresa Donovan In July 2013, Jeannie returns to Salem, this time going by her middle name, Theresa. Initially, she strikes up a connection with resident bad boy JJ Deveraux (Casey Moss) while trying to secure some pot.[28] During a confrontation with JJ and his mother Jennifer Horton (Melissa Reeves) in her office, her aunt Kayla confirms that Theresa is in fact Jeannie and that Jen promised to hire her as her assistant, a promise she reluctantly agrees to. Kayla reminds Theresa it is her last chance at a fresh start.[29] Theresa also strikes up a bad first impression with Jennifer's daughter Abigail Deveraux (Kate Mansi) when Abigail smells pot on Theresa in her mother's office.[30] To continue to battle against Jennifer, she teams up with Anne Milbauer (Meredith Scott Lynn) in hopes of exacting her perfect revenge. In a ploy, Theresa reveals her intentions to hopefully woo Dr. Daniel Jonas (Shawn Christian). After sleeping with JJ, Theresa overdoses on marijuana and GHB. Upon hearing of their daughter's overdose and continuing problems, Shane and Kimberly return to town in the hopes of handling their daughter's problem, together. After believing that Theresa has a handle on her addictions, Shane and Kimberly leave town together. Theresa then teams up with hospital co-worker Anne Milbauer (Meredith Scott Lynn) to conspire against Jennifer, using Daniel as a way to hurt their relationship. In early 2014, following a Narcotics Anonymous (NA) meeting, she begins a sexual and drugged-fused relationship with Brady Black (Eric Martsolf). In 2015, after it is found that Kristen DiMera (Eileen Davidson) stole Theresa's embryo and carried it to term, Brady and Melanie Jonas return her son, Christopher, to her and Brady, and the pair rename him Tate. When Theresa moves into the Kiriakis mansion, tensions arise between her and Victor. She eventually expresses her interest in purchasing Basic Black and running it as her own fashion company, with financial backing from Maggie Horton (Suzanne Rogers). In the hopes of finding the right partner, she teams up with Kate Roberts (Lauren Koslow) and Nicole Walker (Arianne Zucker) to achieve the goal of purchasing Basic Black, with Kate and Nicole's business background and her own interest in fashion design. As she and Brady share several instances of rekindling their romance, she is kicked out of the mansion by Victor; as a result, Brady quits Titan and moves in with Theresa and Tate, in their own penthouse. - source_sentence: where does the last name francisco come from sentences: - Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis). - 'Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]' - Times Square Times Square is a major commercial intersection, tourist destination, entertainment center and neighborhood in the Midtown Manhattan section of New York City at the junction of Broadway and Seventh Avenue. It stretches from West 42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements, Times Square is sometimes referred to as "The Crossroads of the World",[2] "The Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the "heart of the world".[7] One of the world's busiest pedestrian areas,[8] it is also the hub of the Broadway Theater District[9] and a major center of the world's entertainment industry.[10] Times Square is one of the world's most visited tourist attractions, drawing an estimated 50 million visitors annually.[11] Approximately 330,000 people pass through Times Square daily,[12] many of them tourists,[13] while over 460,000 pedestrians walk through Times Square on its busiest days.[7] datasets: - sentence-transformers/natural-questions pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 co2_eq_emissions: emissions: 156.71745272849893 energy_consumed: 0.4031814930936783 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 1.06 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: MPNet base trained on Natural Questions pairs results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.26 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.26 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666663 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.132 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.098 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12166666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.21333333333333335 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2823333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4023333333333333 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3072612507335402 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3923333333333332 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.23491428459601352 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.54 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.82 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.88 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.54 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.49333333333333335 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.452 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.3999999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03532870005653879 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.12890082733478095 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.171758495529932 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.27990780793487774 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4786923942173648 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6884999999999999 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33505815936311906 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.52 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.78 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.88 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.52 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.092 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.51 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.68 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.75 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.85 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6729158648959721 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6254444444444444 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.614761203653674 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.3 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15083333333333335 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.25576984126984126 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.36776984126984125 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4388253968253968 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3428344529352367 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4101904761904761 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2860017356440821 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.56 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.66 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.72 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.56 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2866666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.192 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.102 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.28 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.43 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.48 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.51 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.488503807443355 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6108333333333333 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.43846940314913296 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.32 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.56 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18666666666666668 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.136 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07400000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.32 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.56 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.68 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.529224155417674 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4613571428571428 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.47267860121474675 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.3 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.44 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.46 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.56 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.256 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.206 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.011477084598176458 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.028676292172329844 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.040358577465214304 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.05875427093456358 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.22959434028697892 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3806031746031746 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.07498220009340267 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.4 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.56 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.78 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.38 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.55 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.65 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.559757518165897 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5117460317460317 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5051110779754859 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.84 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.92 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.94 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.98 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.84 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.37999999999999995 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.23999999999999996 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13199999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7406666666666666 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8786666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9093333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.97 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9011957626416093 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.8868571428571428 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8761171188288835 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.4 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.54 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.64 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.24000000000000005 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.17600000000000002 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.08366666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.17366666666666664 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2476666666666667 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3636666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3399485562655788 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5016269841269841 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2597766712058288 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.22 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.62 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.86 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.22 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.172 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.22 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.62 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.86 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.94 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5736165548748362 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.45563492063492056 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.45858965011596586 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.44 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.66 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.44 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.084 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.405 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.63 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.65 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.73 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5809087660276336 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5428571428571428 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5343620568329766 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.5714285714285714 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.8571428571428571 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.9183673469387755 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9795918367346939 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.5714285714285714 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.5306122448979591 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.5183673469387755 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.4163265306122449 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.04042531470555883 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.11796663614343775 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.18934738259789605 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.28088647761316804 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4716177209745631 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7203109815354714 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.36609464219543497 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.43626373626373627 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.632087912087912 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7198744113029828 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7984301412872841 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.43626373626373627 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.28235478806907377 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.22664364207221352 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.15771742543171113 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.25377418713027755 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4051523279682351 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.48296674078432444 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5618749194852313 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4981593188369415 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5529457775784306 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.41976283114374974 name: Cosine Map@100 --- # MPNet base trained on Natural Questions pairs 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. This model was trained using the script from the [Training with Prompts](https://sbert.net/examples/training/prompts/README.html) Sentence Transformers documentation. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("tomaarsen/mpnet-base-nq") # Run inference sentences = [ 'where does the last name francisco come from', 'Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).', 'Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | cosine_accuracy@1 | 0.26 | 0.54 | 0.52 | 0.3 | 0.56 | 0.32 | 0.3 | 0.4 | 0.84 | 0.4 | 0.22 | 0.44 | 0.5714 | | cosine_accuracy@3 | 0.44 | 0.82 | 0.7 | 0.44 | 0.66 | 0.56 | 0.44 | 0.56 | 0.92 | 0.54 | 0.62 | 0.66 | 0.8571 | | cosine_accuracy@5 | 0.58 | 0.88 | 0.78 | 0.58 | 0.68 | 0.68 | 0.46 | 0.68 | 0.94 | 0.64 | 0.86 | 0.68 | 0.9184 | | cosine_accuracy@10 | 0.74 | 0.92 | 0.88 | 0.64 | 0.72 | 0.74 | 0.56 | 0.78 | 0.98 | 0.76 | 0.94 | 0.74 | 0.9796 | | cosine_precision@1 | 0.26 | 0.54 | 0.52 | 0.3 | 0.56 | 0.32 | 0.3 | 0.4 | 0.84 | 0.4 | 0.22 | 0.44 | 0.5714 | | cosine_precision@3 | 0.1667 | 0.4933 | 0.24 | 0.1867 | 0.2867 | 0.1867 | 0.28 | 0.2 | 0.38 | 0.28 | 0.2067 | 0.2333 | 0.5306 | | cosine_precision@5 | 0.132 | 0.452 | 0.16 | 0.16 | 0.192 | 0.136 | 0.256 | 0.144 | 0.24 | 0.24 | 0.172 | 0.144 | 0.5184 | | cosine_precision@10 | 0.098 | 0.4 | 0.092 | 0.094 | 0.102 | 0.074 | 0.206 | 0.082 | 0.132 | 0.176 | 0.094 | 0.084 | 0.4163 | | cosine_recall@1 | 0.1217 | 0.0353 | 0.51 | 0.1508 | 0.28 | 0.32 | 0.0115 | 0.38 | 0.7407 | 0.0837 | 0.22 | 0.405 | 0.0404 | | cosine_recall@3 | 0.2133 | 0.1289 | 0.68 | 0.2558 | 0.43 | 0.56 | 0.0287 | 0.55 | 0.8787 | 0.1737 | 0.62 | 0.63 | 0.118 | | cosine_recall@5 | 0.2823 | 0.1718 | 0.75 | 0.3678 | 0.48 | 0.68 | 0.0404 | 0.65 | 0.9093 | 0.2477 | 0.86 | 0.65 | 0.1893 | | cosine_recall@10 | 0.4023 | 0.2799 | 0.85 | 0.4388 | 0.51 | 0.74 | 0.0588 | 0.74 | 0.97 | 0.3637 | 0.94 | 0.73 | 0.2809 | | **cosine_ndcg@10** | **0.3073** | **0.4787** | **0.6729** | **0.3428** | **0.4885** | **0.5292** | **0.2296** | **0.5598** | **0.9012** | **0.3399** | **0.5736** | **0.5809** | **0.4716** | | cosine_mrr@10 | 0.3923 | 0.6885 | 0.6254 | 0.4102 | 0.6108 | 0.4614 | 0.3806 | 0.5117 | 0.8869 | 0.5016 | 0.4556 | 0.5429 | 0.7203 | | cosine_map@100 | 0.2349 | 0.3351 | 0.6148 | 0.286 | 0.4385 | 0.4727 | 0.075 | 0.5051 | 0.8761 | 0.2598 | 0.4586 | 0.5344 | 0.3661 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.4363 | | cosine_accuracy@3 | 0.6321 | | cosine_accuracy@5 | 0.7199 | | cosine_accuracy@10 | 0.7984 | | cosine_precision@1 | 0.4363 | | cosine_precision@3 | 0.2824 | | cosine_precision@5 | 0.2266 | | cosine_precision@10 | 0.1577 | | cosine_recall@1 | 0.2538 | | cosine_recall@3 | 0.4052 | | cosine_recall@5 | 0.483 | | cosine_recall@10 | 0.5619 | | **cosine_ndcg@10** | **0.4982** | | cosine_mrr@10 | 0.5529 | | cosine_map@100 | 0.4198 | ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 100,231 training samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | who is required to report according to the hmda | 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] | | what is the definition of endoplasmic reticulum in biology | 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... | | what does the ski mean in polish names | Polish name Since the High Middle Ages, Polish-sounding surnames ending with the masculine -ski suffix, including -cki and -dzki, and the corresponding feminine suffix -ska/-cka/-dzka were associated with the nobility (Polish szlachta), which alone, in the early years, had such suffix distinctions.[1] They are widely popular today. | * Loss: [CachedMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 100,231 evaluation samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | query | answer | |:------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | difference between russian blue and british blue cat | 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. | | who played the little girl on mrs doubtfire | 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. | | what year did the movie the sound of music come out | The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000. | * Loss: [CachedMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `seed`: 12 - `bf16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 256 - `per_device_eval_batch_size`: 256 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 12 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| | 0 | 0 | - | - | 0.0419 | 0.1123 | 0.0389 | 0.0309 | 0.0746 | 0.1310 | 0.0311 | 0.0397 | 0.6607 | 0.0638 | 0.2616 | 0.1097 | 0.1098 | 0.1312 | | 0.0026 | 1 | 4.9565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1289 | 50 | 2.0541 | 0.2601 | 0.2710 | 0.4448 | 0.6531 | 0.3607 | 0.4391 | 0.4775 | 0.2046 | 0.4423 | 0.8485 | 0.3347 | 0.5148 | 0.5010 | 0.4544 | 0.4574 | | 0.2577 | 100 | 0.2154 | 0.1422 | 0.2920 | 0.4577 | 0.6635 | 0.3671 | 0.4623 | 0.5067 | 0.2115 | 0.5170 | 0.8845 | 0.3360 | 0.5483 | 0.5044 | 0.4627 | 0.4780 | | 0.3866 | 150 | 0.1503 | 0.1182 | 0.3064 | 0.4665 | 0.6658 | 0.3511 | 0.4935 | 0.5324 | 0.2347 | 0.5320 | 0.8982 | 0.3316 | 0.5674 | 0.5495 | 0.4583 | 0.4913 | | 0.5155 | 200 | 0.1325 | 0.1075 | 0.3205 | 0.4777 | 0.6608 | 0.3588 | 0.4938 | 0.5221 | 0.2285 | 0.5568 | 0.9064 | 0.3321 | 0.5566 | 0.5510 | 0.4693 | 0.4950 | | 0.6443 | 250 | 0.142 | 0.1040 | 0.3326 | 0.4721 | 0.6589 | 0.3671 | 0.4875 | 0.5207 | 0.2392 | 0.5511 | 0.9025 | 0.3336 | 0.5637 | 0.5861 | 0.4738 | 0.4991 | | 0.7732 | 300 | 0.1243 | 0.0989 | 0.3078 | 0.4699 | 0.6560 | 0.3493 | 0.4946 | 0.5268 | 0.2275 | 0.5422 | 0.9071 | 0.3375 | 0.5664 | 0.5850 | 0.4709 | 0.4955 | | 0.9021 | 350 | 0.1161 | 0.0960 | 0.3092 | 0.4781 | 0.6734 | 0.3426 | 0.4971 | 0.5218 | 0.2294 | 0.5608 | 0.9012 | 0.3444 | 0.5742 | 0.5818 | 0.4672 | 0.4986 | | 1.0 | 388 | - | - | 0.3073 | 0.4787 | 0.6729 | 0.3428 | 0.4885 | 0.5292 | 0.2296 | 0.5598 | 0.9012 | 0.3399 | 0.5736 | 0.5809 | 0.4716 | 0.4982 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.403 kWh - **Carbon Emitted**: 0.157 kg of CO2 - **Hours Used**: 1.06 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 3.3.0.dev0 - Transformers: 4.45.2 - PyTorch: 2.5.0+cu121 - Accelerate: 1.0.0 - Datasets: 2.20.0 - Tokenizers: 0.20.1-dev.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CachedMultipleNegativesRankingLoss ```bibtex @misc{gao2021scaling, title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, year={2021}, eprint={2101.06983}, archivePrefix={arXiv}, primaryClass={cs.LG} } ```