DeBERTaV3-small-SenTra-AdaptiveLayers-AllSoft-LowTemp

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 <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
• 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

<!-- - **License:** Unknown -->

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-SenTra-AdaptiveLayers-AllSoft-LowTemp")
# 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]

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-test
• Evaluated with <code>EmbeddingSimilarityEvaluator</code>

<!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* -->

Training Details

Training Datasets

nli-pairs

• Dataset: nli-pairs at d482672
• Size: 6,500 training samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 5 tokens</li><li>mean: 16.62 tokens</li><li>max: 62 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 9.46 tokens</li><li>max: 29 tokens</li></ul>

• Samples:

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

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

sts-label

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

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

• Samples:

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

• Loss: <code>CoSENTLoss</code> with these parameters:

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

vitaminc-pairs

• Dataset: vitaminc-pairs at be6febb
• Size: 3,194 training samples
• Columns: <code>label</code>, <code>sentence1</code>, and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	label	sentence1	sentence2
  type	int	string	string
  details	<ul><li>1: 100.00%</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 16.1 tokens</li><li>max: 75 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 38.59 tokens</li><li>max: 260 tokens</li></ul>

• Samples:

  label	sentence1	sentence2
  <code>1</code>	<code>Based on 31 critics , the film scored more than 43.5 out of 100 .</code>	<code>Metacritic gave the film a score of 44 out of 100 , based on 31 critics , indicating '' mixed or average reviews '' '' . ''</code>
  <code>1</code>	<code>Our Brand Is Crisis scored less than 61/100 , with more than eight critics , which means that the audience was divided about the film .</code>	<code>On Metacritic , the film has a score of 60 out of 100 , based on 9 critics , indicating '' mixed or average reviews '' '' . ''</code>
  <code>1</code>	<code>The film will be screened in less than 3133 theaters .</code>	<code>In its opening weekend , the film is projected to gross $ 15 million from 3,132 theaters.</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

qnli-contrastive

• Dataset: qnli-contrastive at bcdcba7
• Size: 4,000 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	label
  type	string	string	int
  details	<ul><li>min: 6 tokens</li><li>mean: 13.7 tokens</li><li>max: 34 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 35.83 tokens</li><li>max: 201 tokens</li></ul>	<ul><li>0: 100.00%</li></ul>

• Samples:

  sentence1	sentence2	label
  <code>When did Father Jose Manuel Perez Castellano die?</code>	<code>The first public library in Montevideo was formed by the initial donation of the private library of Father José Manuel Pérez Castellano, who died in 1815.</code>	<code>0</code>
  <code>How many singers are typically in the choirs?</code>	<code>The choirs typically range from 40 to 80 singers and are recognized for their efforts to perfect blend, intonation, phrasing and pitch in a large choral setting.</code>	<code>0</code>
  <code>St. George's Town, St. George's Island and St. George's Parish are all referred to as what?</code>	<code>There is a Hamilton Parish in addition to the City of Hamilton (which is in Pembroke Parish).</code>	<code>0</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "OnlineContrastiveLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

scitail-pairs-qa

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

  	sentence2	sentence1
  type	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 16.33 tokens</li><li>max: 41 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 14.66 tokens</li><li>max: 34 tokens</li></ul>

• Samples:

  sentence2	sentence1
  <code>You decrease errors in an experiment by take many measurements.</code>	<code>How do you decrease errors in an experiment?</code>
  <code>A(n) increase in length happens to metal railroad tracks during the heat of a summer day.</code>	<code>What happens to metal railroad tracks during the heat of a summer day?</code>
  <code>Corals build hard exoskeletons that grow to become coral reefs.</code>	<code>Corals build hard exoskeletons that grow to become what?</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

scitail-pairs-pos

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

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 23.38 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 14.97 tokens</li><li>max: 41 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>In fact, copper is a good conductor of electricity.</code>	<code>Copper conducts electricity the best.</code>
  <code>The tadpoles feed on aquatic plants and grow quickly eventually developing limbs and lungs as they emerge out of their pools as tiny frogs.</code>	<code>A frog develops (a/an) lungs as it changes from a tadpole to an adult frog.</code>
  <code>The atomic mass (also referred to as the atomic weight) is the number of protons and neutrons in an atom.</code>	<code>To determine the atomic weight of an element, you should add up protons and neutrons.</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

xsum-pairs

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

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 2 tokens</li><li>mean: 353.38 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 26.97 tokens</li><li>max: 60 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>The 28-year-old centre-back, who signed on a free transfer from Plymouth in June, has started 17 League One games for Rovers so far this season.<br>He picked up the injury in Monday's 4-1 loss at Charlton Athletic.<br>Meanwhile, on-loan midfielder Charlie Colkett, 20, has been recalled to his parent club Chelsea, after 17 Rovers appearances in all competitions.</code>	<code>Bristol Rovers defender Peter Hartley has been sidelined for up to three months with a foot injury.</code>
  <code>Although the 2014 Academy Awards ceremony has come and gone, its after-effects are still being felt in Kenya.<br>The first wave of activity happened soon after Lupita Nyong'o was announced Oscar winner for best actress in a supporting role in 12 Years A Slave.<br>The media, especially on Twitter, went wild with jubilant Kenyans sending and sharing the news, joy and jokes.<br>One Kenyan created much laughter by asking: "Which is this Oscar guy that Lupita has won over?"<br>Yet others began to wonder whether it was now time to seriously analyse what the waters of Lake Victoria - on the shores of Kisumu in Western Kenya - actually contained.<br>That region of the country and its Luo community have made global history for a second time.<br>It was the home of both Barack Obama's father and Lupita Nyong'o's family. Indeed, her father, Anyang Nyong'o is the Senator for Kisumu county and some of Kenya's sharpest minds come from this region.<br>And thanks to her comments, we now have a blue Nairobi. The Kenyan capital has always been known as the green city in the sun, because of its parks and trees. Although with weather patterns running amok, it often feels like the great sun in the city.<br>But not any more. Thanks to Lupita's comment that the beautiful light blue dress she wore at the Oscars reminded her of Nairobi, we now have Nairobi blue.<br>Whether this is a new colour, city, dress or state of mind, I can't tell.<br>What I know for certain is that Lupita's "your dreams are valid" statement in her acceptance speech has become a catch-phrase and a rallying call for Kenyans, especially the youth, to dream big.<br>You'll now hear young artists and upcoming entrepreneurs saying that they will push themselves to rise to the highest ranks - because their dreams are valid.<br>Lupita's victory at the Oscars is a demonstration that the African craft of telling or portraying stories is at the highest global standard.<br>The fact that she won and faced the world without lightening her dark skin complexion, or extending her short African hair, makes another statement - that an authentic African identity does not have to be negotiable for Africa to be heard loud and clear across the planet.<br>Proudly African, Lupita even gave the Western world collective heart failure as they struggled to pronounce her second name correctly.<br>As an ever-optimistic believer that Africa will soon take over the world, I feel the time has come for the continent to dream mega and now use its own voice to narrate the African experience.<br>Hollywood is one of the biggest factories and exporters of Western culture and the American experience. And Africa is a big importer of the same.<br>But the boot is gradually shifting to the other foot with Africa already exporting its sporting talent, its innovations and some of its culture from sources such as the Nigerian Nollywood film industry.<br>Now we must export our thinking. When the world begins to think what we think and why we think it, our story will move from what author Chimamanda Ngozi Adichie describes as the single story, to being understood in its truly complex weave of the African.<br>For this to happen we have to believe that our dreams are valid and that only we can validate those dreams.<br>It's also about believing in ourselves as Africans - that we can change the world, even if it's one colour at a time.<br>Lupita's Nairobi blue certainly seems to have the potential of becoming the new black on the global fashion scene.<br>If you would like to comment on Joseph Warungu's column, please do so below.</code>	<code>In our series of letters from African journalists, broadcaster and media trainer Joseph Warungu reflects on the aftermath of Lupita Nyong'o's historic success at the Oscars.</code>
  <code>He told Newsnight it was "unrealistic" to assume the poll - likely to be held later this year - would be repeated.<br>Mr Osborne, who described himself as a Eurosceptic, said he was "optimistic" about reaching a deal on EU reforms.<br>The chancellor's comments come as Leader of the Commons Chris Grayling said staying in the EU under the current terms would be "disastrous".<br>The in/out referendum on EU membership has been promised by the end of 2017.<br>Prime Minister David Cameron has said he wants to campaign for the UK to stay in a reformed EU, but says he "rules nothing out" if his demands are refused by other EU leaders - a line Mr Osborne reiterated in his interview.<br>"I think anyone who votes out on the assumption that a year or two later you can have another vote to vote back in... is being unrealistic about the nature of the choice," Mr Osborne said.<br>"And I think it's really important that the British people focus on the fact this is the once-in-a-lifetime decision."<br>Timeline: What will happen when?<br>Guide: All you need to know the referendum<br>Explained: What does Britain want from Europe?<br>Analysis: Cameron tries to avert slanging match<br>More: BBC News EU referendum special<br>Mr Cameron has said he hopes to reach a deal on his reform demands, which include curbs on EU migrants' welfare entitlement, at next month's European Council meeting, .<br>His proposal for a four-year freeze on some payments has been resisted by other EU leaders, but Mr Osborne said he saw "the essential pieces of the deal falling into place".<br>Some of the alternative arrangements to remaining within the EU "do not look very attractive", added Mr Osborne.<br>He said: "I want us to be able to stay in a reformed European Union.<br>"And so establishing these principles - that Britain can't be discriminated against because it's not part of the euro, can't pick up the bill for eurozone bailouts, crucially can't have imposed on it changes the eurozone want to make without our consent - these things really matter and they're part of that resettlement."<br>The chancellor - who has a key role in the UK's negotiating team - said the Treasury was "100% focused" on the talks, rather than planning for a UK exit from the EU.<br>He also said the UK would be demanding a permanent guarantee that it would not have to contribute to future eurozone bailout payments.<br>He added: "I've been concerned about some of the things that have happened in the European Union, that's why I want to make those changes.<br>"It's a perfectly respectable position to say 'let's seek those changes, let's achieve those changes, let's have that new settlement, and then we can have the best of both worlds'.<br>"We can be in the European Union, but not run by the European Union."<br>Mr Cameron has said his ministers will be able to campaign for either side in the referendum, but must back the government until negotiations are complete.<br>Leader of the Commons Mr Grayling, writing in the Daily Telegraph on Thursday, said remaining within the European Union under the UK's current membership terms would be "disastrous".<br>He intervention has been seen as the first sign of a minister preparing to campaign to leave the EU in the UK's referendum.<br>Mr Grayling said the UK was at "a crucial crossroads" and "cannot be left in a position where we have no ability to defend our national interest" within the EU".<br>He backed Mr Cameron to secure the reforms he is demanding, a stance supported by Mayor of London Boris Johnson.<br>Mr Johnson has said he remains "very confident" the prime minister will "get a good deal" for the UK in his renegotiation with EU leaders.<br>Asked about Mr Grayling's article in the Telegraph, Mr Johnson said: "He's totally right to say unless you get reform, Europe will continue to be a zone of low growth and stagnation."<br>You can watch Newsnight's full interview with George Osborne on iPlayer .</code>	<code>The UK's EU referendum represents a "once-in-a-lifetime decision", Chancellor George Osborne has said.</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

compression-pairs

• Dataset: compression-pairs at 605bc91
• Size: 4,000 training samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 31.89 tokens</li><li>max: 125 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 10.21 tokens</li><li>max: 28 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>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.</code>	<code>USHL completes expansion draft</code>
  <code>Major League Baseball Commissioner Bud Selig will be speaking at St. Norbert College next month.</code>	<code>Bud Selig to speak at St. Norbert College</code>
  <code>It's fresh cherry time in Michigan and the best time to enjoy this delicious and nutritious fruit.</code>	<code>It's cherry time</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "MultipleNegativesSymmetricRankingLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

sciq_pairs

• Dataset: sciq_pairs at 2c94ad3
• Size: 6,500 training samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 7 tokens</li><li>mean: 17.26 tokens</li><li>max: 60 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 84.37 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>What type of organism is commonly used in preparation of foods such as cheese and yogurt?</code>	<code>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.</code>
  <code>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?</code>	<code>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.</code>
  <code>Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what?</code>	<code>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.</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

qasc_pairs

• Dataset: qasc_pairs at a34ba20
• Size: 6,500 training samples
• Columns: <code>id</code>, <code>sentence1</code>, and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

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

• Samples:

  id	sentence1	sentence2
  <code>3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K</code>	<code>What type of water formation is formed by clouds?</code>	<code>beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds.</code>
  <code>3LS2AMNW5FPNJK3C3PZLZCPX562OQO</code>	<code>Where do beads of water come from?</code>	<code>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</code>
  <code>3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3</code>	<code>What forms beads of water? </code>	<code>beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water.</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

openbookqa_pairs

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

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 3 tokens</li><li>mean: 13.83 tokens</li><li>max: 78 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 11.37 tokens</li><li>max: 30 tokens</li></ul>

• Samples:

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

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

msmarco_pairs

• Dataset: msmarco_pairs at 28ff31e
• Size: 6,500 training samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 4 tokens</li><li>mean: 8.61 tokens</li><li>max: 27 tokens</li></ul>	<ul><li>min: 18 tokens</li><li>mean: 75.09 tokens</li><li>max: 206 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>what are the liberal arts?</code>	<code>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.</code>
  <code>what is the mechanism of action of fibrinolytic or thrombolytic drugs?</code>	<code>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.</code>
  <code>what is normal plat count</code>	<code>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).</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

nq_pairs

• Dataset: nq_pairs at f9e894e
• Size: 6,500 training samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 11.77 tokens</li><li>max: 21 tokens</li></ul>	<ul><li>min: 16 tokens</li><li>mean: 131.57 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>when did richmond last play in a preliminary final</code>	<code>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.</code>
  <code>who sang what in the world's come over you</code>	<code>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.</code>
  <code>who produces the most wool in the world</code>	<code>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.</code>

• Loss: <code>AdaptiveLayerLoss</code> with these parameters:

  {
      "loss": "GISTEmbedLoss",
      "n_layers_per_step": -1,
      "last_layer_weight": 1.5,
      "prior_layers_weight": 0.15,
      "kl_div_weight": 2,
      "kl_temperature": 0.5
  }
  

trivia_pairs

• Dataset: trivia_pairs at a7c36e3
• Size: 6,500 training samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 8 tokens</li><li>mean: 15.16 tokens</li><li>max: 48 tokens</li></ul>	<ul><li>min: 19 tokens</li><li>mean: 456.87 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>Which American-born Sinclair won the Nobel Prize for Literature in 1930?</code>	<code>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/</code>
  <code>Where in England was Dame Judi Dench born?</code>	<code>Judi Dench - IMDb IMDb Actress

The model card is truncated. Read the rest on Hugging Face.