bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-AllSoft

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-v2-AllSoft")
# Run inference
sentences = [
    'How many hymns of Luther were included in the Achtliederbuch?',
    "Luther's hymns were included in early Lutheran hymnals and spread the ideas of the Reformation.",
    'the ABC News building was renamed Peter Jennings Way in 2006 in honor of the recently deceased longtime ABC News chief anchor and anchor of World News Tonight.',
]
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	0.7746
spearman_cosine	0.769
pearson_manhattan	0.7642
spearman_manhattan	0.7545
pearson_euclidean	0.7622
spearman_euclidean	0.7523
pearson_dot	0.6433
spearman_dot	0.6187
pearson_max	0.7746
spearman_max	0.769

Training Details

Training Datasets

nli-pairs

• Dataset: nli-pairs at d482672
• Size: 6,500 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

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: 3,194 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: 15.76 tokens max: 75 tokens	min: 8 tokens mean: 37.3 tokens max: 502 tokens

• Samples:

  label	sentence1	sentence2
  1	The film will be screened in 2200 theaters .	In the United States and Canada , pre-release tracking suggest the film will gross $ 7�8 million from 2,200 theaters in its opening weekend , trailing fellow newcomer 10 Cloverfield Lane ( $ 25�30 million projection ) , but similar t
  1	Neighbors 2 : Sorority Rising ( film ) scored over 65 % on Rotten Tomatoes .	On Rotten Tomatoes , the film has a rating of 67 % , based on 105 reviews , with an average rating of 5.9/10 .
  1	Averaged on more than 65 reviews , The Handmaiden scored 94 % .	On Rotten Tomatoes , the film has a rating of 94 % , based on 67 reviews , with an average rating of 8/10 .

• Loss: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

qnli-contrastive

• Dataset: qnli-contrastive at bcdcba7
• Size: 4,000 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: 13.64 tokens max: 30 tokens	min: 6 tokens mean: 34.57 tokens max: 149 tokens	0: 100.00%

• Samples:

  sentence1	sentence2	label
  What professors established the importance of Whitehead's work?	Professors such as Wieman, Charles Hartshorne, Bernard Loomer, Bernard Meland, and Daniel Day Williams made Whitehead's philosophy arguably the most important intellectual thread running through the Divinity School.	0
  When did people start living on the edge of the desert?	It was long believed that the region had been this way since about 1600 BCE, after shifts in the Earth's axis increased temperatures and decreased precipitation.	0
  What was the title of Gertrude Stein's 1906-1908 book?	Picasso in turn was an important influence on Stein's writing.	0

• Loss: AdaptiveLayerLoss with these parameters:

  {
      "loss": "OnlineContrastiveLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

scitail-pairs-qa

• Dataset: scitail-pairs-qa at 0cc4353
• Size: 4,300 training samples
• Columns: sentence2 and sentence1
• Approximate statistics based on the first 1000 samples:

  	sentence2	sentence1
  type	string	string
  details	min: 7 tokens mean: 16.2 tokens max: 41 tokens	min: 7 tokens mean: 14.65 tokens max: 33 tokens

• Samples:

  sentence2	sentence1
  Ash that enters the air naturally as a result of a volcano eruption is classified as a primary pollutant.	Ash that enters the air naturally as a result of a volcano eruption is classified as what kind of pollutant?
  Exposure to ultraviolet radiation can increase the amount of pigment in the skin and make it appear darker.	Exposure to what can increase the amount of pigment in the skin and make it appear darker?
  A lysozyme destroys bacteria by digesting their cell walls.	How does lysozyme destroy bacteria?

• Loss: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

scitail-pairs-pos

• Dataset: scitail-pairs-pos at 0cc4353
• Size: 2,200 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 7 tokens mean: 23.6 tokens max: 74 tokens	min: 7 tokens mean: 15.23 tokens max: 41 tokens

• Samples:

  sentence1	sentence2
  An atom that gains electrons would be a negative ion.	Atoms that have gained electrons and become negatively charged are called negative ions.
  Scientists will use data collected during the collisions to explore the particles known as quarks and gluons that make up protons and neutrons.	Protons and neutrons are made of quarks, which are fundamental particles of matter.
  Watersheds and divides All of the land area whose water drains into a stream system is called the system's watershed.	All of the land drained by a river system is called its basin, or the "wet" term watershed

• Loss: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

xsum-pairs

• Dataset: xsum-pairs at 788ddaf
• Size: 2,500 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 2 tokens mean: 350.46 tokens max: 512 tokens	min: 6 tokens mean: 27.13 tokens max: 70 tokens

• Samples:

  sentence1	sentence2
  An eyewitness told BBC Persian that the crowds were sharply divided between hardliners and moderates, but it was clear many people had responded to a call from former President Mohammad Khatami to attend the funeral as a show of support for the opposition reform movement. Some were chanting opposition slogans, and others carried placards emphasising Mr Rafsanjani's links to the moderate and reformist camps. "Long live Khatami, Long Live Rouhani. Hashemi, your soul is at peace!" said one banner. "The circle became too closed for the centre," said another, using a quotation from Persian poetry to underline the growing distance in recent years between Mr Rafsanjani and Iran's hardline political establishment. At one stage state television played loud music over its live broadcast of the event in order to drown out opposition slogans being chanted by the crowd. As the official funeral eulogies were relayed to the crowds on the streets, they responded with calls of support for former President Khatami, and opposition leader Mir Hossein Mousavi, and shouts of: "You have the loudspeakers, we have the voice! Shame on you, Shame on State TV!" On Iranian social media the funeral has been the number one topic with many opposition supporters using the hashtag #weallgathered to indicate their support and sympathy. People have been posting photos and videos emphasising the number of opposition supporters out on the streets and showing the opposition slogans which state TV has been trying to obscure. But government supporters have also taken to Twitter to play down the opposition showing at the funeral, accusing them of political opportunism. "A huge army came out of love of the Supreme Leader," wrote a cleric called Sheikh Reza. "While a few foot soldiers came with their cameras to show off." Another conversation engaging many on Twitter involved the wording of the prayers used at the funeral. Did the Supreme Leader Ayatollah Ali Khamenei deliberately leave out a section praising the goodness of the deceased, some opposition supporters asked. And was this a comment on the political tensions between the two? "No," responded another Twitter user, cleric Abbas Zolghadri. "The words of the prayer can be changed. There are no strict rules." He followed this with a poignant photo of an empty grave - "Hashemi's final resting place" was the caption, summing up the sense of loss felt by Iranians of many different political persuasions despite the deep and bitter divisions.	Tehran has seen some of the biggest crowds on the streets since the 2009 "Green Movement" opposition demonstrations, as an estimated 2.5 million people gathered to bid farewell to Akbar Hashemi Rafsanjani, the man universally known as "Hashemi".
  Mark Evans is retracing the same route across the Rub Al Khali, also known as the "Empty Quarter", taken by Bristol pioneer Bertram Thomas in 1930. The 54-year-old Shropshire-born explorer is leading a three-man team to walk the 800 mile (1,300 km) journey from Salalah, Oman to Doha, Qatar. The trek is expected to take 60 days. The Rub Al Khali desert is considered one of the hottest, driest and most inhospitable places on earth. Nearly two decades after Thomas completed his trek, British explorer and writer Sir Wilfred Thesiger crossed the Empty Quarter - mapping it in detail along the way. 60 days To cross the Rub' Al Khali desert * From Salalah in Oman to Doha, Qatar * Walking with camels for 1,300km * Area nearly three times the size of the UK Completed by explorer Bertram Thomas in 1930 Bertram Thomas, who hailed from Pill, near Bristol, received telegrams of congratulation from both King George V and Sultan Taimur, then ruler of Oman. He went on to lecture all over the world about the journey and to write a book called Arabia Felix. Unlike Mr Evans, Thomas did not obtain permission for his expedition. He said: "The biggest challenges for Thomas were warring tribes, lack of water in the waterholes and his total dependence on his Omani companion Sheikh Saleh to negotiate their way through the desert. "The biggest challenge for those who wanted to make the crossing in recent decades has been obtaining government permissions to walk through this desolate and unknown territory."	An explorer has embarked on a challenge to become only the third British person in history to cross the largest sand desert in the world.
  An Olympic gold medallist, he was also three-time world heavyweight champion and took part in some of the most memorable fights in boxing history. He had a professional career spanning 21 years and BBC Sport takes a look at his 61 fights in more detail.	Boxing legend Muhammad Ali, who died at the age of 74, became a sporting icon during his career.

• Loss: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

compression-pairs

• Dataset: compression-pairs at 605bc91
• Size: 4,000 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "MultipleNegativesSymmetricRankingLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

sciq_pairs

• Dataset: sciq_pairs at 2c94ad3
• Size: 6,500 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

qasc_pairs

• Dataset: qasc_pairs at a34ba20
• Size: 6,500 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

msmarco_pairs

• Dataset: msmarco_pairs at 28ff31e
• Size: 6,500 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

nq_pairs

• Dataset: nq_pairs at f9e894e
• Size: 6,500 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

trivia_pairs

• Dataset: trivia_pairs at a7c36e3
• Size: 6,500 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

quora_pairs

• Dataset: quora_pairs at 451a485
• Size: 4,000 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

gooaq_pairs

• Dataset: gooaq_pairs at b089f72
• Size: 6,500 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

Evaluation Datasets

nli-pairs

• Dataset: nli-pairs at d482672
• Size: 750 evaluation samples
• Columns: anchor and positive
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	min: 5 tokens mean: 17.61 tokens max: 51 tokens	min: 4 tokens mean: 9.71 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

scitail-pairs-pos

• Dataset: scitail-pairs-pos at 0cc4353
• Size: 750 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: 22.43 tokens max: 61 tokens	min: 8 tokens mean: 15.3 tokens max: 36 tokens	0: ~50.00% 1: ~50.00%

• 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

qnli-contrastive

• Dataset: qnli-contrastive at bcdcba7
• Size: 750 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: 14.15 tokens max: 36 tokens	min: 4 tokens mean: 36.98 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: AdaptiveLayerLoss with these parameters:

  {
      "loss": "OnlineContrastiveLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 2,
      "prior_layers_weight": 0.1,
      "kl_div_weight": 0.5,
      "kl_temperature": 1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 28
• per_device_eval_batch_size: 18
• learning_rate: 2e-05
• weight_decay: 1e-06
• num_train_epochs: 2
• lr_scheduler_type: cosine_with_restarts
• lr_scheduler_kwargs: {'num_cycles': 3}
• warmup_ratio: 0.25
• save_safetensors: False
• fp16: True
• push_to_hub: True
• hub_model_id: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-2-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: 18
• 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-06
• 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_with_restarts
• lr_scheduler_kwargs: {'num_cycles': 3}
• warmup_ratio: 0.25
• 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-v2-2-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

Click to expand

Epoch	Step	Training Loss	nli-pairs loss	qnli-contrastive loss	scitail-pairs-pos loss	sts-test_spearman_cosine
0	0	-	-	-	-	0.4188
0.0253	71	9.7048	-	-	-	-
0.0503	141	-	7.9860	8.4771	6.6165	-
0.0507	142	8.6743	-	-	-	-
0.0760	213	8.101	-	-	-	-
0.1006	282	-	6.8505	7.5583	4.4099	-
0.1014	284	7.5594	-	-	-	-
0.1267	355	6.3548	-	-	-	-
0.1510	423	-	5.2238	6.2964	2.3430	-
0.1520	426	5.869	-	-	-	-
0.1774	497	5.1134	-	-	-	-
0.2013	564	-	4.5785	5.6786	1.8733	-
0.2027	568	5.1262	-	-	-	-
0.2281	639	3.7625	-	-	-	-
0.2516	705	-	3.9531	5.1247	1.6374	-
0.2534	710	4.5256	-	-	-	-
0.2787	781	3.8572	-	-	-	-
0.3019	846	-	3.5362	4.5487	1.5215	-
0.3041	852	3.9294	-	-	-	-
0.3294	923	3.281	-	-	-	-
0.3522	987	-	3.1562	3.7942	1.4236	-
0.3547	994	3.2531	-	-	-	-
0.3801	1065	3.9305	-	-	-	-
0.4026	1128	-	2.7059	3.4370	1.2689	-
0.4054	1136	3.0324	-	-	-	-
0.4308	1207	3.3544	-	-	-	-
0.4529	1269	-	2.5396	3.0366	1.2415	-
0.4561	1278	3.2331	-	-	-	-
0.4814	1349	3.1913	-	-	-	-
0.5032	1410	-	2.2846	2.7076	1.1422	-
0.5068	1420	2.7389	-	-	-	-
0.5321	1491	2.9541	-	-	-	-
0.5535	1551	-	2.1732	2.3780	1.2127	-
0.5575	1562	3.0911	-	-	-	-
0.5828	1633	2.932	-	-	-	-
0.6039	1692	-	2.0257	1.9252	1.1056	-
0.6081	1704	3.082	-	-	-	-
0.6335	1775	3.0328	-	-	-	-
0.6542	1833	-	1.9588	2.0366	1.1187	-
0.6588	1846	2.9508	-	-	-	-
0.6842	1917	2.7445	-	-	-	-
0.7045	1974	-	1.8310	1.9980	1.0991	-
0.7095	1988	2.8922	-	-	-	-
0.7348	2059	2.7352	-	-	-	-
0.7548	2115	-	1.7650	1.5015	1.1103	-
0.7602	2130	3.2009	-	-	-	-
0.7855	2201	2.6261	-	-	-	-
0.8051	2256	-	1.6932	1.6964	1.0409	-
0.8108	2272	2.6623	-	-	-	-
0.8362	2343	2.8281	-	-	-	-
0.8555	2397	-	1.6844	1.7854	1.0300	-
0.8615	2414	2.3096	-	-	-	-
0.8869	2485	2.4088	-	-	-	-
0.9058	2538	-	1.6698	1.8310	1.0275	-
0.9122	2556	2.6051	-	-	-	-
0.9375	2627	2.972	-	-	-	-
0.9561	2679	-	1.6643	1.8173	1.0215	-
0.9629	2698	2.4207	-	-	-	-
0.9882	2769	2.2772	-	-	-	-
1.0064	2820	-	1.7130	1.7650	1.0496	-
1.0136	2840	2.6348	-	-	-	-
1.0389	2911	2.8271	-	-	-	-
1.0567	2961	-	1.6939	2.1074	0.9858	-
1.0642	2982	2.5215	-	-	-	-
1.0896	3053	2.7442	-	-	-	-
1.1071	3102	-	1.6633	1.5590	0.9903	-
1.1149	3124	2.6155	-	-	-	-
1.1403	3195	2.7053	-	-	-	-
1.1574	3243	-	1.6242	1.6429	0.9740	-
1.1656	3266	2.9191	-	-	-	-
1.1909	3337	2.1112	-	-	-	-
1.2077	3384	-	1.6535	1.6226	0.9516	-
1.2163	3408	2.3519	-	-	-	-
1.2416	3479	1.9416	-	-	-	-
1.2580	3525	-	1.6103	1.6530	0.9357	-
1.2670	3550	2.0859	-	-	-	-
1.2923	3621	2.0109	-	-	-	-
1.3084	3666	-	1.5773	1.4672	0.9155	-
1.3176	3692	2.366	-	-	-	-
1.3430	3763	1.5532	-	-	-	-
1.3587	3807	-	1.5514	1.4451	0.8979	-
1.3683	3834	1.9982	-	-	-	-
1.3936	3905	2.4375	-	-	-	-
1.4090	3948	-	1.5254	1.4050	0.8834	-
1.4190	3976	1.7548	-	-	-	-
1.4443	4047	2.2272	-	-	-	-
1.4593	4089	-	1.5186	1.3720	0.8835	-
1.4697	4118	2.2145	-	-	-	-
1.4950	4189	1.8696	-	-	-	-
1.5096	4230	-	1.5696	1.0682	0.9336	-
1.5203	4260	1.4926	-	-	-	-
1.5457	4331	2.1193	-	-	-	-
1.5600	4371	-	1.5469	0.8180	0.9663	-
1.5710	4402	2.0298	-	-	-	-
1.5964	4473	1.9959	-	-	-	-
1.6103	4512	-	1.4656	1.1725	0.8815	-
1.6217	4544	2.3452	-	-	-	-
1.6470	4615	1.9529	-	-	-	-
1.6606	4653	-	1.4709	1.1081	0.9079	-
1.6724	4686	1.7932	-	-	-	-
1.6977	4757	2.1881	-	-	-	-
1.7109	4794	-	1.4526	0.9851	0.9167	-
1.7231	4828	2.1128	-	-	-	-
1.7484	4899	2.4772	-	-	-	-
1.7612	4935	-	1.4204	0.8683	0.8896	-
1.7737	4970	2.4336	-	-	-	-
1.7991	5041	1.9101	-	-	-	-
1.8116	5076	-	1.3821	1.0420	0.8538	-
1.8244	5112	2.3882	-	-	-	-
1.8498	5183	2.2165	-	-	-	-
1.8619	5217	-	1.3747	1.0753	0.8580	-
1.8751	5254	1.6554	-	-	-	-
1.9004	5325	2.3828	-	-	-	-
1.9122	5358	-	1.3637	1.0699	0.8557	-
1.9258	5396	2.3499	-	-	-	-
1.9511	5467	2.3972	-	-	-	-
1.9625	5499	-	1.3583	1.0596	0.8536	-
1.9764	5538	1.931	-	-	-	-
2.0	5604	-	1.3586	1.0555	0.8543	0.7193

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",
}

AdaptiveLayerLoss

@misc{li20242d,
    title={2D Matryoshka Sentence Embeddings}, 
    author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
    year={2024},
    eprint={2402.14776},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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}
}