bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step1

SentenceTransformer based on microsoft/deberta-v3-small

This is a sentence-transformers model finetuned from microsoft/deberta-v3-small on the nli-pairs, vitaminc-pairs, 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
  • vitaminc-pairs
  • 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-v3-step1")
# Run inference
sentences = [
    "4 Be able to explain why the energy levels in multielectron atoms differ from the levels in the hydrogen-like atom 5 Be able to state and apply the Pauli Exclusion Principle, Hund's Rule, and the Aufbau Principle.",
    'Aufbau principle gives the order of electron filling in an atom.',
    'Birds pair up with the same bird in mating season.',
]
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	0.7946
spearman_cosine	0.8008
pearson_manhattan	0.8013
spearman_manhattan	0.7916
pearson_euclidean	0.8006
spearman_euclidean	0.7918
pearson_dot	0.7592
spearman_dot	0.7392
pearson_max	0.8013
spearman_max	0.8008

Training Details

Training Datasets

nli-pairs

• Dataset: nli-pairs at d482672
• Size: 100,000 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

vitaminc-pairs

• Dataset: vitaminc-pairs at be6febb
• Size: 50,066 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: 17.49 tokens max: 49 tokens	min: 8 tokens mean: 36.16 tokens max: 187 tokens

• Samples:

  label	sentence1	sentence2
  1	Lady Gaga 's second solo album is Born This Way .	Born This Way is the second solo studio album by American singer Lady Gaga , released by Interscope Records on May 23 , 2011 .
  1	Mike Conley Jr. signed a five year deal with the Grizzlies .	On July 1 , 2016 , Conley would agree to sign a five-year deal with the Grizzlies for around the maximum value of $ 153 million.
  1	The Metacritic 's score to Neighbors 2 : Sorority Rising ( film ) was based on more than 14 reviews .	On Metacritic , the film has a score of 54 out of 100 , based on 15 critics , indicating '' mixed or average reviews '' '' . ''

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

scitail-pairs-qa

• Dataset: scitail-pairs-qa at 0cc4353
• Size: 14,987 training samples
• Columns: sentence2 and sentence1
• Approximate statistics based on the first 1000 samples:

  	sentence2	sentence1
  type	string	string
  details	min: 7 tokens mean: 15.79 tokens max: 41 tokens	min: 8 tokens mean: 15.07 tokens max: 33 tokens

• Samples:

  sentence2	sentence1
  The most significant connection between the cerebellum and the rest of the brain is at the pons.	Where is the most significant connection between the cerebellum and the rest of the brain?
  Any large molecule is referred to as a macromolecule.	Any large molecule is referred to as what?
  Melanocytes are located in the bottom layer of the epidermis.	Melanocytes are located in which layer of the epidermis?

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

scitail-pairs-pos

• Dataset: scitail-pairs-pos at 0cc4353
• Size: 8,600 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 7 tokens mean: 23.79 tokens max: 67 tokens	min: 7 tokens mean: 15.51 tokens max: 39 tokens

• Samples:

  sentence1	sentence2
  Cytokinesis is the division of the cytoplasm of a parent cell.	The cytoplasm is divided during cytokinesis.
  And on one of these planets, the Earth, the first living cell arose around four billion years ago.	The first living cells may have evolved around 4 billion years ago.
  lower pH levels indicate increasing acidity, while pH levels higher than 7 indicate increasingly basic solutions.	As ph increases, a solution becomes more basic.

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

xsum-pairs

• Dataset: xsum-pairs at 788ddaf
• Size: 100,000 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 15 tokens mean: 352.28 tokens max: 512 tokens	min: 3 tokens mean: 26.88 tokens max: 62 tokens

• Samples:

  sentence1	sentence2
  The tourists will play three T20 internationals from 17-22 February. Malinga, 33, has not played a competitive game since February 2016 when a knee injury ruled him out of the following month's World Twenty20. He returned to training in September but suffered further setbacks. A bout of dengue fever scuppered hopes that he might return for the limited-overs leg of Sri Lanka's recent tour of South Africa. With an idiosyncratic, slingy bowling action capable of delivering a devastating yorker, Malinga made a huge impression on the international scene after his 2004 debut. He helped Sri Lanka reach the finals of the 2007 and 2011 World Cups, and 2009 and 2012 World T20s, although they were losing finalists on each occasion. Malinga took over as Sri Lanka's Twenty20 captain midway through the 2014 World T20 in Bangladesh, and lifted the trophy after they beat India in the final, but he stepped down as skipper last March. He retired from Test cricket in 2011, citing a "long-standing degenerative condition" which left his right knee unable to stand up to the rigours of five-day cricket. With captain Angelo Mathews still injured, Upul Tharanga will continue to lead the Sri Lanka side down under. Sri Lanka T20 squad for Australia: Upul Tharanga (capt), Niroshan Dickwella (wk), Asela Gunaratna, Chamara Kapugedara, Nuwan Kulasekara, Lasith Malinga, Kusal Mendis, Dilshan Munaweera, Sachith Pathirana, Seekkuge Prasanna, Lakshan Sandakan, Vikum Sanjaya, Dasun Shanaka, Milinda Siriwardana, Isuru Udana.	Pace bowler Lasith Malinga is set to make his comeback from a year out of international cricket after being named in Sri Lanka's Twenty20 squad to tour Australia later this month.
  The International Energy Agency (IEA) estimates 585 million people in sub-Saharan Africa lack access to electricity, with the electrification rate as low as 14.2% in rural areas. The problem is most acute in East Africa, where only 23% of Kenyans; 10.8% of Rwandans; and 14.8% of Tanzanians have access to an electricity supply, according to the World Bank. In spite of efforts to get people onto the grid, population growth has meant these figures stay fairly steady, with the majority of people still using costly and unhealthy forms of energy for cooking and lighting. A number of companies and organisations on the continent have identified solar power as the solution. And a new breed of "solar-preneurs" is emerging, increasing access to power and generating revenues at the same time. "Solar is a valuable source of distributed energy," says Sachi DeCou, co-founder of Juabar, a company operating a network of solar charging kiosks in Tanzania. "In many places in sub-Saharan Africa, populations are quite dispersed. Solar is modular so it can be sized to fit the needs of anywhere, from a light to a business, household to an entire village." In agreement is Jesse Moore, managing director at M-Kopa Solar, which provides "pay-as-you-go" renewable energy for off-grid households in Kenya, Uganda and Tanzania. Off-grid households in East Africa, which also are largely low-income households, spend about $0.50-$0.60 (33p-40p) per day on kerosene lighting and basic charging costs, he says. "With more than 20 million homes off the grid, this means over $3bn (£2bn) is spent each year on these inefficient and unsafe energy substitutes." Given the inefficiencies and high costs associated with alternative power sources, solar has proven hugely popular in places where it has been available. M-Kopa Solar provides power to more than 140,000 households in East Africa for $0.45 per day, and is adding over 4,000 homes each week. And with this increased uptake comes economic opportunities for the companies that provide it. M-Kopa Solar's revenues are nearing $20m per year, and the company is starting to licence its technology in other markets, such as Ghana. "M-Kopa is demonstrating that off-grid energy will be as revolutionary to Africa in the coming decades as mobile telecommunications have been in recent years. Solar is a massive opportunity for entrepreneurs and investors alike," Mr Moore says. Other business models are seeking to allow ordinary African individuals to start their own solar businesses. Ms DeCou's Juabar, for example, builds and operates a network of solar charging kiosks in Tanzania which it leases to entrepreneurs, who then offer electricity services to their communities. Juabar's entrepreneurs are currently earning profits of between $75 and $150 per month, with the company currently leasing out 30 kiosks to Tanzanians and looking to raise $15,000 through crowdfunding in order to increase that number to 50. "There has been a lot of development in the pay-as-you-go solar space over the past few years, facilitating access by reducing the upfront costs of purchasing a solar system," Ms DeCou says. "As we continue to make solar technology more widely available and affordable, one of the most exciting areas of opportunity becomes what you can do with this solar electricity. "Solar is a source of reliable, accessible electricity. Once you can develop that access you have the opportunity to develop new ways to use that electricity to meet community needs. That is what I get most excited about." Henri Nyakarundi is employing a similar model in Rwanda. His company has developed a mobile solar charging kiosk. The kiosks are operated under a franchise model, offering Rwandans the chance to run income-generating businesses by providing services such as charging of electronics and sales of electronic vouchers. Wi-fi hotspots will be available from the kiosks soon - there are already 24 up and running with another 100 due this year. Mr Nyakarundi says he plans to offer a single distribution channel for different products, services and content, while providing opportunities for entrepreneurship through a low-cost franchise model. He believes the opportunities to create solar businesses in Africa are "huge", but as yet, they only exist at the micro level. The next step, he believes, is to move to the macro level - producing power for the grid through solar. "However macro level requires large investment, and unfortunately local banks are still not willing to finance such projects unless you are a big company," Mr Nyakarundi says. Ms DeCou and Mr Moore cite different issues, with the Juabar co-founder saying there was a lack of adequate data on population density in the areas where the company works. "We do our own research to determine ideal places for expansion, as there is limited access to reliable maps of population distribution," she says. Mr Moore says the main obstacle to the growth of solar in Africa is the unaffordability of purchasing solar power "up front" for consumers, though he believes M-Kopa Solar has been so successful to date because it addresses the affordability barrier head on. As the likes of Ms DeCou, Mr Moore and Mr Nyakarundi look to boost access to solar and the entrepreneurial opportunities associated with it, assistance has been on hand from east African governments. Ms DeCou commends the Tanzanian government for not charging Value Added Tax (VAT) on solar products, which she says is a great support to the industry and helps to increase access. "Beyond that, there are specific government programmes to help facilitate rural energy access," she adds. "East African countries offer VAT exemption on all solar products, which is a big saving for a small company like ours," Mr Nyakarundi says. "It will be great to see an east African R&D [research and development] fund for local entrepreneurs that wants to develop new innovative technology to solve our local challenges, so we can stop just importing foreign technology - which most of the time is not designed for the African market - and create a new industry that can level the playing field between Africa and the rest of the world."	African economies may be booming, but continued growth and quality of life are being jeopardised by lack of power.
  A support team is being sent to the Royal Alexandra Hospital in Paisley where, last month, 84% of patients were treated within a four-hour timeframe. The national target is 95%. First Minister Nicola Sturgeon said intervening was a "responsible move". Labour and the Liberal Democrats have claimed the problem is widespread. Jenny Marra, Scottish Labour's health and wellbeing spokeswoman, said the Scottish government's decision to step in at the Royal Alexandra "confirms what Scottish Labour have been saying for weeks - we have an A&E crisis in Scotland". She said: "What they can't do is treat this as a localised problem to Paisley. This is a problem up and down the country. "I was in Fife yesterday and it's a problem there. They really need to be looking at this right across Scotland. "We have heard terrible stories from Glasgow, from Fife, from Paisley, from up north. The Scottish government needs to realise this is a problem across Scotland." Ms Marra called for weekly reporting of accident and emergency (A&E) statistics. The last set of A&E figures were for the period from October to December. From February the figures will be published monthly. Scottish Liberal Democrat health spokesperson Jim Hume also said there was a crisis in A&E departments across Scotland. "The SNP government took its eye off the ball and now it is attempting to whitewash the growing crisis in Scotland's A&E units," he said. "The reality is that the crisis in emergency care is worsening - not improving. Why were the public only told about the extent of the situation facing Royal Alexandra Hospital once the Scottish government sent in an emergency support team? "People will understandably be concerned that other hospitals could be on the brink of a similar situation." Opposition comment came after Ms Sturgeon defended the move to send support to the Paisley hospital. Speaking on BBC Radio Scotland's Good Morning Scotland programme, the first minister said: "Why we've taken the action of sending a support team into the Royal Alexandra Hospital is because we are concerned we're not seeing the degree of recovery in that hospital that we are seeing at other hospitals and that we would be expecting. "It's a responsible move, we will have unscheduled care managers from the Scottish government in there to help the management on the ground look at different ways of speeding up the flow of patients through A&E, and doing what is most important of all - making sure patients get the quick access to treatment that they deserve." Ms Sturgeon said that accident and emergency (A&E) statistics published weeks ago, showed that NHS Tayside's performance for treating patients within the four-hour timeframe was at 98%. She added: "So, what we need to do is make sure that the better working practices, the way in which patients are being taken through A&E that are working well in some hospitals are being applied in all hospitals. "And that's one of the things that the support team that we've sent to the Royal Alexandra, will be helping the local management to achieve." In January, it emerged that one patient at the Royal Alexandra had waited 20 hours for a bed. The failure to meet the national waiting times target prompted an apology from the head of NHS Scotland on Tuesday. Chief executive Paul Gray said it had been a "challenging winter" for A&E departments across Scotland. He said that the support team being sent to the Royal Alexandra Hospital from next week would "help identify issues where they exist and prioritise actions that can be taken to improve A&E performance". NHS Greater Glasgow and Clyde (GGC), which runs the Royal Alexandra, said it hoped the support team would help "improve on our current challenged performance". In January, NHS GGC admitted that 2,400 people across the health board area waited more than four hours for treatment over the Christmas period. Figures published last week showed widespread disparities among health board's in meeting A&E waiting times targets.	Opposition parties have said emergency care in Scotland is "in crisis" after ministers intervened at a hospital that failed to meet waiting times targets.

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

compression-pairs

• Dataset: compression-pairs at 605bc91
• Size: 100,000 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

sciq_pairs

• Dataset: sciq_pairs at 2c94ad3
• Size: 11,679 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

msmarco_pairs

• Dataset: msmarco_pairs at 28ff31e
• Size: 100,000 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

nq_pairs

• Dataset: nq_pairs at f9e894e
• Size: 100,000 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

trivia_pairs

• Dataset: trivia_pairs at a7c36e3
• Size: 73,346 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

quora_pairs

• Dataset: quora_pairs at 451a485
• Size: 100,000 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

gooaq_pairs

• Dataset: gooaq_pairs at b089f72
• Size: 100,000 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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

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: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': 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}
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 250
• per_device_eval_batch_size: 16
• learning_rate: 2e-05
• weight_decay: 1e-08
• num_train_epochs: 1
• lr_scheduler_type: cosine
• warmup_ratio: 0.33
• save_safetensors: False
• fp16: True
• push_to_hub: True
• hub_model_id: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v3-step1-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: 250
• 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-08
• 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: 0.33
• 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-v3-step1-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	scitail-pairs-pos loss	sts-test_spearman_cosine
None	0	-	6.3111	3.8145	-
0.0253	88	7.5831	6.0871	3.7706	-
0.0506	176	6.7273	5.9270	3.7314	-
0.0758	264	5.9091	4.5990	2.8228	-
0.1011	352	4.2126	3.0622	1.7927	-
0.1264	440	3.0055	2.3243	1.1717	-
0.1517	528	2.4462	1.9132	1.1094	-
0.1770	616	2.0925	1.6521	0.9031	-
0.2022	704	2.0016	1.4990	0.8708	-
0.2275	792	1.7607	1.4104	0.8444	-
0.2528	880	1.7801	1.3015	0.8060	-
0.2781	968	1.5522	1.2201	0.7629	-
0.3034	1056	1.4041	1.1747	0.6738	-
0.3286	1144	1.3716	1.1800	0.6005	-
0.3539	1232	1.3107	1.0875	0.6327	-
0.3792	1320	1.3468	1.0540	0.5583	-
0.4045	1408	1.2303	1.0083	0.5666	-
0.4298	1496	1.1907	0.9647	0.5922	-
0.4550	1584	1.1587	0.9537	0.5585	-
0.4803	1672	0.9554	0.9304	0.5592	-
0.5056	1760	0.9837	0.9165	0.5467	-
0.5309	1848	0.8857	0.8931	0.5374	-
0.5562	1936	0.9305	0.8842	0.5331	-
0.5814	2024	0.8061	0.8854	0.5477	-
0.6067	2112	0.8286	0.8693	0.5196	-
0.6320	2200	0.7854	0.8592	0.5159	-
0.6573	2288	0.8374	0.8538	0.5090	-
0.6826	2376	0.7678	0.8425	0.5175	-
0.7078	2464	0.7064	0.8284	0.5046	-
0.7331	2552	0.8849	0.8329	0.4783	-
0.7584	2640	0.801	0.8223	0.4935	-
0.7837	2728	0.8606	0.8147	0.4934	-
0.8090	2816	0.7978	0.8104	0.4984	-
0.8342	2904	0.7671	0.8026	0.4845	-
0.8595	2992	0.766	0.8019	0.4857	-
0.8848	3080	0.9066	0.8000	0.4848	-
0.9101	3168	0.78	0.7986	0.4852	-
0.9354	3256	0.9253	0.7975	0.4853	-
0.9606	3344	0.8153	0.7984	0.4848	-
0.9859	3432	0.8576	0.7989	0.4843	-
1.0	3481	-	0.7990	0.4843	0.8008

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",
}