Add new SentenceTransformer model

6d5b5b1 verified 13 days ago

104 kB

	---
	language:
	- en
	license: apache-2.0
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:99231
	- loss:CachedMultipleNegativesRankingLoss
	base_model: microsoft/mpnet-base
	widget:
	- source_sentence: who led the army that defeated the aztecs
	sentences:
	- Spanish conquest of the Aztec Empire The Spanish conquest of the Aztec Empire,
	or the Spanish-Aztec War (1519-21)[3] was one of the most significant and complex
	events in world history. There are multiple sixteenth-century narratives of the
	events by Spanish conquerors, their indigenous allies, and the defeated Aztecs.
	It was not solely a contest between a small contingent of Spaniards defeating
	the Aztec Empire, but rather the creation of a coalition of Spanish invaders with
	tributaries to the Aztecs, and most especially the Aztecs' indigenous enemies
	and rivals. They combined forces to defeat the Mexica of Tenochtitlan over a two-year
	period. For the Spanish, the expedition to Mexico was part of a project of Spanish
	colonization of the New World after twenty-five years of permanent Spanish settlement
	and further exploration in the Caribbean. The Spanish made landfall in Mexico
	in 1517. A Spanish settler in Cuba, Hernán Cortés, led an expedition (entrada)
	to Mexico, landing in February 1519, following an earlier expedition led by Juan
	de Grijalva to Yucatán in 1517. Two years later Cortés and his retinue set sail,
	thus beginning the expedition of exploration and conquest.[4] The Spanish campaign
	against the Aztec Empire had its final victory on August 13, 1521, when a coalition
	army of Spanish forces and native Tlaxcalan warriors led by Cortés and Xicotencatl
	the Younger captured the emperor Cuauhtemoc and Tenochtitlan, the capital of the
	Aztec Empire. The fall of Tenochtitlan marks the beginning of Spanish rule in
	central Mexico, and they established their capital of Mexico City on the ruins
	of Tenochtitlan.
	- The Girl with All the Gifts Justineau awakens in the Rosalind Franklin. Melanie
	leads her to a group of intelligent hungries, to whom Justineau, wearing an environmental
	protection suit, starts teaching the alphabet.
	- 'Wendy Makkena In 1992 she had a supporting role in the movie Sister Act as the
	shy but talented singing nun Sister Mary Robert, a role she reprised in Sister
	Act 2: Back in the Habit the following year. She appeared in various other television
	roles until 1997, when she starred in Air Bud, followed by the independent film
	Finding North. She continued appearing on television shows such as The Job, Oliver
	Beene, and Listen Up![citation needed]'
	- source_sentence: who went to the most nba finals in a row
	sentences:
	- List of NBA franchise post-season streaks The San Antonio Spurs hold the longest
	active consecutive playoff appearances with 21 appearances, starting in the 1998
	NBA Playoffs (also the longest active playoff streak in any major North American
	sports league as of 2017). The Spurs have won five NBA championships during the
	streak. The Philadelphia 76ers (formerly known as Syracuse Nationals) hold the
	all-time record for consecutive playoff appearances with 22 straight appearances
	between 1950 and 1971. The 76ers won two NBA championships during their streak.
	The Boston Celtics hold the longest consecutive NBA Finals appearance streak with
	ten appearances between 1957 and 1966. During the streak, the Celtics won eight
	consecutive NBA championshipsâ€”also an NBA record.
	- Dear Dumb Diary Dear Dumb Diary is a series of children's novels by Jim Benton.
	Each book is written in the first person view of a middle school girl named Jamie
	Kelly. The series is published by Scholastic in English and Random House in Korean.
	Film rights to the series have been optioned by the Gotham Group.[2]
	- Voting rights in the United States Eligibility to vote in the United States is
	established both through the federal constitution and by state law. Several constitutional
	amendments (the 15th, 19th, and 26th specifically) require that voting rights
	cannot be abridged on account of race, color, previous condition of servitude,
	sex, or age for those above 18; the constitution as originally written did not
	establish any such rights during 1787â€“1870. In the absence of a specific federal
	law or constitutional provision, each state is given considerable discretion to
	establish qualifications for suffrage and candidacy within its own respective
	jurisdiction; in addition, states and lower level jurisdictions establish election
	systems, such as at-large or single member district elections for county councils
	or school boards.
	- source_sentence: who did the vocals on mcdonald's jingle i'm loving it
	sentences:
	- I'm Lovin' It (song) "I'm Lovin' It" is a song recorded by American singer-songwriter
	Justin Timberlake. It was written by Pusha T and produced by The Neptunes.
	- Vallabhbhai Patel As the first Home Minister and Deputy Prime Minister of India,
	Patel organised relief efforts for refugees fleeing from Punjab and Delhi and
	worked to restore peace across the nation. He led the task of forging a united
	India, successfully integrating into the newly independent nation those British
	colonial provinces that had been "allocated" to India. Besides those provinces
	that had been under direct British rule, approximately 565 self-governing princely
	states had been released from British suzerainty by the Indian Independence Act
	of 1947. Employing frank diplomacy with the expressed option to deploy military
	force, Patel persuaded almost every princely state to accede to India. His commitment
	to national integration in the newly independent country was total and uncompromising,
	earning him the sobriquet "Iron Man of India".[3] He is also affectionately remembered
	as the "Patron saint of India's civil servants" for having established the modern
	all-India services system. He is also called the Unifier of India.[4]
	- National debt of the United States As of July 31, 2018, debt held by the public
	was $15.6 trillion and intragovernmental holdings were $5.7 trillion, for a total
	or "National Debt" of $21.3 trillion.[5] Debt held by the public was approximately
	77% of GDP in 2017, ranked 43rd highest out of 207 countries.[6] The Congressional
	Budget Office forecast in April 2018 that the ratio will rise to nearly 100% by
	2028, perhaps higher if current policies are extended beyond their scheduled expiration
	date.[7] As of December 2017, $6.3 trillion or approximately 45% of the debt held
	by the public was owned by foreign investors, the largest being China (about $1.18
	trillion) then Japan (about $1.06 trillion).[8]
	- source_sentence: who is the actress of harley quinn in suicide squad
	sentences:
	- Tariffs in United States history Tariffs were the main source of revenue for the
	federal government from 1789 to 1914. During this period, there was vigorous debate
	between the various political parties over the setting of tariff rates. In general
	Democrats favored a tariff that would pay the cost of government, but no higher.
	Whigs and Republicans favored higher tariffs to protect and encourage American
	industry and industrial workers. Since the early 20th century, however, U.S. tariffs
	have been very low and have been much less a matter of partisan debate.
	- The Rolling Stones The Rolling Stones are an English rock band formed in London,
	England in 1962. The first stable line-up consisted of Brian Jones (guitar, harmonica),
	Mick Jagger (lead vocals), Keith Richards (guitar, backing vocals), Bill Wyman
	(bass), Charlie Watts (drums), and Ian Stewart (piano). Stewart was removed from
	the official line-up in 1963 but continued as a touring member until his death
	in 1985. Jones left the band less than a month prior to his death in 1969, having
	already been replaced by Mick Taylor, who remained until 1974. After Taylor left
	the band, Ronnie Wood took his place in 1975 and has been on guitar in tandem
	with Richards ever since. Following Wyman's departure in 1993, Darryl Jones joined
	as their touring bassist. Touring keyboardists for the band have been Nicky Hopkins
	(1967–1982), Ian McLagan (1978–1981), Billy Preston (through the mid-1970s) and
	Chuck Leavell (1982–present). The band was first led by Brian Jones, but after
	developing into the band's songwriters, Jagger and Richards assumed leadership
	while Jones dealt with legal and personal troubles.
	- Margot Robbie After moving to the United States, Robbie starred in the short-lived
	ABC drama series Pan Am (2011–2012). In 2013, she made her big screen debut in
	Richard Curtis's romantic comedy-drama film About Time and co-starred in Martin
	Scorsese's biographical black comedy The Wolf of Wall Street. In 2015, Robbie
	co-starred in the romantic comedy-drama film Focus, appeared in the romantic World
	War II drama film Suite Française and starred in the science fiction film Z for
	Zachariah. That same year, she played herself in The Big Short. In 2016, she portrayed
	Jane Porter in the action-adventure film The Legend of Tarzan and Harley Quinn
	in the superhero film Suicide Squad. She appeared on Time magazine's "The Most
	Influential People of 2017" list.[4]
	- source_sentence: what is meaning of am and pm in time
	sentences:
	- America's Got Talent America's Got Talent (often abbreviated as AGT) is a televised
	American talent show competition, broadcast on the NBC television network. It
	is part of the global Got Talent franchise created by Simon Cowell, and is produced
	by Fremantle North America and SYCOtv, with distribution done by Fremantle. Since
	its premiere in June 2006, each season is run during the network's summer schedule,
	with the show having featured various hosts - it is currently hosted by Tyra Banks,
	since 2017.[2] It is the first global edition of the franchise, after plans for
	a British edition in 2005 were suspended, following a dispute between Paul O'Grady,
	the planned host, and the British broadcaster ITV; production of this edition
	later resumed in 2007.[3]
	- 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]
	- '12-hour clock The 12-hour clock is a time convention in which the 24 hours of
	the day are divided into two periods:[1] a.m. (from the Latin, ante meridiem,
	meaning before midday) and p.m. (post meridiem, meaning past midday).[2] Each
	period consists of 12 hours numbered: 12 (acting as zero),[3] 1, 2, 3, 4, 5, 6,
	7, 8, 9, 10, and 11. The 24 hour/day cycle starts at 12 midnight (often indicated
	as 12 a.m.), runs through 12 noon (often indicated as 12 p.m.), and continues
	to the midnight at the end of the day. The 12-hour clock was developed over time
	from the mid-second millennium BC to the 16th century AD.'
	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: 100.49635551408996
	energy_consumed: 0.37551604693967594
	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: 0.956
	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: 0.34
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.4
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.52
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.68
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.34
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.15333333333333332
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.124
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08199999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.15666666666666665
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.19666666666666666
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.245
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.32899999999999996
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.2874373031622126
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.40833333333333327
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.23720812652159773
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoDBPedia
	type: NanoDBPedia
	metrics:
	- type: cosine_accuracy@1
	value: 0.5
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.86
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.5
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.49999999999999994
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.456
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.4
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.033460574803481
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.1379456043967994
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.19236008288900686
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.27718360985007373
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4815217166466425
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6581904761904761
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.34038401700749055
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoFEVER
	type: NanoFEVER
	metrics:
	- type: cosine_accuracy@1
	value: 0.56
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.68
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.78
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.84
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.56
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2333333333333333
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.16
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08599999999999998
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.55
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.66
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.75
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.8
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6774195829582105
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6484444444444444
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.6375252923987764
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoFiQA2018
	type: NanoFiQA2018
	metrics:
	- type: cosine_accuracy@1
	value: 0.32
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.48
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.54
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.62
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.32
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.19333333333333336
	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: 0.16969047619047622
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.2751031746031746
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.35526984126984124
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.43926984126984125
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.3484809857651704
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.412
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.29317086609082804
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoHotpotQA
	type: NanoHotpotQA
	metrics:
	- type: cosine_accuracy@1
	value: 0.5
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.62
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.66
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.72
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.5
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.26666666666666666
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.172
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.102
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.25
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.4
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.43
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.51
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4646363664054244
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5711666666666667
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.40372265204584074
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoMSMARCO
	type: NanoMSMARCO
	metrics:
	- type: cosine_accuracy@1
	value: 0.24
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.5
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.6
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.72
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.24
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.16666666666666663
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.12000000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.07200000000000001
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.24
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.5
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.6
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.72
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4662300989052903
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.38596825396825385
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.3966342163469757
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNFCorpus
	type: NanoNFCorpus
	metrics:
	- type: cosine_accuracy@1
	value: 0.38
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.5
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.52
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.6
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.38
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.32
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.28
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.22399999999999998
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.013385350979353738
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.04273144042314974
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.05311513788935319
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.09414123513400076
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.26378524693375555
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.44563492063492055
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.08765440666496092
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNQ
	type: NanoNQ
	metrics:
	- type: cosine_accuracy@1
	value: 0.38
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.6
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.66
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.76
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.38
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.21333333333333332
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.14
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.08199999999999999
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.36
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.59
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.64
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.74
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.56110661357524
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.507888888888889
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.5087680789990747
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoQuoraRetrieval
	type: NanoQuoraRetrieval
	metrics:
	- type: cosine_accuracy@1
	value: 0.9
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.92
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.92
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.94
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.9
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.37999999999999995
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.236
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.12799999999999997
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.7873333333333332
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8786666666666667
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8893333333333334
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.93
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9060776365512109
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.9133333333333334
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.8958434676434677
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoSCIDOCS
	type: NanoSCIDOCS
	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: 0.62
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.84
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.42
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.2866666666666666
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.23600000000000004
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.182
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.08866666666666669
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.1776666666666667
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.2426666666666667
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.3746666666666666
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.3499542026448552
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.524547619047619
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.26373167166015693
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoArguAna
	type: NanoArguAna
	metrics:
	- type: cosine_accuracy@1
	value: 0.16
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.62
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.72
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.88
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.16
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.20666666666666667
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.14400000000000002
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.088
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.16
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.62
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.72
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.88
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5149778577506615
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.39771428571428574
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.4040354269913093
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoSciFact
	type: NanoSciFact
	metrics:
	- type: cosine_accuracy@1
	value: 0.42
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.62
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.64
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.72
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.42
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.21999999999999997
	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: 0.385
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.59
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.61
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.71
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5604524461977042
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.525357142857143
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.5119986720475846
	name: Cosine Map@100
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoTouche2020
	type: NanoTouche2020
	metrics:
	- type: cosine_accuracy@1
	value: 0.5306122448979592
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8571428571428571
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8979591836734694
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9591836734693877
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.5306122448979592
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.5714285714285714
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.5346938775510204
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.41836734693877553
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.04151746748360336
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.12605393943663232
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.1939653912321698
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.2801024018430986
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4767149331432611
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6991739552964044
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.3725605659989721
	name: Cosine Map@100
	- task:
	type: nano-beir
	name: Nano BEIR
	dataset:
	name: NanoBEIR mean
	type: NanoBEIR_mean
	metrics:
	- type: cosine_accuracy@1
	value: 0.43466248037676614
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.6290109890109891
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.6875353218210362
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.7830141287284145
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.43466248037676614
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.28549450549450545
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.2229764521193093
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.15679748822605966
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.24890157970181387
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.3996026276045966
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.45551618871387467
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.5449510580587446
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.48913807620304917
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.54598102464429
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.4117874969551566
	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.

	## Model Details

	### Model Description
	- Model Type: Sentence Transformer
	- Base model: [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
	- 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, 'architecture': 'MPNetModel'})
	(1): Router(
	(sub_modules): ModuleDict(
	(query): Sequential(
	(0): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	)
	(document): Sequential(
	(0): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
	)
	)
	)
	)
	```

	## 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/cls-last-split-pooling")
	# Run inference
	queries = [
	"what is meaning of am and pm in time",
	]
	documents = [
	'12-hour clock The 12-hour clock is a time convention in which the 24 hours of the day are divided into two periods:[1] a.m. (from the Latin, ante meridiem, meaning before midday) and p.m. (post meridiem, meaning past midday).[2] Each period consists of 12 hours numbered: 12 (acting as zero),[3] 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, and 11. The 24 hour/day cycle starts at 12 midnight (often indicated as 12 a.m.), runs through 12 noon (often indicated as 12 p.m.), and continues to the midnight at the end of the day. The 12-hour clock was developed over time from the mid-second millennium BC to the 16th century AD.',
	"America's Got Talent America's Got Talent (often abbreviated as AGT) is a televised American talent show competition, broadcast on the NBC television network. It is part of the global Got Talent franchise created by Simon Cowell, and is produced by Fremantle North America and SYCOtv, with distribution done by Fremantle. Since its premiere in June 2006, each season is run during the network's summer schedule, with the show having featured various hosts - it is currently hosted by Tyra Banks, since 2017.[2] It is the first global edition of the franchise, after plans for a British edition in 2005 were suspended, following a dispute between Paul O'Grady, the planned host, and the British broadcaster ITV; production of this edition later resumed in 2007.[3]",
	'Times Square Times Square is a major commercial intersection, tourist destination, entertainment center and neighborhood in the Midtown Manhattan section of New York City at the junction of Broadway and Seventh Avenue. It stretches from West 42nd to West 47th Streets.[1] Brightly adorned with billboards and advertisements, Times Square is sometimes referred to as "The Crossroads of the World",[2] "The Center of the Universe",[3] "the heart of The Great White Way",[4][5][6] and the "heart of the world".[7] One of the world\'s busiest pedestrian areas,[8] it is also the hub of the Broadway Theater District[9] and a major center of the world\'s entertainment industry.[10] Times Square is one of the world\'s most visited tourist attractions, drawing an estimated 50 million visitors annually.[11] Approximately 330,000 people pass through Times Square daily,[12] many of them tourists,[13] while over 460,000 pedestrians walk through Times Square on its busiest days.[7]',
	]
	query_embeddings = model.encode_query(queries)
	document_embeddings = model.encode_document(documents)
	print(query_embeddings.shape, document_embeddings.shape)
	# [1, 768] [3, 768]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(query_embeddings, document_embeddings)
	print(similarities)
	# tensor([[0.5485, 0.0270, 0.1584]])
	```

	<!--
	### 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 \| 0.34 \| 0.5 \| 0.56 \| 0.32 \| 0.5 \| 0.24 \| 0.38 \| 0.38 \| 0.9 \| 0.42 \| 0.16 \| 0.42 \| 0.5306 \|
	\| cosine_accuracy@3 \| 0.4 \| 0.8 \| 0.68 \| 0.48 \| 0.62 \| 0.5 \| 0.5 \| 0.6 \| 0.92 \| 0.58 \| 0.62 \| 0.62 \| 0.8571 \|
	\| cosine_accuracy@5 \| 0.52 \| 0.86 \| 0.78 \| 0.54 \| 0.66 \| 0.6 \| 0.52 \| 0.66 \| 0.92 \| 0.62 \| 0.72 \| 0.64 \| 0.898 \|
	\| cosine_accuracy@10 \| 0.68 \| 0.9 \| 0.84 \| 0.62 \| 0.72 \| 0.72 \| 0.6 \| 0.76 \| 0.94 \| 0.84 \| 0.88 \| 0.72 \| 0.9592 \|
	\| cosine_precision@1 \| 0.34 \| 0.5 \| 0.56 \| 0.32 \| 0.5 \| 0.24 \| 0.38 \| 0.38 \| 0.9 \| 0.42 \| 0.16 \| 0.42 \| 0.5306 \|
	\| cosine_precision@3 \| 0.1533 \| 0.5 \| 0.2333 \| 0.1933 \| 0.2667 \| 0.1667 \| 0.32 \| 0.2133 \| 0.38 \| 0.2867 \| 0.2067 \| 0.22 \| 0.5714 \|
	\| cosine_precision@5 \| 0.124 \| 0.456 \| 0.16 \| 0.16 \| 0.172 \| 0.12 \| 0.28 \| 0.14 \| 0.236 \| 0.236 \| 0.144 \| 0.136 \| 0.5347 \|
	\| cosine_precision@10 \| 0.082 \| 0.4 \| 0.086 \| 0.092 \| 0.102 \| 0.072 \| 0.224 \| 0.082 \| 0.128 \| 0.182 \| 0.088 \| 0.082 \| 0.4184 \|
	\| cosine_recall@1 \| 0.1567 \| 0.0335 \| 0.55 \| 0.1697 \| 0.25 \| 0.24 \| 0.0134 \| 0.36 \| 0.7873 \| 0.0887 \| 0.16 \| 0.385 \| 0.0415 \|
	\| cosine_recall@3 \| 0.1967 \| 0.1379 \| 0.66 \| 0.2751 \| 0.4 \| 0.5 \| 0.0427 \| 0.59 \| 0.8787 \| 0.1777 \| 0.62 \| 0.59 \| 0.1261 \|
	\| cosine_recall@5 \| 0.245 \| 0.1924 \| 0.75 \| 0.3553 \| 0.43 \| 0.6 \| 0.0531 \| 0.64 \| 0.8893 \| 0.2427 \| 0.72 \| 0.61 \| 0.194 \|
	\| cosine_recall@10 \| 0.329 \| 0.2772 \| 0.8 \| 0.4393 \| 0.51 \| 0.72 \| 0.0941 \| 0.74 \| 0.93 \| 0.3747 \| 0.88 \| 0.71 \| 0.2801 \|
	\| cosine_ndcg@10 \| 0.2874 \| 0.4815 \| 0.6774 \| 0.3485 \| 0.4646 \| 0.4662 \| 0.2638 \| 0.5611 \| 0.9061 \| 0.35 \| 0.515 \| 0.5605 \| 0.4767 \|
	\| cosine_mrr@10 \| 0.4083 \| 0.6582 \| 0.6484 \| 0.412 \| 0.5712 \| 0.386 \| 0.4456 \| 0.5079 \| 0.9133 \| 0.5245 \| 0.3977 \| 0.5254 \| 0.6992 \|
	\| cosine_map@100 \| 0.2372 \| 0.3404 \| 0.6375 \| 0.2932 \| 0.4037 \| 0.3966 \| 0.0877 \| 0.5088 \| 0.8958 \| 0.2637 \| 0.404 \| 0.512 \| 0.3726 \|

	#### Nano BEIR

	* Dataset: `NanoBEIR_mean`
	* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
	```json
	{
	"dataset_names": [
	"climatefever",
	"dbpedia",
	"fever",
	"fiqa2018",
	"hotpotqa",
	"msmarco",
	"nfcorpus",
	"nq",
	"quoraretrieval",
	"scidocs",
	"arguana",
	"scifact",
	"touche2020"
	]
	}
	```

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.4347 \|
	\| cosine_accuracy@3 \| 0.629 \|
	\| cosine_accuracy@5 \| 0.6875 \|
	\| cosine_accuracy@10 \| 0.783 \|
	\| cosine_precision@1 \| 0.4347 \|
	\| cosine_precision@3 \| 0.2855 \|
	\| cosine_precision@5 \| 0.223 \|
	\| cosine_precision@10 \| 0.1568 \|
	\| cosine_recall@1 \| 0.2489 \|
	\| cosine_recall@3 \| 0.3996 \|
	\| cosine_recall@5 \| 0.4555 \|
	\| cosine_recall@10 \| 0.545 \|
	\| cosine_ndcg@10 \| 0.4891 \|
	\| cosine_mrr@10 \| 0.546 \|
	\| cosine_map@100 \| 0.4118 \|

	<!--
	## 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: 99,231 training samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 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",
	"mini_batch_size": 16,
	"gather_across_devices": false
	}
	```

	### Evaluation Dataset

	#### natural-questions

	* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
	* Size: 1,000 evaluation samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 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",
	"mini_batch_size": 16,
	"gather_across_devices": false
	}
	```

	### 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
	- `router_mapping`: {'query': 'query', 'answer': 'document'}

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

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

	</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 \|
	\|:------:\|:----:\|:-------------:\|:---------------:\|:-------------------------------:\|:--------------------------:\|:------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:---------------------:\|:---------------------------------:\|:--------------------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|:----------------------------:\|
	\| -1 \| -1 \| - \| - \| 0.0630 \| 0.1506 \| 0.1219 \| 0.0264 \| 0.1597 \| 0.0674 \| 0.0332 \| 0.0715 \| 0.3045 \| 0.0708 \| 0.1367 \| 0.1019 \| 0.1166 \| 0.1096 \|
	\| 0.0026 \| 1 \| 5.5974 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0129 \| 5 \| 5.3551 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0258 \| 10 \| 5.0092 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0387 \| 15 \| 4.4557 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0515 \| 20 \| 3.2672 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0644 \| 25 \| 1.9936 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0773 \| 30 \| 1.3587 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0902 \| 35 \| 1.0273 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1031 \| 40 \| 0.7769 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1160 \| 45 \| 0.5939 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1289 \| 50 \| 0.4739 \| 0.2552 \| 0.2749 \| 0.4683 \| 0.6305 \| 0.3643 \| 0.4449 \| 0.4521 \| 0.1968 \| 0.4122 \| 0.8490 \| 0.3413 \| 0.5203 \| 0.4890 \| 0.4388 \| 0.4525 \|
	\| 0.1418 \| 55 \| 0.417 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1546 \| 60 \| 0.4114 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1675 \| 65 \| 0.3787 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1804 \| 70 \| 0.3349 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1933 \| 75 \| 0.3161 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2062 \| 80 \| 0.3358 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2191 \| 85 \| 0.2999 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2320 \| 90 \| 0.3039 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2448 \| 95 \| 0.2502 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2577 \| 100 \| 0.225 \| 0.1430 \| 0.2907 \| 0.4866 \| 0.6736 \| 0.3518 \| 0.4464 \| 0.4605 \| 0.2073 \| 0.4936 \| 0.8874 \| 0.3542 \| 0.5146 \| 0.5442 \| 0.4653 \| 0.4751 \|
	\| 0.2706 \| 105 \| 0.263 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2835 \| 110 \| 0.3001 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2964 \| 115 \| 0.224 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3093 \| 120 \| 0.2394 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3222 \| 125 \| 0.2487 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3351 \| 130 \| 0.1954 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3479 \| 135 \| 0.2194 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3608 \| 140 \| 0.2514 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3737 \| 145 \| 0.2145 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3866 \| 150 \| 0.2053 \| 0.1190 \| 0.2912 \| 0.4807 \| 0.6543 \| 0.3429 \| 0.4563 \| 0.4598 \| 0.2422 \| 0.5075 \| 0.9008 \| 0.3552 \| 0.5323 \| 0.5575 \| 0.4581 \| 0.4799 \|
	\| 0.3995 \| 155 \| 0.2405 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4124 \| 160 \| 0.2207 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4253 \| 165 \| 0.1908 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4381 \| 170 \| 0.1832 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4510 \| 175 \| 0.2108 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4639 \| 180 \| 0.1901 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4768 \| 185 \| 0.2118 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4897 \| 190 \| 0.1813 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5026 \| 195 \| 0.1848 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5155 \| 200 \| 0.1932 \| 0.1043 \| 0.2857 \| 0.4838 \| 0.6747 \| 0.3592 \| 0.4611 \| 0.4738 \| 0.2415 \| 0.5336 \| 0.8939 \| 0.3539 \| 0.5101 \| 0.5442 \| 0.4726 \| 0.4837 \|
	\| 0.5284 \| 205 \| 0.2004 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5412 \| 210 \| 0.1874 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5541 \| 215 \| 0.1548 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5670 \| 220 \| 0.1662 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5799 \| 225 \| 0.158 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5928 \| 230 \| 0.1951 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6057 \| 235 \| 0.1935 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6186 \| 240 \| 0.1665 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6314 \| 245 \| 0.1557 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6443 \| 250 \| 0.1987 \| 0.0963 \| 0.2834 \| 0.4801 \| 0.6737 \| 0.3522 \| 0.4610 \| 0.4736 \| 0.2478 \| 0.5643 \| 0.8999 \| 0.3437 \| 0.5225 \| 0.5353 \| 0.4802 \| 0.4860 \|
	\| 0.6572 \| 255 \| 0.1612 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6701 \| 260 \| 0.1859 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6830 \| 265 \| 0.1983 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6959 \| 270 \| 0.1688 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7088 \| 275 \| 0.1949 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7216 \| 280 \| 0.1684 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7345 \| 285 \| 0.1834 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7474 \| 290 \| 0.1673 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7603 \| 295 \| 0.185 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7732 \| 300 \| 0.1529 \| 0.0902 \| 0.2827 \| 0.4798 \| 0.6636 \| 0.3486 \| 0.4528 \| 0.4634 \| 0.2530 \| 0.5602 \| 0.9064 \| 0.3519 \| 0.5204 \| 0.5531 \| 0.4727 \| 0.4853 \|
	\| 0.7861 \| 305 \| 0.2042 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7990 \| 310 \| 0.1995 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8119 \| 315 \| 0.1579 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8247 \| 320 \| 0.1711 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8376 \| 325 \| 0.17 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8505 \| 330 \| 0.1539 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8634 \| 335 \| 0.151 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8763 \| 340 \| 0.1642 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8892 \| 345 \| 0.1669 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9021 \| 350 \| 0.1475 \| 0.0911 \| 0.2874 \| 0.4843 \| 0.6724 \| 0.3450 \| 0.4536 \| 0.4590 \| 0.2616 \| 0.5611 \| 0.9064 \| 0.3501 \| 0.5114 \| 0.5675 \| 0.4718 \| 0.4870 \|
	\| 0.9149 \| 355 \| 0.1842 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9278 \| 360 \| 0.1858 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9407 \| 365 \| 0.2033 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9536 \| 370 \| 0.181 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9665 \| 375 \| 0.1525 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9794 \| 380 \| 0.1722 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9923 \| 385 \| 0.1547 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| -1 \| -1 \| - \| - \| 0.2874 \| 0.4815 \| 0.6774 \| 0.3485 \| 0.4646 \| 0.4662 \| 0.2638 \| 0.5611 \| 0.9061 \| 0.3500 \| 0.5150 \| 0.5605 \| 0.4767 \| 0.4891 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.376 kWh
	- Carbon Emitted: 0.100 kg of CO2
	- Hours Used: 0.956 hours

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

	### Framework Versions
	- Python: 3.11.6
	- Sentence Transformers: 5.2.0.dev0
	- Transformers: 4.56.0.dev0
	- PyTorch: 2.7.1+cu126
	- Accelerate: 1.6.0
	- Datasets: 3.6.0
	- Tokenizers: 0.21.1

	## Citation

	### BibTeX

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

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

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