Update README.md

f44b248 verified about 2 months ago

97.4 kB

	---
	language:
	- en
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dense
	- generated_from_trainer
	- dataset_size:4314846
	- loss:CachedMultipleNegativesBidirectionalRankingLoss
	base_model: answerdotai/ModernBERT-base
	widget:
	- source_sentence: what is grade 7 gcse equivalent to?
	sentences:
	- >-
	Unlike the Google Home Mini (First Gen), the Nest Mini (Second Gen) can be
	used to actually enjoy music in every room of the house. While the Google
	Home Mini (First Gen) is a decent way to get music in every room of your
	home for cheap, the sound quality that comes from the speaker reflects the
	price of the product.
	- >-
	In general, a grade 7-9 is roughly equivalent to A-A* under the old system,
	while a grade 4 and above is roughly equivalent to a C and above. Fewer
	students will receive a grade 9 than would have received an A* under the old
	grading system.
	- >-
	['Pulling at a wet or dirty diaper.', 'Hiding to pee or poop.', "Interest in
	others' use of the potty, or copying their behavior.", 'Having a dry diaper
	for a longer-than-usual time.', 'Awakening dry from a nap.', "Telling you
	that they're about to go, are going or have just gone in their diaper."]
	- source_sentence: >-
	Desire For Sex Drops As You Age, But You Can Still Have A Satisfactory Sex
	Life
	sentences:
	- >-
	ADVERTISEMENT

	Those who have been in long-term relationships know that sex can start to
	fall by the wayside the longer you're together.

	Whether you have children, a busy career, an active social life, a job that
	takes you away from home often, or a chronic illness, there are plenty of
	reasons why couples have less sex compared to when they first started
	dating.

	And it's not just stuff like that that's keeping you away from fun between
	the sheets; according to research from the Kinsey Institute, age plays a
	factor in your sex drive, for both men and women.

	Unsurprisingly, younger people are having the most sex compared to other age
	groups.

	Those aged 18 to 29 years old are having sex an average of 112 times a year
	(about every three days), and, as Indy100 notes, most people lose their
	virginity when they're teenagers, with men having sex for the first time
	around 16.8 years, and women losing theirs at 17.2 years.

	By comparison, 30 to 39-year-olds have sex on average 86 times a year, which
	is around 1.6 times per week.

	The study notes that this drop-off coincides with the age people choose to
	start having children, which, as parents know, can really kill the mood,
	especially if there's a baby crying at the exact same time you feel like
	getting it on. (Which is most likely in the morning.)

	And it only lessens the older you get. Those who are in their 40s have sex
	an average of 69 times a year, due to factors such as family obligations,
	day-to-day stresses, and possible illnesses.

	"The basic storyline that has emerged from these studies is that, as we get
	older, our odds of developing chronic health conditions increases and this,
	in turn, negatively impacts the frequency and quality of sexual activity,"
	notes Dr. Justin Lehmiller of the Kinsey Institute.

	Unfortunately, the study didn't look into the sex lives of those 50 and
	older, but there is other research out there. According to a study published
	in the Archives of Sexual Behavior, couples who have been married for more
	than 25 years have a 40 per cent chance of having sex two or three times a
	week, but that statistic drops to 35 per cent for couples who have been
	married for 50 or more years.

	Surprisingly, couples who have been together for 65 years are 42 per cent
	more likely to have sex a couple times a week.

	As we get older, our odds of developing chronic health conditions increases
	and this, in turn, negatively impacts the frequency and quality of sexual
	activity.

	According to a study published in the Journal of Sex Research, those who
	"feel their age" tended to have less sex, while those who remained in better
	health had more active and satisfying sex lives.

	"The younger people feel, the more likely they are to maintain high sexual
	satisfaction as they get older (or at least they'll experience a much less
	noticeable change)," wrote Lehmiller.

	It's worth noting that these study results come from a small sample of the
	population, and it shouldn't be the standard for how much sex we should be
	having.

	However, there is plenty of research that backs up the claim that sex is
	great for one's health, so the more you get busy, the better!

	Also on HuffPost:
	- >-
	HONOLULU — A former Hawaii state worker who sent a false missile alert last
	month said Friday that he's devastated for causing panic but was "100 per
	cent sure" at the time that the attack was real.

	The man in his 50s spoke to reporters on the condition that he not be
	identified because he fears for his safety after receiving threats.

	He says the on-duty call he received on Jan. 13 didn't sound like a drill.
	However, state officials say other workers clearly heard the word "exercise"
	repeated several times.

	He said it felt like he had been hit with a "body blow" when he realized it
	was just a drill and he has had difficulty eating and sleeping since.

	The Hawaii Emergency Management Agency fired him.

	The man's superiors said they knew for years that he had problems performing
	his job. The worker had mistakenly believed drills for tsunami and fire
	warnings were actual events, and colleagues were not comfortable working
	with him, the state said.

	His supervisors counselled him but kept him for a decade in a position that
	had to be renewed each year.

	The ex-worker disputed that, saying he wasn't aware of any performance
	problems.

	While working at the state warning site in a former bunker in Honolulu's
	Diamond Head crater on Jan. 13, the man said, he took a call that sounded
	like a real warning from U.S Pacific Command. He said he didn't hear that it
	was a drill.

	But the problems at the agency went beyond the one employee.

	Federal and state reports say the agency had a vague checklist for missile
	alerts, allowing workers to interpret the steps they should follow
	differently. Managers didn't require a second person to sign off on alerts
	before they were sent, and the agency lacked any preparation on how to
	correct a false warning.

	Those details emerged Tuesday in reports on investigations about how the
	agency mistakenly blasted cellphones and broadcast stations with the missile
	warning.

	It took nearly 40 minutes for the agency to figure out a way to retract the
	false alert on the same platforms it was sent to.

	"The protocols were not in place. It was a sense of urgency to put it in
	place as soon as possible. But those protocols were not developed to the
	point they should have," retired Brig. Gen. Bruce Oliveira, who wrote the
	report on Hawaii's internal investigation, said at a news conference.

	Hawaii Emergency Management Agency Administrator Vern Miyagi resigned as the
	reports were released. Officials revealed that the employee who sent the
	alert was fired Jan. 26. The state did not name him.

	The agency's executive officer, Toby Clairmont, said Wednesday that he
	stepped down because it was clear action would be taken against agency
	leaders after the alert.
	- >-
	Pompeii’s Final Hours: New Evidence (C5)

	Rating:

	The Big Crash Diet Experiment (BBC1)

	Rating:

	With his rosy cheeks and nose, and a crown of laurel leaves drooping over
	one eye, former political journalist John Sergeant looked like jolly little
	Bacchus, the Roman god of wine, as he tucked into an ancient feast on
	Pompeii’s Final Hours: New Evidence (C5).

	A game soul, whether strutting the pasa doble on Strictly or bartering in a
	Naples marketplace, John munched fried sea urchins and braised moray eel —
	with plenty of red vino to slosh the taste away.

	He did blanch at the thought of bulls’ testicles stuffed with pepper and
	herbs.

	John Sergeant on an hour-long archaeological romp in Pompeii’s Final Hours:
	New Evidence

	Apparently this delicacy was a great favourite in Pompeii — but then, the
	decadent Romans drenched every meal in lashings of garum, a sauce made from
	rotting fish. Anything would taste better than that.

	Noble as Brutus, John held his nose and chewed a mouthful of cobbler. ‘I
	wouldn’t have it every night,’ he muttered.

	It’s an astonishing thought that Julius Caesar conquered most of the known
	world, when he must have been suffering from chronic indigestion.

	Imagine what the Romans might have done if they’d invented the pizza a
	couple of thousand years earlier.

	This hour-long archaeological romp was the first of three surveys of life in
	the shadow of Vesuvius, set to continue tonight and tomorrow.

	The ‘new evidence’ in the title came from computer X-ray scans of some of
	Pompeii’s famous casts.

	These detailed figurines were created by the 19th-century archaeologist
	Giuseppe Fiorelli, who injected liquid plaster into the cavities where Roman
	bodies had been buried by ash in the volcanic eruption in AD79.

	Fiorelli’s casts are the most moving and tragic death masks ever made. Every
	plaster corpse is writhing in agony, suffocated by poisonous gases.

	For 150 years, the victims’ skeletal remains have been locked in their
	cases. It is only now that the technology exists to examine the bones
	without destroying the casts.

	What the first CT scans revealed swept old theories away. One figure long
	believed to be a man appeared, in fact, to be female.

	Another, thought for decades to be a male gladiator in his prime, turned out
	to be a teenage boy.

	Presenters Bettany Hughes and Raksha Dave didn’t make enough of these
	dramatic finds. The CT results were held back to the end of the hour, so
	that the discoveries were inevitably rushed.

	Dr Javid Abdelmoneim in The Big Crash Diet Experiment challenges
	conventional wisdom on food and exercise

	Don’t blame John Sergeant, though. While the others were in the lab, he was
	still polishing off his meal of eels and urchins. Say what you like, this
	man believes in doing his research.

	After that, he’d probably welcome a few days of starvation. The powdered
	soups and shakes fed to four slimmers by Dr Javid Abdelmoneim in The Big
	Crash Diet Experiment (BBC1) looked worse than any classical culinary
	torture, though.

	To challenge conventional wisdom that brief bursts of intensive dieting
	rarely bring long-term results, Dr Javid had his guinea pigs living on 800
	calories a day for nine weeks.

	All lost plenty of weight. But it was the switch to healthy-eating
	afterwards that seemed to bring the best results.

	The show had plenty of useful advice for dieters. Don’t pretend fast food is
	‘addictive’ — greasy take-aways are just a bad habit. Only eat in the dining
	room, never on the sofa . . . or in bed.

	Remember, burger bars are in the cynical business of selling you empty
	calories.

	Follow those rules, and you might not need the powdered shakes. Or the foul
	fish sauce.
	- source_sentence: Berlin startup offers a year with no money worries
	sentences:
	- >-
	Get daily updates directly to your inbox + Subscribe Thank you for
	subscribing! Could not subscribe, try again later Invalid Email

	Nuneaton's hospital has been given the all-clear after a previously closed
	ward has now been re-opened.

	Bosses at the George Eliot Hospital were forced to close the Adam Bede ward
	due to an outbreak of Norovirus.

	It remained closed over the weekend but on Monday they said that ward had
	now been decontaminated and re-opened.

	Martina Morris, deputy director of nursing at George Eliot Hospital NHS
	Trust, said: “The patients on Adam Bede ward have been clear of symptoms for
	the last 48 hours, and following a full decontamination, we have re-opened
	the ward.

	“Any patients in the hospital who continue to present with symptoms of
	norovirus have been isolated in side rooms.”

	“But they are keen to prevent any further outbreaks and are appealing to
	anyone from suffering from the sickness and diarrhoea to steer clear.

	“We ask that the public continue to avoid the hospital, if they have
	symptoms of diarrhoea and vomiting and do not visit until they have been
	symptom free for at least 48 hours,” the deputy director of nursing said.

	“Good hand hygiene is key to limiting the spread of these infections and it
	is important to wash your hands thoroughly with soap and warm water as using
	just an anti-bacterial hand gel is not sufficient.”
	- >-
	Comedy cabaret team All That Malarkey are promising to end 2017 with a
	festive bang with their new show Camp as Christmas.

	They will be playing The Groundlings Theatre in Portsmouth on December 20 at
	7.30pm (www.groundlings.co.uk) and Chichester’s St John’s Chapel on December
	21, also at 7.30pm (07722 824696).

	Spokesman David Harrington said: “We spent a sizzling summer strutting our
	stuff at the Edinburgh Fringe Festival, where we performed to an
	international audience and gained excellent reviews.”

	Now they are back on the road for Christmas: “We’re excited to have dates
	including our London debut at the magnificent King’s Head Theatre, as well
	as other performances in Wales and the South, though we always finish at
	Chichester as that is where our journey began.

	“The four classically-trained singers of ATM are geared up and ready to sing
	their hearts out, fling themselves around the stage and present popular
	Christmas songs from pop to classics and carols, all musically arranged in
	unexpected ways that will surprise and entertain, accompanied and compered
	by yours truly at the keyboard. Known for our unique four-part harmony
	arrangements of family favourites, laced with fun, sparkle and
	tongue-in-cheek frivolity, our new programme will include wonderful new
	renditions of Do you ACTUALLY wish it could be Christmas everyday, Christmas
	No.1 Medley and We Need a Little Christmas.

	“Always drawing an amazing and welcoming crowd, our performance this year
	will be at St John’s Chapel, Chichester, hometown of the unmissable
	ginger-haired ATM soprano, Amy Fuller, and the city where ATM started four
	Christmases ago.

	“Promising to be an energetic and impossibly-festive evening, we’ll also be
	holding a collection for St Wilfrid’s Hospice at the end, particularly close
	to our hearts this year. Also in the diary for this tour is an appearance at
	my hometown of Portsmouth (Wednesday, December 20 at The Groundlings
	Theatre). Having gone to Padnell school and Oaklands Catholic school and
	sixth form, it will be a treat to bring our outrageous act to old friends
	and family, and show them what I do for a living…flick my hair around and
	make funny faces at the piano like a maniac. Amy Fuller had made herself a
	complete stranger to me by growing up in Chichester and going to Bishop
	Luffa and Parklands Primary, but we fortunately crossed paths when studying
	together.”
	- >-
	Michael Bohmeyer, the founder of Mein Grundeinkommen (My Basic Income).
	Photo: DPA

	Miko from Berlin may only be five, but he already has €1,000 ($1,063) per
	month to live on -- not from hard graft, but as part of an experiment into
	universal basic income.

	He is one of 85 people, including around 10 children, chosen by startup Mein
	Grundeinkommen (My Basic Income) to receive the payments for a year since
	2014.

	Founder Michael Bohmeyer has set out to prove to a sceptical public in
	Germany and further afield that the universal basic income (UBI) idea is
	workable.

	"Thanks to my first startup, I got a regular income, my life became more
	creative and healthy. So I wanted to launch a social experiment,"
	31-year-old Bohmeyer told AFP.

	And he wasn't alone in wanting to test the idea, as some 55,000 donors have
	stumped up the cash for the payments in a "crowdfunding" model -- with the
	final recipients picked out in a "wheel of fortune" event livestreamed
	online.

	Mother Birgit Kaulfuss said little Miko "can't really understand, but for
	the whole family it was exhilarating" when he was chosen -- offering a
	chance to live "in a more relaxed way" and take a first-ever family holiday.

	Trying things out

	"Everyone sleeps more soundly and no one become a layabout," Bohmeyer said
	of his beneficiaries.

	Recipients' experiences range from a welcome spell without financial worries
	to major turning points in their lives.

	"Without day-to-day pressures, you can be more creative and try things out,"
	Valerie Rupp told public broadcaster ARD in a recent interview.

	She was able both to take care of her baby and start a career as a decorator
	-- even as her husband, newly arrived from Mali, was taking German

	lessons.

	Winners have left jobs that were doing little more for them than put bread
	on the table to become teachers, taken time out to address chronic illness,
	broken alcohol addiction, taken care of loved ones, or paid for children's
	studies.

	"It's at once a gift and a prompt" to make a change, explained Astrid
	Lobeyer, who used the money to give eulogies at funerals and studied the

	therapeutic Alexander technique, a method for relieving stress in the
	muscles.

	Bohmeyer's experiment has fascinated social media and boosted discussion
	about a universal income in Germany.

	At the same time, Finland is testing the idea with 2,000 homeless recipients
	and the idea is a flagship policy for French Socialist presidential

	candidate Benoit Hamon.

	Reward for laziness?

	In 2009, the German parliament flatly rejected a petition from some 50,000
	Germans demanding a universal income.

	Nevertheless, some 40 percent of the public still think it's a good idea,
	according to a survey last June by pollsters Emnid.

	Supporters have formed a campaign group called "Buendnis Grundeinkommen"
	(Basic income federation) with their sights on September's legislative
	elections, but so far no major party has taken up the cause.

	There are pockets of support among left-wingers, the right, Catholic
	organisations and even industry leaders, whose reasoning ranges from
	fighting poverty to simplifying bureaucracy or smoothing the transition into
	the

	digital era.

	Resistance to the idea is more focused, centering on how UBI would change
	people's relationship to work.

	Right-wingers dismiss it as a "reward for laziness", while the Social
	Democratic Party (SPD) worried in 2006 about unemployed recipients being

	"labelled useless" rather than getting help to find jobs.

	Meanwhile, major unions like IG Metall and Verdi denounce the idea as a
	"liberal Trojan horse" that would "boost inequality" by paying millionaires
	and poor people alike.

	Thankless jobs

	Mein Grundeinkommen is "poorly thought out" as a response to broader social
	questions, University of Freiburg economist Alexander Spermann told AFP.

	The startup's 20 employees eat up "60 percent of the budget", founder
	Michael Bohmeyer admits -- while the idea of basing the funding on curiosity
	or activism by thousands of donors is hardly applicable on a large scale.

	For Spermann, the Berliners' experiment has only succeeded in answering the
	question "what would I do with a blank cheque if I got one for Christmas?"

	People's choices in terms of qualifications or work if they were guaranteed
	the payments for life are the real mystery, the economist argues.

	"Who will take on the exhausting and sometimes less attractive tasks, like
	emptying bins or taking care of the elderly?" asked Werner Eichhorst of the
	Bonn Centre for the Future of Work (IZA) in 2013.

	UBI supporters argue such jobs would either be taken over by robots or find
	a new place of honour in society if the policy were enacted.

	"No machine will take over working for us and pay our taxes at the same
	time," Eichhorst and opponents shoot back.
	- source_sentence: population of artesia
	sentences:
	- >-
	Meanwhile, bring 4 cups of water to a boil and add the barley. Simmer
	uncovered for 30 minutes, drain, and set aside. When the soup is ready, add
	the barley and cook the soup for another 15 or 20 minutes, until the barley
	is tender.
	- >-
	The 2016 Artesia, New Mexico, population is 12,036. There are 1,211 people
	per square mile (population density).
	- >-
	There are 30 calories in one cup of chopped green peppers and approximately
	6 calories in 1 ounce or 28g of green peppers.
	- source_sentence: what is the best paying engineering job
	sentences:
	- >-
	The 20 highest-paying jobs for engineering majors. Engineering jobs pay
	well. To find out just how lucrative they really are, we turned to PayScale,
	the creator of the world's largest compensation database. To find the 20
	highest-paying jobs for engineering majors, PayScale first identified the
	most common jobs for those with a bachelor's degree (and nothing more) who
	work full-time in the US. Chief architects and vice president's of business
	development topped the list, both earning an impressive $151,000 a year.
	- "Depending on the thickness and size of the chop, it can take anywhere from eight to 30 minutes. Hereâ\x80\x99s a helpful cooking chart and some tips to achieve delicious pork chops every time. Pork chops are a crowd pleaser, especially once you master your grilling technique. For safe consumption, itâ\x80\x99s recommended to cook pork until it reaches an internal temperature of 145Â°F or 65Â°C. Depending on the cut and thickness of your chop, the time it may take to reach this can vary. To make sure your chops are the right temperature, use a digital meat thermometer."
	- >-
	Aviation is a combat arms branch which encompasses 80 percent of the
	commissioned officer operational flying positions within the Army (less
	those in Aviation Material Management and Medical Service Corps).
	datasets:
	- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
	- sentence-transformers/natural-questions
	- sentence-transformers/gooaq
	- sentence-transformers/ccnews
	- sentence-transformers/hotpotqa
	pipeline_tag: sentence-similarity
	library_name: sentence-transformers
	metrics:
	- cosine_accuracy@10
	- cosine_precision@10
	- cosine_recall@10
	- cosine_ndcg@10
	- cosine_mrr@10
	- cosine_map@10
	model-index:
	- name: SentenceTransformer based on answerdotai/ModernBERT-base
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoClimateFEVER
	type: NanoClimateFEVER
	metrics:
	- type: cosine_accuracy@10
	value: 0.68
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.09
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.374
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.3204103646278264
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.42072222222222216
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.2384825396825397
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoDBPedia
	type: NanoDBPedia
	metrics:
	- type: cosine_accuracy@10
	value: 0.94
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.39000000000000007
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.2684345324233032
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5013173913967965
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7496666666666667
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.3713051587301587
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoFEVER
	type: NanoFEVER
	metrics:
	- type: cosine_accuracy@10
	value: 0.98
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.10199999999999998
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.9333333333333332
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7970708195176515
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7731666666666667
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.7398333333333332
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoFiQA2018
	type: NanoFiQA2018
	metrics:
	- type: cosine_accuracy@10
	value: 0.74
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.122
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.5628492063492063
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.45952453703882723
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5142222222222222
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.3760648589065255
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoHotpotQA
	type: NanoHotpotQA
	metrics:
	- type: cosine_accuracy@10
	value: 0.94
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.12999999999999998
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.65
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6496205965616751
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.8305555555555556
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.5639444444444445
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoMSMARCO
	type: NanoMSMARCO
	metrics:
	- type: cosine_accuracy@10
	value: 0.84
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.08399999999999999
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.84
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5914940146382726
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5118333333333333
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.5118333333333334
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNFCorpus
	type: NanoNFCorpus
	metrics:
	- type: cosine_accuracy@10
	value: 0.7
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.256
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.13512669313971043
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.29812924809751384
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.4497777777777777
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.20484007936507936
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoNQ
	type: NanoNQ
	metrics:
	- type: cosine_accuracy@10
	value: 0.78
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.08399999999999999
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.76
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6278509641999098
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5953333333333333
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.5760000000000001
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoQuoraRetrieval
	type: NanoQuoraRetrieval
	metrics:
	- type: cosine_accuracy@10
	value: 1
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.132
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.986
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.9386568522919021
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.9366666666666665
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.9120888888888888
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoSCIDOCS
	type: NanoSCIDOCS
	metrics:
	- type: cosine_accuracy@10
	value: 0.82
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.176
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.35966666666666663
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.3412893142888829
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.5091904761904761
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.23048174603174598
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoArguAna
	type: NanoArguAna
	metrics:
	- type: cosine_accuracy@10
	value: 0.9
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.09
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.9
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.589790277339453
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.49080158730158724
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.4908015873015873
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoSciFact
	type: NanoSciFact
	metrics:
	- type: cosine_accuracy@10
	value: 0.8
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.092
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.8
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.6514145845317466
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6098333333333332
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.5992222222222222
	name: Cosine Map@10
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: NanoTouche2020
	type: NanoTouche2020
	metrics:
	- type: cosine_accuracy@10
	value: 0.9387755102040817
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.4102040816326531
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.2819732491937568
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.4762218106016415
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.720262390670554
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.3260029000262236
	name: Cosine Map@10
	- task:
	type: nano-beir
	name: Nano BEIR
	dataset:
	name: NanoBEIR mean
	type: NanoBEIR_mean
	metrics:
	- type: cosine_accuracy@10
	value: 0.8506750392464679
	name: Cosine Accuracy@10
	- type: cosine_precision@10
	value: 0.16601569858712717
	name: Cosine Precision@10
	- type: cosine_recall@10
	value: 0.603952590854306
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.5571377519332383
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.6240024793800304
	name: Cosine Mrr@10
	- type: cosine_map@10
	value: 0.4723770070973909
	name: Cosine Map@10
	license: apache-2.0
	---

	# SentenceTransformer based on answerdotai/ModernBERT-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1), [natural_questions](https://huggingface.co/datasets/sentence-transformers/natural-questions), [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq), [ccnews](https://huggingface.co/datasets/sentence-transformers/ccnews) and [hotpotqa](https://huggingface.co/datasets/sentence-transformers/hotpotqa) datasets. 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: [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) <!-- at revision 8949b909ec900327062f0ebf497f51aef5e6f0c8 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 dimensions
	- Similarity Function: Cosine Similarity
	- Training Datasets:
	- [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1)
	- [natural_questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
	- [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq)
	- [ccnews](https://huggingface.co/datasets/sentence-transformers/ccnews)
	- [hotpotqa](https://huggingface.co/datasets/sentence-transformers/hotpotqa)
	- Language: en
	<!-- - License: Unknown -->

	### Model Sources

	- Documentation: [Sentence Transformers Documentation](https://sbert.net)
	- Repository: [Sentence Transformers on GitHub](https://github.com/huggingface/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': 'ModernBertModel'})
	(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("hotchpotch/ModernBERT-embedding-CMNBRL")
	# Run inference
	queries = [
	"what is the best paying engineering job",
	]
	documents = [
	"The 20 highest-paying jobs for engineering majors. Engineering jobs pay well. To find out just how lucrative they really are, we turned to PayScale, the creator of the world's largest compensation database. To find the 20 highest-paying jobs for engineering majors, PayScale first identified the most common jobs for those with a bachelor's degree (and nothing more) who work full-time in the US. Chief architects and vice president's of business development topped the list, both earning an impressive $151,000 a year.",
	'Aviation is a combat arms branch which encompasses 80 percent of the commissioned officer operational flying positions within the Army (less those in Aviation Material Management and Medical Service Corps).',
	'Depending on the thickness and size of the chop, it can take anywhere from eight to 30 minutes. Hereâ\x80\x99s a helpful cooking chart and some tips to achieve delicious pork chops every time. Pork chops are a crowd pleaser, especially once you master your grilling technique. For safe consumption, itâ\x80\x99s recommended to cook pork until it reaches an internal temperature of 145Â°F or 65Â°C. Depending on the cut and thickness of your chop, the time it may take to reach this can vary. To make sure your chops are the right temperature, use a digital meat thermometer.',
	]
	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([[0.9709, 0.7909, 0.6977]])
	```

	<!--
	### 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@10 \| 0.68 \| 0.94 \| 0.98 \| 0.74 \| 0.94 \| 0.84 \| 0.7 \| 0.78 \| 1.0 \| 0.82 \| 0.9 \| 0.8 \| 0.9388 \|
	\| cosine_precision@10 \| 0.09 \| 0.39 \| 0.102 \| 0.122 \| 0.13 \| 0.084 \| 0.256 \| 0.084 \| 0.132 \| 0.176 \| 0.09 \| 0.092 \| 0.4102 \|
	\| cosine_recall@10 \| 0.374 \| 0.2684 \| 0.9333 \| 0.5628 \| 0.65 \| 0.84 \| 0.1351 \| 0.76 \| 0.986 \| 0.3597 \| 0.9 \| 0.8 \| 0.282 \|
	\| cosine_ndcg@10 \| 0.3204 \| 0.5013 \| 0.7971 \| 0.4595 \| 0.6496 \| 0.5915 \| 0.2981 \| 0.6279 \| 0.9387 \| 0.3413 \| 0.5898 \| 0.6514 \| 0.4762 \|
	\| cosine_mrr@10 \| 0.4207 \| 0.7497 \| 0.7732 \| 0.5142 \| 0.8306 \| 0.5118 \| 0.4498 \| 0.5953 \| 0.9367 \| 0.5092 \| 0.4908 \| 0.6098 \| 0.7203 \|
	\| cosine_map@10 \| 0.2385 \| 0.3713 \| 0.7398 \| 0.3761 \| 0.5639 \| 0.5118 \| 0.2048 \| 0.576 \| 0.9121 \| 0.2305 \| 0.4908 \| 0.5992 \| 0.326 \|

	#### 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"
	],
	"dataset_id": "sentence-transformers/NanoBEIR-en"
	}
	```

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@10 \| 0.8507 \|
	\| cosine_precision@10 \| 0.166 \|
	\| cosine_recall@10 \| 0.604 \|
	\| cosine_ndcg@10 \| 0.5571 \|
	\| cosine_mrr@10 \| 0.624 \|
	\| cosine_map@10 \| 0.4724 \|

	<!--
	## 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 Datasets
	<details><summary>msmarco</summary>

	#### msmarco

	* Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1) at [84ed2d3](https://huggingface.co/datasets/sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1/tree/84ed2d35626f617d890bd493b4d6db69a741e0e2)
	* Size: 502,939 training samples
	* Columns: <code>query</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \|
	\|:--------\|:---------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 9.26 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>min: 19 tokens</li><li>mean: 80.68 tokens</li><li>max: 230 tokens</li></ul> \|
	* Samples:
	\| query \| positive \|
	\|:-------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>is cabinet refacing worth the cost?</code> \| <code>Fans of refacing say this mini-makeover can give a kitchen a whole new look at a much lower cost than installing all-new cabinets. Cabinet refacing can save up to 50 percent compared to the cost of replacing, says Cheryl Catalano, owner of Kitchen Solvers, a cabinet refacing franchise in Napierville, Illinois. From.</code> \|
	\| <code>is the fovea ethmoidalis a bone</code> \| <code>Ethmoid bone/fovea ethmoidalis. The medial portion of the ethmoid bone is a cruciate membranous bone composed of the crista galli, cribriform plate, and perpendicular ethmoidal plate. The crista is a thick piece of bone, shaped like a âcock's comb,â that projects intracranially and attaches to the falx cerebri.</code> \|
	\| <code>average pitches per inning</code> \| <code>The likelihood of a pitcher completing nine innings if he throws an average of 14 pitches or less per inning is reinforced by the totals of the 89 games in which pitchers did actually complete nine innings of work.</code> \|
	* Loss: [<code>CachedMultipleNegativesBidirectionalRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesbidirectionalrankingloss) with these parameters:
	```json
	{
	"temperature": 0.01,
	"similarity_fct": "cos_sim",
	"mini_batch_size": 128,
	"gather_across_devices": false
	}
	```
	</details>
	<details><summary>natural_questions</summary>

	#### 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>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 12.46 tokens</li><li>max: 22 tokens</li></ul> \| <ul><li>min: 12 tokens</li><li>mean: 137.8 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| query \| positive \|
	\|:------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <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>CachedMultipleNegativesBidirectionalRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesbidirectionalrankingloss) with these parameters:
	```json
	{
	"temperature": 0.01,
	"similarity_fct": "cos_sim",
	"mini_batch_size": 128,
	"gather_across_devices": false
	}
	```
	</details>
	<details><summary>gooaq</summary>

	#### gooaq

	* Dataset: [gooaq](https://huggingface.co/datasets/sentence-transformers/gooaq) at [b089f72](https://huggingface.co/datasets/sentence-transformers/gooaq/tree/b089f728748a068b7bc5234e5bcf5b25e3c8279c)
	* Size: 3,012,496 training samples
	* Columns: <code>query</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 12.05 tokens</li><li>max: 21 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 59.08 tokens</li><li>max: 116 tokens</li></ul> \|
	* Samples:
	\| query \| positive \|
	\|:-----------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>how do i program my directv remote with my tv?</code> \| <code>['Press MENU on your remote.', 'Select Settings & Help > Settings > Remote Control > Program Remote.', 'Choose the device (TV, audio, DVD) you wish to program. ... ', 'Follow the on-screen prompts to complete programming.']</code> \|
	\| <code>are rodrigues fruit bats nocturnal?</code> \| <code>Before its numbers were threatened by habitat destruction, storms, and hunting, some of those groups could number 500 or more members. Sunrise, sunset. Rodrigues fruit bats are most active at dawn, at dusk, and at night.</code> \|
	\| <code>why does your heart rate increase during exercise bbc bitesize?</code> \| <code>During exercise there is an increase in physical activity and muscle cells respire more than they do when the body is at rest. The heart rate increases during exercise. The rate and depth of breathing increases - this makes sure that more oxygen is absorbed into the blood, and more carbon dioxide is removed from it.</code> \|
	* Loss: [<code>CachedMultipleNegativesBidirectionalRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesbidirectionalrankingloss) with these parameters:
	```json
	{
	"temperature": 0.01,
	"similarity_fct": "cos_sim",
	"mini_batch_size": 128,
	"gather_across_devices": false
	}
	```
	</details>
	<details><summary>ccnews</summary>

	#### ccnews

	* Dataset: [ccnews](https://huggingface.co/datasets/sentence-transformers/ccnews) at [6118cc0](https://huggingface.co/datasets/sentence-transformers/ccnews/tree/6118cc09daf7977d6dddef2c6e4b7a4c92db9f57)
	* Size: 614,664 training samples
	* Columns: <code>query</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 16.71 tokens</li><li>max: 56 tokens</li></ul> \| <ul><li>min: 18 tokens</li><li>mean: 349.3 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| query \| positive \|
	\|:----------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Rupee rises for 2nd consecutive day, gains 8 paise against US dollar today</code> \| <code>The rupee rose 8 paise to close at 64.37 apiece US dollar at the interbank foreign exchange market today.<br>The Indian rupee appreciated for the second consecutive day and gained over 8 paise against the US dollar on Monday. The domestic currency opened unchanged today, very quickly edged higher and extended the gains to hit a day’s high of 64.34. The rupee rose 8 paise to close at 64.37 apiece US dollar at the interbank foreign exchange market today. The Reserve Bank of India fixed the reference rate of the rupee at 64.3616 against the US dollar on Monday. The Indian rupee moved up 23 paise against the US dollar in just 2 days as Narendra Modi led BJP is most likely to conquer Gujarat for the fifth consecutive time in the state elections. Way back in March 2017, the rupee appreciated as much as 79 paise in a single day to close at a 16-month high against the US dollar after Bharatiya Janata Party’s landslide victory in Uttar Pradesh state elections.<br>Finance Minister Arun Jaitley is all ...</code> \|
	\| <code>Microsoft pushes for ‘Digital Geneva Convention’ for cybercrimes</code> \| <code>Technology companies, he added, need to preserve trust and stability online by pledging neutrality in cyber conflict. ( Image for representation, Source: Reuters) Technology companies, he added, need to preserve trust and stability online by pledging neutrality in cyber conflict. ( Image for representation, Source: Reuters)<br>Microsoft President Brad Smith on Tuesday pressed the world’s governments to form an international body to protect civilians from state-sponsored hacking, saying recent high-profile attacks showed a need for global norms to police government activity in cyberspace.<br>Countries need to develop and abide by global rules for cyber attacks similar to those established for armed conflict at the 1949 Geneva Convention that followed World War Two, Smith said. Technology companies, he added, need to preserve trust and stability online by pledging neutrality in cyber conflict.<br>Watch all our videos from Express Technology<br>“We need a Digital Geneva Convention that will commit go...</code> \|
	\| <code>Prince Gets Purple Pantone Color ‘Love Symbol #2’</code> \| <code>By Abby Hassler<br>Prince, also known as “The Purple One” is finally getting his very own Pantone color. Pantone and Prince’s Estate announced today (August 14) that the late singer has his own purple hue, “Love Symbol #2,” which is named after the iconic symbol the singer used as an emblem for his name.<br>Related: Wesley Snipes Beat Out Prince for His Role in Michael Jackson’s ‘Bad’<br>“The color purple was synonymous with who Prince was and will always be. This is an incredible way for his legacy to live on forever,” Troy Carter, entertainment adviser to Prince’s Estate, said.<br>“We are honored to have worked on the development of Love Symbol #2, a distinctive new purple shade created in memory of Prince, ‘the purple one,'” added Laurie Pressman, vice president of the Pantone Color Institute. “A musical icon known for his artistic brilliance, Love Symbol #2 is emblematic of Prince’s distinctive style. Long associated with the purple family, Love Symbol #2 enables Prince’s unique purple shade t...</code> \|
	* Loss: [<code>CachedMultipleNegativesBidirectionalRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesbidirectionalrankingloss) with these parameters:
	```json
	{
	"temperature": 0.01,
	"similarity_fct": "cos_sim",
	"mini_batch_size": 128,
	"gather_across_devices": false
	}
	```
	</details>
	<details><summary>hotpotqa</summary>

	#### hotpotqa

	* Dataset: [hotpotqa](https://huggingface.co/datasets/sentence-transformers/hotpotqa) at [f07d3cd](https://huggingface.co/datasets/sentence-transformers/hotpotqa/tree/f07d3cd2d290ea2e83ed35e33d67d6a4658b8786)
	* Size: 84,516 training samples
	* Columns: <code>query</code> and <code>positive</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| positive \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 8 tokens</li><li>mean: 25.82 tokens</li><li>max: 140 tokens</li></ul> \| <ul><li>min: 18 tokens</li><li>mean: 103.34 tokens</li><li>max: 350 tokens</li></ul> \|
	* Samples:
	\| query \| positive \|
	\|:------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Which magazine covers a wider range of topics, Decibel or Paper?</code> \| <code>Decibel (magazine) Decibel is a monthly heavy metal magazine published by the Philadelphia-based Red Flag Media since October 2004. Its sections include Upfront, Features, Reviews, Guest Columns and the Decibel Hall of Fame. The magazine's tag-line is currently "Extremely Extreme" (previously "The New Noise"); the editor-in-chief is Albert Mudrian.</code> \|
	\| <code>what bbc drama features such actors as Sian Reeves and Ben Daniels?</code> \| <code>Siân Reeves Siân Reeves (born Siân Rivers on May 9, 1966 in West Bromwich) is a British actress, most famous for playing the role of Sydney Henshall in the BBC drama "Cutting It", and for playing villain Sally Spode in "Emmerdale".</code> \|
	\| <code>What size population does the County Connection public transit in Concord, California service?</code> \| <code>County Connection The County Connection (officially, the Central Contra Costa Transit Authority, CCCTA) is a Concord-based public transit agency operating fixed-route bus and ADA paratransit (County Connection LINK) service in and around central Contra Costa County in the San Francisco Bay Area. Established in 1980 as a joint powers authority, CCCTA assumed control of public bus service within central Contra Costa first begun by Oakland-based AC Transit as it expanded into suburban Contra Costa County in the mid-1970s (especially after the opening of BART).</code> \|
	* Loss: [<code>CachedMultipleNegativesBidirectionalRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesbidirectionalrankingloss) with these parameters:
	```json
	{
	"temperature": 0.01,
	"similarity_fct": "cos_sim",
	"mini_batch_size": 128,
	"gather_across_devices": false
	}
	```
	</details>

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

	- `per_device_train_batch_size`: 8192
	- `per_device_eval_batch_size`: 512
	- `learning_rate`: 0.0001
	- `weight_decay`: 0.01
	- `num_train_epochs`: 1
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.1
	- `seed`: 12
	- `bf16`: True
	- `dataloader_drop_last`: True
	- `dataloader_num_workers`: 12
	- `dataloader_prefetch_factor`: 2
	- `remove_unused_columns`: False
	- `optim`: adamw_torch
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: no
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 8192
	- `per_device_eval_batch_size`: 512
	- `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`: 0.0001
	- `weight_decay`: 0.01
	- `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`: cosine
	- `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
	- `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`: True
	- `dataloader_num_workers`: 12
	- `dataloader_prefetch_factor`: 2
	- `past_index`: -1
	- `disable_tqdm`: False
	- `remove_unused_columns`: False
	- `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}
	- `parallelism_config`: None
	- `deepspeed`: None
	- `label_smoothing_factor`: 0.0
	- `optim`: adamw_torch
	- `optim_args`: None
	- `adafactor`: False
	- `group_by_length`: False
	- `length_column_name`: length
	- `project`: huggingface
	- `trackio_space_id`: trackio
	- `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`: no
	- `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`: True
	- `prompts`: None
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional
	- `router_mapping`: {}
	- `learning_rate_mapping`: {}

	</details>

	### Training Logs
	\| Epoch \| Step \| Training 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 \|
	\|:------:\|:----:\|:-------------:\|:-------------------------------:\|:--------------------------:\|:------------------------:\|:---------------------------:\|:---------------------------:\|:--------------------------:\|:---------------------------:\|:---------------------:\|:---------------------------------:\|:--------------------------:\|:--------------------------:\|:--------------------------:\|:-----------------------------:\|:----------------------------:\|
	\| 0.0190 \| 10 \| 11.3289 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0381 \| 20 \| 7.5743 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0571 \| 30 \| 5.4003 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0762 \| 40 \| 3.399 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0952 \| 50 \| 2.7399 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1143 \| 60 \| 2.415 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1333 \| 70 \| 2.3843 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1524 \| 80 \| 1.9827 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1714 \| 90 \| 1.8858 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1905 \| 100 \| 1.7143 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2095 \| 110 \| 2.0079 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2286 \| 120 \| 1.8461 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2476 \| 130 \| 1.7032 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2667 \| 140 \| 1.6531 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2857 \| 150 \| 1.9902 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3048 \| 160 \| 1.6245 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3238 \| 170 \| 1.685 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3429 \| 180 \| 1.657 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3619 \| 190 \| 1.8747 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3810 \| 200 \| 1.4671 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4 \| 210 \| 1.5957 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4190 \| 220 \| 1.5083 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4381 \| 230 \| 1.5014 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4571 \| 240 \| 1.4548 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4762 \| 250 \| 1.5598 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4952 \| 260 \| 1.3879 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5143 \| 270 \| 1.5633 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5333 \| 280 \| 1.5092 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5524 \| 290 \| 1.4434 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5714 \| 300 \| 1.5024 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5905 \| 310 \| 1.511 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6095 \| 320 \| 1.4404 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6286 \| 330 \| 1.6083 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6476 \| 340 \| 1.4197 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6667 \| 350 \| 1.5548 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6857 \| 360 \| 1.5642 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7048 \| 370 \| 1.4709 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7238 \| 380 \| 1.482 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7429 \| 390 \| 1.5472 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7619 \| 400 \| 1.4899 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7810 \| 410 \| 1.3321 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8 \| 420 \| 1.5174 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8190 \| 430 \| 1.3945 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8381 \| 440 \| 1.5877 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8571 \| 450 \| 1.3143 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8762 \| 460 \| 1.5343 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8952 \| 470 \| 1.4968 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9143 \| 480 \| 1.4361 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9333 \| 490 \| 1.4353 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9524 \| 500 \| 1.3146 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9714 \| 510 \| 1.3722 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9905 \| 520 \| 1.3098 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0 \| 521 \| - \| 0.3204 \| 0.5013 \| 0.7971 \| 0.4595 \| 0.6496 \| 0.5915 \| 0.2981 \| 0.6279 \| 0.9387 \| 0.3413 \| 0.5898 \| 0.6514 \| 0.4762 \| 0.5571 \|


	### Framework Versions
	- Python: 3.11.14
	- Sentence Transformers: 5.3.0.dev0
	- Transformers: 4.57.1
	- PyTorch: 2.8.0+cu129
	- Accelerate: 1.12.0
	- Datasets: 4.4.1
	- Tokenizers: 0.22.1

	## 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",
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->