XLMRoBERTaM3-CustomPoolin-v1-s1

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3 on the negation-triplets, vitaminc-pairs, scitail-pairs-qa, scitail-pairs-pos, xsum-pairs, sciq_pairs, qasc_pairs, openbookqa_pairs, nq_pairs, trivia_pairs, gooaq_pairs, paws-pos and global_dataset datasets. It maps sentences & paragraphs to a 1024-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: BAAI/bge-m3 <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Datasets:
  • negation-triplets
  • vitaminc-pairs
  • scitail-pairs-qa
  • scitail-pairs-pos
  • xsum-pairs
  • sciq_pairs
  • qasc_pairs
  • openbookqa_pairs
  • nq_pairs
  • trivia_pairs
  • gooaq_pairs
  • paws-pos
  • global_dataset
• 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': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): AdvancedWeightedPooling(
    (mha): MultiheadAttention(
      (out_proj): NonDynamicallyQuantizableLinear(in_features=1024, out_features=1024, bias=True)
    )
    (MLP): Sequential(
      (0): Linear(in_features=1024, out_features=2048, bias=True)
      (1): ReLU()
      (2): Linear(in_features=2048, out_features=1024, bias=True)
    )
    (layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=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/XLMRoBERTaM3-CustomPoolin-v1-s1")
# Run inference
sentences = [
    'Which hand puppet would you associate with Shari Lewis?',
    'Shari Lewis Puppets | eBay Shari Lewis Puppets Buy It Now Free Shipping This baby doll puppet was part of my mom\'s doll collection. Shari\'s Baby: Teach Baby to Talk. Baby Puppet with Blanket. This doll has been on display, but has not been played with. It comes from a smo... $11.70 Buy It Now Shari Lewis. Lamb Chop and 2 Charlie the Horse Puppets. lamb chop: 5" X 8". horse: 6" X 11". horse: 5.5" X 10". Classic children\'s entertainer!all the rubber faces have some areas of rubbing and tiny ...',
    'Names of Presidents and First Ladies President\'s and First Ladies\' Names Alphabetically Presidents\' First Names Presidents\' Middle Names Presidents\' Last Names First Ladies\' First Names First Ladies\' Maiden names Abraham Abram Adams x 2 Abigail Appleton Andrew Alan Arthur Anna x 3 Bouvier Andrew Baines Buchanan Barbara Carow Benjamin Birchard Bush x 2 Caroline Childress Calvin Clark Carter Claudia "Lady Bird" Custis Chester David Cleveland Dolly Davis Dwight Delano Clinton Edith x 2 Dent Franklin x 2 Earl Coolidge Eleanor x 2 deWolf George x 3 Fitzgerald Eisenhower Eliza Doud Gerald Gamaliel Fillmore Elizabeth "Bess" Fulsom Grover Henry Ford Elizabeth "Betty" Galt Harry Herbert Walker Garfield Elizabeth "Eliza" Gardiner Herbert Howard Grant Ellen Goodhew James x 5 Jefferson Harding Florence Henry John x 4 Knox Harrison x 2 Frances Herndon Lyndon Milhous Hayes Hannah Herron Martin Quincy Hoover Helen Hoes Millard Rudolph Jackson Hillary Johnson Richard S Jefferson Ida Kortright Ronald Simpson Johnson x 2 Jacqueline McCardle Rutherford Walker Kennedy Jane Pierce Theodore Wilson Lincoln Julia x 2 Powers Thomas Madison Laura Robards Ulysses McKinley Lou Rodham Warren Monroe Louisa Roosevelt William x 4 Nixon Lucretia Rudolph Woodrow Pierce Lucy Ryan Zachary Polk Margaret Saxton Reagan Martha x 2 Scott Roosevelt x 2 Mary Skelton Taft Mary "Mamie" Smith x 3 Taylor Nancy Symmes Truman Rachel Taylor Tyler Rosalynn Todd x 2 VanBuren Sarah Wallace Washington Thelma "Patricia" Warren Wilson Webb Welch',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# 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>

Binary Classification

• Datasets: allNLI-dev and Qnli-dev
• Evaluated with <code>BinaryClassificationEvaluator</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

negation-triplets

• Dataset: negation-triplets at d43e6fe
• Size: 5,025 training samples
• Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	entailment	negative
  type	string	string	string
  details	<ul><li>min: 6 tokens</li><li>mean: 25.85 tokens</li><li>max: 106 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 16.35 tokens</li><li>max: 58 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 16.38 tokens</li><li>max: 54 tokens</li></ul>

• Samples:

  anchor	entailment	negative
  <code>a small clean bathroom with a blue towel and a white wash cloth</code>	<code>A big bathroom with sink and mirror in back. </code>	<code>A big bathroom without sink and mirror in back.</code>
  <code>A bathroom counter that has a vase with flowers inside of it.</code>	<code>a bathroom with a big bathtub and a big mirror above the sinks </code>	<code>a bathroom with a small bathtub and a small mirror above the sinks </code>
  <code>A new outdoor vintage flea market will open on Sun., April 22 at Westfield Promenade, 6100 Topanga Canyon Blvd., Woodland Hills.</code>	<code>Vintage flea market opens April 22</code>	<code>Antique store closes doors on April 22</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

vitaminc-pairs

• Dataset: vitaminc-pairs at be6febb
• Size: 5,025 training samples
• Columns: <code>claim</code> and <code>evidence</code>
• Approximate statistics based on the first 1000 samples:

  	claim	evidence
  type	string	string
  details	<ul><li>min: 9 tokens</li><li>mean: 20.19 tokens</li><li>max: 63 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 45.84 tokens</li><li>max: 294 tokens</li></ul>

• Samples:

  claim	evidence
  <code>The McLaren 720S is considered a sports car .</code>	<code>The McLaren 720S is a British sports car designed and manufactured by McLaren Automotive .</code>
  <code>Snow Patrol is a Northern Irish band .</code>	<code>`` } } '' '' Chasing Cars '' '' is a song by Northern Irish/Scottish alternative rock band Snow Patrol . ''</code>
  <code>Rachid Ghezzal has one nationality .</code>	<code>Rachid Ghezzal ( born 9 May 1992 ) is Algerian footballer who plays for French club Lyon in Ligue 1 and the Algeria national team .</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

scitail-pairs-qa

• Dataset: scitail-pairs-qa at 0cc4353
• Size: 5,025 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: 9 tokens</li><li>mean: 19.0 tokens</li><li>max: 51 tokens</li></ul>	<ul><li>min: 8 tokens</li><li>mean: 17.33 tokens</li><li>max: 48 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>Gas bubble holes helps pumice float in water.</code>	<code>What helps pumice float in water?</code>
  <code>The law of parsimony is the scientific concept stating that when looking at two competing theories, the one with fewer assumptions should be chosen.</code>	<code>What the scientific concept stating that when looking at two competing theories, the one with fewer assumptions should be chosen?</code>
  <code>Community interactions are important factors in natural selection.</code>	<code>Community interactions are important factors in what?</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

scitail-pairs-pos

• Dataset: scitail-pairs-pos at 0cc4353
• Size: 5,025 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: 28.99 tokens</li><li>max: 75 tokens</li></ul>	<ul><li>min: 9 tokens</li><li>mean: 19.18 tokens</li><li>max: 51 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>Neutrons have no electric charge.</code>	<code>A neutron has neutral or no charge.</code>
  <code>Blood vessels are part of the circulatory system, the "highway" system of the human body that transports materials to all of its cells.</code>	<code>The main function of the circulatory system is to transport materials.</code>
  <code>Clouds form by the condensation of water vapor.</code>	<code>Condensation must occur for clouds to form.</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

xsum-pairs

• Dataset: xsum-pairs at 044020f
• Size: 131,779 training samples
• Columns: <code>summary</code> and <code>document</code>
• Approximate statistics based on the first 1000 samples:

  	summary	document
  type	string	string
  details	<ul><li>min: 11 tokens</li><li>mean: 30.04 tokens</li><li>max: 58 tokens</li></ul>	<ul><li>min: 61 tokens</li><li>mean: 250.1 tokens</li><li>max: 401 tokens</li></ul>

• Samples:

  summary	document
  <code>Australia international Christian Lealiifano says there were times during his recovery from leukaemia that he thought he would not play rugby again.</code>	<code>The 29-year-old was diagnosed in August 2016, but made his comeback in the Brumbies' Super Rugby quarter-final loss in July.<br>Lealiifano has been linked with a short-term deal at Pro14 side Ulster.<br>"When I first started training I thought I would never play football again," Lealiifano said.<br>"When the doctor gave me the all clear that I could return back to work, that was when I had my eye on the prize."<br>Lealiifano said his cancer battle has "changed" his outlook on life.<br>He underwent chemotherapy and a bone marrow transplant, and was told the cancer was in remission in February.<br>"I would go through this 10 times again for the person I am today," he said. "The journey I have been through and the person that I have become."<br>Lealiifano say he is "exploring options" in Australia's National Rugby Championship and playing abroad.<br>Addressing the Ulster rumours, he said: "Nothing has been locked away yet - it is something in the pipeline."<br>Lealiifano, who the last of his 19 caps for the Wallabi...</code>
  <code>A charity said one of its boats suffered "catastrophic damage" searching a river for a missing man.</code>	<code>West Midlands Search & Rescue (WMSAR), which is run by volunteers, was on the River Severn in Shrewsbury after Daniel Hodgin, 20, went missing last weekend.<br>He failed to return home after a night out with friends at The Buttermarket nightclub in the town.<br>The damage on Saturday was believed to have been caused by submerged debris, WMSAR said.<br>Nobody was hurt in this weekend's incident and the crew was rescued by another of its boats nearby, it said.<br>A spokesman said: "We are a small charity, run wholly by specialist volunteers and funded by donations.<br>"The loss of a boat will have a significant financial impact and we desperately need help to replace the boat and get the team back up to full operational capacity."<br>WMSAR provides support to the emergency services in Shropshire, Herefordshire and Worcestershire.<br>West Mercia Police said it believed Mr Hodgin, from Madeley, left the club at about 03:30 GMT on 23 November.<br>Dozens of people assembled at the nightclub on Saturday to search fo...</code>
  <code>Final preparations are being made for the staging of Scotland's biggest Gaelic cultural festival in Stornoway on Lewis.</code>	<code>The nine-day Royal National Mòd, which will take place from 14-22 October, was last held in the Western Isles in 2011.<br>Organisers An Comunn Gàidhealach said last year's event in Oban was estimated to have generated about £3m for the local economy.<br>The Mòd features music, dance and arts competitions and performances.<br>This year's opening ceremony will include headline performances by Gaelic group Dàimh and the Mischa Macpherson Trio.<br>Lewis Pipe Band will also lead the festival's traditional torchlight procession, which is held on the opening night of the Mòd.<br>John Morrison, chief executive of An Comunn Gàidhealach, said a "tremendous amount" of entries had been received for this year's competitions.<br>He added: "The Mòd is a huge highlight in Scotland's cultural calendar, attracting Gaels and non-Gaels from across the world to celebrate our diverse range of events and competitions.<br>"We're delighted to have the Mischa Macpherson Trio and Dàimh play at this year's opening ceremony, setting t...</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

sciq_pairs

• Dataset: sciq_pairs at 2c94ad3
• Size: 5,025 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: 20.04 tokens</li><li>max: 79 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 102.5 tokens</li><li>max: 683 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>What astronomical phenomenon is often comprised of remnants of the earliest material that formed in the solar system?</code>	<code>Many meteorites are remnants of the earliest material that formed in the solar system.</code>
  <code>Female monotremes share what external opening with reptiles and birds?</code>	<code>Female monotremes lack a uterus and vagina. Instead, they have a cloaca with one external opening, like the cloaca of reptiles and birds. The opening is used to excrete wastes as well as lay eggs. The eggs of monotremes have a leathery shell, like the eggs of reptiles. Female monotremes have mammary glands but not nipples. They secrete milk to feed their young from a patch on their belly. This form of reproduction is least risky for the mother but most risky for the offspring.</code>
  <code>Diarrhea due to food poisoning is an example of a problem involving what organ system?</code>	<code>Much of the time, you probably aren’t aware of your digestive system. It works well without causing any problems. But most people have problems with their digestive system at least once in a while. Did you ever eat something that didn’t “agree” with you? Maybe you had a stomachache or felt sick to your stomach. Perhaps you had diarrhea. These can be symptoms of food poisoning.</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

qasc_pairs

• Dataset: qasc_pairs at a34ba20
• Size: 5,025 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: 13.09 tokens</li><li>max: 27 tokens</li></ul>	<ul><li>min: 22 tokens</li><li>mean: 42.35 tokens</li><li>max: 82 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>What generate heat mainly by keeping their metabolic rate high?</code>	<code>Mammals generate heat mainly by keeping their metabolic rate high.. Gray Squirrel Management Tree squirrels are common Alabama mammals. <br> tree squirrels generate heat mainly by keeping their metabolic rate high</code>
  <code>Reducing paper production would directly help stop what international problem?</code>	<code>creating paper requires cutting down trees. Saving trees helps stop global warming. <br> Reductions in paper production would help stop global warming</code>
  <code>Cells form tissues, which form?</code>	<code>Cells are organized into tissues, and tissues form organs.. Skin is a protective organ. <br> Cells form tissues, and tissues form skin</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

openbookqa_pairs

• Dataset: openbookqa_pairs at 3c2724c
• Size: 4,957 training samples
• Columns: <code>question</code> and <code>fact</code>
• Approximate statistics based on the first 1000 samples:

  	question	fact
  type	string	string
  details	<ul><li>min: 4 tokens</li><li>mean: 15.73 tokens</li><li>max: 73 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 13.69 tokens</li><li>max: 33 tokens</li></ul>

• Samples:

  question	fact
  <code>Who is most likely to get a job at a lumber yard?</code>	<code>timber companies cut down trees</code>
  <code>Food is a source of energy for what?</code>	<code>food is a source of energy for animals</code>
  <code>Porpoises move fastest in</code>	<code>large fins can be used to move quickly through water</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

nq_pairs

• Dataset: nq_pairs at f9e894e
• Size: 5,025 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: 13.41 tokens</li><li>max: 27 tokens</li></ul>	<ul><li>min: 20 tokens</li><li>mean: 148.54 tokens</li><li>max: 905 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>who is the guy on the cover of you've come a long way baby</code>	<code>You've Come a Long Way, Baby The title You've Come a Long Way, Baby was derived from a marketing slogan for Virginia Slims cigarettes.[9] Conceived by Red Design, the album's primary cover art features an obese young man dressed in a T-shirt bearing the words "I'm #1 so why try harder" while holding a cigarette in his left hand.[10][11] The original photograph was taken at the 1983 Fat People's Festival in Danville, Virginia and provided by the Rex Features photo library.[12] Despite a series of enquiries, the man has not been identified.[13] Additional photography for the You've Come a Long Way, Baby liner notes was provided by Simon Thornton.[10] The cover image was changed in North America to an image of shelves stacked with records.</code>
  <code>govt of india refers to the absolute poverty line in terms of</code>	<code>Below Poverty Line (India) Internationally, an income of less than $1.90 per day per head of purchasing power parity is defined as extreme poverty. By this estimate, about 21.2% of Indians are extremely poor. Income-based poverty lines consider the bare minimum income to provide basic food requirements; it does not account for other essentials such as health care and education.[2] India is an extremely poor country according to this.</code>
  <code>when did zody's go out of business</code>	<code>Zody's Bankrupt again by the early 1980s, the parent company, now known as HRT Industries, began closing stores in 1984. The remaining Zodys stores in California were shuttered in March 1986,[2][3][4] with many locations being sold to Federated Stores, the parent company of Ralphs supermarket chain,[5][6] while other locations were purchased by HomeClub, a home improvement store chain.[7]</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

trivia_pairs

• Dataset: trivia_pairs at a7c36e3
• Size: 5,025 training samples
• Columns: <code>query</code> and <code>answer</code>
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	<ul><li>min: 9 tokens</li><li>mean: 19.59 tokens</li><li>max: 62 tokens</li></ul>	<ul><li>min: 27 tokens</li><li>mean: 246.6 tokens</li><li>max: 575 tokens</li></ul>

• Samples:

  query	answer
  <code>What is the official language of The Netherlands?</code>	<code>Dutch language
  <code>Which painting of 1851 by Sir Edwin Landseer features a great stag/deer which stands on a rise among cloud-covered mountains?</code>	<code>Monarch of the Glen Home A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Monarch of the Glen Monarch of the Glen is one of the most famous paintings completed by the English artist Sir Edwin Landseer (1802-1873). Lions might be king’s of the jungle but the message here is clear that this great stag deer is king of the glen. He stands on a rise in a glen among cloud-covered mountains. The magnificent animal surveys the viewer while exhibiting a quiet strength. It was painted in 1851 and was intended to be displayed in the refreshment-room of the House of Lords. The Commons refused the price. (The Famous Artists did not find the original asking price, but did find that the painting was later sold for 7,000 Pounds.) As of when we wrote this post, Monarch of the Glen was housed in the National Museum of Scotland. The world has Andrew McMillan to thank for making this photograph available and placing it into the public domain. If you think this painting looks familiar, that might be bec...</code>
  <code>"Shirley Conran's book ""Superwoman"", published in 1975, was about what?"</code>	<code>SUPERWOMAN by Shirley Conran

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

gooaq_pairs

• Dataset: gooaq_pairs at b089f72
• Size: 5,025 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: 12.84 tokens</li><li>max: 22 tokens</li></ul>	<ul><li>min: 14 tokens</li><li>mean: 64.33 tokens</li><li>max: 141 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>what is the difference between job work and career?</code>	<code>Work is anything where you expend effort, whether paid or not. A job can be either a piece of work, or an activity for which you are paid. A career is a lifetime progression of paid occupation. A profession is a career with high status and one requiring training, often at degree level.</code>
  <code>are dlr tickets valid on underground?</code>	<code>While the tickets you buy for the Tube are valid on the DLR, if you are using a Oyster Card with pay as you go credit then you must ensure you swipe your card when getting on and off the DLR at each station - the DLR stations are different in that there are no barriers or gates to prevent you exiting without paying.</code>
  <code>in which season does ross and rachel get together?</code>	<code>The producers had kept Ross and Rachel from being together throughout the first season, eventually bringing them together in the second-season episode "The One Where Ross Finds Out", only to split them up in the following episode.</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

paws-pos

• Dataset: paws-pos at 161ece9
• Size: 5,025 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: 12 tokens</li><li>mean: 31.83 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 12 tokens</li><li>mean: 31.86 tokens</li><li>max: 64 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>Phyllonorycter fraxinella is a moth of the Gracillariidae family , which is found from Germany and Poland to Sicily and Greece and from France to Southern Russia .</code>	<code>Phyllonorycter fraxinella is a moth of the Gracillariidae family . It is found from Germany and Poland tot Sicily and Greece and from France to southern Russia .</code>
  <code>Upernivik Island is an uninhabited island in the Qaasuitsup municipality in northwest Greenland .</code>	<code>Upernivik Island is an uninhabited island in the Qaasuitsup municipality in northwestern Greenland .</code>
  <code>The following night , Delirious put his problems with Pearce aside to challenge Danielson .</code>	<code>The next night , Delirious put aside his problems with Pearce to challenge Danielson .</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

global_dataset

• Dataset: global_dataset
• Size: 33,818 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: 36.58 tokens</li><li>max: 368 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 57.39 tokens</li><li>max: 690 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>The `` Real World Suite '' costs $ 25,000 per night .</code>	<code>The level they rebuilt to accommodate MTV is now the '' Real World Suite '' '' billed at $ 25,000 per night . ''</code>
  <code>A young child colors with crayons at his dinner table.</code>	<code>Child colors with crayons at dinner table.</code>
  <code>Sensori-neural hearing loss is usually permanent and is caused by damage to hair cells in the inner ear.</code>	<code>Loud sounds that cause hearing loss damage the hair cells in the inner ear.</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

Evaluation Datasets

vitaminc-pairs

• Dataset: vitaminc-pairs at be6febb
• Size: 128 evaluation samples
• Columns: <code>claim</code> and <code>evidence</code>
• Approximate statistics based on the first 128 samples:

  	claim	evidence
  type	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 23.52 tokens</li><li>max: 48 tokens</li></ul>	<ul><li>min: 12 tokens</li><li>mean: 39.63 tokens</li><li>max: 92 tokens</li></ul>

• Samples:

  claim	evidence
  <code>Dragon Con had over 5000 guests .</code>	<code>Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .</code>
  <code>COVID-19 has reached more than 185 countries .</code>	<code>As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .</code>
  <code>In March , Italy had 3.6x times more cases of coronavirus than China .</code>	<code>As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

negation-triplets

• Dataset: negation-triplets at d43e6fe
• Size: 128 evaluation samples
• Columns: <code>anchor</code>, <code>entailment</code>, and <code>negative</code>
• Approximate statistics based on the first 128 samples:

  	anchor	entailment	negative
  type	string	string	string
  details	<ul><li>min: 8 tokens</li><li>mean: 15.96 tokens</li><li>max: 32 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 14.28 tokens</li><li>max: 25 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 14.54 tokens</li><li>max: 26 tokens</li></ul>

• Samples:

  anchor	entailment	negative
  <code>Two sheep are looking over a cement wall.</code>	<code>Two sheep poking their heads over a white cement wall</code>	<code>Two sheep hiding their bodies under a white cement wall</code>
  <code>A stone building with a clock displayed on the outside. </code>	<code>A tall multi-story building with a large clock atop it.</code>	<code>A short single-story building with a small clock atop it.</code>
  <code>An older women tending to a garden.</code>	<code>The lady has a garden</code>	<code>The lady has no garden.</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

scitail-pairs-pos

• Dataset: scitail-pairs-pos at 0cc4353
• Size: 128 evaluation samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 128 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 11 tokens</li><li>mean: 26.51 tokens</li><li>max: 62 tokens</li></ul>	<ul><li>min: 11 tokens</li><li>mean: 18.73 tokens</li><li>max: 28 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase;</code>	<code>Wavelength is the distance between two corresponding points of adjacent waves called.</code>
  <code>humans normally have 23 pairs of chromosomes.</code>	<code>Humans typically have 23 pairs pairs of chromosomes.</code>
  <code>kinetic energy  the energy of motion.</code>	<code>Kinetic energy is the energy of motion.</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

scitail-pairs-qa

• Dataset: scitail-pairs-qa at 0cc4353
• Size: 128 evaluation samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 128 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 9 tokens</li><li>mean: 19.12 tokens</li><li>max: 42 tokens</li></ul>	<ul><li>min: 9 tokens</li><li>mean: 16.72 tokens</li><li>max: 41 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>The term immunodeficiency means a failure, insufficiency, or delay in the response of the immune system, which may be acquired or inherited.</code>	<code>What term means a failure, insufficiency, or delay in the response of the immune system, which may be acquired or inherited?</code>
  <code>Each lymph organ has a different job in the immune system.</code>	<code>Each lymph organ has a different job in what system?</code>
  <code>A(n) an axillary bud is above each leaf scar.</code>	<code>What is above each leaf scar?</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

xsum-pairs

• Dataset: xsum-pairs at 044020f
• Size: 131,779 evaluation samples
• Columns: <code>summary</code> and <code>document</code>
• Approximate statistics based on the first 1000 samples:

  	summary	document
  type	string	string
  details	<ul><li>min: 18 tokens</li><li>mean: 29.7 tokens</li><li>max: 47 tokens</li></ul>	<ul><li>min: 63 tokens</li><li>mean: 235.77 tokens</li><li>max: 405 tokens</li></ul>

• Samples:

  summary	document
  <code>Glasgow Warriors' Pro12 match against Leinster has been postponed due to severe weather conditions.</code>	<code>Heavy rain overnight on Friday which continued into Saturday rendered the pitch at Scotstoun unplayable.<br>The postponement creates something of a fixture headache for Warriors.<br>They have already had to squeeze their rearranged Champions Cup fixture against Racing 92 into an already packed calendar, and may now need to face Leinster on a midweek date.<br>"The pitch at Scotstoun Stadium is unplayable due to surface water and there is a concern for both players and supporters safety given the adverse weather conditions," a Warriors statement said.<br>Several Scottish football fixtures have also fallen victim to the weather.<br>Pro12<br>Glasgow Warriors P-P Leinster<br>Scottish Premiership<br>Celtic P-P Hamilton Academical<br>Hearts P-P Inverness CT<br>Partick Thistle P-P Motherwell<br>Scottish Championship<br>Falkirk P-P Livingston<br>Raith Rovers P-P Rangers<br>St Mirren P-P Queen of the South<br>Scottish League One<br>Albion Rovers P-P Stranraer<br>Scottish League Two<br>East Stirlingshire P-P Clyde<br>Scottish Cup<br>Lothian Thistle Hutchi...</code>
  <code>A former consultant has been charged with 10 counts of sexual assault at a hospital in Surrey.</code>	<code>Cardiologist Neil Ineson, 60, of Sandhurst, Berkshire is charged with a series of sexual assaults at Frimley Park Hospital in Surrey.<br>The alleged assaults took place between August 2007 and November 2014.<br>Surrey Police said Dr Ineson had been released on conditional bail and would appear at Guildford Magistrates' Court on 10 May.</code>
  <code>Jenny Lewis has been photographing artists who live and work in Hackney - but rather than setting out with a list of names, she asks each sitter to suggest the next person in the chain.</code>	<code>This gives the project a vitality that sees her moving from established names to those on the first rung of the ladder, and back again.<br>Lewis says: "Hackney is still full of creative people working down side-alleys, tucked behind residential streets, in old factories and warehouses as well as new studio communities."<br>She is aware that as time passes, the area is changing, and this could be last chance to capture this creative community.<br>Lewis says that even some of those she has photographed have since had to move out.<br>"The borough is evolving so quickly it sometimes feels like historical documentation, recording the way things once were," she says, "capturing a studio with the 20 or so years build up of detritus before the slate is wiped clean and a block of flats takes its place."<br>You can see more of Jenny Lewis's work on her website.</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

sciq_pairs

• Dataset: sciq_pairs at 2c94ad3
• Size: 128 evaluation samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 128 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 9 tokens</li><li>mean: 19.49 tokens</li><li>max: 45 tokens</li></ul>	<ul><li>min: 2 tokens</li><li>mean: 102.95 tokens</li><li>max: 579 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>How does the atomic number change during beta emission?</code>	<code>Beta (β) decay is a more complicated process. Unlike the α-emission, which simply expels a particle, the β-emission involves the transformation of a neutron in the nucleus to a proton and an electron. The electron is then ejected from the nucleus. In the process, the atomic number increases by one while the atomic weight stays the same. As is the case with α-emissions, β-emissions are often accompanied by γ-radiation.</code>
  <code>Circular water waves decrease in what property as they move away from where a rock is dropped?</code>	<code>16.11 Energy in Waves: Intensity 17. Two identical waves undergo pure constructive interference. Is the resultant intensity twice that of the individual waves? Explain your answer. Circular water waves decrease in amplitude as they move away from where a rock is dropped. Explain why.</code>
  <code>What are are organisms that feed on small pieces of organic matter?</code>	<code>Deposit feeders, which are organisms that feed on small pieces of organic matter, usually in the top layer of soil. Sea cucumbers are deposit feeders, living on the ocean floor. They eat the tiny scrap particles that are usually abundant in the environments that they inhabit.</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

qasc_pairs

• Dataset: qasc_pairs at a34ba20
• Size: 128 evaluation samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 128 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 6 tokens</li><li>mean: 13.09 tokens</li><li>max: 27 tokens</li></ul>	<ul><li>min: 23 tokens</li><li>mean: 41.5 tokens</li><li>max: 75 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>What is a major food source for other organisms, including humans?</code>	<code>Mollusks are a major food source for other organisms, including humans.. Mollusks are invertebrates and are no exception. <br> Invertebrates are a major food source for other organisms, including humans.</code>
  <code>What does not change during melting?</code>	<code>phase changes do not change mass. Melting is a phase change. <br> Mass does not change during melting</code>
  <code>What causes water to expand as it becomes ice?</code>	<code>Hydrogen bonds cause water to expand when it freezes.. Water freezes into ice when cooled. <br> hydrogen bonds cause water to expand as it becomes ice</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

openbookqa_pairs

• Dataset: openbookqa_pairs at 3c2724c
• Size: 500 evaluation samples
• Columns: <code>question</code> and <code>fact</code>
• Approximate statistics based on the first 500 samples:

  	question	fact
  type	string	string
  details	<ul><li>min: 5 tokens</li><li>mean: 15.98 tokens</li><li>max: 54 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 14.32 tokens</li><li>max: 30 tokens</li></ul>

• Samples:

  question	fact
  <code>The thermal production of a stove is generically used for</code>	<code>a stove generates heat for cooking usually</code>
  <code>What creates a valley?</code>	<code>a valley is formed by a river flowing</code>
  <code>when it turns day and night on a planet, what cause this?</code>	<code>a planet rotating causes cycles of day and night on that planet</code>

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

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
    (2): Normalize()
  ), 'temperature': 0.025}
  

nq_pairs

• Dataset: nq_pairs at f9e894e
• Size: 128 evaluation samples
• Columns: <code>sentence1</code> and <code>sentence2</code>
• Approximate statistics based on the first 128 samples:

  	sentence1	sentence2
  type	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 13.3 tokens</li><li>max: 24 tokens</li></ul>	<ul><li>min: 34 tokens</li><li>mean: 165.62 tokens</li><li>max: 525 tokens</li></ul>

• Samples:

  sentence1	sentence2
  <code>when did the singer of linkin park die</code>	<code>Chester Bennington Chester Charles Bennington (March 20, 1976 – July 20, 2017) was an American singer, songwriter, musician, and actor. He served as lead singer for the bands Linkin Park, Dead by Sunrise, Grey Daze, and Stone Temple Pilots.</code>

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