bobox/DeBERTaV3-small-GeneralSentenceTransformer

SentenceTransformer based on microsoft/deberta-v3-small

This is a sentence-transformers model finetuned from microsoft/deberta-v3-small on the nli-pairs, sts-label, vitaminc-pairs, qnli-contrastive, scitail-pairs-qa, scitail-pairs-pos, xsum-pairs, compression-pairs, sciq_pairs, qasc_pairs, openbookqa_pairs, msmarco_pairs, nq_pairs, trivia_pairs, quora_pairs and gooaq_pairs 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: microsoft/deberta-v3-small
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Datasets:
  • nli-pairs
  • sts-label
  • vitaminc-pairs
  • qnli-contrastive
  • scitail-pairs-qa
  • scitail-pairs-pos
  • xsum-pairs
  • compression-pairs
  • sciq_pairs
  • qasc_pairs
  • openbookqa_pairs
  • msmarco_pairs
  • nq_pairs
  • trivia_pairs
  • quora_pairs
  • gooaq_pairs
• Language: en

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model 
  (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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("bobox/DeBERTaV3-small-GeneralSentenceTransformer")
# Run inference
sentences = [
    'Why did oil start getting priced in terms of gold?',
    "Because oil was priced in dollars, oil producers' real income decreased.",
    'This allows all set top boxes in a household to share recordings and other media.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.774
spearman_cosine	0.7727
pearson_manhattan	0.7871
spearman_manhattan	0.7758
pearson_euclidean	0.7842
spearman_euclidean	0.7724
pearson_dot	0.7059
spearman_dot	0.6868
pearson_max	0.7871
spearman_max	0.7758

Semantic Similarity

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.774
spearman_cosine	0.7727
pearson_manhattan	0.7871
spearman_manhattan	0.7758
pearson_euclidean	0.7842
spearman_euclidean	0.7724
pearson_dot	0.7059
spearman_dot	0.6868
pearson_max	0.7871
spearman_max	0.7758

Training Details

Training Datasets

nli-pairs

• Dataset: nli-pairs at d482672
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 5 tokens mean: 16.62 tokens max: 62 tokens	min: 4 tokens mean: 9.46 tokens max: 29 tokens

• Samples:

  sentence1	sentence2
  A person on a horse jumps over a broken down airplane.	A person is outdoors, on a horse.
  Children smiling and waving at camera	There are children present
  A boy is jumping on skateboard in the middle of a red bridge.	The boy does a skateboarding trick.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

sts-label

• Dataset: sts-label at ab7a5ac
• Size: 5,749 training samples
• Columns: sentence1, sentence2, and score
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	min: 6 tokens mean: 9.81 tokens max: 27 tokens	min: 5 tokens mean: 9.74 tokens max: 25 tokens	min: 0.0 mean: 0.54 max: 1.0

• Samples:

  sentence1	sentence2	score
  A plane is taking off.	An air plane is taking off.	1.0
  A man is playing a large flute.	A man is playing a flute.	0.76
  A man is spreading shreded cheese on a pizza.	A man is spreading shredded cheese on an uncooked pizza.	0.76

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

vitaminc-pairs

• Dataset: vitaminc-pairs at be6febb
• Size: 4,943 training samples
• Columns: label, sentence1, and sentence2
• Approximate statistics based on the first 1000 samples:

  	label	sentence1	sentence2
  type	int	string	string
  details	1: 100.00%	min: 6 tokens mean: 16.56 tokens max: 54 tokens	min: 8 tokens mean: 37.97 tokens max: 184 tokens

• Samples:

  label	sentence1	sentence2
  1	The film scored above 85.5 % based on more than 127 reviews .	On Rotten Tomatoes the film has a rating of 86 % , based on 128 reviews , with an average rating of 7.2/10 .
  1	The film has more than 59 reviews .	On Rotten Tomatoes , the film has a rating of 47 % , based on 60 reviews , with an average rating of 5.4/10 .
  1	More than 100 cases of COVID-19 have been reported .	Tamil Nadu reported more than 190 cases in a day , all who had been to the event.

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, '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})
    (2): Normalize()
  ), 'temperature': 0.05}
  

qnli-contrastive

• Dataset: qnli-contrastive at bcdcba7
• Size: 10,000 training samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 6 tokens mean: 13.69 tokens max: 40 tokens	min: 6 tokens mean: 35.17 tokens max: 499 tokens	0: 100.00%

• Samples:

  sentence1	sentence2	label
  What year was the Southampton Docks company created?	The Southampton Docks company had been formed in 1835.	0
  what happens to the reflected wave which allows the antenna to reach its asympotic feedpoint impedance?	Thus the antenna's impedance, given by the ratio of feedpoint voltage to current, is altered due to the antenna's proximity to the ground.	0
  West 132nd Street is interrupted by St. Nicholas Park and which college?	The main portion of 132nd Street runs eastbound from Frederick Douglass Boulevard to northern end of Park Avenue where there is a southbound exit from/entrance to the Harlem River Drive.	0

• Loss: OnlineContrastiveLoss

scitail-pairs-qa

• Dataset: scitail-pairs-qa at 0cc4353
• Size: 6,595 training samples
• Columns: sentence2 and sentence1
• Approximate statistics based on the first 1000 samples:

  	sentence2	sentence1
  type	string	string
  details	min: 7 tokens mean: 16.18 tokens max: 41 tokens	min: 7 tokens mean: 15.15 tokens max: 41 tokens

• Samples:

  sentence2	sentence1
  Nearly five million people die yearly due to air pollution.	Nearly how many millions of people die yearly due to air pollution?
  Exercising every day is an example of a good health habit.	Which activity is an example of a good health habit?
  A(n) fungus causes ergot, a disease that impacts crops directly and has more devastating effects on animals.	A type of what organism causes ergot, a disease that impacts crops directly and has more devastating effects on animals?

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

scitail-pairs-pos

• Dataset: scitail-pairs-pos at 0cc4353
• Size: 3,405 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 8 tokens mean: 23.97 tokens max: 65 tokens	min: 7 tokens mean: 15.1 tokens max: 39 tokens

• Samples:

  sentence1	sentence2
  Tornadoes Tornadoes are most common during the period of spring and early summer, but can occur in any month of the year.	Tornadoes can occur in any.
  A watershed is the land area that drains water into a river system or other body of water.	All of the land drained by a river system is called its basin, or the "wet" term watershed
  Humans have 23 pairs of chromosomes (one set from each parent).	Humans have 23 pairs pairs of chromosomes.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

xsum-pairs

• Dataset: xsum-pairs at 788ddaf
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 10 tokens mean: 355.4 tokens max: 512 tokens	min: 10 tokens mean: 27.07 tokens max: 70 tokens

• Samples:

  sentence1	sentence2
  Former Royal Marine Stephen Gough, 55, was found guilty by a jury at Winchester Crown Court of breaching an anti-social behaviour order (Asbo). The order bans him from taking off his clothes in public. He had refused to put on clothes as he left Winchester Prison after being imprisoned for a previous Asbo breach. Judge Jane Miller QC suggested moves should be made to find Gough a closed nudist community to live in to prevent the cycle of imprisonment which has seen him jailed for much of the past eight years. Gough earned his nickname when he completed a naked trek from Land's End to John O'Groats in 2003. A BBC documentary team followed his journey. He had previously argued there was nothing intimidating about him appearing "in his natural human state".	A man known as the "naked rambler" has been jailed for two-and-a-half years after walking out of prison wearing only his boots and socks.
  Colbert, 49, is host of Comedy Central's acclaimed late-night satire programme The Colbert Report. Letterman, 66, said last week he was retiring after 21 years hosting the CBS show and 11 years on NBC's Late Night. On his show, Colbert plays a satirical version of himself to mock right-wing pundits. He has suggested he will retire the character for the new show. "Stephen Colbert is one of the most inventive and respected forces on television," CBS president Leslie Moonves wrote in a statement following the announcement. "David Letterman's legacy and accomplishments are an incredible source of pride for all of us here, and today's announcement speaks to our commitment of upholding what he established for CBS in late night." On his popular and influential Emmy-winning Comedy Central programme, Colbert's biting brand of satire has drawn critical acclaim as well as provoking ire, often from the Republicans and conservatives he skewers. Recently he was attacked on social media for a joke some viewed as disparaging toward Asian Americans but which he meant as a satirical jab at the owner of the Washington Redskins American football team. Of his hiring to the Late Show top spot, Colbert said, "simply being a guest on David Letterman's show has been a highlight of my career." "I never dreamed that I would follow in his footsteps, though everyone in late night follows Dave's lead."	Stephen Colbert will succeed Late Show host David Letterman upon his retirement next year, CBS has said.
  The pair are both through to the last eight of the Welsh Open at Cardiff, with White beating defending champion John Higgins 4-1. "Michael White is an amazing player. He has absolutely everything," O'Sullivan, 40, told BBC Wales. "Wales is going to have another world champion... maybe a multiple world champion." O'Sullivan added: "John Higgins behind Stephen Hendry is the best player I have seen and he has lost to Michael. "He [White] is fearless, he plays everybody like they are the same. He doesn't care who is sat in that seat against him and that is a gift. "Michael is now a top player. He's definitely knocking on the doo. If he won the world title this year I wouldn't be surprised." Englishman O'Sullivan had little trouble beating China's Yu Delu 4-1 after losing the opening frame. White, 24, described the victory over four-time world champion Higgins as a career highlight. "It is a fantastic win, [it] would have to be up there with the best of my career," White said. "I am feeling very relaxed and laid back and it showed in my performance. "To get that win in my home event makes it more special. It is good for Wales and the tournament. "Who knows, maybe a Welsh player could get to the final."	Welshman Michael White can become a world champion, according to five-time winner Ronnie O'Sullivan.

• Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

compression-pairs

• Dataset: compression-pairs at 605bc91
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 10 tokens mean: 31.89 tokens max: 125 tokens	min: 5 tokens mean: 10.21 tokens max: 28 tokens

• Samples:

  sentence1	sentence2
  The USHL completed an expansion draft on Monday as 10 players who were on the rosters of USHL teams during the 2009-10 season were selected by the League's two newest entries, the Muskegon Lumberjacks and Dubuque Fighting Saints.	USHL completes expansion draft
  Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month.	Bud Selig to speak at St. Norbert College
  It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit.	It's cherry time

• Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

sciq_pairs

• Dataset: sciq_pairs at 2c94ad3
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 7 tokens mean: 17.26 tokens max: 60 tokens	min: 2 tokens mean: 84.37 tokens max: 512 tokens

• Samples:

  sentence1	sentence2
  What type of organism is commonly used in preparation of foods such as cheese and yogurt?	Mesophiles grow best in moderate temperature, typically between 25°C and 40°C (77°F and 104°F). Mesophiles are often found living in or on the bodies of humans or other animals. The optimal growth temperature of many pathogenic mesophiles is 37°C (98°F), the normal human body temperature. Mesophilic organisms have important uses in food preparation, including cheese, yogurt, beer and wine.
  What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere?	Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to southwest or the reverse in the Northern Hemisphere. The winds blow northwest to southeast or the reverse in the southern hemisphere.
  Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what?	Summary Changes of state are examples of phase changes, or phase transitions. All phase changes are accompanied by changes in the energy of a system. Changes from a more-ordered state to a less-ordered state (such as a liquid to a gas) areendothermic. Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always exothermic. The conversion of a solid to a liquid is called fusion (or melting). The energy required to melt 1 mol of a substance is its enthalpy of fusion (ΔHfus). The energy change required to vaporize 1 mol of a substance is the enthalpy of vaporization (ΔHvap). The direct conversion of a solid to a gas is sublimation. The amount of energy needed to sublime 1 mol of a substance is its enthalpy of sublimation (ΔHsub) and is the sum of the enthalpies of fusion and vaporization. Plots of the temperature of a substance versus heat added or versus heating time at a constant rate of heating are calledheating curves. Heating curves relate temperature changes to phase transitions. A superheated liquid, a liquid at a temperature and pressure at which it should be a gas, is not stable. A cooling curve is not exactly the reverse of the heating curve because many liquids do not freeze at the expected temperature. Instead, they form a supercooled liquid, a metastable liquid phase that exists below the normal melting point. Supercooled liquids usually crystallize on standing, or adding a seed crystal of the same or another substance can induce crystallization.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

qasc_pairs

• Dataset: qasc_pairs at a34ba20
• Size: 8,134 training samples
• Columns: id, sentence1, and sentence2
• Approximate statistics based on the first 1000 samples:

  	id	sentence1	sentence2
  type	string	string	string
  details	min: 17 tokens mean: 21.35 tokens max: 27 tokens	min: 5 tokens mean: 11.47 tokens max: 25 tokens	min: 14 tokens mean: 35.55 tokens max: 66 tokens

• Samples:

  id	sentence1	sentence2
  3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K	What type of water formation is formed by clouds?	beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds.
  3LS2AMNW5FPNJK3C3PZLZCPX562OQO	Where do beads of water come from?	beads of water are formed by water vapor condensing. Condensation is the change of water vapor to a liquid.. Vapor turning into a liquid leaves behind beads of water
  3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3	What forms beads of water?	beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water.

• Loss: GISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, '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})
    (2): Normalize()
  ), 'temperature': 0.05}
  

openbookqa_pairs

• Dataset: openbookqa_pairs at 388097e
• Size: 2,740 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 3 tokens mean: 13.83 tokens max: 78 tokens	min: 4 tokens mean: 11.37 tokens max: 30 tokens

• Samples:

  sentence1	sentence2
  The sun is responsible for	the sun is the source of energy for physical cycles on Earth
  When food is reduced in the stomach	digestion is when stomach acid breaks down food
  Stars are	a star is made of gases

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

msmarco_pairs

• Dataset: msmarco_pairs at 28ff31e
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 4 tokens mean: 8.61 tokens max: 27 tokens	min: 18 tokens mean: 75.09 tokens max: 206 tokens

• Samples:

  sentence1	sentence2
  what are the liberal arts?	liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects.
  what is the mechanism of action of fibrinolytic or thrombolytic drugs?	Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure.
  what is normal plat count	78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

nq_pairs

• Dataset: nq_pairs at f9e894e
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 10 tokens mean: 11.77 tokens max: 21 tokens	min: 16 tokens mean: 131.57 tokens max: 512 tokens

• Samples:

  sentence1	sentence2
  when did richmond last play in a preliminary final	Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.
  who sang what in the world's come over you	Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.
  who produces the most wool in the world	Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

trivia_pairs

• Dataset: trivia_pairs at a7c36e3
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 8 tokens mean: 15.16 tokens max: 48 tokens	min: 19 tokens mean: 456.87 tokens max: 512 tokens

• Samples:

  sentence1	sentence2
  Which American-born Sinclair won the Nobel Prize for Literature in 1930?	The Nobel Prize in Literature 1930 The Nobel Prize in Literature 1930 Sinclair Lewis The Nobel Prize in Literature 1930 Sinclair Lewis Prize share: 1/1 The Nobel Prize in Literature 1930 was awarded to Sinclair Lewis "for his vigorous and graphic art of description and his ability to create, with wit and humour, new types of characters". Photos: Copyright © The Nobel Foundation Share this: To cite this page MLA style: "The Nobel Prize in Literature 1930". Nobelprize.org. Nobel Media AB 2014. Web. 18 Jan 2017. http://www.nobelprize.org/nobel_prizes/literature/laureates/1930/
  Where in England was Dame Judi Dench born?	Judi Dench - IMDb IMDb Actress
  In which decade did Billboard magazine first publish and American hit chart?	The US Billboard song chart The US Billboard song chart Search this site with Google Song chart US Billboard The Billboard magazine has published various music charts starting (with sheet music) in 1894, the first "Music Hit Parade" was published in 1936 , the first "Music Popularity Chart" was calculated in 1940 . These charts became less irregular until the weekly "Hot 100" was started in 1958 . The current chart combines sales, airplay and downloads. A music collector that calls himself Bullfrog has been consolidating the complete chart from 1894 to the present day. he has published this information in a comprehenive spreadsheet (which can be obtained at bullfrogspond.com/ ). The Bullfrog data assigns each song a unique identifier, something like "1968_076" (which just happens to be the Bee Gees song "I've Gotta Get A Message To You"). This "Whitburn Number" is provided to match with the books of Joel Whitburn and consists of the year and a ranking within the year. A song that first entered the charts in December and has a long run is listed the following year. This numbering scheme means that songs which are still in the charts cannot be assigned a final id, because their ranking might change. So the definitive listing for a year cannot be final until about April. In our listing we only use songs with finalised IDs, this means that every year we have to wait until last year's entries are finalised before using them. (Source bullfrogspond.com/ , the original version used here was 20090808 with extra data from: the 2009 data from 20091219 the 2010 data from 20110305 the 2011 data from 20120929 the 2012 data from 20130330 the 2013 data from 20150328 The 20150328 data was the last one produced before the Billboard company forced the data to be withdrawn. As far as we know there are no more recent data sets available. This pattern of obtaining the data for a particular year in the middle of the following one comes from the way that the Bullfrog project generates the identifier for a song (what they call the "Prefix" in the spreadsheet). Recent entries are identified with keys like "2015-008" while older ones have keys like "2013_177". In the second case the underscore is significant, it indicates that this was the 177th biggest song released in 2013. Now, of course, during the year no one knows where a particular song will rank, so the underscore names can't be assigned until every song from a particular year has dropped out of the charts, so recent records are temporarily assigned a name with a dash. In about May of the following year the rankings are calculated and the final identifiers are assigned. That is why we at the Turret can only grab this data retrospectively. Attributes The original spreadsheet has a number of attributes, we have limited our attention to just a few of them: 134 9 The songs with the most entries on the chart were White Christmas (with 33 versions and a total of 110 weeks) and Stardust (with 19 and a total of 106 weeks). position The peak position that songs reached in the charts should show an smooth curve from number one down to the lowest position. This chart has more songs in the lower peak positions than one would expect. Before 1991 the profile of peak positions was exactly as you would expect, that year Billboard introduced the concept of "Recurrent" tracks, that is they removed any track from the chart which had spent more than twenty weeks in the chart and had fallen to the lower positions. weeks The effect of the "Recurrent" process, by which tracks are removed if they have spent at least twenty weeks in the chart and have fallen to the lower reaches, can clearly be seen in the strange spike in this attribute. This "adjustment" was intended to promote newer songs and ensure the chart does not become "stale". In fact since it was introduced in 1991 the length of long chart runs has increased, this might reflect the more conscious efforts of record companies to "game" the charts by controlling release times and promotions, or it coul

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

quora_pairs

• Dataset: quora_pairs at 451a485
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 6 tokens mean: 13.53 tokens max: 42 tokens	min: 6 tokens mean: 13.68 tokens max: 43 tokens

• Samples:

  sentence1	sentence2
  Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?	I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?
  How can I be a good geologist?	What should I do to be a great geologist?
  How do I read and find my YouTube comments?	How can I see all my Youtube comments?

• Loss: MultipleNegativesSymmetricRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

gooaq_pairs

• Dataset: gooaq_pairs at b089f72
• Size: 10,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 8 tokens mean: 11.6 tokens max: 21 tokens	min: 13 tokens mean: 57.74 tokens max: 127 tokens

• Samples:

  sentence1	sentence2
  is toprol xl the same as metoprolol?	Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure.
  are you experienced cd steve hoffman?	The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design.
  how are babushka dolls made?	Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Datasets

nli-pairs

• Dataset: nli-pairs at d482672
• Size: 6,808 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 17.64 tokens max: 63 tokens	min: 4 tokens mean: 9.67 tokens max: 29 tokens

• Samples:

  anchor	positive
  Two women are embracing while holding to go packages.	Two woman are holding packages.
  Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.	Two kids in numbered jerseys wash their hands.
  A man selling donuts to a customer during a world exhibition event held in the city of Angeles	A man selling donuts to a customer.

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

scitail-pairs-pos

• Dataset: scitail-pairs-pos at 0cc4353
• Size: 1,304 evaluation samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 5 tokens mean: 22.52 tokens max: 67 tokens	min: 8 tokens mean: 15.34 tokens max: 36 tokens	0: ~47.50% 1: ~52.50%

• Samples:

  sentence1	sentence2	label
  An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions.	Replace another in a molecule happens to atoms during a substitution reaction.	0
  Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase;	Wavelength is the distance between two corresponding points of adjacent waves called.	1
  humans normally have 23 pairs of chromosomes.	Humans typically have 23 pairs pairs of chromosomes.	1

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

qnli-contrastive

• Dataset: qnli-contrastive at bcdcba7
• Size: 5,463 evaluation samples
• Columns: sentence1, sentence2, and label
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	min: 6 tokens mean: 14.13 tokens max: 36 tokens	min: 4 tokens mean: 36.58 tokens max: 225 tokens	0: 100.00%

• Samples:

  sentence1	sentence2	label
  What came into force after the new constitution was herald?	As of that day, the new constitution heralding the Second Republic came into force.	0
  What is the first major city in the stream of the Rhine?	The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz.	0
  What is the minimum required if you want to teach in Canada?	In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher.	0

• Loss: OnlineContrastiveLoss

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 28
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• weight_decay: 1e-10
• num_train_epochs: 2
• lr_scheduler_type: cosine
• warmup_ratio: 0.33
• save_safetensors: False
• fp16: True
• push_to_hub: True
• hub_model_id: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp
• hub_strategy: checkpoint
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 28
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• learning_rate: 2e-05
• weight_decay: 1e-10
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 2
• max_steps: -1
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.33
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: False
• 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: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• 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: True
• resume_from_checkpoint: None
• hub_model_id: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp
• hub_strategy: checkpoint
• hub_private_repo: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	nli-pairs loss	qnli-contrastive loss	scitail-pairs-pos loss	sts-test_spearman_cosine
None	0	-	2.9404	4.1500	2.3949	-
0.1	471	3.3296	1.8879	2.2598	1.3439	-
0.2	942	1.8704	0.9546	1.8402	0.5629	-
0.3	1413	1.2621	0.7152	1.3887	0.4553	-
0.4	1884	1.2512	0.5274	0.8418	0.3621	-
0.5	2355	1.1724	0.4927	0.1424	0.3924	-
0.6	2826	0.9036	0.4621	0.3409	0.3777	-
0.7	3297	1.0374	0.4111	0.2125	0.3417	-
0.8	3768	0.9259	0.3853	0.1646	0.2819	-
0.9	4239	0.8709	0.3749	0.1157	0.2912	-
1.0	4710	0.8686	0.3636	0.0961	0.3109	-
1.1	5181	0.726	0.3744	0.0453	0.3424	-
1.2	5652	0.8151	0.3502	0.1835	0.2602	-
1.3	6123	0.7127	0.3362	0.1089	0.2460	-
1.4	6594	0.8408	0.3184	0.0701	0.2784	-
1.5	7065	0.7845	0.3191	0.0318	0.2822	-
1.6	7536	0.5766	0.3056	0.0566	0.2774	-
1.7	8007	0.7304	0.2991	0.0542	0.2736	-
1.8	8478	0.6639	0.2949	0.0515	0.2694	-
1.9	8949	0.6153	0.2938	0.0589	0.2718	-
2.0	9420	0.6665	0.2937	0.0569	0.2724	0.7727

Framework Versions

• Python: 3.10.13
• Sentence Transformers: 3.0.1
• Transformers: 4.41.2
• PyTorch: 2.1.2
• Accelerate: 0.30.1
• Datasets: 2.19.2
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}

GISTEmbedLoss

@misc{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning}, 
    author={Aivin V. Solatorio},
    year={2024},
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}