bobox/XLMRoBERTaM3-CustomPoolin-v1.02-1024dMLP-s1

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3 on the NLI, natural-questions, vitaminc, xsum, paws 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
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 1024 dimensions
• Similarity Function: Cosine Similarity
• Training Datasets:
  • NLI
  • natural-questions
  • vitaminc
  • xsum
  • paws
  • global_dataset
• Language: en

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 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): SwiGLUBlock(
        (in_proj_swish): Linear(in_features=1024, out_features=2048, bias=True)
        (in_proj_gate): Linear(in_features=1024, out_features=2048, bias=True)
      )
      (1): Dropout(p=0.05, inplace=False)
      (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-v1.02-1024dMLP-s1")
# Run inference
sentences = [
    'In mathematical astronomy , his fame is due to the introduction of the astronomical globe , and his early contributions to understanding the movement of the planets .',
    'His fame is due in mathematical astronomy to the introduction of the astronomical globe and to his early contributions to the understanding of the movement of the planets .',
    'In 1994 , Rodrigo Leão left the band to start a solo career , replaced by Carlos Maria Trindade ( keyboard synthesizer ) .',
]
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

• Dataset: sts-test
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.9061
spearman_cosine	0.9181

Binary Classification

• Datasets: allNLI-dev and Qnli-dev
• Evaluated with BinaryClassificationEvaluator

Metric	allNLI-dev	Qnli-dev
cosine_accuracy	0.7734	0.7344
cosine_accuracy_threshold	0.669	0.6468
cosine_f1	0.7071	0.7153
cosine_f1_threshold	0.6514	0.5601
cosine_precision	0.625	0.6282
cosine_recall	0.814	0.8305
cosine_ap	0.6771	0.7422
cosine_mcc	0.5397	0.4191

Training Details

Training Datasets

NLI

NLI

• Dataset: NLI at d43e6fe
• Size: 750 training samples
• Columns: anchor, entailment, and negative
• Approximate statistics based on the first 750 samples:

  	anchor	entailment	negative
  type	string	string	string
  details	min: 5 tokens mean: 24.9 tokens max: 176 tokens	min: 5 tokens mean: 16.4 tokens max: 54 tokens	min: 6 tokens mean: 16.53 tokens max: 49 tokens

• Samples:

  anchor	entailment	negative
  09:00 On Thursday, AT&T said they have teamed with Juniper Networks to develop a mobile security platform for both businesses and consumers.	AT&T and Juniper to develop mobile security platform	AT&T and Juniper disassemble mobile security platform
  two police motorcycles driving down the road in front of a cop car	Two motorcycle cops and a police car on a street.	No motorcycle cops and a police car on a street.
  I've told you about their size.	I have told you about their size.	I have not told you about their size.

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

natural-questions

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 750 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 750 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 10 tokens mean: 13.32 tokens max: 25 tokens	min: 17 tokens mean: 148.26 tokens max: 651 tokens

• Samples:

  sentence1	sentence2
  who is winner in bigg boss season 5 kannada	Bigg Boss Kannada 5 Bigg Boss Kannada 5 (BBK5) was the fifth season of the Kannada television series Bigg Boss Kannada, that premiered on 15 October 2017.[1] Sudeep reprised his role as the host of the show.[2] The finale of the season took place 28 January 2018, and rapper Chandan Shetty was declared the winner of the show and the prize money of ₹50 lakh. Sales representative Diwaker was voted the runner-up.[3]
  what side of the street do they drive on in sweden	Left- and right-hand traffic Sweden was LHT from about 1734 to 1967,[17] despite having land borders with RHT countries, and approximately 90 percent of cars being left-hand drive (LHD) vehicles.[18] A referendum was held in 1955, with an overwhelming majority voting against a change to RHT. Nevertheless, some years later the government ordered a conversion, which took place at 5 am on Sunday, 3 September 1967. The accident rate dropped sharply after the change,[19] but soon rose back to near its original level.[20] The day was known as Dagen H ("H-Day"), the 'H' being for Högertrafik or right traffic. When Iceland switched the following year, it was known as H-dagurinn, again meaning "H-Day".[21]
  what is the difference between mandelbrot and biscotti	Mandelbrot (cookie) Its precise origin is unknown, as is its historic relationship with biscotti, a similar Italian cookie. While mandelbrot and biscotti both have a crunchy exterior, mandelbrot is slightly softer than biscotti due to its higher oil and/or butter content.

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

vitaminc

vitaminc

• Dataset: vitaminc at be6febb
• Size: 370,653 training samples
• Columns: claim and evidence
• Approximate statistics based on the first 1000 samples:

  	claim	evidence
  type	string	string
  details	min: 9 tokens mean: 20.44 tokens max: 73 tokens	min: 9 tokens mean: 44.61 tokens max: 191 tokens

• Samples:

  claim	evidence
  The Script is a pop band .	The Script are an Irish pop band formed in 2007 in Dublin , Ireland .
  Scott Skiles scored fewer than 55 points in home games .	He set several records during high school , including most points in a home game ( 53 ) and most points in an away game ( 56 ) .
  The Black Cauldron was released before July 25 , 1985 .	The film was distributed theatrically through Buena Vista Distribution on July 24 , 1985 .

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

xsum

xsum

• Dataset: xsum 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: 30.72 tokens max: 62 tokens	min: 63 tokens mean: 311.14 tokens max: 550 tokens

• Samples:

  summary	document
  The amount of time spent needing daily care in late life has doubled in England over the past two decades, a study suggests.	The Newcastle University study found men spent 2.4 years on average needing regular care and women three years. This includes everything from help with washing and dressing each day to round-the-clock care. Researchers said it suggested there needed to be a sharp increase in the number of care home places to cope. It comes as ministers consider a new way to fund the system. The government has promised major reform amid reports that councils are struggling to provide enough support to cope with the ageing population. The latest research, published in the Lancet, looked at not just the growth in the numbers of older people but also how many of those years were spent needing daily care. Between 1991 and 2011, life expectancy increased by more than four years for both men and women to 82.6 and 85.6 respectively. But the number of those years spent with substantial care needs rose much more rapidly, from 1.1 to 2.4 for men and 1.6 to three for women. Looking ahead to 2025, it means there wi...
  A man has admitted sexually assaulting two women in the same street two months apart.	Craig Perkins had initially denied being involved in the attacks in Bournemouth's Boundary Road in September and December of last year. But on Wednesday at Bournemouth Crown Court he pleaded guilty to two counts of sexual assault. The 29-year-old, of Victoria Park Road, Bournemouth, has been remanded in custody and will be sentenced on 5 May. Police said the victims were both in their 20s - the first was assaulted on Tuesday 13 September and the second attack happened on Thursday 24 November. Perkins was arrested on 14 December. Det Ch Insp Sarah Derbyshire, of Dorset Police's major crime investigation team, said: "Stranger sex attacks such as these are very rare in Dorset and we are committed toward investigating them thoroughly and bringing the offender to justice. "The victims in this case have been updated about Perkins' guilty pleas and I would like to pay tribute to them for having the confidence to report these offences to Dorset Police and the assistance they have given to the ...
  Durham produced a below-par batting display as they lost by seven wickets to Worcestershire in the One-Day Cup.	A 22 overs-a-side game was all that was possible after a long rain delay, but the home side were bowled out for 90. Mark Stoneman top-scored with 29 and the only other batsman to reach double figures was Paul Collingwood (17). Chris Rushworth took 3-19 as the visitors began their reply, but Alexei Kervezee (37) and Brett D'Oliveira (20) saw them to 91-3 with 17 balls in hand. Their unbroken partnership was worth 60 after Kervezee collected the winning single from the bowling of Usman Arshad. Durham reached 35-1 at the start of their innings after play got under way at 15:30 BST, but then lost four wickets for 11 runs. D'Oliveira, Ed Barnard, Joe Leach and Chris Russell took two wickets each as they were finally dismissed at the start of the 22nd over. Durham's total was their seventh-lowest in non-Twenty20 limited-overs matches games. Rushworth exploited the conditions superbly at the start of Worcestershire's innings, but once he was out of the attack, Kervezee and D'Oliveira were abl...

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

paws

paws

• Dataset: paws at 161ece9
• Size: 49,401 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 12 tokens mean: 30.94 tokens max: 56 tokens	min: 11 tokens mean: 30.97 tokens max: 55 tokens

• Samples:

  sentence1	sentence2
  Charley Frazier ( born August 12 , 1939 in Houston , Texas ) is a former American Football Wide Receiver from the NFL and the American Football League .	Charley Frazier ( born August 12 , 1939 in Houston , Texas ) is a former American football receiver in the American Football League and the NFL .
  Indonesian dumplings were influenced and brought by Chinese immigrants to Indonesia .	Indonesian dumplings were influenced and brought to Indonesia by Chinese immigrants .
  The SSSI has an area of 190.3 hectares , while the SAC has 168.3 hectares .	The SSSI has an area of 190.3 hectares while the SAC covers 168.3 hectares .

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

global_dataset

global_dataset

• Dataset: global_dataset
• Size: 71,250 training samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 7 tokens mean: 24.45 tokens max: 115 tokens	min: 6 tokens mean: 106.19 tokens max: 564 tokens

• Samples:

  sentence1	sentence2
  Taobao is a Chinese online shopping site similar to eBay , Amazon and Rakuten , which is operated by Alibaba Group in Hangzhou , Zhejiang .	Taobao is a Chinese online shopping website similar to eBay , Amazon and Rakuten , which is operated in Hangzhou , Zhejiang by Alibaba Group .
  Because of the lack of wood , boats were bundled with made papyrus reeds .	Because of the lack of wood , boats with papyrus reeds were bundled .
  New Zealand leg-spinner Ish Sodhi hopes his stint playing in Nottinghamshire's T20 campaign this summer will lead to a longer stay in England.	The 24-year-old has played 41 international matches in all formats. He has been particularly effective in T20, with 21 wickets at 14.47 and a strike-rate of a wicket every 13 balls. "In the last year or so I have definitely been a lot more successful in the T20 stuff than in the other stuff," he told BBC Radio Nottingham. "But in the last six months I have been finding my way in the four-stuff and one-dayers. "In the future I would love to come over and play all the forms. At this stage the T20 is the main focus. It is still a wee, wee way away but I will continue to look to hone my T20 skills and try to be in tip-top condition when I come over." Sodhi says playing in England is a "great opportunity" with the 2019 World Cup in mind. "It's great to play at these grounds where I will potentially play a World Cup, which I am targeting," he said. "It will be great to get used to conditions. The opportunity came up and I will try to grab it with both hands." Sodhi will be the second of two ...

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

Evaluation Datasets

NLI

NLI

• Dataset: NLI at d43e6fe
• Size: 85 evaluation samples
• Columns: anchor, entailment, and negative
• Approximate statistics based on the first 85 samples:

  	anchor	entailment	negative
  type	string	string	string
  details	min: 9 tokens mean: 17.02 tokens max: 36 tokens	min: 6 tokens mean: 12.96 tokens max: 25 tokens	min: 6 tokens mean: 13.53 tokens max: 26 tokens

• Samples:

  anchor	entailment	negative
  The girls walk down the street.	Girls walk down the street.	Girls do not walk down the street.
  Two computers sitting on top of a desk.	A laptop computer and a desktop computer on a white desk	A laptop computer and a desktop computer on a black desk
  A bathroom with a toilette with it's seat down.	A bathroom with a sink and a toilet	A bathroom without a sink or a toilet

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

natural-questions

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 113 evaluation samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 113 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 10 tokens mean: 13.57 tokens max: 23 tokens	min: 34 tokens mean: 176.3 tokens max: 2497 tokens

• Samples:

  sentence1	sentence2
  kiss him not me where does the anime end in the manga	Kiss Him, Not Me Kiss Him, Not Me, known in Japan as Watashi ga Motete Dōsunda (Japanese: 私がモテてどうすんだ, Hepburn: lit. What's the Point of Me Getting Popular?), is a Japanese romantic comedy shōjo manga series written and illustrated by Junko.[2] It is published by Kodansha since 2013 on Bessatsu Friend magazine.[3] Twelve volumes compiling the chapters have been released so far.[2] It is published online in English by Crunchyroll and the volumes will be published by Kodansha USA.[3] An audio drama adaptation of the first chapter was released on January 13, 2015.[4] An anime adaptation by Brain's Base aired in Japan between October and December 2016.[5][6] The manga won Best Shōjo Manga at the 40th Kodansha Manga Awards.
  who sings i just want to use your love	Your Love (The Outfield song) "Your Love" is a song by the English rock band the Outfield, taken from their debut album Play Deep (1985). The song was penned by the band's guitarist John Spinks.
  how many episodes of westworld are in season 1	Westworld (season 1) The first season of the American science fiction western television series Westworld (subtitled The Maze) premiered on HBO on October 2, 2016, and concluded on December 4, 2016. It consisted of ten episodes, each running approximately 60 minutes in length and was broadcast on Sundays in the United States. The complete first season was released on home media on November 7, 2017.

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

vitaminc

vitaminc

• Dataset: vitaminc at be6febb
• Size: 63,054 evaluation samples
• Columns: claim and evidence
• Approximate statistics based on the first 1000 samples:

  	claim	evidence
  type	string	string
  details	min: 10 tokens mean: 22.35 tokens max: 48 tokens	min: 11 tokens mean: 37.94 tokens max: 75 tokens

• Samples:

  claim	evidence
  More than 273 people have died from the 2019-20 coronavirus outside mainland China .	More than 3,200 people have died : almost 3,000 in mainland China and around 275 in other countries .
  More than 146,500 people have been infected with coronavirus globally , during the 2019�20 pandemic .	more than 147,000 cases have been confirmed worldwide .
  Over 278,000 coronavirus cases had been confirmed around the world by March 21 , 2020 .	As of 21 March , more than 278,000 cases of COVID-19 have been reported in over 186 countries and territories , resulting in more than 11,500 deaths and 92,000 recoveries. virus seems to mostly spread between people via respiratory droplets .

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

xsum

xsum

• Dataset: xsum 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: 17 tokens mean: 30.71 tokens max: 43 tokens	min: 70 tokens mean: 305.77 tokens max: 543 tokens

• Samples:

  summary	document
  A new species of moss has been found growing on 10 maple trees in a Carmarthenshire car park, but experts are in two minds about its origins.	Welsh Bristle-moss was discovered near Dryslwyn Castle, close to Llandeilo, by the Countryside Council for Wales. It said it might have evolved from a genetically similar moss. But it could be an undiscovered species that was imported from the Continent on maples used to landscape the car park in the 1990s. There are about 900 species of moss in Britain and 587 of those are found in Wales. The Welsh Bristle-moss was discovered during a survey which is recording mosses growing on trees in south Wales. Experts said the moss had a unique combination of distinctive traits. It differed from related mosses because of its round-tipped leaf tips and flat leaf edges. Countryside Council for Wales (CCW) moss ecologist Sam Bosanquet, who made the new find, said: "Welsh Bristle-moss highlights the need to be ever vigilant and open-minded, even when looking at plants in mundane places like car parks. "This is a high-point in our regular work of recording mosses which grow on trees in south Wales. "...
  A former Soviet army officer has been convicted by a US jury of planning and leading a Taliban attack on American forces in Afghanistan in 2009.	The jury found Irek Hamidullin guilty on 15 counts, including supporting terrorists and conspiracy to use a weapon of mass destruction. The 55-year-old is the first military prisoner from Afghanistan to be tried in a US federal court. Some of the charges carry a mandatory life sentence. About 30 insurgents died in the attack, with Hamidullin the only survivor, while no American or Afghan soldiers were killed. Hamidullin, who did not testify during the trial, is expected to be sentenced on 6 November. Lawyers say it is unusual for someone captured on the battlefield in Afghanistan to be transferred to the United States for trial in a federal court. Hamidullin's defence lawyers had tried unsuccessfully to have the charges dismissed, saying their client was a prisoner of war and ineligible for trial in civilian court. Prosecutors argued federal law protected US soldiers no matter where they were. The jury in Richmond. Virginia, reached its verdict after five days of testimony and eight ho...
  UK troops could be deployed to train moderate Syrian rebels in the fight against Islamic State militants (IS), the defence secretary has said.	Michael Fallon told BBC News that UK troops could be sent to a country neighbouring Syria, possibly Jordan. He insisted however that UK forces would not engage in direct combat. The US is leading efforts to train a Syrian opposition to fight IS, also known as ISIS, which has captured large parts of of the country. The country's National Security Adviser Susan Rice said a deal had been reached with Turkey to allow the US to train Syrian rebels on its soil, although this has been denied by Turkish officials. Mr Fallon discussed the possibility of launching training operations, while visiting the Royal Fleet Auxiliary Ship, Argus, in Falmouth. A specialist team of 12 soldiers from the Yorkshire Regiment is already training Kurdish fighters in Iraq to use UK-supplied heavy machine guns. And the UK is to fund bomb disposal training for the Kurdish Peshmerga forces to counter the threat of Improvised Explosive Devices (IEDs), Foreign Secretary Philip Hammond announced on Monday. The Prime Mi...

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

paws

paws

• Dataset: paws at 161ece9
• Size: 8,000 evaluation samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 11 tokens mean: 31.67 tokens max: 56 tokens	min: 10 tokens mean: 31.33 tokens max: 54 tokens

• Samples:

  sentence1	sentence2
  He also wrote a large number of vocal arrangements and orchestral accompaniments to varieties .	He also wrote a large number of vocal arrangements and orchestral accompaniments for varieties .
  In 1994 , Rodrigo Leão left the band to start a solo career , being replaced by Carlos Maria Trindade ( keyboard synthesizer ) .	In 1994 , Rodrigo Leão left the band to start a solo career , replaced by Carlos Maria Trindade ( keyboard synthesizer ) .
  Until 1951 , he was active as a socialist in post-war legislation when he decided to focus on local politics .	He was active as a socialist in the post-war legislature until 1951 , when he decided to focus on local politics .

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

global_dataset

global_dataset

• Dataset: global_dataset
• Size: 256 evaluation samples
• Columns: sentence1 and sentence2
• Approximate statistics based on the first 256 samples:

  	sentence1	sentence2
  type	string	string
  details	min: 10 tokens mean: 23.68 tokens max: 51 tokens	min: 6 tokens mean: 112.38 tokens max: 511 tokens

• Samples:

  sentence1	sentence2
  All babies born from Tuesday across the UK will have an anti-hepatitis B injection added to the other routine vaccinations they are given in their early life.	The jab protects against viral infections that cause cirrhosis and liver cancer. Babies are already vaccinated against diphtheria, tetanus, whooping cough, Hib and polio. Public Health England said the new vaccine had been "shown to be safe". Babies are currently given vaccinations when they are eight, 12 and 16 weeks old and the new injection will be given at the same time as the others. Previously, the hepatitis B vaccine was available on the NHS as a separate jab and was only administered to infants considered at risk, such as those born to infected mothers. While hepatitis B rates in the UK are generally very low, in some inner city areas up to 1% of antenatal women are infected. The infection has no symptoms so many of these women will be unaware they are ill, while their babies are considered at high risk. Mary Ramsay, head of immunisation at Public Health England, said: "The Hexavalent vaccine has been extensively tested and shown to be safe and is widely used internationally wi...
  A black man in a long sleeves white collared shirt and a tie is walking to work in a big city.	The man is wearing work attire and is walking to his job.
  ACVM is based in Glasgow and has offices in Edinburgh , Aberdeen , Newcastle , Manchester and Milton Keynes .	ACVM is based in Glasgow and has subsidiaries in Edinburgh , Aberdeen , Newcastle , Manchester and Milton Keynes .

• Loss: CachedGISTEmbedLoss with these parameters:

  {'guide': SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
    (1): Pooling({'word_embedding_dimension': 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': 0.02}
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 192
• per_device_eval_batch_size: 256
• learning_rate: 0.0001
• weight_decay: 0.001
• lr_scheduler_type: cosine_with_min_lr
• lr_scheduler_kwargs: {'num_cycles': 0.5, 'min_lr': 3.3333333333333335e-05}
• warmup_ratio: 0.15
• save_safetensors: False
• fp16: True
• remove_unused_columns: False
• push_to_hub: True
• hub_model_id: bobox/XLMRoBERTaM3-CustomPoolin-v1.02-1024dMLP-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: 192
• 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: 0.0001
• 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': 3.3333333333333335e-05}
• warmup_ratio: 0.15
• 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}
• tp_size: 0
• 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-v1.02-1024dMLP-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
• 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	NLI loss	natural-questions loss	vitaminc loss	xsum loss	paws loss	global dataset loss	sts-test_spearman_cosine	allNLI-dev_cosine_ap	Qnli-dev_cosine_ap
0.0026	1	0.7912	-	-	-	-	-	-	-	-	-
0.0051	2	3.5781	-	-	-	-	-	-	-	-	-
0.0077	3	0.8711	-	-	-	-	-	-	-	-	-
0.0102	4	0.9923	-	-	-	-	-	-	-	-	-
0.0128	5	0.6723	-	-	-	-	-	-	-	-	-
0.0153	6	1.0542	-	-	-	-	-	-	-	-	-
0.0179	7	0.8721	-	-	-	-	-	-	-	-	-
0.0204	8	0.8121	-	-	-	-	-	-	-	-	-
0.0230	9	0.9226	-	-	-	-	-	-	-	-	-
0.0255	10	0.7534	-	-	-	-	-	-	-	-	-
0.0281	11	0.9769	-	-	-	-	-	-	-	-	-
0.0306	12	1.1295	-	-	-	-	-	-	-	-	-
0.0332	13	0.9773	-	-	-	-	-	-	-	-	-
0.0357	14	0.7239	-	-	-	-	-	-	-	-	-
0.0383	15	0.6364	-	-	-	-	-	-	-	-	-
0.0408	16	0.7573	-	-	-	-	-	-	-	-	-
0.0434	17	0.7629	-	-	-	-	-	-	-	-	-
0.0459	18	0.8665	-	-	-	-	-	-	-	-	-
0.0485	19	0.6049	-	-	-	-	-	-	-	-	-
0.0510	20	0.6587	-	-	-	-	-	-	-	-	-
0.0536	21	0.5717	-	-	-	-	-	-	-	-	-
0.0561	22	0.4781	-	-	-	-	-	-	-	-	-
0.0587	23	0.4699	-	-	-	-	-	-	-	-	-
0.0612	24	1.7145	-	-	-	-	-	-	-	-	-
0.0638	25	0.531	-	-	-	-	-	-	-	-	-
0.0663	26	0.5584	-	-	-	-	-	-	-	-	-
0.0689	27	0.398	-	-	-	-	-	-	-	-	-
0.0714	28	0.5015	-	-	-	-	-	-	-	-	-
0.0740	29	0.4741	-	-	-	-	-	-	-	-	-
0.0765	30	0.3762	-	-	-	-	-	-	-	-	-
0.0791	31	0.6952	-	-	-	-	-	-	-	-	-
0.0816	32	0.2723	-	-	-	-	-	-	-	-	-
0.0842	33	0.4301	-	-	-	-	-	-	-	-	-
0.0867	34	0.3839	-	-	-	-	-	-	-	-	-
0.0893	35	0.3154	-	-	-	-	-	-	-	-	-
0.0918	36	0.2796	-	-	-	-	-	-	-	-	-
0.0944	37	0.2964	-	-	-	-	-	-	-	-	-
0.0969	38	0.2232	-	-	-	-	-	-	-	-	-
0.0995	39	0.2661	-	-	-	-	-	-	-	-	-
0.1020	40	0.3133	-	-	-	-	-	-	-	-	-
0.1046	41	0.2047	-	-	-	-	-	-	-	-	-
0.1071	42	0.2206	-	-	-	-	-	-	-	-	-
0.1097	43	0.1694	-	-	-	-	-	-	-	-	-
0.1122	44	0.1864	-	-	-	-	-	-	-	-	-
0.1148	45	0.2126	-	-	-	-	-	-	-	-	-
0.1173	46	0.1589	-	-	-	-	-	-	-	-	-
0.1199	47	0.2539	-	-	-	-	-	-	-	-	-
0.1224	48	0.2403	-	-	-	-	-	-	-	-	-
0.125	49	0.1666	-	-	-	-	-	-	-	-	-
0.1276	50	0.1633	-	-	-	-	-	-	-	-	-
0.1301	51	0.2204	-	-	-	-	-	-	-	-	-
0.1327	52	0.0716	-	-	-	-	-	-	-	-	-
0.1352	53	0.1254	-	-	-	-	-	-	-	-	-
0.1378	54	0.3478	-	-	-	-	-	-	-	-	-
0.1403	55	0.2607	-	-	-	-	-	-	-	-	-
0.1429	56	0.2158	-	-	-	-	-	-	-	-	-
0.1454	57	0.2082	-	-	-	-	-	-	-	-	-
0.1480	58	0.2334	-	-	-	-	-	-	-	-	-
0.1505	59	0.2203	0.9447	0.2167	2.4175	0.1710	0.0204	0.2824	0.9129	0.6641	0.7343
0.1531	60	0.1368	-	-	-	-	-	-	-	-	-
0.1556	61	0.2153	-	-	-	-	-	-	-	-	-
0.1582	62	0.0711	-	-	-	-	-	-	-	-	-
0.1607	63	0.2255	-	-	-	-	-	-	-	-	-
0.1633	64	0.0982	-	-	-	-	-	-	-	-	-
0.1658	65	0.1388	-	-	-	-	-	-	-	-	-
0.1684	66	0.1797	-	-	-	-	-	-	-	-	-
0.1709	67	0.4173	-	-	-	-	-	-	-	-	-
0.1735	68	0.0102	-	-	-	-	-	-	-	-	-
0.1760	69	0.0634	-	-	-	-	-	-	-	-	-
0.1786	70	0.1956	-	-	-	-	-	-	-	-	-
0.1811	71	0.2188	-	-	-	-	-	-	-	-	-
0.1837	72	0.1399	-	-	-	-	-	-	-	-	-
0.1862	73	0.1489	-	-	-	-	-	-	-	-	-
0.1888	74	0.1567	-	-	-	-	-	-	-	-	-
0.1913	75	0.2404	-	-	-	-	-	-	-	-	-
0.1939	76	0.1295	-	-	-	-	-	-	-	-	-
0.1964	77	0.4541	-	-	-	-	-	-	-	-	-
0.1990	78	0.2364	-	-	-	-	-	-	-	-	-
0.2015	79	0.0929	-	-	-	-	-	-	-	-	-
0.2041	80	0.1699	-	-	-	-	-	-	-	-	-
0.2066	81	0.1846	-	-	-	-	-	-	-	-	-
0.2092	82	0.1126	-	-	-	-	-	-	-	-	-
0.2117	83	0.1151	-	-	-	-	-	-	-	-	-
0.2143	84	0.2015	-	-	-	-	-	-	-	-	-
0.2168	85	0.1028	-	-	-	-	-	-	-	-	-
0.2194	86	0.2284	-	-	-	-	-	-	-	-	-
0.2219	87	0.1368	-	-	-	-	-	-	-	-	-
0.2245	88	0.0836	-	-	-	-	-	-	-	-	-
0.2270	89	0.1276	-	-	-	-	-	-	-	-	-
0.2296	90	0.181	-	-	-	-	-	-	-	-	-
0.2321	91	0.1516	-	-	-	-	-	-	-	-	-
0.2347	92	0.1769	-	-	-	-	-	-	-	-	-
0.2372	93	0.1261	-	-	-	-	-	-	-	-	-
0.2398	94	0.2324	-	-	-	-	-	-	-	-	-
0.2423	95	0.1046	-	-	-	-	-	-	-	-	-
0.2449	96	0.1372	-	-	-	-	-	-	-	-	-
0.2474	97	0.0654	-	-	-	-	-	-	-	-	-
0.25	98	0.2279	-	-	-	-	-	-	-	-	-
0.2526	99	0.0807	-	-	-	-	-	-	-	-	-
0.2551	100	0.123	-	-	-	-	-	-	-	-	-
0.2577	101	0.1464	-	-	-	-	-	-	-	-	-
0.2602	102	0.0897	-	-	-	-	-	-	-	-	-
0.2628	103	0.1612	-	-	-	-	-	-	-	-	-
0.2653	104	0.1289	-	-	-	-	-	-	-	-	-
0.2679	105	0.7234	-	-	-	-	-	-	-	-	-
0.2704	106	0.1004	-	-	-	-	-	-	-	-	-
0.2730	107	0.1227	-	-	-	-	-	-	-	-	-
0.2755	108	0.2446	-	-	-	-	-	-	-	-	-
0.2781	109	0.1338	-	-	-	-	-	-	-	-	-
0.2806	110	0.0427	-	-	-	-	-	-	-	-	-
0.2832	111	0.1149	-	-	-	-	-	-	-	-	-
0.2857	112	0.1524	-	-	-	-	-	-	-	-	-
0.2883	113	0.1308	-	-	-	-	-	-	-	-	-
0.2908	114	0.192	-	-	-	-	-	-	-	-	-
0.2934	115	0.141	-	-	-	-	-	-	-	-	-
0.2959	116	0.1539	-	-	-	-	-	-	-	-	-
0.2985	117	0.1548	-	-	-	-	-	-	-	-	-
0.3010	118	0.1284	0.8682	0.1388	2.3304	0.1062	0.0200	0.2694	0.9151	0.6651	0.7364
0.3036	119	0.0939	-	-	-	-	-	-	-	-	-
0.3061	120	0.2675	-	-	-	-	-	-	-	-	-
0.3087	121	0.1542	-	-	-	-	-	-	-	-	-
0.3112	122	0.1347	-	-	-	-	-	-	-	-	-
0.3138	123	0.1285	-	-	-	-	-	-	-	-	-
0.3163	124	0.1025	-	-	-	-	-	-	-	-	-
0.3189	125	0.0879	-	-	-	-	-	-	-	-	-
0.3214	126	0.0446	-	-	-	-	-	-	-	-	-
0.3240	127	0.1739	-	-	-	-	-	-	-	-	-
0.3265	128	0.1309	-	-	-	-	-	-	-	-	-
0.3291	129	0.1737	-	-	-	-	-	-	-	-	-
0.3316	130	0.1063	-	-	-	-	-	-	-	-	-
0.3342	131	0.0568	-	-	-	-	-	-	-	-	-
0.3367	132	0.1966	-	-	-	-	-	-	-	-	-
0.3393	133	0.2336	-	-	-	-	-	-	-	-	-
0.3418	134	0.1716	-	-	-	-	-	-	-	-	-
0.3444	135	0.0979	-	-	-	-	-	-	-	-	-
0.3469	136	0.1319	-	-	-	-	-	-	-	-	-
0.3495	137	0.1058	-	-	-	-	-	-	-	-	-
0.3520	138	0.225	-	-	-	-	-	-	-	-	-
0.3546	139	0.1045	-	-	-	-	-	-	-	-	-
0.3571	140	0.1066	-	-	-	-	-	-	-	-	-
0.3597	141	0.1234	-	-	-	-	-	-	-	-	-
0.3622	142	0.1707	-	-	-	-	-	-	-	-	-
0.3648	143	0.1204	-	-	-	-	-	-	-	-	-
0.3673	144	0.2086	-	-	-	-	-	-	-	-	-
0.3699	145	0.0982	-	-	-	-	-	-	-	-	-
0.3724	146	0.0937	-	-	-	-	-	-	-	-	-
0.375	147	0.1763	-	-	-	-	-	-	-	-	-
0.3776	148	0.0601	-	-	-	-	-	-	-	-	-
0.3801	149	0.1354	-	-	-	-	-	-	-	-	-
0.3827	150	0.1135	-	-	-	-	-	-	-	-	-
0.3852	151	0.2146	-	-	-	-	-	-	-	-	-
0.3878	152	0.0868	-	-	-	-	-	-	-	-	-
0.3903	153	0.2428	-	-	-	-	-	-	-	-	-
0.3929	154	0.0582	-	-	-	-	-	-	-	-	-
0.3954	155	0.1299	-	-	-	-	-	-	-	-	-
0.3980	156	0.0911	-	-	-	-	-	-	-	-	-
0.4005	157	0.1184	-	-	-	-	-	-	-	-	-
0.4031	158	0.0692	-	-	-	-	-	-	-	-	-
0.4056	159	0.1228	-	-	-	-	-	-	-	-	-
0.4082	160	0.0574	-	-	-	-	-	-	-	-	-
0.4107	161	0.0822	-	-	-	-	-	-	-	-	-
0.4133	162	0.1071	-	-	-	-	-	-	-	-	-
0.4158	163	0.0544	-	-	-	-	-	-	-	-	-
0.4184	164	0.1261	-	-	-	-	-	-	-	-	-
0.4209	165	0.094	-	-	-	-	-	-	-	-	-
0.4235	166	0.1539	-	-	-	-	-	-	-	-	-
0.4260	167	0.045	-	-	-	-	-	-	-	-	-
0.4286	168	0.1074	-	-	-	-	-	-	-	-	-
0.4311	169	0.1626	-	-	-	-	-	-	-	-	-
0.4337	170	0.1337	-	-	-	-	-	-	-	-	-
0.4362	171	0.1737	-	-	-	-	-	-	-	-	-
0.4388	172	0.104	-	-	-	-	-	-	-	-	-
0.4413	173	0.0989	-	-	-	-	-	-	-	-	-
0.4439	174	0.2015	-	-	-	-	-	-	-	-	-
0.4464	175	0.1364	-	-	-	-	-	-	-	-	-
0.4490	176	0.0968	-	-	-	-	-	-	-	-	-
0.4515	177	0.0868	0.8198	0.0984	2.3936	0.0804	0.0204	0.2730	0.9166	0.6676	0.7384
0.4541	178	0.0538	-	-	-	-	-	-	-	-	-
0.4566	179	0.0855	-	-	-	-	-	-	-	-	-
0.4592	180	0.1492	-	-	-	-	-	-	-	-	-
0.4617	181	0.0799	-	-	-	-	-	-	-	-	-
0.4643	182	0.0979	-	-	-	-	-	-	-	-	-
0.4668	183	0.087	-	-	-	-	-	-	-	-	-
0.4694	184	0.1763	-	-	-	-	-	-	-	-	-
0.4719	185	0.1646	-	-	-	-	-	-	-	-	-
0.4745	186	0.1483	-	-	-	-	-	-	-	-	-
0.4770	187	0.1098	-	-	-	-	-	-	-	-	-
0.4796	188	0.6778	-	-	-	-	-	-	-	-	-
0.4821	189	0.116	-	-	-	-	-	-	-	-	-
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0.9031	354	0.0857	0.8229	0.1095	2.6739	0.0849	0.0206	0.3026	0.9160	0.6698	0.7347
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0.9082	356	0.0759	-	-	-	-	-	-	-	-	-
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0.9668	379	0.0953	-	-	-	-	-	-	-	-	-
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0.9719	381	0.0953	-	-	-	-	-	-	-	-	-
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0.9770	383	0.149	-	-	-	-	-	-	-	-	-
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0.9821	385	0.0856	-	-	-	-	-	-	-	-	-
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0.9949	390	0.1441	-	-	-	-	-	-	-	-	-
0.9974	391	0.1154	-	-	-	-	-	-	-	-	-
1.0	392	0.0001	-	-	-	-	-	-	-	-	-
1.0026	393	0.0247	-	-	-	-	-	-	-	-	-
1.0051	394	0.0772	-	-	-	-	-	-	-	-	-
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1.0102	396	0.0985	-	-	-	-	-	-	-	-	-
1.0128	397	0.0509	-	-	-	-	-	-	-	-	-
1.0153	398	0.0791	-	-	-	-	-	-	-	-	-
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1.0204	400	0.0549	-	-	-	-	-	-	-	-	-
1.0230	401	0.0274	-	-	-	-	-	-	-	-	-
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1.0306	404	0.0085	-	-	-	-	-	-	-	-	-
1.0332	405	0.0883	-	-	-	-	-	-	-	-	-
1.0357	406	0.0261	-	-	-	-	-	-	-	-	-
1.0383	407	0.0662	-	-	-	-	-	-	-	-	-
1.0408	408	0.088	-	-	-	-	-	-	-	-	-
1.0434	409	0.0632	-	-	-	-	-	-	-	-	-
1.0459	410	0.0569	-	-	-	-	-	-	-	-	-
1.0485	411	0.0421	-	-	-	-	-	-	-	-	-
1.0510	412	0.2399	-	-	-	-	-	-	-	-	-
1.0536	413	0.04	0.7573	0.1220	2.5621	0.0854	0.0209	0.3192	0.9170	0.6756	0.7336
1.0561	414	0.0331	-	-	-	-	-	-	-	-	-
1.0587	415	0.0403	-	-	-	-	-	-	-	-	-
1.0612	416	0.1124	-	-	-	-	-	-	-	-	-
1.0638	417	0.0718	-	-	-	-	-	-	-	-	-
1.0663	418	0.0764	-	-	-	-	-	-	-	-	-
1.0689	419	0.1252	-	-	-	-	-	-	-	-	-
1.0714	420	0.0224	-	-	-	-	-	-	-	-	-
1.0740	421	0.0655	-	-	-	-	-	-	-	-	-
1.0765	422	0.0611	-	-	-	-	-	-	-	-	-
1.0791	423	0.0579	-	-	-	-	-	-	-	-	-
1.0816	424	0.0462	-	-	-	-	-	-	-	-	-
1.0842	425	0.1243	-	-	-	-	-	-	-	-	-
1.0867	426	0.0635	-	-	-	-	-	-	-	-	-
1.0893	427	0.0932	-	-	-	-	-	-	-	-	-
1.0918	428	0.0568	-	-	-	-	-	-	-	-	-
1.0944	429	0.0716	-	-	-	-	-	-	-	-	-
1.0969	430	0.0734	-	-	-	-	-	-	-	-	-
1.0995	431	0.0533	-	-	-	-	-	-	-	-	-
1.1020	432	0.0593	-	-	-	-	-	-	-	-	-
1.1046	433	0.0198	-	-	-	-	-	-	-	-	-
1.1071	434	0.0484	-	-	-	-	-	-	-	-	-
1.1097	435	0.0656	-	-	-	-	-	-	-	-	-
1.1122	436	0.0263	-	-	-	-	-	-	-	-	-
1.1148	437	0.0753	-	-	-	-	-	-	-	-	-
1.1173	438	0.0478	-	-	-	-	-	-	-	-	-
1.1199	439	0.0375	-	-	-	-	-	-	-	-	-
1.1224	440	0.0822	-	-	-	-	-	-	-	-	-
1.125	441	0.0826	-	-	-	-	-	-	-	-	-
1.1276	442	0.0906	-	-	-	-	-	-	-	-	-
1.1301	443	0.0251	-	-	-	-	-	-	-	-	-
1.1327	444	0.034	-	-	-	-	-	-	-	-	-
1.1352	445	0.0149	-	-	-	-	-	-	-	-	-
1.1378	446	0.063	-	-	-	-	-	-	-	-	-
1.1403	447	0.0517	-	-	-	-	-	-	-	-	-
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2.4592	964	0.0282	-	-	-	-	-	-	-	-	-
2.4617	965	0.0464	-	-	-	-	-	-	-	-	-
2.4643	966	0.0204	-	-	-	-	-	-	-	-	-
2.4668	967	0.0209	-	-	-	-	-	-	-	-	-
2.4694	968	0.042	-	-	-	-	-	-	-	-	-
2.4719	969	0.0653	-	-	-	-	-	-	-	-	-
2.4745	970	0.0481	-	-	-	-	-	-	-	-	-
2.4770	971	0.0421	-	-	-	-	-	-	-	-	-
2.4796	972	0.0737	-	-	-	-	-	-	-	-	-
2.4821	973	0.032	-	-	-	-	-	-	-	-	-
2.4847	974	0.0607	-	-	-	-	-	-	-	-	-
2.4872	975	0.0784	-	-	-	-	-	-	-	-	-
2.4898	976	0.028	-	-	-	-	-	-	-	-	-
2.4923	977	0.0922	-	-	-	-	-	-	-	-	-
2.4949	978	0.0376	-	-	-	-	-	-	-	-	-
2.4974	979	0.0346	-	-	-	-	-	-	-	-	-
2.5	980	0.0977	-	-	-	-	-	-	-	-	-
2.5026	981	0.0471	-	-	-	-	-	-	-	-	-
2.5051	982	0.0616	-	-	-	-	-	-	-	-	-
2.5077	983	0.0368	-	-	-	-	-	-	-	-	-
2.5102	984	0.0169	-	-	-	-	-	-	-	-	-
2.5128	985	0.0298	-	-	-	-	-	-	-	-	-
2.5153	986	0.0187	-	-	-	-	-	-	-	-	-
2.5179	987	0.0529	-	-	-	-	-	-	-	-	-
2.5204	988	0.0346	-	-	-	-	-	-	-	-	-
2.5230	989	0.0879	-	-	-	-	-	-	-	-	-
2.5255	990	0.0234	-	-	-	-	-	-	-	-	-
2.5281	991	0.0493	-	-	-	-	-	-	-	-	-
2.5306	992	0.0901	-	-	-	-	-	-	-	-	-
2.5332	993	0.0377	-	-	-	-	-	-	-	-	-
2.5357	994	0.031	-	-	-	-	-	-	-	-	-
2.5383	995	0.0572	-	-	-	-	-	-	-	-	-
2.5408	996	0.0454	-	-	-	-	-	-	-	-	-
2.5434	997	0.0433	-	-	-	-	-	-	-	-	-
2.5459	998	0.0589	-	-	-	-	-	-	-	-	-
2.5485	999	0.0217	-	-	-	-	-	-	-	-	-
2.5510	1000	0.0483	-	-	-	-	-	-	-	-	-
2.5536	1001	0.0604	-	-	-	-	-	-	-	-	-
2.5561	1002	0.021	-	-	-	-	-	-	-	-	-
2.5587	1003	0.0577	0.7841	0.1216	2.3861	0.1020	0.0196	0.2750	0.9167	0.6718	0.7341
2.5612	1004	0.0405	-	-	-	-	-	-	-	-	-
2.5638	1005	0.0543	-	-	-	-	-	-	-	-	-
2.5663	1006	0.0141	-	-	-	-	-	-	-	-	-
2.5689	1007	0.0132	-	-	-	-	-	-	-	-	-
2.5714	1008	0.0195	-	-	-	-	-	-	-	-	-
2.5740	1009	0.0438	-	-	-	-	-	-	-	-	-
2.5765	1010	0.1235	-	-	-	-	-	-	-	-	-
2.5791	1011	0.0168	-	-	-	-	-	-	-	-	-
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2.5842	1013	0.0596	-	-	-	-	-	-	-	-	-
2.5867	1014	0.0371	-	-	-	-	-	-	-	-	-
2.5893	1015	0.0174	-	-	-	-	-	-	-	-	-
2.5918	1016	0.057	-	-	-	-	-	-	-	-	-
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2.625	1029	0.0667	-	-	-	-	-	-	-	-	-
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2.6301	1031	0.0331	-	-	-	-	-	-	-	-	-
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2.6352	1033	0.0123	-	-	-	-	-	-	-	-	-
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2.6403	1035	0.1093	-	-	-	-	-	-	-	-	-
2.6429	1036	0.0494	-	-	-	-	-	-	-	-	-
2.6454	1037	0.0495	-	-	-	-	-	-	-	-	-
2.6480	1038	0.025	-	-	-	-	-	-	-	-	-
2.6505	1039	0.0098	-	-	-	-	-	-	-	-	-
2.6531	1040	0.0376	-	-	-	-	-	-	-	-	-
2.6556	1041	0.053	-	-	-	-	-	-	-	-	-
2.6582	1042	0.0332	-	-	-	-	-	-	-	-	-
2.6607	1043	0.0272	-	-	-	-	-	-	-	-	-
2.6633	1044	0.0708	-	-	-	-	-	-	-	-	-
2.6658	1045	0.01	-	-	-	-	-	-	-	-	-
2.6684	1046	0.0141	-	-	-	-	-	-	-	-	-
2.6709	1047	0.046	-	-	-	-	-	-	-	-	-
2.6735	1048	0.0093	-	-	-	-	-	-	-	-	-
2.6760	1049	0.0335	-	-	-	-	-	-	-	-	-
2.6786	1050	0.0021	-	-	-	-	-	-	-	-	-
2.6811	1051	0.0201	-	-	-	-	-	-	-	-	-
2.6837	1052	0.0459	-	-	-	-	-	-	-	-	-
2.6862	1053	0.0247	-	-	-	-	-	-	-	-	-
2.6888	1054	0.0535	-	-	-	-	-	-	-	-	-
2.6913	1055	0.0482	-	-	-	-	-	-	-	-	-
2.6939	1056	0.0147	-	-	-	-	-	-	-	-	-
2.6964	1057	0.1053	-	-	-	-	-	-	-	-	-
2.6990	1058	0.0165	-	-	-	-	-	-	-	-	-
2.7015	1059	0.0115	-	-	-	-	-	-	-	-	-
2.7041	1060	0.0541	-	-	-	-	-	-	-	-	-
2.7066	1061	0.0487	-	-	-	-	-	-	-	-	-
2.7092	1062	0.0567	0.7855	0.1111	2.3604	0.1043	0.0194	0.2682	0.9186	0.6740	0.7294
2.7117	1063	0.0173	-	-	-	-	-	-	-	-	-
2.7143	1064	0.386	-	-	-	-	-	-	-	-	-
2.7168	1065	0.0455	-	-	-	-	-	-	-	-	-
2.7194	1066	0.0453	-	-	-	-	-	-	-	-	-
2.7219	1067	0.0704	-	-	-	-	-	-	-	-	-
2.7245	1068	0.0129	-	-	-	-	-	-	-	-	-
2.7270	1069	0.0238	-	-	-	-	-	-	-	-	-
2.7296	1070	0.0703	-	-	-	-	-	-	-	-	-
2.7321	1071	0.0956	-	-	-	-	-	-	-	-	-
2.7347	1072	0.0322	-	-	-	-	-	-	-	-	-
2.7372	1073	0.0213	-	-	-	-	-	-	-	-	-
2.7398	1074	0.0077	-	-	-	-	-	-	-	-	-
2.7423	1075	0.0184	-	-	-	-	-	-	-	-	-
2.7449	1076	0.0324	-	-	-	-	-	-	-	-	-
2.7474	1077	0.022	-	-	-	-	-	-	-	-	-
2.75	1078	0.0265	-	-	-	-	-	-	-	-	-
2.7526	1079	0.0667	-	-	-	-	-	-	-	-	-
2.7551	1080	0.0125	-	-	-	-	-	-	-	-	-
2.7577	1081	0.0455	-	-	-	-	-	-	-	-	-
2.7602	1082	0.0046	-	-	-	-	-	-	-	-	-
2.7628	1083	0.0617	-	-	-	-	-	-	-	-	-
2.7653	1084	0.0301	-	-	-	-	-	-	-	-	-
2.7679	1085	0.0391	-	-	-	-	-	-	-	-	-
2.7704	1086	0.0852	-	-	-	-	-	-	-	-	-
2.7730	1087	0.0808	-	-	-	-	-	-	-	-	-
2.7755	1088	0.0322	-	-	-	-	-	-	-	-	-
2.7781	1089	0.0151	-	-	-	-	-	-	-	-	-
2.7806	1090	0.0493	-	-	-	-	-	-	-	-	-
2.7832	1091	0.0409	-	-	-	-	-	-	-	-	-
2.7857	1092	0.0839	-	-	-	-	-	-	-	-	-
2.7883	1093	0.0813	-	-	-	-	-	-	-	-	-
2.7908	1094	0.0592	-	-	-	-	-	-	-	-	-
2.7934	1095	0.0299	-	-	-	-	-	-	-	-	-
2.7959	1096	0.0131	-	-	-	-	-	-	-	-	-
2.7985	1097	0.0302	-	-	-	-	-	-	-	-	-
2.8010	1098	0.0203	-	-	-	-	-	-	-	-	-
2.8036	1099	0.0117	-	-	-	-	-	-	-	-	-
2.8061	1100	0.0427	-	-	-	-	-	-	-	-	-
2.8087	1101	0.0816	-	-	-	-	-	-	-	-	-
2.8112	1102	0.05	-	-	-	-	-	-	-	-	-
2.8138	1103	0.069	-	-	-	-	-	-	-	-	-
2.8163	1104	0.0815	-	-	-	-	-	-	-	-	-
2.8189	1105	0.0403	-	-	-	-	-	-	-	-	-
2.8214	1106	0.0384	-	-	-	-	-	-	-	-	-
2.8240	1107	0.0339	-	-	-	-	-	-	-	-	-
2.8265	1108	0.0588	-	-	-	-	-	-	-	-	-
2.8291	1109	0.0066	-	-	-	-	-	-	-	-	-
2.8316	1110	0.0282	-	-	-	-	-	-	-	-	-
2.8342	1111	0.0259	-	-	-	-	-	-	-	-	-
2.8367	1112	0.1058	-	-	-	-	-	-	-	-	-
2.8393	1113	0.0195	-	-	-	-	-	-	-	-	-
2.8418	1114	0.0261	-	-	-	-	-	-	-	-	-
2.8444	1115	0.0771	-	-	-	-	-	-	-	-	-
2.8469	1116	0.0581	-	-	-	-	-	-	-	-	-
2.8495	1117	0.0372	-	-	-	-	-	-	-	-	-
2.8520	1118	0.0361	-	-	-	-	-	-	-	-	-
2.8546	1119	0.0232	-	-	-	-	-	-	-	-	-
2.8571	1120	0.0169	-	-	-	-	-	-	-	-	-
2.8597	1121	0.0344	0.7702	0.1186	2.3960	0.0989	0.0195	0.2707	0.9181	0.6759	0.7356
2.8622	1122	0.0285	-	-	-	-	-	-	-	-	-
2.8648	1123	0.07	-	-	-	-	-	-	-	-	-
2.8673	1124	0.0527	-	-	-	-	-	-	-	-	-
2.8699	1125	0.0342	-	-	-	-	-	-	-	-	-
2.8724	1126	0.0263	-	-	-	-	-	-	-	-	-
2.875	1127	0.2125	-	-	-	-	-	-	-	-	-
2.8776	1128	0.0312	-	-	-	-	-	-	-	-	-
2.8801	1129	0.0213	-	-	-	-	-	-	-	-	-
2.8827	1130	0.0524	-	-	-	-	-	-	-	-	-
2.8852	1131	0.0095	-	-	-	-	-	-	-	-	-
2.8878	1132	0.0402	-	-	-	-	-	-	-	-	-
2.8903	1133	0.0556	-	-	-	-	-	-	-	-	-
2.8929	1134	0.0355	-	-	-	-	-	-	-	-	-
2.8954	1135	0.0458	-	-	-	-	-	-	-	-	-
2.8980	1136	0.0469	-	-	-	-	-	-	-	-	-
2.9005	1137	0.0389	-	-	-	-	-	-	-	-	-
2.9031	1138	0.0627	-	-	-	-	-	-	-	-	-
2.9056	1139	0.0535	-	-	-	-	-	-	-	-	-
2.9082	1140	0.0671	-	-	-	-	-	-	-	-	-
2.9107	1141	0.0137	-	-	-	-	-	-	-	-	-
2.9133	1142	0.0502	-	-	-	-	-	-	-	-	-
2.9158	1143	0.0259	-	-	-	-	-	-	-	-	-
2.9184	1144	0.0085	-	-	-	-	-	-	-	-	-
2.9209	1145	0.0387	-	-	-	-	-	-	-	-	-
2.9235	1146	0.0438	-	-	-	-	-	-	-	-	-
2.9260	1147	0.0072	-	-	-	-	-	-	-	-	-
2.9286	1148	0.0209	-	-	-	-	-	-	-	-	-
2.9311	1149	0.0483	-	-	-	-	-	-	-	-	-
2.9337	1150	0.0628	-	-	-	-	-	-	-	-	-
2.9362	1151	0.0612	-	-	-	-	-	-	-	-	-
2.9388	1152	0.0419	-	-	-	-	-	-	-	-	-
2.9413	1153	0.5139	-	-	-	-	-	-	-	-	-
2.9439	1154	0.0633	-	-	-	-	-	-	-	-	-
2.9464	1155	0.0127	-	-	-	-	-	-	-	-	-
2.9490	1156	0.0822	-	-	-	-	-	-	-	-	-
2.9515	1157	0.0175	-	-	-	-	-	-	-	-	-
2.9541	1158	0.0047	-	-	-	-	-	-	-	-	-
2.9566	1159	0.0447	-	-	-	-	-	-	-	-	-
2.9592	1160	0.0375	-	-	-	-	-	-	-	-	-
2.9617	1161	0.0581	-	-	-	-	-	-	-	-	-
2.9643	1162	0.0733	-	-	-	-	-	-	-	-	-
2.9668	1163	0.0211	-	-	-	-	-	-	-	-	-
2.9694	1164	0.1113	-	-	-	-	-	-	-	-	-
2.9719	1165	0.0554	-	-	-	-	-	-	-	-	-
2.9745	1166	0.0672	-	-	-	-	-	-	-	-	-
2.9770	1167	0.0374	-	-	-	-	-	-	-	-	-
2.9796	1168	0.0059	-	-	-	-	-	-	-	-	-
2.9821	1169	0.0237	-	-	-	-	-	-	-	-	-
2.9847	1170	0.064	-	-	-	-	-	-	-	-	-
2.9872	1171	0.0497	-	-	-	-	-	-	-	-	-
2.9898	1172	0.012	-	-	-	-	-	-	-	-	-
2.9923	1173	0.0413	-	-	-	-	-	-	-	-	-
2.9949	1174	0.0304	-	-	-	-	-	-	-	-	-
2.9974	1175	0.0196	-	-	-	-	-	-	-	-	-
3.0	1176	0.0002	0.7696	0.1121	2.3511	0.0822	0.0197	0.2703	0.9181	0.6771	0.7422
-1	-1	-	-	-	-	-	-	-	0.9181	0.6771	0.7422

Framework Versions

• Python: 3.11.11
• Sentence Transformers: 3.4.1
• Transformers: 4.51.1
• PyTorch: 2.5.1+cu124
• Accelerate: 1.3.0
• Datasets: 3.5.0
• 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",
}