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 Sources

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]

Evaluation

Metrics

Semantic Similarity

Metric Value
pearson_cosine 0.8665
spearman_cosine 0.9018

Binary Classification

Metric allNLI-dev Qnli-dev
cosine_accuracy 0.7578 0.6855
cosine_accuracy_threshold 0.8307 0.7555
cosine_f1 0.6865 0.6831
cosine_f1_threshold 0.7192 0.6664
cosine_precision 0.5682 0.5843
cosine_recall 0.8671 0.822
cosine_ap 0.665 0.7074

Training Details

Training Datasets

negation-triplets

  • Dataset: negation-triplets at d43e6fe
  • Size: 5,025 training samples
  • Columns: anchor, entailment, and negative
  • Approximate statistics based on the first 1000 samples:
    anchor entailment negative
    type string string string
    details
    • min: 6 tokens
    • mean: 25.85 tokens
    • max: 106 tokens
    • min: 4 tokens
    • mean: 16.35 tokens
    • max: 58 tokens
    • min: 4 tokens
    • mean: 16.38 tokens
    • max: 54 tokens
  • Samples:
    anchor entailment negative
    a small clean bathroom with a blue towel and a white wash cloth A big bathroom with sink and mirror in back. A big bathroom without sink and mirror in back.
    A bathroom counter that has a vase with flowers inside of it. a bathroom with a big bathtub and a big mirror above the sinks a bathroom with a small bathtub and a small mirror above the sinks
    A new outdoor vintage flea market will open on Sun., April 22 at Westfield Promenade, 6100 Topanga Canyon Blvd., Woodland Hills. Vintage flea market opens April 22 Antique store closes doors on April 22
  • Loss: GISTEmbedLoss 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: claim and evidence
  • Approximate statistics based on the first 1000 samples:
    claim evidence
    type string string
    details
    • min: 9 tokens
    • mean: 20.19 tokens
    • max: 63 tokens
    • min: 8 tokens
    • mean: 45.84 tokens
    • max: 294 tokens
  • Samples:
    claim evidence
    The McLaren 720S is considered a sports car . The McLaren 720S is a British sports car designed and manufactured by McLaren Automotive .
    Snow Patrol is a Northern Irish band . `` } } '' '' Chasing Cars '' '' is a song by Northern Irish/Scottish alternative rock band Snow Patrol . ''
    Rachid Ghezzal has one nationality . Rachid Ghezzal ( born 9 May 1992 ) is Algerian footballer who plays for French club Lyon in Ligue 1 and the Algeria national team .
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2
    type string string
    details
    • min: 9 tokens
    • mean: 19.0 tokens
    • max: 51 tokens
    • min: 8 tokens
    • mean: 17.33 tokens
    • max: 48 tokens
  • Samples:
    sentence1 sentence2
    Gas bubble holes helps pumice float in water. What helps pumice float in water?
    The law of parsimony is the scientific concept stating that when looking at two competing theories, the one with fewer assumptions should be chosen. What the scientific concept stating that when looking at two competing theories, the one with fewer assumptions should be chosen?
    Community interactions are important factors in natural selection. Community interactions are important factors in what?
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2
    type string string
    details
    • min: 8 tokens
    • mean: 28.99 tokens
    • max: 75 tokens
    • min: 9 tokens
    • mean: 19.18 tokens
    • max: 51 tokens
  • Samples:
    sentence1 sentence2
    Neutrons have no electric charge. A neutron has neutral or no charge.
    Blood vessels are part of the circulatory system, the "highway" system of the human body that transports materials to all of its cells. The main function of the circulatory system is to transport materials.
    Clouds form by the condensation of water vapor. Condensation must occur for clouds to form.
  • Loss: GISTEmbedLoss 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: summary and document
  • Approximate statistics based on the first 1000 samples:
    summary document
    type string string
    details
    • min: 11 tokens
    • mean: 30.04 tokens
    • max: 58 tokens
    • min: 61 tokens
    • mean: 250.1 tokens
    • max: 401 tokens
  • Samples:
    summary document
    Australia international Christian Lealiifano says there were times during his recovery from leukaemia that he thought he would not play rugby again. The 29-year-old was diagnosed in August 2016, but made his comeback in the Brumbies' Super Rugby quarter-final loss in July.
    Lealiifano has been linked with a short-term deal at Pro14 side Ulster.
    "When I first started training I thought I would never play football again," Lealiifano said.
    "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."
    Lealiifano said his cancer battle has "changed" his outlook on life.
    He underwent chemotherapy and a bone marrow transplant, and was told the cancer was in remission in February.
    "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."
    Lealiifano say he is "exploring options" in Australia's National Rugby Championship and playing abroad.
    Addressing the Ulster rumours, he said: "Nothing has been locked away yet - it is something in the pipeline."
    Lealiifano, who the last of his 19 caps for the Wallabi...
    A charity said one of its boats suffered "catastrophic damage" searching a river for a missing man. 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.
    He failed to return home after a night out with friends at The Buttermarket nightclub in the town.
    The damage on Saturday was believed to have been caused by submerged debris, WMSAR said.
    Nobody was hurt in this weekend's incident and the crew was rescued by another of its boats nearby, it said.
    A spokesman said: "We are a small charity, run wholly by specialist volunteers and funded by donations.
    "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."
    WMSAR provides support to the emergency services in Shropshire, Herefordshire and Worcestershire.
    West Mercia Police said it believed Mr Hodgin, from Madeley, left the club at about 03:30 GMT on 23 November.
    Dozens of people assembled at the nightclub on Saturday to search fo...
    Final preparations are being made for the staging of Scotland's biggest Gaelic cultural festival in Stornoway on Lewis. The nine-day Royal National Mòd, which will take place from 14-22 October, was last held in the Western Isles in 2011.
    Organisers An Comunn Gàidhealach said last year's event in Oban was estimated to have generated about £3m for the local economy.
    The Mòd features music, dance and arts competitions and performances.
    This year's opening ceremony will include headline performances by Gaelic group Dàimh and the Mischa Macpherson Trio.
    Lewis Pipe Band will also lead the festival's traditional torchlight procession, which is held on the opening night of the Mòd.
    John Morrison, chief executive of An Comunn Gàidhealach, said a "tremendous amount" of entries had been received for this year's competitions.
    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.
    "We're delighted to have the Mischa Macpherson Trio and Dàimh play at this year's opening ceremony, setting t...
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2
    type string string
    details
    • min: 7 tokens
    • mean: 20.04 tokens
    • max: 79 tokens
    • min: 2 tokens
    • mean: 102.5 tokens
    • max: 683 tokens
  • Samples:
    sentence1 sentence2
    What astronomical phenomenon is often comprised of remnants of the earliest material that formed in the solar system? Many meteorites are remnants of the earliest material that formed in the solar system.
    Female monotremes share what external opening with reptiles and birds? 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.
    Diarrhea due to food poisoning is an example of a problem involving what organ system? 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.
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2
    type string string
    details
    • min: 5 tokens
    • mean: 13.09 tokens
    • max: 27 tokens
    • min: 22 tokens
    • mean: 42.35 tokens
    • max: 82 tokens
  • Samples:
    sentence1 sentence2
    What generate heat mainly by keeping their metabolic rate high? Mammals generate heat mainly by keeping their metabolic rate high.. Gray Squirrel Management Tree squirrels are common Alabama mammals.
    tree squirrels generate heat mainly by keeping their metabolic rate high
    Reducing paper production would directly help stop what international problem? creating paper requires cutting down trees. Saving trees helps stop global warming.
    Reductions in paper production would help stop global warming
    Cells form tissues, which form? Cells are organized into tissues, and tissues form organs.. Skin is a protective organ.
    Cells form tissues, and tissues form skin
  • Loss: GISTEmbedLoss 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: question and fact
  • Approximate statistics based on the first 1000 samples:
    question fact
    type string string
    details
    • min: 4 tokens
    • mean: 15.73 tokens
    • max: 73 tokens
    • min: 5 tokens
    • mean: 13.69 tokens
    • max: 33 tokens
  • Samples:
    question fact
    Who is most likely to get a job at a lumber yard? timber companies cut down trees
    Food is a source of energy for what? food is a source of energy for animals
    Porpoises move fastest in large fins can be used to move quickly through water
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2
    type string string
    details
    • min: 10 tokens
    • mean: 13.41 tokens
    • max: 27 tokens
    • min: 20 tokens
    • mean: 148.54 tokens
    • max: 905 tokens
  • Samples:
    sentence1 sentence2
    who is the guy on the cover of you've come a long way baby 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.
    govt of india refers to the absolute poverty line in terms of 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.
    when did zody's go out of business 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]
  • Loss: GISTEmbedLoss 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: query and answer
  • Approximate statistics based on the first 1000 samples:
    query answer
    type string string
    details
    • min: 9 tokens
    • mean: 19.59 tokens
    • max: 62 tokens
    • min: 27 tokens
    • mean: 246.6 tokens
    • max: 575 tokens
  • Samples:
    query answer
    What is the official language of The Netherlands? Dutch language
    Which painting of 1851 by Sir Edwin Landseer features a great stag/deer which stands on a rise among cloud-covered mountains? 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...
    "Shirley Conran's book ""Superwoman"", published in 1975, was about what?" SUPERWOMAN by Shirley Conran
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2
    type string string
    details
    • min: 8 tokens
    • mean: 12.84 tokens
    • max: 22 tokens
    • min: 14 tokens
    • mean: 64.33 tokens
    • max: 141 tokens
  • Samples:
    sentence1 sentence2
    what is the difference between job work and career? 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.
    are dlr tickets valid on underground? 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.
    in which season does ross and rachel get together? 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.
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2
    type string string
    details
    • min: 12 tokens
    • mean: 31.83 tokens
    • max: 64 tokens
    • min: 12 tokens
    • mean: 31.86 tokens
    • max: 64 tokens
  • Samples:
    sentence1 sentence2
    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 . 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 .
    Upernivik Island is an uninhabited island in the Qaasuitsup municipality in northwest Greenland . Upernivik Island is an uninhabited island in the Qaasuitsup municipality in northwestern Greenland .
    The following night , Delirious put his problems with Pearce aside to challenge Danielson . The next night , Delirious put aside his problems with Pearce to challenge Danielson .
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 1000 samples:
    sentence1 sentence2
    type string string
    details
    • min: 5 tokens
    • mean: 36.58 tokens
    • max: 368 tokens
    • min: 2 tokens
    • mean: 57.39 tokens
    • max: 690 tokens
  • Samples:
    sentence1 sentence2
    The `` Real World Suite '' costs $ 25,000 per night . The level they rebuilt to accommodate MTV is now the '' Real World Suite '' '' billed at $ 25,000 per night . ''
    A young child colors with crayons at his dinner table. Child colors with crayons at dinner table.
    Sensori-neural hearing loss is usually permanent and is caused by damage to hair cells in the inner ear. Loud sounds that cause hearing loss damage the hair cells in the inner ear.
  • Loss: GISTEmbedLoss 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: claim and evidence
  • Approximate statistics based on the first 128 samples:
    claim evidence
    type string string
    details
    • min: 10 tokens
    • mean: 23.52 tokens
    • max: 48 tokens
    • min: 12 tokens
    • mean: 39.63 tokens
    • max: 92 tokens
  • Samples:
    claim evidence
    Dragon Con had over 5000 guests . 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 .
    COVID-19 has reached more than 185 countries . As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .
    In March , Italy had 3.6x times more cases of coronavirus than China . 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 .
  • Loss: GISTEmbedLoss 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: anchor, entailment, and negative
  • Approximate statistics based on the first 128 samples:
    anchor entailment negative
    type string string string
    details
    • min: 8 tokens
    • mean: 15.96 tokens
    • max: 32 tokens
    • min: 6 tokens
    • mean: 14.28 tokens
    • max: 25 tokens
    • min: 6 tokens
    • mean: 14.54 tokens
    • max: 26 tokens
  • Samples:
    anchor entailment negative
    Two sheep are looking over a cement wall. Two sheep poking their heads over a white cement wall Two sheep hiding their bodies under a white cement wall
    A stone building with a clock displayed on the outside. A tall multi-story building with a large clock atop it. A short single-story building with a small clock atop it.
    An older women tending to a garden. The lady has a garden The lady has no garden.
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 128 samples:
    sentence1 sentence2
    type string string
    details
    • min: 11 tokens
    • mean: 26.51 tokens
    • max: 62 tokens
    • min: 11 tokens
    • mean: 18.73 tokens
    • max: 28 tokens
  • Samples:
    sentence1 sentence2
    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.
    humans normally have 23 pairs of chromosomes. Humans typically have 23 pairs pairs of chromosomes.
    kinetic energy  the energy of motion. Kinetic energy is the energy of motion.
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 128 samples:
    sentence1 sentence2
    type string string
    details
    • min: 9 tokens
    • mean: 19.12 tokens
    • max: 42 tokens
    • min: 9 tokens
    • mean: 16.72 tokens
    • max: 41 tokens
  • Samples:
    sentence1 sentence2
    The term immunodeficiency means a failure, insufficiency, or delay in the response of the immune system, which may be acquired or inherited. What term means a failure, insufficiency, or delay in the response of the immune system, which may be acquired or inherited?
    Each lymph organ has a different job in the immune system. Each lymph organ has a different job in what system?
    A(n) an axillary bud is above each leaf scar. What is above each leaf scar?
  • Loss: GISTEmbedLoss 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: summary and document
  • Approximate statistics based on the first 1000 samples:
    summary document
    type string string
    details
    • min: 18 tokens
    • mean: 29.7 tokens
    • max: 47 tokens
    • min: 63 tokens
    • mean: 235.77 tokens
    • max: 405 tokens
  • Samples:
    summary document
    Glasgow Warriors' Pro12 match against Leinster has been postponed due to severe weather conditions. Heavy rain overnight on Friday which continued into Saturday rendered the pitch at Scotstoun unplayable.
    The postponement creates something of a fixture headache for Warriors.
    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.
    "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.
    Several Scottish football fixtures have also fallen victim to the weather.
    Pro12
    Glasgow Warriors P-P Leinster
    Scottish Premiership
    Celtic P-P Hamilton Academical
    Hearts P-P Inverness CT
    Partick Thistle P-P Motherwell
    Scottish Championship
    Falkirk P-P Livingston
    Raith Rovers P-P Rangers
    St Mirren P-P Queen of the South
    Scottish League One
    Albion Rovers P-P Stranraer
    Scottish League Two
    East Stirlingshire P-P Clyde
    Scottish Cup
    Lothian Thistle Hutchi...
    A former consultant has been charged with 10 counts of sexual assault at a hospital in Surrey. Cardiologist Neil Ineson, 60, of Sandhurst, Berkshire is charged with a series of sexual assaults at Frimley Park Hospital in Surrey.
    The alleged assaults took place between August 2007 and November 2014.
    Surrey Police said Dr Ineson had been released on conditional bail and would appear at Guildford Magistrates' Court on 10 May.
    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. 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.
    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."
    She is aware that as time passes, the area is changing, and this could be last chance to capture this creative community.
    Lewis says that even some of those she has photographed have since had to move out.
    "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."
    You can see more of Jenny Lewis's work on her website.
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 128 samples:
    sentence1 sentence2
    type string string
    details
    • min: 9 tokens
    • mean: 19.49 tokens
    • max: 45 tokens
    • min: 2 tokens
    • mean: 102.95 tokens
    • max: 579 tokens
  • Samples:
    sentence1 sentence2
    How does the atomic number change during beta emission? 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.
    Circular water waves decrease in what property as they move away from where a rock is dropped? 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.
    What are are organisms that feed on small pieces of organic matter? 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.
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 128 samples:
    sentence1 sentence2
    type string string
    details
    • min: 6 tokens
    • mean: 13.09 tokens
    • max: 27 tokens
    • min: 23 tokens
    • mean: 41.5 tokens
    • max: 75 tokens
  • Samples:
    sentence1 sentence2
    What is a major food source for other organisms, including humans? Mollusks are a major food source for other organisms, including humans.. Mollusks are invertebrates and are no exception.
    Invertebrates are a major food source for other organisms, including humans.
    What does not change during melting? phase changes do not change mass. Melting is a phase change.
    Mass does not change during melting
    What causes water to expand as it becomes ice? Hydrogen bonds cause water to expand when it freezes.. Water freezes into ice when cooled.
    hydrogen bonds cause water to expand as it becomes ice
  • Loss: GISTEmbedLoss 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: question and fact
  • Approximate statistics based on the first 500 samples:
    question fact
    type string string
    details
    • min: 5 tokens
    • mean: 15.98 tokens
    • max: 54 tokens
    • min: 6 tokens
    • mean: 14.32 tokens
    • max: 30 tokens
  • Samples:
    question fact
    The thermal production of a stove is generically used for a stove generates heat for cooking usually
    What creates a valley? a valley is formed by a river flowing
    when it turns day and night on a planet, what cause this? a planet rotating causes cycles of day and night on that planet
  • Loss: GISTEmbedLoss 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: sentence1 and sentence2
  • Approximate statistics based on the first 128 samples:
    sentence1 sentence2
    type string string
    details
    • min: 10 tokens
    • mean: 13.3 tokens
    • max: 24 tokens
    • min: 34 tokens
    • mean: 165.62 tokens
    • max: 525 tokens
  • Samples:
    sentence1 sentence2
    when did the singer of linkin park die 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.
    when does rose tyler come back to doctor who Rose Tyler In spin-off series Torchwood (2006–2011), the audience learns that Rose's act of resurrecting Jack cursed him with being unable to die.[14] Her absence and the Doctor's pained estrangement from her proves a point of contention for the Doctor's series 3 companion Martha (Freema Agyeman); when Martha protects the Doctor, living as a human without his memories, it is still Rose that he dreams of.[15] When the Doctor is reunited with Donna Noble (Catherine Tate) in the show's fourth series (2008), Rose mysteriously begins to appear in the Doctor's life—first seen only by Donna, and later in silent video messages which the Doctor fails to notice.[16][17] When a "Time Beetle" creates an alternate universe in which Donna never meets the Doctor and the Doctor dies, Rose travels from her parallel world to this world, working alongside the organisation UNIT to send Donna back in time, and make Donna's younger self turn left at a junction and not right. Rose tells Donna to say two word...
    who plays simba's mom in the lion king List of The Lion King characters Sarabi (voiced by Madge Sinclair in the first film, Alfre Woodard in the live-action film) is Mufasa's mate, Simba's mother and Kiara and Kion's paternal grandmother. Her name means "mirage" in Swahili. In The Lion King, she serves as the Queen of Pride Rock. Years after Scar usurps the throne, Sarabi helps Simba fight against Scar and his hyenas. When Simba defeats Scar, Nala becomes Queen and Sarabi becomes the Queen Dowager.
  • Loss: GISTEmbedLoss 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: 128 evaluation samples
  • Columns: query and answer
  • Approximate statistics based on the first 128 samples:
    query answer
    type string string
    details
    • min: 9 tokens
    • mean: 18.95 tokens
    • max: 55 tokens
    • min: 35 tokens
    • mean: 231.95 tokens
    • max: 575 tokens
  • Samples:
    query answer
    Excluding Australia, what is the third largest island in the world? Excluding Australia, what is the third largest island in the world? Excluding Australia, what is the third largest island in the world? +3 votes posted Jun 22, 2016 by Aastha Joshi Share this question   Your comment on this question: Email me at this address if a comment is added after mine:Email me if a comment is added after mine Privacy: Your email address will only be used for sending these notifications. Anti-spam verification: To avoid this verification in future, please log in or register . 1 Answer 0 votes Borneo is the largest of the Malay Archipelago and third largest island in the world. Covered by 744,108 area sq. km located at West mid-Pacific (Indonesian, south part; Brunei and Malaysian, north part) However Australia is defined as a continent rather than an island. answer Jun 22, 2016 by Atindra Kumar Nath Your comment on this answer: Email me at this address if a comment is added after mine:Email me if a comment is added after mine Privacy: Your email address will only ...
    What is a car marque, a software company and the national flower of Egypt? Home 2016
    What was the first name of the first First Lady of the USA? 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 S...
  • Loss: GISTEmbedLoss 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: 128 evaluation samples
  • Columns: sentence1 and sentence2
  • Approximate statistics based on the first 128 samples:
    sentence1 sentence2
    type string string
    details
    • min: 8 tokens
    • mean: 12.64 tokens
    • max: 21 tokens
    • min: 24 tokens
    • mean: 64.33 tokens
    • max: 130 tokens
  • Samples:
    sentence1 sentence2
    is flylady app available for android? FlyHelper is a Free android app to use with Flylady. It has customizable checklists for daily, weekly and monthly routines; checklisted zone lists; a meal planner; a place for your task list and a link to the Flylady day - plan. You also need to download the license key, also for Free (it wasn't always free).
    can you install vinyl plank flooring over existing ceramic tile? Vinyl plank flooring works well in any tightly sealed, smooth or seamless surface. Vinyl flooring can be installed over ceramic tile if the grout lines are thin enough.
    how does your digestive system help us obtain nutrients? The digestive system converts the foods we eat into their simplest forms, like glucose (sugars), amino acids (that make up protein) or fatty acids (that make up fats). The broken-down food is then absorbed into the bloodstream from the small intestine and the nutrients are carried to each cell in the body.
  • Loss: GISTEmbedLoss 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: 128 evaluation samples
  • Columns: sentence1 and sentence2
  • Approximate statistics based on the first 128 samples:
    sentence1 sentence2
    type string string
    details
    • min: 11 tokens
    • mean: 31.43 tokens
    • max: 56 tokens
    • min: 10 tokens
    • mean: 31.14 tokens
    • max: 54 tokens
  • Samples:
    sentence1 sentence2
    They were there to enjoy us and they were there to pray for us . They were there for us to enjoy and they were there for us to pray .
    After the end of the war in June 1902 , Higgins left Southampton in the `` SSBavarian '' in August , returning to Cape Town the following month . In August , after the end of the war in June 1902 , Higgins Southampton left the `` SSBavarian '' and returned to Cape Town the following month .
    From the merger of the Four Rivers Council and the Audubon Council , the Shawnee Trails Council was born . Shawnee Trails Council was formed from the merger of the Four Rivers Council and the Audubon Council .
  • Loss: GISTEmbedLoss 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: 384 evaluation samples
  • Columns: sentence1 and sentence2
  • Approximate statistics based on the first 384 samples:
    sentence1 sentence2
    type string string
    details
    • min: 5 tokens
    • mean: 35.66 tokens
    • max: 341 tokens
    • min: 2 tokens
    • mean: 63.78 tokens
    • max: 579 tokens
  • Samples:
    sentence1 sentence2
    What is the attachment of ducklings to their mother an example of? Figure 45.39 The attachment of ducklings to their mother is an example of imprinting. (credit: modification of work by Mark Harkin).
    Worldwide , there have been more than 152,000 coronavirus cases during 2019-2020 . more than 154,000 cases have been confirmed worldwide .
    What helps maintain body temperature? an animal usually requires a warm body temperature for survival. Mammals are also warm-blooded and are covered with hair or fur.
    fur helps maintain body temperature
  • Loss: GISTEmbedLoss 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}
    

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: steps
  • per_device_train_batch_size: 128
  • per_device_eval_batch_size: 256
  • learning_rate: 0.001
  • weight_decay: 0.001
  • lr_scheduler_type: cosine_with_min_lr
  • lr_scheduler_kwargs: {'num_cycles': 0.5, 'min_lr': 0.0001}
  • warmup_ratio: 0.25
  • save_safetensors: False
  • fp16: True
  • remove_unused_columns: False
  • push_to_hub: True
  • hub_model_id: bobox/XLMRoBERTaM3-CustomPoolin-v1-s1-checkpoints-tmp
  • hub_strategy: all_checkpoints
  • hub_private_repo: False
  • 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: 128
  • per_device_eval_batch_size: 256
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 1
  • eval_accumulation_steps: None
  • torch_empty_cache_steps: None
  • learning_rate: 0.001
  • weight_decay: 0.001
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 3
  • max_steps: -1
  • lr_scheduler_type: cosine_with_min_lr
  • lr_scheduler_kwargs: {'num_cycles': 0.5, 'min_lr': 0.0001}
  • warmup_ratio: 0.25
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: False
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: None
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: False
  • 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/XLMRoBERTaM3-CustomPoolin-v1-s1-checkpoints-tmp
  • hub_strategy: all_checkpoints
  • hub_private_repo: False
  • hub_always_push: False
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • include_inputs_for_metrics: False
  • include_for_metrics: []
  • 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
  • eval_on_start: False
  • use_liger_kernel: False
  • eval_use_gather_object: False
  • average_tokens_across_devices: False
  • prompts: None
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Click to expand
Epoch Step Training Loss vitaminc-pairs loss negation-triplets loss scitail-pairs-pos loss scitail-pairs-qa loss xsum-pairs loss sciq pairs loss qasc pairs loss openbookqa pairs loss nq pairs loss trivia pairs loss gooaq pairs loss paws-pos loss global dataset loss sts-test_spearman_cosine allNLI-dev_cosine_ap Qnli-dev_cosine_ap
0.0014 1 1.2468 - - - - - - - - - - - - - - - -
0.0027 2 1.4692 - - - - - - - - - - - - - - - -
0.0041 3 1.2457 - - - - - - - - - - - - - - - -
0.0055 4 1.1859 - - - - - - - - - - - - - - - -
0.0069 5 1.2404 - - - - - - - - - - - - - - - -
0.0082 6 0.042 - - - - - - - - - - - - - - - -
0.0096 7 0.8856 - - - - - - - - - - - - - - - -
0.0110 8 1.5417 - - - - - - - - - - - - - - - -
0.0123 9 0.023 - - - - - - - - - - - - - - - -
0.0137 10 0.8655 - - - - - - - - - - - - - - - -
0.0151 11 0.5894 - - - - - - - - - - - - - - - -
0.0165 12 0.7053 - - - - - - - - - - - - - - - -
0.0178 13 0.5857 - - - - - - - - - - - - - - - -
0.0192 14 0.8375 - - - - - - - - - - - - - - - -
0.0206 15 0.9043 - - - - - - - - - - - - - - - -
0.0219 16 0.8756 - - - - - - - - - - - - - - - -
0.0233 17 0.5076 - - - - - - - - - - - - - - - -
0.0247 18 0.4757 - - - - - - - - - - - - - - - -
0.0261 19 0.9993 - - - - - - - - - - - - - - - -
0.0274 20 0.2622 - - - - - - - - - - - - - - - -
0.0288 21 0.3497 - - - - - - - - - - - - - - - -
0.0302 22 0.2514 - - - - - - - - - - - - - - - -
0.0316 23 0.1673 - - - - - - - - - - - - - - - -
0.0329 24 0.203 - - - - - - - - - - - - - - - -
0.0343 25 0.698 - - - - - - - - - - - - - - - -
0.0357 26 0.3401 - - - - - - - - - - - - - - - -
0.0370 27 0.2185 - - - - - - - - - - - - - - - -
0.0384 28 0.4424 - - - - - - - - - - - - - - - -
0.0398 29 0.0381 - - - - - - - - - - - - - - - -
0.0412 30 0.8215 - - - - - - - - - - - - - - - -
0.0425 31 0.1542 - - - - - - - - - - - - - - - -
0.0439 32 0.6893 - - - - - - - - - - - - - - - -
0.0453 33 0.3773 - - - - - - - - - - - - - - - -
0.0466 34 0.538 - - - - - - - - - - - - - - - -
0.0480 35 0.0073 - - - - - - - - - - - - - - - -
0.0494 36 2.378 - - - - - - - - - - - - - - - -
0.0508 37 0.5949 - - - - - - - - - - - - - - - -
0.0521 38 0.7071 - - - - - - - - - - - - - - - -
0.0535 39 0.1607 - - - - - - - - - - - - - - - -
0.0549 40 0.7735 - - - - - - - - - - - - - - - -
0.0562 41 0.7594 - - - - - - - - - - - - - - - -
0.0576 42 0.3569 - - - - - - - - - - - - - - - -
0.0590 43 0.2454 - - - - - - - - - - - - - - - -
0.0604 44 0.2723 - - - - - - - - - - - - - - - -
0.0617 45 0.5338 - - - - - - - - - - - - - - - -
0.0631 46 0.1891 - - - - - - - - - - - - - - - -
0.0645 47 0.3647 - - - - - - - - - - - - - - - -
0.0658 48 0.383 - - - - - - - - - - - - - - - -
0.0672 49 0.2353 - - - - - - - - - - - - - - - -
0.0686 50 0.5541 - - - - - - - - - - - - - - - -
0.0700 51 0.4908 - - - - - - - - - - - - - - - -
0.0713 52 0.586 - - - - - - - - - - - - - - - -
0.0727 53 0.2241 - - - - - - - - - - - - - - - -
0.0741 54 0.6046 - - - - - - - - - - - - - - - -
0.0754 55 0.231 - - - - - - - - - - - - - - - -
0.0768 56 0.7105 - - - - - - - - - - - - - - - -
0.0782 57 0.5591 - - - - - - - - - - - - - - - -
0.0796 58 0.5194 - - - - - - - - - - - - - - - -
0.0809 59 0.3297 - - - - - - - - - - - - - - - -
0.0823 60 0.0299 - - - - - - - - - - - - - - - -
0.0837 61 0.3514 - - - - - - - - - - - - - - - -
0.0850 62 0.1932 - - - - - - - - - - - - - - - -
0.0864 63 0.4035 - - - - - - - - - - - - - - - -
0.0878 64 0.0094 - - - - - - - - - - - - - - - -
0.0892 65 0.0148 - - - - - - - - - - - - - - - -
0.0905 66 0.0231 - - - - - - - - - - - - - - - -
0.0919 67 0.3204 - - - - - - - - - - - - - - - -
0.0933 68 0.3011 - - - - - - - - - - - - - - - -
0.0947 69 0.3871 - - - - - - - - - - - - - - - -
0.0960 70 0.1823 - - - - - - - - - - - - - - - -
0.0974 71 0.3572 - - - - - - - - - - - - - - - -
0.0988 72 0.5289 - - - - - - - - - - - - - - - -
0.1001 73 0.3223 - - - - - - - - - - - - - - - -
0.1015 74 0.3247 - - - - - - - - - - - - - - - -
0.1029 75 0.2133 - - - - - - - - - - - - - - - -
0.1043 76 0.8249 - - - - - - - - - - - - - - - -
0.1056 77 0.4341 - - - - - - - - - - - - - - - -
0.1070 78 0.2932 - - - - - - - - - - - - - - - -
0.1084 79 0.0099 - - - - - - - - - - - - - - - -
0.1097 80 0.3348 - - - - - - - - - - - - - - - -
0.1111 81 0.6405 - - - - - - - - - - - - - - - -
0.1125 82 0.1536 - - - - - - - - - - - - - - - -
0.1139 83 0.1299 - - - - - - - - - - - - - - - -
0.1152 84 0.5863 - - - - - - - - - - - - - - - -
0.1166 85 0.7205 - - - - - - - - - - - - - - - -
0.1180 86 0.4052 - - - - - - - - - - - - - - - -
0.1193 87 0.3953 - - - - - - - - - - - - - - - -
0.1207 88 0.5598 - - - - - - - - - - - - - - - -
0.1221 89 0.2856 - - - - - - - - - - - - - - - -
0.1235 90 0.2277 - - - - - - - - - - - - - - - -
0.1248 91 0.3296 - - - - - - - - - - - - - - - -
0.1262 92 0.3079 - - - - - - - - - - - - - - - -
0.1276 93 0.4867 - - - - - - - - - - - - - - - -
0.1289 94 0.4319 - - - - - - - - - - - - - - - -
0.1303 95 0.2952 - - - - - - - - - - - - - - - -
0.1317 96 0.5531 - - - - - - - - - - - - - - - -
0.1331 97 0.0296 - - - - - - - - - - - - - - - -
0.1344 98 0.8536 - - - - - - - - - - - - - - - -
0.1358 99 0.4879 - - - - - - - - - - - - - - - -
0.1372 100 0.67 - - - - - - - - - - - - - - - -
0.1385 101 0.4813 - - - - - - - - - - - - - - - -
0.1399 102 0.0488 - - - - - - - - - - - - - - - -
0.1413 103 0.5388 - - - - - - - - - - - - - - - -
0.1427 104 0.376 - - - - - - - - - - - - - - - -
0.1440 105 0.017 - - - - - - - - - - - - - - - -
0.1454 106 0.7542 - - - - - - - - - - - - - - - -
0.1468 107 0.4063 - - - - - - - - - - - - - - - -
0.1481 108 0.3658 - - - - - - - - - - - - - - - -
0.1495 109 0.4389 - - - - - - - - - - - - - - - -
0.1509 110 0.3803 2.0093 1.5957 0.0618 0.0092 0.3721 0.0412 0.4749 1.1541 0.2363 0.6520 0.2262 0.0282 0.3067 0.9137 0.6627 0.7567
0.1523 111 0.2478 - - - - - - - - - - - - - - - -
0.1536 112 0.8402 - - - - - - - - - - - - - - - -
0.1550 113 0.6608 - - - - - - - - - - - - - - - -
0.1564 114 0.0934 - - - - - - - - - - - - - - - -
0.1578 115 0.3907 - - - - - - - - - - - - - - - -
0.1591 116 0.449 - - - - - - - - - - - - - - - -
0.1605 117 0.4041 - - - - - - - - - - - - - - - -
0.1619 118 0.6749 - - - - - - - - - - - - - - - -
0.1632 119 0.4847 - - - - - - - - - - - - - - - -
0.1646 120 0.0526 - - - - - - - - - - - - - - - -
0.1660 121 0.6795 - - - - - - - - - - - - - - - -
0.1674 122 0.0064 - - - - - - - - - - - - - - - -
0.1687 123 0.5918 - - - - - - - - - - - - - - - -
0.1701 124 0.3544 - - - - - - - - - - - - - - - -
0.1715 125 0.3849 - - - - - - - - - - - - - - - -
0.1728 126 0.2051 - - - - - - - - - - - - - - - -
0.1742 127 0.2129 - - - - - - - - - - - - - - - -
0.1756 128 2.7937 - - - - - - - - - - - - - - - -
0.1770 129 0.0166 - - - - - - - - - - - - - - - -
0.1783 130 0.7856 - - - - - - - - - - - - - - - -
0.1797 131 0.8368 - - - - - - - - - - - - - - - -
0.1811 132 0.3813 - - - - - - - - - - - - - - - -
0.1824 133 0.5695 - - - - - - - - - - - - - - - -
0.1838 134 0.351 - - - - - - - - - - - - - - - -
0.1852 135 0.3821 - - - - - - - - - - - - - - - -
0.1866 136 0.3249 - - - - - - - - - - - - - - - -
0.1879 137 0.3404 - - - - - - - - - - - - - - - -
0.1893 138 0.4535 - - - - - - - - - - - - - - - -
0.1907 139 0.0577 - - - - - - - - - - - - - - - -
0.1920 140 0.7431 - - - - - - - - - - - - - - - -
0.1934 141 0.6778 - - - - - - - - - - - - - - - -
0.1948 142 0.5436 - - - - - - - - - - - - - - - -
0.1962 143 0.3582 - - - - - - - - - - - - - - - -
0.1975 144 0.316 - - - - - - - - - - - - - - - -
0.1989 145 0.4446 - - - - - - - - - - - - - - - -
0.2003 146 0.7792 - - - - - - - - - - - - - - - -
0.2016 147 1.1147 - - - - - - - - - - - - - - - -
0.2030 148 0.8267 - - - - - - - - - - - - - - - -
0.2044 149 0.8149 - - - - - - - - - - - - - - - -
0.2058 150 0.942 - - - - - - - - - - - - - - - -
0.2071 151 2.4865 - - - - - - - - - - - - - - - -
0.2085 152 1.0715 - - - - - - - - - - - - - - - -
0.2099 153 0.6219 - - - - - - - - - - - - - - - -
0.2112 154 0.8705 - - - - - - - - - - - - - - - -
0.2126 155 0.2407 - - - - - - - - - - - - - - - -
0.2140 156 0.4925 - - - - - - - - - - - - - - - -
0.2154 157 0.0316 - - - - - - - - - - - - - - - -
0.2167 158 0.3935 - - - - - - - - - - - - - - - -
0.2181 159 0.2083 - - - - - - - - - - - - - - - -
0.2195 160 0.2798 - - - - - - - - - - - - - - - -
0.2209 161 0.8777 - - - - - - - - - - - - - - - -
0.2222 162 0.0002 - - - - - - - - - - - - - - - -
0.2236 163 0.2736 - - - - - - - - - - - - - - - -
0.2250 164 2.4185 - - - - - - - - - - - - - - - -
0.2263 165 0.7767 - - - - - - - - - - - - - - - -
0.2277 166 0.7971 - - - - - - - - - - - - - - - -
0.2291 167 0.4535 - - - - - - - - - - - - - - - -
0.2305 168 0.6654 - - - - - - - - - - - - - - - -
0.2318 169 0.3985 - - - - - - - - - - - - - - - -
0.2332 170 0.0338 - - - - - - - - - - - - - - - -
0.2346 171 0.1834 - - - - - - - - - - - - - - - -
0.2359 172 0.603 - - - - - - - - - - - - - - - -
0.2373 173 0.7871 - - - - - - - - - - - - - - - -
0.2387 174 0.4304 - - - - - - - - - - - - - - - -
0.2401 175 0.649 - - - - - - - - - - - - - - - -
0.2414 176 0.048 - - - - - - - - - - - - - - - -
0.2428 177 0.4079 - - - - - - - - - - - - - - - -
0.2442 178 0.4627 - - - - - - - - - - - - - - - -
0.2455 179 0.3703 - - - - - - - - - - - - - - - -
0.2469 180 0.8343 - - - - - - - - - - - - - - - -
0.2483 181 0.692 - - - - - - - - - - - - - - - -
0.2497 182 2.7071 - - - - - - - - - - - - - - - -
0.2510 183 0.8451 - - - - - - - - - - - - - - - -
0.2524 184 0.635 - - - - - - - - - - - - - - - -
0.2538 185 0.312 - - - - - - - - - - - - - - - -
0.2551 186 0.6996 - - - - - - - - - - - - - - - -
0.2565 187 0.4432 - - - - - - - - - - - - - - - -
0.2579 188 0.375 - - - - - - - - - - - - - - - -
0.2593 189 0.9366 - - - - - - - - - - - - - - - -
0.2606 190 0.755 - - - - - - - - - - - - - - - -
0.2620 191 0.6068 - - - - - - - - - - - - - - - -
0.2634 192 0.5336 - - - - - - - - - - - - - - - -
0.2647 193 0.8783 - - - - - - - - - - - - - - - -
0.2661 194 0.3576 - - - - - - - - - - - - - - - -
0.2675 195 2.1854 - - - - - - - - - - - - - - - -
0.2689 196 0.7835 - - - - - - - - - - - - - - - -
0.2702 197 0.5668 - - - - - - - - - - - - - - - -
0.2716 198 0.7033 - - - - - - - - - - - - - - - -
0.2730 199 0.0002 - - - - - - - - - - - - - - - -
0.2743 200 0.5791 - - - - - - - - - - - - - - - -
0.2757 201 0.2697 - - - - - - - - - - - - - - - -
0.2771 202 0.6261 - - - - - - - - - - - - - - - -
0.2785 203 0.3253 - - - - - - - - - - - - - - - -
0.2798 204 0.8323 - - - - - - - - - - - - - - - -
0.2812 205 0.4472 - - - - - - - - - - - - - - - -
0.2826 206 0.3342 - - - - - - - - - - - - - - - -
0.2840 207 0.6313 - - - - - - - - - - - - - - - -
0.2853 208 0.059 - - - - - - - - - - - - - - - -
0.2867 209 0.1195 - - - - - - - - - - - - - - - -
0.2881 210 0.0296 - - - - - - - - - - - - - - - -
0.2894 211 0.5316 - - - - - - - - - - - - - - - -
0.2908 212 0.5201 - - - - - - - - - - - - - - - -
0.2922 213 0.6602 - - - - - - - - - - - - - - - -
0.2936 214 0.9578 - - - - - - - - - - - - - - - -
0.2949 215 0.2089 - - - - - - - - - - - - - - - -
0.2963 216 1.2112 - - - - - - - - - - - - - - - -
0.2977 217 0.3294 - - - - - - - - - - - - - - - -
0.2990 218 0.867 - - - - - - - - - - - - - - - -
0.3004 219 1.1745 - - - - - - - - - - - - - - - -
0.3018 220 0.7287 2.5132 1.5992 0.1210 0.0290 0.6064 0.0521 0.7245 1.4260 0.7105 0.8537 0.5030 0.0315 0.1892 0.8907 0.6542 0.7693
0.3032 221 0.5484 - - - - - - - - - - - - - - - -
0.3045 222 0.9396 - - - - - - - - - - - - - - - -
0.3059 223 0.4335 - - - - - - - - - - - - - - - -
0.3073 224 0.9026 - - - - - - - - - - - - - - - -
0.3086 225 0.7214 - - - - - - - - - - - - - - - -
0.3100 226 0.4794 - - - - - - - - - - - - - - - -
0.3114 227 0.0003 - - - - - - - - - - - - - - - -
0.3128 228 0.3003 - - - - - - - - - - - - - - - -
0.3141 229 0.4667 - - - - - - - - - - - - - - - -
0.3155 230 0.5006 - - - - - - - - - - - - - - - -
0.3169 231 0.5555 - - - - - - - - - - - - - - - -
0.3182 232 0.4437 - - - - - - - - - - - - - - - -
0.3196 233 0.8813 - - - - - - - - - - - - - - - -
0.3210 234 1.1836 - - - - - - - - - - - - - - - -
0.3224 235 0.3176 - - - - - - - - - - - - - - - -
0.3237 236 0.6248 - - - - - - - - - - - - - - - -
0.3251 237 0.3623 - - - - - - - - - - - - - - - -
0.3265 238 0.3205 - - - - - - - - - - - - - - - -
0.3278 239 0.439 - - - - - - - - - - - - - - - -
0.3292 240 0.653 - - - - - - - - - - - - - - - -
0.3306 241 0.5743 - - - - - - - - - - - - - - - -
0.3320 242 0.5844 - - - - - - - - - - - - - - - -
0.3333 243 0.9451 - - - - - - - - - - - - - - - -
0.3347 244 0.7071 - - - - - - - - - - - - - - - -
0.3361 245 0.0226 - - - - - - - - - - - - - - - -
0.3374 246 1.0585 - - - - - - - - - - - - - - - -
0.3388 247 1.0764 - - - - - - - - - - - - - - - -
0.3402 248 0.0289 - - - - - - - - - - - - - - - -
0.3416 249 0.5588 - - - - - - - - - - - - - - - -
0.3429 250 0.7509 - - - - - - - - - - - - - - - -
0.3443 251 0.8388 - - - - - - - - - - - - - - - -
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2.5542 1862 0.1346 - - - - - - - - - - - - - - - -
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2.5597 1866 0.2162 - - - - - - - - - - - - - - - -
2.5610 1867 0.3087 - - - - - - - - - - - - - - - -
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2.5638 1869 0.151 - - - - - - - - - - - - - - - -
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2.5665 1871 0.0053 - - - - - - - - - - - - - - - -
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2.5761 1878 0.2512 - - - - - - - - - - - - - - - -
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2.5789 1880 0.353 - - - - - - - - - - - - - - - -
2.5802 1881 0.5225 - - - - - - - - - - - - - - - -
2.5816 1882 0.5316 - - - - - - - - - - - - - - - -
2.5830 1883 0.2855 - - - - - - - - - - - - - - - -
2.5844 1884 0.2006 - - - - - - - - - - - - - - - -
2.5857 1885 0.1881 - - - - - - - - - - - - - - - -
2.5871 1886 0.1617 - - - - - - - - - - - - - - - -
2.5885 1887 0.2923 - - - - - - - - - - - - - - - -
2.5898 1888 2.1382 - - - - - - - - - - - - - - - -
2.5912 1889 0.1644 - - - - - - - - - - - - - - - -
2.5926 1890 0.4955 - - - - - - - - - - - - - - - -
2.5940 1891 0.3882 - - - - - - - - - - - - - - - -
2.5953 1892 0.5865 - - - - - - - - - - - - - - - -
2.5967 1893 0.2798 - - - - - - - - - - - - - - - -
2.5981 1894 2.4055 - - - - - - - - - - - - - - - -
2.5995 1895 1.7765 - - - - - - - - - - - - - - - -
2.6008 1896 0.1194 - - - - - - - - - - - - - - - -
2.6022 1897 0.3848 - - - - - - - - - - - - - - - -
2.6036 1898 0.1345 - - - - - - - - - - - - - - - -
2.6049 1899 0.3411 - - - - - - - - - - - - - - - -
2.6063 1900 0.2154 - - - - - - - - - - - - - - - -
2.6077 1901 0.11 - - - - - - - - - - - - - - - -
2.6091 1902 0.2541 - - - - - - - - - - - - - - - -
2.6104 1903 0.4773 - - - - - - - - - - - - - - - -
2.6118 1904 0.4979 - - - - - - - - - - - - - - - -
2.6132 1905 0.3372 - - - - - - - - - - - - - - - -
2.6145 1906 0.1801 - - - - - - - - - - - - - - - -
2.6159 1907 0.1492 - - - - - - - - - - - - - - - -
2.6173 1908 0.3447 - - - - - - - - - - - - - - - -
2.6187 1909 0.0362 - - - - - - - - - - - - - - - -
2.6200 1910 0.0451 - - - - - - - - - - - - - - - -
2.6214 1911 0.2264 - - - - - - - - - - - - - - - -
2.6228 1912 0.0963 - - - - - - - - - - - - - - - -
2.6241 1913 0.0314 - - - - - - - - - - - - - - - -
2.6255 1914 0.0738 - - - - - - - - - - - - - - - -
2.6269 1915 0.2773 - - - - - - - - - - - - - - - -
2.6283 1916 0.4213 - - - - - - - - - - - - - - - -
2.6296 1917 0.5221 - - - - - - - - - - - - - - - -
2.6310 1918 0.0931 - - - - - - - - - - - - - - - -
2.6324 1919 0.4674 - - - - - - - - - - - - - - - -
2.6337 1920 0.2365 - - - - - - - - - - - - - - - -
2.6351 1921 0.0485 - - - - - - - - - - - - - - - -
2.6365 1922 0.1861 - - - - - - - - - - - - - - - -
2.6379 1923 0.1627 - - - - - - - - - - - - - - - -
2.6392 1924 0.1757 - - - - - - - - - - - - - - - -
2.6406 1925 0.1972 - - - - - - - - - - - - - - - -
2.6420 1926 0.186 - - - - - - - - - - - - - - - -
2.6433 1927 0.0332 - - - - - - - - - - - - - - - -
2.6447 1928 0.078 - - - - - - - - - - - - - - - -
2.6461 1929 0.1772 - - - - - - - - - - - - - - - -
2.6475 1930 0.4478 - - - - - - - - - - - - - - - -
2.6488 1931 0.0961 - - - - - - - - - - - - - - - -
2.6502 1932 0.3922 - - - - - - - - - - - - - - - -
2.6516 1933 0.3992 - - - - - - - - - - - - - - - -
2.6529 1934 0.271 - - - - - - - - - - - - - - - -
2.6543 1935 0.2431 - - - - - - - - - - - - - - - -
2.6557 1936 0.0837 - - - - - - - - - - - - - - - -
2.6571 1937 0.1267 - - - - - - - - - - - - - - - -
2.6584 1938 0.0443 - - - - - - - - - - - - - - - -
2.6598 1939 0.1071 - - - - - - - - - - - - - - - -
2.6612 1940 0.1947 - - - - - - - - - - - - - - - -
2.6626 1941 0.4155 - - - - - - - - - - - - - - - -
2.6639 1942 0.0514 - - - - - - - - - - - - - - - -
2.6653 1943 0.247 - - - - - - - - - - - - - - - -
2.6667 1944 0.4753 - - - - - - - - - - - - - - - -
2.6680 1945 0.1326 - - - - - - - - - - - - - - - -
2.6694 1946 0.2399 - - - - - - - - - - - - - - - -
2.6708 1947 0.0177 - - - - - - - - - - - - - - - -
2.6722 1948 0.1975 - - - - - - - - - - - - - - - -
2.6735 1949 0.2081 - - - - - - - - - - - - - - - -
2.6749 1950 0.1391 - - - - - - - - - - - - - - - -
2.6763 1951 0.1537 - - - - - - - - - - - - - - - -
2.6776 1952 0.5363 - - - - - - - - - - - - - - - -
2.6790 1953 0.0103 - - - - - - - - - - - - - - - -
2.6804 1954 0.1783 - - - - - - - - - - - - - - - -
2.6818 1955 0.443 - - - - - - - - - - - - - - - -
2.6831 1956 0.1407 - - - - - - - - - - - - - - - -
2.6845 1957 0.2194 - - - - - - - - - - - - - - - -
2.6859 1958 0.1627 - - - - - - - - - - - - - - - -
2.6872 1959 0.0303 - - - - - - - - - - - - - - - -
2.6886 1960 0.0011 - - - - - - - - - - - - - - - -
2.6900 1961 0.1819 - - - - - - - - - - - - - - - -
2.6914 1962 0.1631 - - - - - - - - - - - - - - - -
2.6927 1963 0.0161 - - - - - - - - - - - - - - - -
2.6941 1964 0.302 - - - - - - - - - - - - - - - -
2.6955 1965 0.3284 - - - - - - - - - - - - - - - -
2.6968 1966 0.245 - - - - - - - - - - - - - - - -
2.6982 1967 0.3598 - - - - - - - - - - - - - - - -
2.6996 1968 0.025 - - - - - - - - - - - - - - - -
2.7010 1969 0.7513 - - - - - - - - - - - - - - - -
2.7023 1970 0.2268 - - - - - - - - - - - - - - - -
2.7037 1971 0.3263 - - - - - - - - - - - - - - - -
2.7051 1972 0.0968 - - - - - - - - - - - - - - - -
2.7064 1973 0.3619 - - - - - - - - - - - - - - - -
2.7078 1974 0.1079 - - - - - - - - - - - - - - - -
2.7092 1975 0.0233 - - - - - - - - - - - - - - - -
2.7106 1976 0.6683 - - - - - - - - - - - - - - - -
2.7119 1977 0.0714 - - - - - - - - - - - - - - - -
2.7133 1978 0.0838 - - - - - - - - - - - - - - - -
2.7147 1979 0.1391 - - - - - - - - - - - - - - - -
2.7160 1980 0.384 2.6477 1.6870 0.0557 0.0038 0.2160 0.0433 0.3512 1.0100 0.2625 0.9187 0.3336 0.0325 0.1483 0.8992 0.6639 0.7138
2.7174 1981 0.2393 - - - - - - - - - - - - - - - -
2.7188 1982 0.0828 - - - - - - - - - - - - - - - -
2.7202 1983 0.0991 - - - - - - - - - - - - - - - -
2.7215 1984 0.024 - - - - - - - - - - - - - - - -
2.7229 1985 0.2985 - - - - - - - - - - - - - - - -
2.7243 1986 0.0929 - - - - - - - - - - - - - - - -
2.7257 1987 0.334 - - - - - - - - - - - - - - - -
2.7270 1988 0.5975 - - - - - - - - - - - - - - - -
2.7284 1989 0.4524 - - - - - - - - - - - - - - - -
2.7298 1990 0.1184 - - - - - - - - - - - - - - - -
2.7311 1991 0.6318 - - - - - - - - - - - - - - - -
2.7325 1992 0.208 - - - - - - - - - - - - - - - -
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2.9986 2186 0.0 - - - - - - - - - - - - - - - -
3.0 2187 0.1497 2.6774 1.7258 0.0517 0.0039 0.2146 0.0453 0.3271 0.9940 0.2478 0.9024 0.3085 0.0318 0.1462 0.9018 0.6650 0.7074

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.3.1
  • Transformers: 4.47.0
  • PyTorch: 2.5.1+cu121
  • Accelerate: 1.2.1
  • Datasets: 3.3.1
  • Tokenizers: 0.21.0

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",
}

GISTEmbedLoss

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