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	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:100231
	- loss:CachedMultipleNegativesRankingLoss
	base_model: google-bert/bert-base-uncased
	widget:
	- source_sentence: who ordered the charge of the light brigade
	sentences:
	- Charge of the Light Brigade The Charge of the Light Brigade was a charge of British
	light cavalry led by Lord Cardigan against Russian forces during the Battle of
	Balaclava on 25 October 1854 in the Crimean War. Lord Raglan, overall commander
	of the British forces, had intended to send the Light Brigade to prevent the Russians
	from removing captured guns from overrun Turkish positions, a task well-suited
	to light cavalry.
	- UNICEF The United Nations International Children's Emergency Fund was created
	by the United Nations General Assembly on 11 December 1946, to provide emergency
	food and healthcare to children in countries that had been devastated by World
	War II. The Polish physician Ludwik Rajchman is widely regarded as the founder
	of UNICEF and served as its first chairman from 1946. On Rajchman's suggestion,
	the American Maurice Pate was appointed its first executive director, serving
	from 1947 until his death in 1965.[5][6] In 1950, UNICEF's mandate was extended
	to address the long-term needs of children and women in developing countries everywhere.
	In 1953 it became a permanent part of the United Nations System, and the words
	"international" and "emergency" were dropped from the organization's name, making
	it simply the United Nations Children's Fund, retaining the original acronym,
	"UNICEF".[3]
	- Marcus Jordan Marcus James Jordan (born December 24, 1990) is an American former
	college basketball player who played for the UCF Knights men's basketball team
	of Conference USA.[1] He is the son of retired Hall of Fame basketball player
	Michael Jordan.
	- source_sentence: what part of the cow is the rib roast
	sentences:
	- Standing rib roast A standing rib roast, also known as prime rib, is a cut of
	beef from the primal rib, one of the nine primal cuts of beef. While the entire
	rib section comprises ribs six through 12, a standing rib roast may contain anywhere
	from two to seven ribs.
	- Blaine Anderson Kurt begins to mend their relationship in "Thanksgiving", just
	before New Directions loses at Sectionals to the Warblers, and they spend Christmas
	together in New York City.[29][30] Though he and Kurt continue to be on good terms,
	Blaine finds himself developing a crush on his best friend, Sam, which he knows
	will come to nothing as he knows Sam is not gay; the two of them team up to find
	evidence that the Warblers cheated at Sectionals, which means New Directions will
	be competing at Regionals. He ends up going to the Sadie Hawkins dance with Tina
	Cohen-Chang (Jenna Ushkowitz), who has developed a crush on him, but as friends
	only.[31] When Kurt comes to Lima for the wedding of glee club director Will (Matthew
	Morrison) and Emma (Jayma Mays)—which Emma flees—he and Blaine make out beforehand,
	and sleep together afterward, though they do not resume a permanent relationship.[32]
	- 'Soviet Union The Soviet Union (Russian: Сове́тский Сою́з, tr. Sovétsky Soyúz,
	IPA: [sɐˈvʲɛt͡skʲɪj sɐˈjus] ( listen)), officially the Union of Soviet Socialist
	Republics (Russian: Сою́з Сове́тских Социалисти́ческих Респу́блик, tr. Soyúz Sovétskikh
	Sotsialistícheskikh Respúblik, IPA: [sɐˈjus sɐˈvʲɛtskʲɪx sətsɨəlʲɪsˈtʲitɕɪskʲɪx
	rʲɪˈspublʲɪk] ( listen)), abbreviated as the USSR (Russian: СССР, tr. SSSR), was
	a socialist state in Eurasia that existed from 1922 to 1991. Nominally a union
	of multiple national Soviet republics,[a] its government and economy were highly
	centralized. The country was a one-party state, governed by the Communist Party
	with Moscow as its capital in its largest republic, the Russian Soviet Federative
	Socialist Republic. The Russian nation had constitutionally equal status among
	the many nations of the union but exerted de facto dominance in various respects.[7]
	Other major urban centres were Leningrad, Kiev, Minsk, Alma-Ata and Novosibirsk.
	The Soviet Union was one of the five recognized nuclear weapons states and possessed
	the largest stockpile of weapons of mass destruction.[8] It was a founding permanent
	member of the United Nations Security Council, as well as a member of the Organization
	for Security and Co-operation in Europe (OSCE) and the leading member of the Council
	for Mutual Economic Assistance (CMEA) and the Warsaw Pact.'
	- source_sentence: what is the current big bang theory season
	sentences:
	- Byzantine army From the seventh to the 12th centuries, the Byzantine army was
	among the most powerful and effective military forces in the world â€“ neither
	Middle Ages Europe nor (following its early successes) the fracturing Caliphate
	could match the strategies and the efficiency of the Byzantine army. Restricted
	to a largely defensive role in the 7th to mid-9th centuries, the Byzantines developed
	the theme-system to counter the more powerful Caliphate. From the mid-9th century,
	however, they gradually went on the offensive, culminating in the great conquests
	of the 10th century under a series of soldier-emperors such as Nikephoros II Phokas,
	John Tzimiskes and Basil II. The army they led was less reliant on the militia
	of the themes; it was by now a largely professional force, with a strong and well-drilled
	infantry at its core and augmented by a revived heavy cavalry arm. With one of
	the most powerful economies in the world at the time, the Empire had the resources
	to put to the field a powerful host when needed, in order to reclaim its long-lost
	territories.
	- The Big Bang Theory The Big Bang Theory is an American television sitcom created
	by Chuck Lorre and Bill Prady, both of whom serve as executive producers on the
	series, along with Steven Molaro. All three also serve as head writers. The show
	premiered on CBS on September 24, 2007.[3] The series' tenth season premiered
	on September 19, 2016.[4] In March 2017, the series was renewed for two additional
	seasons, bringing its total to twelve, and running through the 2018â€“19 television
	season. The eleventh season is set to premiere on September 25, 2017.[5]
	- 2016 NCAA Division I Softball Tournament The 2016 NCAA Division I Softball Tournament
	was held from May 20 through June 8, 2016 as the final part of the 2016 NCAA Division
	I softball season. The 64 NCAA Division I college softball teams were to be selected
	out of an eligible 293 teams on May 15, 2016. Thirty-two teams were awarded an
	automatic bid as champions of their conference, and thirty-two teams were selected
	at-large by the NCAA Division I softball selection committee. The tournament culminated
	with eight teams playing in the 2016 Women's College World Series at ASA Hall
	of Fame Stadium in Oklahoma City in which the Oklahoma Sooners were crowned the
	champions.
	- source_sentence: what happened to tates mom on days of our lives
	sentences:
	- 'Paige O''Hara Donna Paige Helmintoller, better known as Paige O''Hara (born May
	10, 1956),[1] is an American actress, voice actress, singer and painter. O''Hara
	began her career as a Broadway actress in 1983 when she portrayed Ellie May Chipley
	in the musical Showboat. In 1991, she made her motion picture debut in Disney''s
	Beauty and the Beast, in which she voiced the film''s heroine, Belle. Following
	the critical and commercial success of Beauty and the Beast, O''Hara reprised
	her role as Belle in the film''s two direct-to-video follow-ups, Beauty and the
	Beast: The Enchanted Christmas and Belle''s Magical World.'
	- M. Shadows Matthew Charles Sanders (born July 31, 1981), better known as M. Shadows,
	is an American singer, songwriter, and musician. He is best known as the lead
	vocalist, songwriter, and a founding member of the American heavy metal band Avenged
	Sevenfold. In 2017, he was voted 3rd in the list of Top 25 Greatest Modern Frontmen
	by Ultimate Guitar.[1]
	- Theresa Donovan In July 2013, Jeannie returns to Salem, this time going by her
	middle name, Theresa. Initially, she strikes up a connection with resident bad
	boy JJ Deveraux (Casey Moss) while trying to secure some pot.[28] During a confrontation
	with JJ and his mother Jennifer Horton (Melissa Reeves) in her office, her aunt
	Kayla confirms that Theresa is in fact Jeannie and that Jen promised to hire her
	as her assistant, a promise she reluctantly agrees to. Kayla reminds Theresa it
	is her last chance at a fresh start.[29] Theresa also strikes up a bad first impression
	with Jennifer's daughter Abigail Deveraux (Kate Mansi) when Abigail smells pot
	on Theresa in her mother's office.[30] To continue to battle against Jennifer,
	she teams up with Anne Milbauer (Meredith Scott Lynn) in hopes of exacting her
	perfect revenge. In a ploy, Theresa reveals her intentions to hopefully woo Dr.
	Daniel Jonas (Shawn Christian). After sleeping with JJ, Theresa overdoses on marijuana
	and GHB. Upon hearing of their daughter's overdose and continuing problems, Shane
	and Kimberly return to town in the hopes of handling their daughter's problem,
	together. After believing that Theresa has a handle on her addictions, Shane and
	Kimberly leave town together. Theresa then teams up with hospital co-worker Anne
	Milbauer (Meredith Scott Lynn) to conspire against Jennifer, using Daniel as a
	way to hurt their relationship. In early 2014, following a Narcotics Anonymous
	(NA) meeting, she begins a sexual and drugged-fused relationship with Brady Black
	(Eric Martsolf). In 2015, after it is found that Kristen DiMera (Eileen Davidson)
	stole Theresa's embryo and carried it to term, Brady and Melanie Jonas return
	her son, Christopher, to her and Brady, and the pair rename him Tate. When Theresa
	moves into the Kiriakis mansion, tensions arise between her and Victor. She eventually
	expresses her interest in purchasing Basic Black and running it as her own fashion
	company, with financial backing from Maggie Horton (Suzanne Rogers). In the hopes
	of finding the right partner, she teams up with Kate Roberts (Lauren Koslow) and
	Nicole Walker (Arianne Zucker) to achieve the goal of purchasing Basic Black,
	with Kate and Nicole's business background and her own interest in fashion design.
	As she and Brady share several instances of rekindling their romance, she is kicked
	out of the mansion by Victor; as a result, Brady quits Titan and moves in with
	Theresa and Tate, in their own penthouse.
	- source_sentence: where does the last name francisco come from
	sentences:
	- Francisco Francisco is the Spanish and Portuguese form of the masculine given
	name Franciscus (corresponding to English Francis).
	- 'Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah),
	is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the
	Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls
	(Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia,
	born as Hadassah but known as Esther, who becomes queen of Persia and thwarts
	a genocide of her people. The story forms the core of the Jewish festival of Purim,
	during which it is read aloud twice: once in the evening and again the following
	morning. The books of Esther and Song of Songs are the only books in the Hebrew
	Bible that do not explicitly mention God.[2]'
	- Times Square Times Square is a major commercial intersection, tourist destination,
	entertainment center and neighborhood in the Midtown Manhattan section of New
	York City at the junction of Broadway and Seventh Avenue. It stretches from West
	42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements,
	Times Square is sometimes referred to as "The Crossroads of the World",[2] "The
	Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the
	"heart of the world".[7] One of the world's busiest pedestrian areas,[8] it is
	also the hub of the Broadway Theater District[9] and a major center of the world's
	entertainment industry.[10] Times Square is one of the world's most visited tourist
	attractions, drawing an estimated 50 million visitors annually.[11] Approximately
	330,000 people pass through Times Square daily,[12] many of them tourists,[13]
	while over 460,000 pedestrians walk through Times Square on its busiest days.[7]
	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: 104.55706247798686
	energy_consumed: 0.2689902868469595
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 0.782
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: BERT base trained on Natural Questions pairs
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoClimateFEVER
	type: NanoClimateFEVER
	metrics:
	- type: cosine_accuracy@1
	value: 0.22
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.44
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.52
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.62
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.22
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.18
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.136
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08199999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.09833333333333333
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.23566666666666666
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.27899999999999997
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.3256666666666666
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.272752213638476
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.35327777777777775
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.22112124218381526
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoDBPedia
	type: NanoDBPedia
	metrics:
	- type: cosine_accuracy@1
	value: 0.64
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.74
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.84
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.64
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.46
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.44000000000000006
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.39000000000000007
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.06726969384535866
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.10829208895108451
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.16535944626004745
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.2529406741716369
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4767334977280841
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7150238095238095
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.35379332004025005
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoFEVER
	type: NanoFEVER
	metrics:
	- type: cosine_accuracy@1
	value: 0.52
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.72
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.74
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.8
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.52
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.24
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.15200000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08399999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.51
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.69
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.72
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.79
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6577792985828184
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6226904761904761
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.6145173719999807
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoFiQA2018
	type: NanoFiQA2018
	metrics:
	- type: cosine_accuracy@1
	value: 0.2
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.3
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.36
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.42
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.2
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.11999999999999998
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.1
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.064
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.08807936507936509
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.15074603174603174
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.20407936507936508
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.2524126984126984
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.20563274962284733
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.26849999999999996
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.17422019443090375
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoHotpotQA
	type: NanoHotpotQA
	metrics:
	- type: cosine_accuracy@1
	value: 0.58
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.68
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.7
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.8
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.58
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.3
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.19199999999999995
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.11399999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.29
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.45
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.48
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.57
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5242406054056967
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.643968253968254
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.4580837397163866
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoMSMARCO
	type: NanoMSMARCO
	metrics:
	- type: cosine_accuracy@1
	value: 0.22
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.44
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.5
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.68
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.22
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.14666666666666664
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.10000000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.068
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.22
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.44
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.5
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.68
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.43963932117536886
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.3651269841269841
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.3787220730332196
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNFCorpus
	type: NanoNFCorpus
	metrics:
	- type: cosine_accuracy@1
	value: 0.3
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.44
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.48
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.58
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.3
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2733333333333333
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.21600000000000003
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.17800000000000002
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.021273572702979568
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.05401978082802253
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.06417737946426297
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.09731580553141
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.22424563391154495
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.3820476190476191
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.0898107385158962
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNQ
	type: NanoNQ
	metrics:
	- type: cosine_accuracy@1
	value: 0.42
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.58
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.66
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.72
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.42
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.19333333333333333
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.136
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.07400000000000001
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.41
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.56
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.65
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.69
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.558255406320814
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5235
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.5201771704107404
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoQuoraRetrieval
	type: NanoQuoraRetrieval
	metrics:
	- type: cosine_accuracy@1
	value: 0.8
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.94
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.96
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.98
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.8
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.3666666666666666
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.23999999999999996
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.13399999999999998
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.7040000000000001
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8786666666666667
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.9193333333333333
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.97
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.8832492246317404
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8669999999999999
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8484825708061002
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoSCIDOCS
	type: NanoSCIDOCS
	metrics:
	- type: cosine_accuracy@1
	value: 0.32
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.44
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.52
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.6
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.32
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.22
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.192
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.136
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.06766666666666668
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.1366666666666667
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.19766666666666666
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.2776666666666667
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.2659892317280556
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.40594444444444444
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.2092360599636062
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoArguAna
	type: NanoArguAna
	metrics:
	- type: cosine_accuracy@1
	value: 0.2
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.6
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.72
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.8
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.2
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.14400000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.2
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.6
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.72
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.8
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5055380364663926
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.40949999999999986
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.4212792624042624
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoSciFact
	type: NanoSciFact
	metrics:
	- type: cosine_accuracy@1
	value: 0.34
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.52
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.58
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.66
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.34
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.18
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.132
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.07600000000000001
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.305
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.485
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.565
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.65
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.48695696351260737
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.44471428571428573
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.4344481461926843
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoTouche2020
	type: NanoTouche2020
	metrics:
	- type: cosine_accuracy@1
	value: 0.4897959183673469
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.7755102040816326
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.9387755102040817
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9795918367346939
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.4897959183673469
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.44897959183673464
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.4448979591836735
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.37755102040816335
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.038721005631019066
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.10614028485651965
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.16536659269196569
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.2595083655687104
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.418741764200365
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6490038872691933
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.32735138614786596
	name: Cosine Map@100
	- task:
	type: nano-beir
	name: Nano BEIR
	dataset:
	name: NanoBEIR mean
	type: NanoBEIR_mean
	metrics:
	- type: cosine_accuracy@1
	value: 0.4038304552590267
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.5858084772370487
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.6552904238618524
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.7338147566718993
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.4038304552590267
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2560753532182104
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.20191522762951336
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.14288854003139717
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.23233412594297867
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.37655370664474297
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.43307559873043383
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.5088854520782915
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4553656882249855
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5115613490817572
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.38855717506505477
	name: Cosine Map@100
	---

	# BERT base trained on Natural Questions pairs

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

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

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
	- Language: en
	- License: apache-2.0

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
	- Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

	### Full Model Architecture

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

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

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

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

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Information Retrieval

	* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \|
	\|:--------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:-----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\|
	\| cosine_accuracy@1 \| 0.22 \| 0.64 \| 0.52 \| 0.2 \| 0.58 \| 0.22 \| 0.3 \| 0.42 \| 0.8 \| 0.32 \| 0.2 \| 0.34 \| 0.4898 \|
	\| cosine_accuracy@3 \| 0.44 \| 0.74 \| 0.72 \| 0.3 \| 0.68 \| 0.44 \| 0.44 \| 0.58 \| 0.94 \| 0.44 \| 0.6 \| 0.52 \| 0.7755 \|
	\| cosine_accuracy@5 \| 0.52 \| 0.84 \| 0.74 \| 0.36 \| 0.7 \| 0.5 \| 0.48 \| 0.66 \| 0.96 \| 0.52 \| 0.72 \| 0.58 \| 0.9388 \|
	\| cosine_accuracy@10 \| 0.62 \| 0.9 \| 0.8 \| 0.42 \| 0.8 \| 0.68 \| 0.58 \| 0.72 \| 0.98 \| 0.6 \| 0.8 \| 0.66 \| 0.9796 \|
	\| cosine_precision@1 \| 0.22 \| 0.64 \| 0.52 \| 0.2 \| 0.58 \| 0.22 \| 0.3 \| 0.42 \| 0.8 \| 0.32 \| 0.2 \| 0.34 \| 0.4898 \|
	\| cosine_precision@3 \| 0.18 \| 0.46 \| 0.24 \| 0.12 \| 0.3 \| 0.1467 \| 0.2733 \| 0.1933 \| 0.3667 \| 0.22 \| 0.2 \| 0.18 \| 0.449 \|
	\| cosine_precision@5 \| 0.136 \| 0.44 \| 0.152 \| 0.1 \| 0.192 \| 0.1 \| 0.216 \| 0.136 \| 0.24 \| 0.192 \| 0.144 \| 0.132 \| 0.4449 \|
	\| cosine_precision@10 \| 0.082 \| 0.39 \| 0.084 \| 0.064 \| 0.114 \| 0.068 \| 0.178 \| 0.074 \| 0.134 \| 0.136 \| 0.08 \| 0.076 \| 0.3776 \|
	\| cosine_recall@1 \| 0.0983 \| 0.0673 \| 0.51 \| 0.0881 \| 0.29 \| 0.22 \| 0.0213 \| 0.41 \| 0.704 \| 0.0677 \| 0.2 \| 0.305 \| 0.0387 \|
	\| cosine_recall@3 \| 0.2357 \| 0.1083 \| 0.69 \| 0.1507 \| 0.45 \| 0.44 \| 0.054 \| 0.56 \| 0.8787 \| 0.1367 \| 0.6 \| 0.485 \| 0.1061 \|
	\| cosine_recall@5 \| 0.279 \| 0.1654 \| 0.72 \| 0.2041 \| 0.48 \| 0.5 \| 0.0642 \| 0.65 \| 0.9193 \| 0.1977 \| 0.72 \| 0.565 \| 0.1654 \|
	\| cosine_recall@10 \| 0.3257 \| 0.2529 \| 0.79 \| 0.2524 \| 0.57 \| 0.68 \| 0.0973 \| 0.69 \| 0.97 \| 0.2777 \| 0.8 \| 0.65 \| 0.2595 \|
	\| cosine_ndcg@10 \| 0.2728 \| 0.4767 \| 0.6578 \| 0.2056 \| 0.5242 \| 0.4396 \| 0.2242 \| 0.5583 \| 0.8832 \| 0.266 \| 0.5055 \| 0.487 \| 0.4187 \|
	\| cosine_mrr@10 \| 0.3533 \| 0.715 \| 0.6227 \| 0.2685 \| 0.644 \| 0.3651 \| 0.382 \| 0.5235 \| 0.867 \| 0.4059 \| 0.4095 \| 0.4447 \| 0.649 \|
	\| cosine_map@100 \| 0.2211 \| 0.3538 \| 0.6145 \| 0.1742 \| 0.4581 \| 0.3787 \| 0.0898 \| 0.5202 \| 0.8485 \| 0.2092 \| 0.4213 \| 0.4344 \| 0.3274 \|

	#### Nano BEIR

	* Dataset: `NanoBEIR_mean`
	* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.4038 \|
	\| cosine_accuracy@3 \| 0.5858 \|
	\| cosine_accuracy@5 \| 0.6553 \|
	\| cosine_accuracy@10 \| 0.7338 \|
	\| cosine_precision@1 \| 0.4038 \|
	\| cosine_precision@3 \| 0.2561 \|
	\| cosine_precision@5 \| 0.2019 \|
	\| cosine_precision@10 \| 0.1429 \|
	\| cosine_recall@1 \| 0.2323 \|
	\| cosine_recall@3 \| 0.3766 \|
	\| cosine_recall@5 \| 0.4331 \|
	\| cosine_recall@10 \| 0.5089 \|
	\| cosine_ndcg@10 \| 0.4554 \|
	\| cosine_mrr@10 \| 0.5116 \|
	\| cosine_map@100 \| 0.3886 \|

	<!--
	## Bias, Risks and Limitations

	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: 100,231 training samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 11.74 tokens</li><li>max: 24 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 137.2 tokens</li><li>max: 508 tokens</li></ul> \|
	* Samples:
	\| query \| answer \|
	\|:------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>who is required to report according to the hmda</code> \| <code>Home Mortgage Disclosure Act US financial institutions must report HMDA data to their regulator if they meet certain criteria, such as having assets above a specific threshold. The criteria is different for depository and non-depository institutions and are available on the FFIEC website.[4] In 2012, there were 7,400 institutions that reported a total of 18.7 million HMDA records.[5]</code> \|
	\| <code>what is the definition of endoplasmic reticulum in biology</code> \| <code>Endoplasmic reticulum The endoplasmic reticulum (ER) is a type of organelle in eukaryotic cells that forms an interconnected network of flattened, membrane-enclosed sacs or tube-like structures known as cisternae. The membranes of the ER are continuous with the outer nuclear membrane. The endoplasmic reticulum occurs in most types of eukaryotic cells, but is absent from red blood cells and spermatozoa. There are two types of endoplasmic reticulum: rough and smooth. The outer (cytosolic) face of the rough endoplasmic reticulum is studded with ribosomes that are the sites of protein synthesis. The rough endoplasmic reticulum is especially prominent in cells such as hepatocytes. The smooth endoplasmic reticulum lacks ribosomes and functions in lipid manufacture and metabolism, the production of steroid hormones, and detoxification.[1] The smooth ER is especially abundant in mammalian liver and gonad cells. The lacy membranes of the endoplasmic reticulum were first seen in 1945 using elect...</code> \|
	\| <code>what does the ski mean in polish names</code> \| <code>Polish name Since the High Middle Ages, Polish-sounding surnames ending with the masculine -ski suffix, including -cki and -dzki, and the corresponding feminine suffix -ska/-cka/-dzka were associated with the nobility (Polish szlachta), which alone, in the early years, had such suffix distinctions.[1] They are widely popular today.</code> \|
	* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### natural-questions

	* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
	* Size: 100,231 evaluation samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 11.78 tokens</li><li>max: 22 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 135.64 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| query \| answer \|
	\|:------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>difference between russian blue and british blue cat</code> \| <code>Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.</code> \|
	\| <code>who played the little girl on mrs doubtfire</code> \| <code>Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.</code> \|
	\| <code>what year did the movie the sound of music come out</code> \| <code>The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000.</code> \|
	* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 256
	- `per_device_eval_batch_size`: 256
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `seed`: 12
	- `bf16`: True
	- `batch_sampler`: no_duplicates

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 256
	- `per_device_eval_batch_size`: 256
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 2e-05
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 1
	- `max_steps`: -1
	- `lr_scheduler_type`: linear
	- `lr_scheduler_kwargs`: {}
	- `warmup_ratio`: 0.1
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: True
	- `save_on_each_node`: False
	- `save_only_model`: False
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 12
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: True
	- `fp16`: False
	- `fp16_opt_level`: O1
	- `half_precision_backend`: auto
	- `bf16_full_eval`: False
	- `fp16_full_eval`: False
	- `tf32`: None
	- `local_rank`: 0
	- `ddp_backend`: None
	- `tpu_num_cores`: None
	- `tpu_metrics_debug`: False
	- `debug`: []
	- `dataloader_drop_last`: False
	- `dataloader_num_workers`: 0
	- `dataloader_prefetch_factor`: None
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: True
	- `label_names`: None
	- `load_best_model_at_end`: False
	- `ignore_data_skip`: False
	- `fsdp`: []
	- `fsdp_min_num_params`: 0
	- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
	- `fsdp_transformer_layer_cls_to_wrap`: None
	- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `ddp_find_unused_parameters`: None
	- `ddp_bucket_cap_mb`: None
	- `ddp_broadcast_buffers`: False
	- `dataloader_pin_memory`: True
	- `dataloader_persistent_workers`: False
	- `skip_memory_metrics`: True
	- `use_legacy_prediction_loop`: False
	- `push_to_hub`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `include_for_metrics`: []
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `eval_on_start`: False
	- `use_liger_kernel`: False
	- `eval_use_gather_object`: False
	- `average_tokens_across_devices`: False
	- `prompts`: None
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| Validation Loss \| NanoClimateFEVER_cosine_ndcg@10 \| NanoDBPedia_cosine_ndcg@10 \| NanoFEVER_cosine_ndcg@10 \| NanoFiQA2018_cosine_ndcg@10 \| NanoHotpotQA_cosine_ndcg@10 \| NanoMSMARCO_cosine_ndcg@10 \| NanoNFCorpus_cosine_ndcg@10 \| NanoNQ_cosine_ndcg@10 \| NanoQuoraRetrieval_cosine_ndcg@10 \| NanoSCIDOCS_cosine_ndcg@10 \| NanoArguAna_cosine_ndcg@10 \| NanoSciFact_cosine_ndcg@10 \| NanoTouche2020_cosine_ndcg@10 \| NanoBEIR_mean_cosine_ndcg@10 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:-------------------------------:\|:--------------------------:\|:------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:---------------------:\|:---------------------------------:\|:--------------------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|:----------------------------:\|
	\| 0 \| 0 \| - \| - \| 0.1046 \| 0.2182 \| 0.1573 \| 0.0575 \| 0.2597 \| 0.1602 \| 0.0521 \| 0.0493 \| 0.7310 \| 0.1320 \| 0.2309 \| 0.1240 \| 0.0970 \| 0.1826 \|
	\| 0.0026 \| 1 \| 2.9906 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0129 \| 5 \| 3.0069 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0258 \| 10 \| 2.715 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0387 \| 15 \| 2.1234 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0515 \| 20 \| 1.5407 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0644 \| 25 \| 1.2437 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0773 \| 30 \| 0.9972 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0902 \| 35 \| 0.7725 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1031 \| 40 \| 0.6277 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1160 \| 45 \| 0.5025 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1289 \| 50 \| 0.3382 \| 0.3345 \| 0.2316 \| 0.4851 \| 0.6465 \| 0.2296 \| 0.4966 \| 0.4263 \| 0.2129 \| 0.4993 \| 0.8656 \| 0.2590 \| 0.4917 \| 0.4712 \| 0.4293 \| 0.4419 \|
	\| 0.1418 \| 55 \| 0.3324 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1546 \| 60 \| 0.2682 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1675 \| 65 \| 0.2473 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1804 \| 70 \| 0.1919 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1933 \| 75 \| 0.2543 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2062 \| 80 \| 0.2179 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2191 \| 85 \| 0.2067 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2320 \| 90 \| 0.2065 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2448 \| 95 \| 0.2104 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2577 \| 100 \| 0.2068 \| 0.1892 \| 0.2690 \| 0.4640 \| 0.6510 \| 0.2334 \| 0.4844 \| 0.4357 \| 0.2162 \| 0.5012 \| 0.8806 \| 0.2673 \| 0.5057 \| 0.4322 \| 0.4265 \| 0.4436 \|
	\| 0.2706 \| 105 \| 0.1864 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2835 \| 110 \| 0.1897 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2964 \| 115 \| 0.1828 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3093 \| 120 \| 0.1715 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3222 \| 125 \| 0.1786 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3351 \| 130 \| 0.1505 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3479 \| 135 \| 0.1672 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3608 \| 140 \| 0.1742 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3737 \| 145 \| 0.1582 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3866 \| 150 \| 0.1289 \| 0.1575 \| 0.2760 \| 0.4735 \| 0.6475 \| 0.2177 \| 0.5005 \| 0.4452 \| 0.2185 \| 0.5302 \| 0.8873 \| 0.2687 \| 0.4949 \| 0.4475 \| 0.4220 \| 0.4484 \|
	\| 0.3995 \| 155 \| 0.1566 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4124 \| 160 \| 0.1502 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4253 \| 165 \| 0.143 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4381 \| 170 \| 0.1592 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4510 \| 175 \| 0.1398 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4639 \| 180 \| 0.1173 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4768 \| 185 \| 0.123 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4897 \| 190 \| 0.1752 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5026 \| 195 \| 0.1433 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5155 \| 200 \| 0.1719 \| 0.1433 \| 0.2796 \| 0.4769 \| 0.6449 \| 0.2145 \| 0.5111 \| 0.4484 \| 0.2239 \| 0.5568 \| 0.8901 \| 0.2709 \| 0.4841 \| 0.4647 \| 0.4184 \| 0.4526 \|
	\| 0.5284 \| 205 \| 0.1596 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5412 \| 210 \| 0.145 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5541 \| 215 \| 0.1654 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5670 \| 220 \| 0.1477 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5799 \| 225 \| 0.1559 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5928 \| 230 \| 0.147 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6057 \| 235 \| 0.1645 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6186 \| 240 \| 0.1504 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6314 \| 245 \| 0.1648 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6443 \| 250 \| 0.1399 \| 0.1345 \| 0.2707 \| 0.4727 \| 0.6523 \| 0.2101 \| 0.5168 \| 0.4432 \| 0.2236 \| 0.5542 \| 0.8904 \| 0.2637 \| 0.5034 \| 0.4737 \| 0.4207 \| 0.4535 \|
	\| 0.6572 \| 255 \| 0.1562 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6701 \| 260 \| 0.1532 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6830 \| 265 \| 0.1367 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6959 \| 270 \| 0.1166 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7088 \| 275 \| 0.1307 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7216 \| 280 \| 0.1365 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7345 \| 285 \| 0.1279 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7474 \| 290 \| 0.1338 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7603 \| 295 \| 0.1448 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7732 \| 300 \| 0.1313 \| 0.1278 \| 0.2737 \| 0.4750 \| 0.6552 \| 0.2246 \| 0.5280 \| 0.4390 \| 0.2247 \| 0.5609 \| 0.8832 \| 0.2661 \| 0.5092 \| 0.4865 \| 0.4224 \| 0.4576 \|
	\| 0.7861 \| 305 \| 0.1376 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7990 \| 310 \| 0.106 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8119 \| 315 \| 0.1331 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8247 \| 320 \| 0.1376 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8376 \| 325 \| 0.1349 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8505 \| 330 \| 0.1392 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8634 \| 335 \| 0.1394 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8763 \| 340 \| 0.1276 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8892 \| 345 \| 0.1228 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9021 \| 350 \| 0.112 \| 0.1244 \| 0.2807 \| 0.4747 \| 0.6564 \| 0.2063 \| 0.5249 \| 0.4373 \| 0.2238 \| 0.5576 \| 0.8832 \| 0.2641 \| 0.5038 \| 0.5028 \| 0.4181 \| 0.4564 \|
	\| 0.9149 \| 355 \| 0.1258 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9278 \| 360 \| 0.1304 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9407 \| 365 \| 0.1372 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9536 \| 370 \| 0.145 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9665 \| 375 \| 0.1423 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9794 \| 380 \| 0.1463 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9923 \| 385 \| 0.1101 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.0 \| 388 \| - \| - \| 0.2728 \| 0.4767 \| 0.6578 \| 0.2056 \| 0.5242 \| 0.4396 \| 0.2242 \| 0.5583 \| 0.8832 \| 0.2660 \| 0.5055 \| 0.4870 \| 0.4187 \| 0.4554 \|


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

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

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

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### CachedMultipleNegativesRankingLoss
	```bibtex
	@misc{gao2021scaling,
	title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
	author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
	year={2021},
	eprint={2101.06983},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

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