bobox/XLMRoBERTaM3-CustomPoolin-v1.01-1024d-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)
    )
    (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-v1.01-2048d-s1")
# Run inference
sentences = [
    'Simyo belongs to the Dutch telecommunications group KPN , after acquisition of the remainder of E-Plus on March 14 .',
    'Following the acquisition of the remainder of E-Plus on 14 March , Simyo belongs to the Dutch telecommunications group KPN .',
    'In total , 28 US victims were killed , while Viet Cong losses were killed 345 and a further 192 estimated killed .',
]
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	0.9057
spearman_cosine	0.9187

Binary Classification

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

Metric	allNLI-dev	Qnli-dev
cosine_accuracy	0.7656	0.7109
cosine_accuracy_threshold	0.7216	0.6857
cosine_f1	0.7091	0.7101
cosine_f1_threshold	0.6112	0.5354
cosine_precision	0.5821	0.6203
cosine_recall	0.907	0.8305
cosine_ap	0.66	0.717
cosine_mcc	0.5462	0.4058

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: 4 tokens mean: 25.11 tokens max: 209 tokens	min: 5 tokens mean: 16.61 tokens max: 50 tokens	min: 5 tokens mean: 16.79 tokens max: 50 tokens

• Samples:

  anchor	entailment	negative
  a child at a table preparing food	A child mixing food in a bowl on a table.	A child pouring food out of a bowl on a table.
  right exactly and then and then uh uh i'm i'm just not sure if i see you know if i see it going like i say to the right places i mean it'd be it'd be one thing if it were taking a bite out of your savings and then you were getting it all back when you got older	The money needs to go to the right place because it not any you pay with your savings you might not get it all back when you get older.	The money needs to go to the wrong place because it is not any you pay with your savings you might not get it all back when you get older.
  A bent over man is working on a toilet.	a man kneeling by a toilet while holding onto a hammer	a man kneeling by a toilet while holding onto a feather

• 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: 13.29 tokens max: 25 tokens	min: 7 tokens mean: 149.14 tokens max: 775 tokens

• Samples:

  sentence1	sentence2
  what is g's name in ncis la	G. Callen In season seven, Callen admits that he and Joelle are not suited and rowing a lot. They break up off screen but still spend Christmas together to avoid being lonely. In "Matryoshka, Part 2", Callen meets his father in Russia who reveals that his birth name is Grisha Alekandrovich Nikolaev but had to leave before he could find out any more information. Callen has since updated his personnel records and credentials with his full name.[1]
  who won the afl grand final in 1997	1997 AFL Grand Final The 1997 AFL Grand Final was an Australian rules football game contested between the Adelaide Football Club and the St Kilda Football Club, held at the Melbourne Cricket Ground in Melbourne on 27 September 1997. It was the 101st annual Grand Final of the Australian Football League (formerly the Victorian Football League), staged to determine the premiers for the 1997 AFL season. The match, attended by 99,645 spectators, was won by Adelaide by a margin of 31 points, marking that club's first premiership victory.
  what kind of ape is king louie in jungle book	King Louie King Louie is a fictional character introduced in Walt Disney's 1967 animated musical film, The Jungle Book. Unlike the majority of the adapted characters in the film, Louie was not featured in Rudyard Kipling's original works. King Louie was portrayed as an orangutan who was the leader of the other jungle primates, and who attempted to gain knowledge of fire from Mowgli, in order to become more human.

• 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: 20.27 tokens max: 92 tokens	min: 9 tokens mean: 44.98 tokens max: 294 tokens

• Samples:

  claim	evidence
  XL Recordings was founded after 1989 .	The label originated as a 1990 offshoot of Beggars Banquet Records .
  The Florida Project is a comedy-drama film .	The Florida Project is a 2017 American comedy-drama film directed by Sean Baker , from a screenplay by Baker and Chris Bergoch .
  Neighbors 2 : Sorority Rising ( film ) was reviewed by more than 45 critics .	On Rotten Tomatoes , the film has a rating of 67 % , based on 46 reviews , with an average rating of 6.2/10 .

• 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: 15 tokens mean: 30.64 tokens max: 49 tokens	min: 60 tokens mean: 313.48 tokens max: 576 tokens

• Samples:

  summary	document
  A car has been shunted several feet down the track in a crash with a tram.	The collision involving a Midland Metro tram happened at the station in Bilston Road in Ettingshall, Wolverhampton, at about 16:00 BST on Sunday. The car driver, a man in his 60s, was taken to hospital as a precaution. Midland Metro said: "The incident happened at the Priestfield tram stop car park. Emergency services were called and the full service was later resumed at around 17:30 BST." British Transport Police said it was treating the collision as an accident and no arrests had been made. The car was shunted about 10ft (3m) down the track in the crash. A West Midlands Ambulance Service spokesman said: "On arrival, crews found a car that had suffered significant damage to the passenger side having been shunted down the tracks." He said the driver of the car suffered chest pain from his seatbelt and was taken to New Cross Hospital in Wolverhampton.
  An audit has found that Nigeria's state oil company overpaid the government $750m (£490m), but also found it had not properly accounted for $1.48bn.	The financial report follows allegations in 2013 by then-central bank chief Lamido Sanusi that the firm had failed to account for about $20bn. It caused a huge uproar in Nigeria, forcing President Goodluck Jonathan to order an independent audit. His office released the findings as he prepares to step down in a month. For the latest news, views and analysis see the BBC Africa Live page. Ex-military ruler Muhammadu Buhari will be inaugurated on 29 May after he defeated Mr Jonathan in elections last month. He has vowed to tackle corruption in Nigeria, Africa's main oil producer where most people live on less than $2 a day. The BBC's Chris Ewokor in the capital, Abuja, says the findings suggest that Mr Sanusi's claims were exaggerated. However, many Nigerians still believe that corruption in the oil sector runs deep, our correspondent adds. The audit into the accounts of the Nigerian National Petroleum Corporation (NNPC) was carried out by PwC, one of the world's leading accounting firms. ...
  Chilean President Michelle Bachelet has asked all her ministers to resign and says she will choose a new cabinet in the coming days.	In an interview with broadcaster Canal 13, Ms Bachelet said changes were needed to promote reforms. Ms Bachelet's approval ratings have recently slumped amid a series of political scandals. In February her son resigned as head of a government charity over allegations of influence-peddling. The BBC's Gideon Long in Santiago says Ms Bachelet's dismissal of the entire cabinet is without precedent in recent Chilean history. The move gives some indication of the gravity of the crisis she faces, he says. "A few hours ago I requested the resignation of all the ministers, and I will take 72 hours to decide who will stay and who will go," Ms Bachelet said on Wednesday evening. "This is the time for a cabinet change." Ms Bachelet's son, Sebastian Davalos, was accused by the opposition of using his influence to get a $10m (£6.5m) bank loan for his wife. His wife's company used the money to buy plots of land in central Chile which her company then resold for profit. Although Chile's national bank...

• 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: 11 tokens mean: 31.42 tokens max: 55 tokens	min: 11 tokens mean: 31.27 tokens max: 55 tokens

• Samples:

  sentence1	sentence2
  Agathe knows Guillaume had a lover , but believes it was a young woman , Guillaume and Tom 's co-worker Sarah .	Agathe knows that Guillaume had a lover , but believes that it was a young woman , Guillaume and Tom 's co-worker Sarah .
  It is available in areas including Phibsboro and Castleknock , Finglas , Cabra and Ballymun .	It is available in areas including Phibsboro and Castleknock , Finglas , Cabra , Ballymun .
  More recently , the band Extra Life has combined aspects of early music with the modern genre of math rock .	More recently , the band has combined Extra Life Aspects of Old Music with the modern genre of Math Rock .

• 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: 24.11 tokens max: 88 tokens	min: 5 tokens mean: 104.68 tokens max: 536 tokens

• Samples:

  sentence1	sentence2
  Audubon Park was founded in 1941 as a community within Audubon with the construction of 500 accommodation units for the employees of New York Shipbuilding in Camden , New Jersey .	Audubon Park was established as a community within Audubon in 1941 with the construction of 500 housing units for employees of New York Shipbuilding in Camden , New Jersey .
  Highsmith is the father of the current former NFL player Ali Highsmith and the uncle of the former NFL player Alonzo Highsmith .	Highsmith is the father of current former NFL player Ali Highsmith and uncle of former NFL player Alonzo Highsmith .
  Work has begun to construct Britain's first new permanent military base in the Middle East since 1971.	Foreign Secretary Philip Hammond and naval personnel attended a ceremony to mark the start of construction of HMS Juffair at Mina Salman Port in Bahrain. The establishment is being developed to support Royal Navy deployments in the Gulf through the creation of a permanent and improved base. Mr Hammond said it showed the UK's commitment to the region. Mr Hammond said: "The presence of the Royal Navy in Bahrain is guaranteed into the future, ensuring Britain's sustained presence east of Suez. "The new facility will enable Britain to work with our allies to reinforce stability in the Gulf and beyond." Bahrain has been criticised over allegations of serious human rights abuses, but Mr Hammond said the UK was helping the Persian Gulf State to change. He said: "Bahrain is not perfect by any means, but it at least knows what it has to do and it is taking steps to do it. In some cases, he said, the state's authorities were "seeking our support to help them reform., for example, their police fo...

• 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: 8 tokens mean: 17.14 tokens max: 37 tokens	min: 6 tokens mean: 13.35 tokens max: 29 tokens	min: 6 tokens mean: 13.65 tokens max: 28 tokens

• Samples:

  anchor	entailment	negative
  A tabby cat laying on a cat scratcher in front of a bicycle wheel.	A cat is falling asleep on top of a scratching pad.	A cat is wide awake on top of a scratching pad.
  Bruce Springsteen, with one arm outstretched, is singing in the spotlight in a dark concert hall.	Bruce Springsteen is a singer.	Bruce Springsteen is not a singer.
  A man and a woman cook in the kitchen.	A married couple preparing food in a house kitchen.	A married couple not preparing food in a house kitchen.

• 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: 13.14 tokens max: 22 tokens	min: 34 tokens mean: 151.35 tokens max: 390 tokens

• Samples:

  sentence1	sentence2
  what do the colors of germany flag mean	Flag of Germany The colours of the modern flag are associated with the republican democracy first proposed in 1848, formed after World War I, and represent German unity and freedom.[3] During the Weimar Republic, the black-red-gold colours were the colours of the democratic, centrist, and republican political parties, as seen in the name of Reichsbanner Schwarz-Rot-Gold, formed by members of the Social Democratic, the Centre, and the Democratic parties to defend the republic against extremists on the right and left.
  where do goldfish come from in the wild	Goldfish A relatively small member of the carp family (which also includes the Prussian carp and the crucian carp), the goldfish is native to east Asia. It was first selectively bred in China more than a thousand years ago, and several distinct breeds have since been developed. Goldfish breeds vary greatly in size, body shape, fin configuration and colouration (various combinations of white, yellow, orange, red, brown, and black are known).
  what was the name of punky brewsters dog	Punky Brewster Penelope "Punky" Brewster (Soleil Moon Frye) is a warm, funny and bright child. Her father walked out on her family, then her mother abandoned her at a Chicago shopping center, leaving Punky alone with her dog Brandon. Afterwards, Punky discovered a vacant apartment in a local building.[3]

• 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: 22.49 tokens max: 48 tokens	min: 11 tokens mean: 38.29 tokens max: 92 tokens

• Samples:

  claim	evidence
  Coronavirus has killed more than 7,050 people .	more than 7,100 deaths have been attributed to COVID-19 .
  Less than 91,000 recoveries from coronavirus had been reported by 21 March .	As of 21 March , more than 275,000 cases of COVID-19 have been reported in over 185 countries and territories , resulting in more than 11,300 deaths and 90,000 recoveries .
  More than 31,800 COVID-19 fatalities have been confirmed globally in 2019-20 .	more than 680,000 cases of COVID-19 have been reported in over 190 countries and territories , resulting in approximately 31,900 deaths .

• 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: 12 tokens mean: 29.78 tokens max: 47 tokens	min: 41 tokens mean: 304.01 tokens max: 501 tokens

• Samples:

  summary	document
  Hibernian striker Anthony Stokes believes Easter Road boss Alan Stubbs has all the attributes to become a top manager.	Stokes, on loan at Hibs from Celtic, refused to say whether he thought Stubbs would one day be Celtic boss. But the Irishman feels Stubbs would cope admirably with everything that comes with being an Old Firm manager. "From the managers I have seen there he would have no issue in coping with the Celtic job," said Stokes. "That is not me trying to put anything out there. You look at him and he is obviously not in the manager's job that long but he is like a manager that has been around for 10 or 15 years in how he handles the boys. "He has played the game himself and he understands how players react and how they handle themselves and adapt to situations." Stokes says it has taken him some time to recapture the sharpness he was looking for when he joined the club in January. He insists this is down to his lack of game time, having not featured for Celtic since a 3-1 win against Dundee United back in August. "It's just down to fitness and match sharpness," explained the Republic of Irelan...
  Scarlets have confirmed Cardiff Blues wing Harry Robinson is joining them ahead of the 2014-15 season.	The move was revealed to BBC Radio Wales by joint Blues coach Dale McIntosh on 20 March. Robinson, 20, has made three Wales appearances - one against Barbarians and two against Japan, and has played for Wales Under-20. He has scored eight tries in 17 Blues appearances, having made his debut in the 2012-13 season. Caps against Barbarians in June 2013 and two more on tour in Japan followed. But he has not figured since for Wales and will hope to progress at Scarlets. Scarlets head coach Simon Easterby said: "We believe Harry will thrive in our young and ambitious environment but will also benefit from playing alongside a world-class and experienced player like [returning centre] Regan King. "He's an ambitious player who has great pace and the supporters can look forward to what he can bring next season in addition to existing Scarlets talent in the back three." McIntosh said: "A lot to do with it [Robinson's move] is the budget, the financial situation at the moment in Wales. "It's about...
  A knife attacker who lashed out at Tube passengers cutting the throat of one had images of murdered Lee Rigby on his phone, a jury has heard.	Muhiddin Mire, 30, injured musician Lyle Zimmerman at Leytonstone station, the Old Bailey heard. As he was led away by police on 5 December he said "this is for Syria, for my Muslim brothers", the jury was told. Mr Mire admits attacking Mr Zimmerman, 56, but denies attempted murder. Mr Zimmerman suffered "a deep and ragged wound" to his neck and his windpipe was exposed, the court has heard. Images of murdered soldier Lee Rigby and British Islamic State executioner Jihadi John, were among extremist material found on Mr Mire's phone, prosecutors said. As he was led away by police Mr Mire explained his actions were in response to the bombings of hospitals in Syria, the jury heard. Prosecutor Jonathan Rees QC said: "The contents of the defendant's phone, when looked at alongside what he said during the course of the incident... provides an insight as to what motivated the defendant to do what he did and what he was seeking to do to Mr Zimmerman." Minutes after he attacked Mr Zimmerman, th...

• 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: 15 tokens mean: 31.14 tokens max: 56 tokens	min: 16 tokens mean: 31.13 tokens max: 54 tokens

• Samples:

  sentence1	sentence2
  It was designed in 1983 by architects Philip Johnson ( alumnus of the University ) and John Burgee .	It was designed by architects Philip Johnson ( alumnus of the University ) and John Burgee in 1983 .
  Mandarin - Chinese has specific terms and racial euphemisms for different races and ethnicities , and some discriminatory attacks against representatives of certain governments and backgrounds .	Mandarin Chinese has specific terms and racial euphemisms for different races and ethnicities , and some discriminatory slurs against representatives from certain governments and backgrounds .
  Adam Surat ( `` Inner Strength '' ) is a documentary film directed by Sheikh Mohammed Sultan in 1989 about the Bangladeshi painter Tareque Masud .	Adam Surat ( `` The Inner Strength '' ) is a 1989 Bangladeshi documentary film about the Bangladeshi painter Tareque Masud , directed by Sheikh Mohammed Sultan .

• 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: 23.88 tokens max: 50 tokens	min: 7 tokens mean: 117.77 tokens max: 526 tokens

• Samples:

  sentence1	sentence2
  Dominique Cottrez, the Frenchwoman who confessed in 2010 to killing eight of her own newborn babies, has been freed under supervision, French media report.	She was released untagged by an appeals court in the northern town of Douai on condition she continued to receive psychological and psychiatric care. Mrs Cottrez had been in custody since July 2010, when remains were found in the garden of her parents' home. She was awaiting trial on eight charges of voluntary homicide. It was not immediately clear on what grounds she had been released. While she admitted killing her eight newborn babies between 1989 and 2006, in the village of Villers-au-Tertre near Lille, she said her husband had known nothing about them. He has not been charged. She told an examining magistrate she had been a victim of incest and feared that her own father, who died in 2007, had fathered the babies, Le Figaro newspaper reports. Mrs Cottrez, who is in her late forties, reportedly managed to conceal the pregnancies because of her obesity. Two sets of remains were found in the parents' house, wrapped in plastic bags. The other six bodies were found in the garage of a s...
  An empty bathroom with a shower next to a toilet.	A small bathroom with a toilet and a bathtub with an open shower curtain.
  Lobethal Bierhaus is a German beer brewery , with regional style influences .	Lobethal Bierhaus is a German brewery with regional influences of style .

• 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-v1.01-2048d-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-v1.01-2048d-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	0.8688	-	-	-	-	-	-	-	-	-
0.0051	2	3.538	-	-	-	-	-	-	-	-	-
0.0077	3	0.6752	-	-	-	-	-	-	-	-	-
0.0102	4	0.8807	-	-	-	-	-	-	-	-	-
0.0128	5	0.6127	-	-	-	-	-	-	-	-	-
0.0153	6	0.9364	-	-	-	-	-	-	-	-	-
0.0179	7	0.6662	-	-	-	-	-	-	-	-	-
0.0204	8	0.8483	-	-	-	-	-	-	-	-	-
0.0230	9	1.0163	-	-	-	-	-	-	-	-	-
0.0255	10	0.7548	-	-	-	-	-	-	-	-	-
0.0281	11	0.8119	-	-	-	-	-	-	-	-	-
0.0306	12	0.9089	-	-	-	-	-	-	-	-	-
0.0332	13	0.8262	-	-	-	-	-	-	-	-	-
0.0357	14	0.7469	-	-	-	-	-	-	-	-	-
0.0383	15	0.7397	-	-	-	-	-	-	-	-	-
0.0408	16	0.532	-	-	-	-	-	-	-	-	-
0.0434	17	0.6666	-	-	-	-	-	-	-	-	-
0.0459	18	0.7882	-	-	-	-	-	-	-	-	-
0.0485	19	0.7006	-	-	-	-	-	-	-	-	-
0.0510	20	0.7633	-	-	-	-	-	-	-	-	-
0.0536	21	0.6789	-	-	-	-	-	-	-	-	-
0.0561	22	0.6284	-	-	-	-	-	-	-	-	-
0.0587	23	0.5713	-	-	-	-	-	-	-	-	-
0.0612	24	1.7645	-	-	-	-	-	-	-	-	-
0.0638	25	0.4332	-	-	-	-	-	-	-	-	-
0.0663	26	0.4666	-	-	-	-	-	-	-	-	-
0.0689	27	0.3851	-	-	-	-	-	-	-	-	-
0.0714	28	0.6867	-	-	-	-	-	-	-	-	-
0.0740	29	0.3746	-	-	-	-	-	-	-	-	-
0.0765	30	0.3221	-	-	-	-	-	-	-	-	-
0.0791	31	0.6967	-	-	-	-	-	-	-	-	-
0.0816	32	0.3859	-	-	-	-	-	-	-	-	-
0.0842	33	0.4324	-	-	-	-	-	-	-	-	-
0.0867	34	0.2536	-	-	-	-	-	-	-	-	-
0.0893	35	0.2255	-	-	-	-	-	-	-	-	-
0.0918	36	0.3757	-	-	-	-	-	-	-	-	-
0.0944	37	0.305	-	-	-	-	-	-	-	-	-
0.0969	38	0.2694	-	-	-	-	-	-	-	-	-
0.0995	39	0.3352	-	-	-	-	-	-	-	-	-
0.1020	40	0.2138	-	-	-	-	-	-	-	-	-
0.1046	41	0.1574	-	-	-	-	-	-	-	-	-
0.1071	42	0.3109	-	-	-	-	-	-	-	-	-
0.1097	43	0.2689	-	-	-	-	-	-	-	-	-
0.1122	44	0.198	-	-	-	-	-	-	-	-	-
0.1148	45	0.2007	-	-	-	-	-	-	-	-	-
0.1173	46	0.1973	-	-	-	-	-	-	-	-	-
0.1199	47	0.1172	-	-	-	-	-	-	-	-	-
0.1224	48	0.2643	-	-	-	-	-	-	-	-	-
0.125	49	0.1813	-	-	-	-	-	-	-	-	-
0.1276	50	0.095	-	-	-	-	-	-	-	-	-
0.1301	51	0.1818	-	-	-	-	-	-	-	-	-
0.1327	52	0.2118	-	-	-	-	-	-	-	-	-
0.1352	53	0.107	-	-	-	-	-	-	-	-	-
0.1378	54	0.1999	-	-	-	-	-	-	-	-	-
0.1403	55	0.1951	-	-	-	-	-	-	-	-	-
0.1429	56	0.2001	-	-	-	-	-	-	-	-	-
0.1454	57	0.2214	-	-	-	-	-	-	-	-	-
0.1480	58	0.1678	-	-	-	-	-	-	-	-	-
0.1505	59	0.0841	0.7537	0.3439	1.5805	0.2436	0.0212	0.5505	0.9131	0.6599	0.7294
0.1531	60	0.135	-	-	-	-	-	-	-	-	-
0.1556	61	0.0853	-	-	-	-	-	-	-	-	-
0.1582	62	0.3284	-	-	-	-	-	-	-	-	-
0.1607	63	0.2188	-	-	-	-	-	-	-	-	-
0.1633	64	0.1539	-	-	-	-	-	-	-	-	-
0.1658	65	0.1776	-	-	-	-	-	-	-	-	-
0.1684	66	0.2858	-	-	-	-	-	-	-	-	-
0.1709	67	0.3067	-	-	-	-	-	-	-	-	-
0.1735	68	0.0222	-	-	-	-	-	-	-	-	-
0.1760	69	0.1468	-	-	-	-	-	-	-	-	-
0.1786	70	0.2655	-	-	-	-	-	-	-	-	-
0.1811	71	0.1122	-	-	-	-	-	-	-	-	-
0.1837	72	0.0744	-	-	-	-	-	-	-	-	-
0.1862	73	0.1609	-	-	-	-	-	-	-	-	-
0.1888	74	0.1659	-	-	-	-	-	-	-	-	-
0.1913	75	0.186	-	-	-	-	-	-	-	-	-
0.1939	76	0.1391	-	-	-	-	-	-	-	-	-
0.1964	77	0.3234	-	-	-	-	-	-	-	-	-
0.1990	78	0.179	-	-	-	-	-	-	-	-	-
0.2015	79	0.191	-	-	-	-	-	-	-	-	-
0.2041	80	0.3031	-	-	-	-	-	-	-	-	-
0.2066	81	0.1225	-	-	-	-	-	-	-	-	-
0.2092	82	0.1261	-	-	-	-	-	-	-	-	-
0.2117	83	0.124	-	-	-	-	-	-	-	-	-
0.2143	84	0.1981	-	-	-	-	-	-	-	-	-
0.2168	85	0.0966	-	-	-	-	-	-	-	-	-
0.2194	86	0.1433	-	-	-	-	-	-	-	-	-
0.2219	87	0.1127	-	-	-	-	-	-	-	-	-
0.2245	88	0.0791	-	-	-	-	-	-	-	-	-
0.2270	89	0.1543	-	-	-	-	-	-	-	-	-
0.2296	90	0.1623	-	-	-	-	-	-	-	-	-
0.2321	91	0.1875	-	-	-	-	-	-	-	-	-
0.2347	92	0.2052	-	-	-	-	-	-	-	-	-
0.2372	93	0.1369	-	-	-	-	-	-	-	-	-
0.2398	94	0.1689	-	-	-	-	-	-	-	-	-
0.2423	95	0.0761	-	-	-	-	-	-	-	-	-
0.2449	96	0.0837	-	-	-	-	-	-	-	-	-
0.2474	97	0.1575	-	-	-	-	-	-	-	-	-
0.25	98	0.1799	-	-	-	-	-	-	-	-	-
0.2526	99	0.1809	-	-	-	-	-	-	-	-	-
0.2551	100	0.119	-	-	-	-	-	-	-	-	-
0.2577	101	0.1464	-	-	-	-	-	-	-	-	-
0.2602	102	0.041	-	-	-	-	-	-	-	-	-
0.2628	103	0.19	-	-	-	-	-	-	-	-	-
0.2653	104	0.1423	-	-	-	-	-	-	-	-	-
0.2679	105	0.4117	-	-	-	-	-	-	-	-	-
0.2704	106	0.1147	-	-	-	-	-	-	-	-	-
0.2730	107	0.1455	-	-	-	-	-	-	-	-	-
0.2755	108	0.1134	-	-	-	-	-	-	-	-	-
0.2781	109	0.114	-	-	-	-	-	-	-	-	-
0.2806	110	0.0946	-	-	-	-	-	-	-	-	-
0.2832	111	0.161	-	-	-	-	-	-	-	-	-
0.2857	112	0.1212	-	-	-	-	-	-	-	-	-
0.2883	113	0.1292	-	-	-	-	-	-	-	-	-
0.2908	114	0.1122	-	-	-	-	-	-	-	-	-
0.2934	115	0.1526	-	-	-	-	-	-	-	-	-
0.2959	116	0.1103	-	-	-	-	-	-	-	-	-
0.2985	117	0.1331	-	-	-	-	-	-	-	-	-
0.3010	118	0.0865	0.6081	0.2691	1.5269	0.1412	0.0212	0.5289	0.9173	0.6602	0.7240
0.3036	119	0.0885	-	-	-	-	-	-	-	-	-
0.3061	120	0.1277	-	-	-	-	-	-	-	-	-
0.3087	121	0.0942	-	-	-	-	-	-	-	-	-
0.3112	122	0.0684	-	-	-	-	-	-	-	-	-
0.3138	123	0.193	-	-	-	-	-	-	-	-	-
0.3163	124	0.1047	-	-	-	-	-	-	-	-	-
0.3189	125	0.1641	-	-	-	-	-	-	-	-	-
0.3214	126	0.0988	-	-	-	-	-	-	-	-	-
0.3240	127	0.1354	-	-	-	-	-	-	-	-	-
0.3265	128	0.1431	-	-	-	-	-	-	-	-	-
0.3291	129	0.135	-	-	-	-	-	-	-	-	-
0.3316	130	0.1668	-	-	-	-	-	-	-	-	-
0.3342	131	0.0339	-	-	-	-	-	-	-	-	-
0.3367	132	0.0782	-	-	-	-	-	-	-	-	-
0.3393	133	0.2163	-	-	-	-	-	-	-	-	-
0.3418	134	0.1736	-	-	-	-	-	-	-	-	-
0.3444	135	0.166	-	-	-	-	-	-	-	-	-
0.3469	136	0.0804	-	-	-	-	-	-	-	-	-
0.3495	137	0.0902	-	-	-	-	-	-	-	-	-
0.3520	138	0.0847	-	-	-	-	-	-	-	-	-
0.3546	139	0.2095	-	-	-	-	-	-	-	-	-
0.3571	140	0.0682	-	-	-	-	-	-	-	-	-
0.3597	141	0.0645	-	-	-	-	-	-	-	-	-
0.3622	142	0.2498	-	-	-	-	-	-	-	-	-
0.3648	143	0.2704	-	-	-	-	-	-	-	-	-
0.3673	144	0.2131	-	-	-	-	-	-	-	-	-
0.3699	145	0.1247	-	-	-	-	-	-	-	-	-
0.3724	146	0.1979	-	-	-	-	-	-	-	-	-
0.375	147	0.0845	-	-	-	-	-	-	-	-	-
0.3776	148	0.1752	-	-	-	-	-	-	-	-	-
0.3801	149	0.1824	-	-	-	-	-	-	-	-	-
0.3827	150	0.1249	-	-	-	-	-	-	-	-	-
0.3852	151	0.1261	-	-	-	-	-	-	-	-	-
0.3878	152	0.0839	-	-	-	-	-	-	-	-	-
0.3903	153	0.1659	-	-	-	-	-	-	-	-	-
0.3929	154	0.0955	-	-	-	-	-	-	-	-	-
0.3954	155	0.0917	-	-	-	-	-	-	-	-	-
0.3980	156	0.0496	-	-	-	-	-	-	-	-	-
0.4005	157	0.0665	-	-	-	-	-	-	-	-	-
0.4031	158	0.1232	-	-	-	-	-	-	-	-	-
0.4056	159	0.1529	-	-	-	-	-	-	-	-	-
0.4082	160	0.0806	-	-	-	-	-	-	-	-	-
0.4107	161	0.0901	-	-	-	-	-	-	-	-	-
0.4133	162	0.0882	-	-	-	-	-	-	-	-	-
0.4158	163	0.1155	-	-	-	-	-	-	-	-	-
0.4184	164	0.1194	-	-	-	-	-	-	-	-	-
0.4209	165	0.0513	-	-	-	-	-	-	-	-	-
0.4235	166	0.1288	-	-	-	-	-	-	-	-	-
0.4260	167	0.0825	-	-	-	-	-	-	-	-	-
0.4286	168	0.0897	-	-	-	-	-	-	-	-	-
0.4311	169	0.0669	-	-	-	-	-	-	-	-	-
0.4337	170	0.1707	-	-	-	-	-	-	-	-	-
0.4362	171	0.1525	-	-	-	-	-	-	-	-	-
0.4388	172	0.0807	-	-	-	-	-	-	-	-	-
0.4413	173	0.1249	-	-	-	-	-	-	-	-	-
0.4439	174	0.1338	-	-	-	-	-	-	-	-	-
0.4464	175	0.0788	-	-	-	-	-	-	-	-	-
0.4490	176	0.1216	-	-	-	-	-	-	-	-	-
0.4515	177	0.1544	0.6024	0.2676	1.5297	0.1515	0.0211	0.5435	0.9172	0.6573	0.7200
0.4541	178	0.1201	-	-	-	-	-	-	-	-	-
0.4566	179	0.1198	-	-	-	-	-	-	-	-	-
0.4592	180	0.1066	-	-	-	-	-	-	-	-	-
0.4617	181	0.1532	-	-	-	-	-	-	-	-	-
0.4643	182	0.1067	-	-	-	-	-	-	-	-	-
0.4668	183	0.1158	-	-	-	-	-	-	-	-	-
0.4694	184	0.0746	-	-	-	-	-	-	-	-	-
0.4719	185	0.1679	-	-	-	-	-	-	-	-	-
0.4745	186	0.1911	-	-	-	-	-	-	-	-	-
0.4770	187	0.0588	-	-	-	-	-	-	-	-	-
0.4796	188	0.5639	-	-	-	-	-	-	-	-	-
0.4821	189	0.1161	-	-	-	-	-	-	-	-	-
0.4847	190	0.062	-	-	-	-	-	-	-	-	-
0.4872	191	0.0967	-	-	-	-	-	-	-	-	-
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1.0	392	0.0001	-	-	-	-	-	-	-	-	-
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1.0383	407	0.1511	-	-	-	-	-	-	-	-	-
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1.0510	412	0.1195	-	-	-	-	-	-	-	-	-
1.0536	413	0.0857	0.5814	0.2668	1.5742	0.1667	0.0213	0.5629	0.9171	0.6630	0.7375
1.0561	414	0.0531	-	-	-	-	-	-	-	-	-
1.0587	415	0.1254	-	-	-	-	-	-	-	-	-
1.0612	416	0.061	-	-	-	-	-	-	-	-	-
1.0638	417	0.0661	-	-	-	-	-	-	-	-	-
1.0663	418	0.0534	-	-	-	-	-	-	-	-	-
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1.0740	421	0.0593	-	-	-	-	-	-	-	-	-
1.0765	422	0.0	-	-	-	-	-	-	-	-	-
1.0791	423	0.0774	-	-	-	-	-	-	-	-	-
1.0816	424	0.0382	-	-	-	-	-	-	-	-	-
1.0842	425	0.0461	-	-	-	-	-	-	-	-	-
1.0867	426	0.1043	-	-	-	-	-	-	-	-	-
1.0893	427	0.0891	-	-	-	-	-	-	-	-	-
1.0918	428	0.1286	-	-	-	-	-	-	-	-	-
1.0944	429	0.0437	-	-	-	-	-	-	-	-	-
1.0969	430	0.0282	-	-	-	-	-	-	-	-	-
1.0995	431	0.1001	-	-	-	-	-	-	-	-	-
1.1020	432	0.0303	-	-	-	-	-	-	-	-	-
1.1046	433	0.0907	-	-	-	-	-	-	-	-	-
1.1071	434	0.0265	-	-	-	-	-	-	-	-	-
1.1097	435	0.0788	-	-	-	-	-	-	-	-	-
1.1122	436	0.1319	-	-	-	-	-	-	-	-	-
1.1148	437	0.0869	-	-	-	-	-	-	-	-	-
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1.1199	439	0.1199	-	-	-	-	-	-	-	-	-
1.1224	440	0.0415	-	-	-	-	-	-	-	-	-
1.125	441	0.0656	-	-	-	-	-	-	-	-	-
1.1276	442	0.0487	-	-	-	-	-	-	-	-	-
1.1301	443	0.0429	-	-	-	-	-	-	-	-	-
1.1327	444	0.1154	-	-	-	-	-	-	-	-	-
1.1352	445	0.0361	-	-	-	-	-	-	-	-	-
1.1378	446	0.0302	-	-	-	-	-	-	-	-	-
1.1403	447	0.0468	-	-	-	-	-	-	-	-	-
1.1429	448	0.0416	-	-	-	-	-	-	-	-	-
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2.4592	964	0.0597	-	-	-	-	-	-	-	-	-
2.4617	965	0.0629	-	-	-	-	-	-	-	-	-
2.4643	966	0.0441	-	-	-	-	-	-	-	-	-
2.4668	967	0.0252	-	-	-	-	-	-	-	-	-
2.4694	968	0.0704	-	-	-	-	-	-	-	-	-
2.4719	969	0.0514	-	-	-	-	-	-	-	-	-
2.4745	970	0.0951	-	-	-	-	-	-	-	-	-
2.4770	971	0.0579	-	-	-	-	-	-	-	-	-
2.4796	972	0.048	-	-	-	-	-	-	-	-	-
2.4821	973	0.0841	-	-	-	-	-	-	-	-	-
2.4847	974	0.0271	-	-	-	-	-	-	-	-	-
2.4872	975	0.0176	-	-	-	-	-	-	-	-	-
2.4898	976	0.0422	-	-	-	-	-	-	-	-	-
2.4923	977	0.0535	-	-	-	-	-	-	-	-	-
2.4949	978	0.019	-	-	-	-	-	-	-	-	-
2.4974	979	0.0683	-	-	-	-	-	-	-	-	-
2.5	980	0.0244	-	-	-	-	-	-	-	-	-
2.5026	981	0.0607	-	-	-	-	-	-	-	-	-
2.5051	982	0.0455	-	-	-	-	-	-	-	-	-
2.5077	983	0.0383	-	-	-	-	-	-	-	-	-
2.5102	984	0.0095	-	-	-	-	-	-	-	-	-
2.5128	985	0.0067	-	-	-	-	-	-	-	-	-
2.5153	986	0.0837	-	-	-	-	-	-	-	-	-
2.5179	987	0.0376	-	-	-	-	-	-	-	-	-
2.5204	988	0.0194	-	-	-	-	-	-	-	-	-
2.5230	989	0.0411	-	-	-	-	-	-	-	-	-
2.5255	990	0.0919	-	-	-	-	-	-	-	-	-
2.5281	991	0.0899	-	-	-	-	-	-	-	-	-
2.5306	992	0.013	-	-	-	-	-	-	-	-	-
2.5332	993	0.0149	-	-	-	-	-	-	-	-	-
2.5357	994	0.0179	-	-	-	-	-	-	-	-	-
2.5383	995	0.0082	-	-	-	-	-	-	-	-	-
2.5408	996	0.0289	-	-	-	-	-	-	-	-	-
2.5434	997	0.0295	-	-	-	-	-	-	-	-	-
2.5459	998	0.0288	-	-	-	-	-	-	-	-	-
2.5485	999	0.0399	-	-	-	-	-	-	-	-	-
2.5510	1000	0.0546	-	-	-	-	-	-	-	-	-
2.5536	1001	0.0789	-	-	-	-	-	-	-	-	-
2.5561	1002	0.0178	-	-	-	-	-	-	-	-	-
2.5587	1003	0.0564	0.5442	0.3005	1.5761	0.1217	0.0214	0.4938	0.9185	0.6620	0.7122
2.5612	1004	0.0572	-	-	-	-	-	-	-	-	-
2.5638	1005	0.0457	-	-	-	-	-	-	-	-	-
2.5663	1006	0.0613	-	-	-	-	-	-	-	-	-
2.5689	1007	0.029	-	-	-	-	-	-	-	-	-
2.5714	1008	0.0578	-	-	-	-	-	-	-	-	-
2.5740	1009	0.102	-	-	-	-	-	-	-	-	-
2.5765	1010	0.0496	-	-	-	-	-	-	-	-	-
2.5791	1011	0.0757	-	-	-	-	-	-	-	-	-
2.5816	1012	0.009	-	-	-	-	-	-	-	-	-
2.5842	1013	0.0422	-	-	-	-	-	-	-	-	-
2.5867	1014	0.0791	-	-	-	-	-	-	-	-	-
2.5893	1015	0.0576	-	-	-	-	-	-	-	-	-
2.5918	1016	0.024	-	-	-	-	-	-	-	-	-
2.5944	1017	0.0318	-	-	-	-	-	-	-	-	-
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2.5995	1019	0.0206	-	-	-	-	-	-	-	-	-
2.6020	1020	0.0404	-	-	-	-	-	-	-	-	-
2.6046	1021	0.0212	-	-	-	-	-	-	-	-	-
2.6071	1022	0.0359	-	-	-	-	-	-	-	-	-
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2.6122	1024	0.0574	-	-	-	-	-	-	-	-	-
2.6148	1025	0.0897	-	-	-	-	-	-	-	-	-
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2.6199	1027	0.0214	-	-	-	-	-	-	-	-	-
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2.625	1029	0.0524	-	-	-	-	-	-	-	-	-
2.6276	1030	0.0039	-	-	-	-	-	-	-	-	-
2.6301	1031	0.0356	-	-	-	-	-	-	-	-	-
2.6327	1032	0.0296	-	-	-	-	-	-	-	-	-
2.6352	1033	0.0302	-	-	-	-	-	-	-	-	-
2.6378	1034	0.063	-	-	-	-	-	-	-	-	-
2.6403	1035	0.0958	-	-	-	-	-	-	-	-	-
2.6429	1036	0.1315	-	-	-	-	-	-	-	-	-
2.6454	1037	0.0489	-	-	-	-	-	-	-	-	-
2.6480	1038	0.0611	-	-	-	-	-	-	-	-	-
2.6505	1039	0.0323	-	-	-	-	-	-	-	-	-
2.6531	1040	0.0299	-	-	-	-	-	-	-	-	-
2.6556	1041	0.0456	-	-	-	-	-	-	-	-	-
2.6582	1042	0.0459	-	-	-	-	-	-	-	-	-
2.6607	1043	0.023	-	-	-	-	-	-	-	-	-
2.6633	1044	0.0214	-	-	-	-	-	-	-	-	-
2.6658	1045	0.031	-	-	-	-	-	-	-	-	-
2.6684	1046	0.0231	-	-	-	-	-	-	-	-	-
2.6709	1047	0.0342	-	-	-	-	-	-	-	-	-
2.6735	1048	0.0216	-	-	-	-	-	-	-	-	-
2.6760	1049	0.0106	-	-	-	-	-	-	-	-	-
2.6786	1050	0.064	-	-	-	-	-	-	-	-	-
2.6811	1051	0.0303	-	-	-	-	-	-	-	-	-
2.6837	1052	0.0118	-	-	-	-	-	-	-	-	-
2.6862	1053	0.0762	-	-	-	-	-	-	-	-	-
2.6888	1054	0.0126	-	-	-	-	-	-	-	-	-
2.6913	1055	0.0348	-	-	-	-	-	-	-	-	-
2.6939	1056	0.0543	-	-	-	-	-	-	-	-	-
2.6964	1057	0.0408	-	-	-	-	-	-	-	-	-
2.6990	1058	0.0279	-	-	-	-	-	-	-	-	-
2.7015	1059	0.0379	-	-	-	-	-	-	-	-	-
2.7041	1060	0.0831	-	-	-	-	-	-	-	-	-
2.7066	1061	0.0712	-	-	-	-	-	-	-	-	-
2.7092	1062	0.0258	0.5653	0.3086	1.5462	0.1225	0.0214	0.4793	0.9196	0.6645	0.7119
2.7117	1063	0.0321	-	-	-	-	-	-	-	-	-
2.7143	1064	0.1742	-	-	-	-	-	-	-	-	-
2.7168	1065	0.0447	-	-	-	-	-	-	-	-	-
2.7194	1066	0.0714	-	-	-	-	-	-	-	-	-
2.7219	1067	0.0286	-	-	-	-	-	-	-	-	-
2.7245	1068	0.0214	-	-	-	-	-	-	-	-	-
2.7270	1069	0.0136	-	-	-	-	-	-	-	-	-
2.7296	1070	0.0784	-	-	-	-	-	-	-	-	-
2.7321	1071	0.0489	-	-	-	-	-	-	-	-	-
2.7347	1072	0.028	-	-	-	-	-	-	-	-	-
2.7372	1073	0.0921	-	-	-	-	-	-	-	-	-
2.7398	1074	0.0097	-	-	-	-	-	-	-	-	-
2.7423	1075	0.0216	-	-	-	-	-	-	-	-	-
2.7449	1076	0.0492	-	-	-	-	-	-	-	-	-
2.7474	1077	0.0472	-	-	-	-	-	-	-	-	-
2.75	1078	0.0419	-	-	-	-	-	-	-	-	-
2.7526	1079	0.0327	-	-	-	-	-	-	-	-	-
2.7551	1080	0.0487	-	-	-	-	-	-	-	-	-
2.7577	1081	0.0392	-	-	-	-	-	-	-	-	-
2.7602	1082	0.0413	-	-	-	-	-	-	-	-	-
2.7628	1083	0.034	-	-	-	-	-	-	-	-	-
2.7653	1084	0.0368	-	-	-	-	-	-	-	-	-
2.7679	1085	0.0724	-	-	-	-	-	-	-	-	-
2.7704	1086	0.1611	-	-	-	-	-	-	-	-	-
2.7730	1087	0.0366	-	-	-	-	-	-	-	-	-
2.7755	1088	0.0394	-	-	-	-	-	-	-	-	-
2.7781	1089	0.0321	-	-	-	-	-	-	-	-	-
2.7806	1090	0.0392	-	-	-	-	-	-	-	-	-
2.7832	1091	0.0284	-	-	-	-	-	-	-	-	-
2.7857	1092	0.0568	-	-	-	-	-	-	-	-	-
2.7883	1093	0.0197	-	-	-	-	-	-	-	-	-
2.7908	1094	0.0567	-	-	-	-	-	-	-	-	-
2.7934	1095	0.0294	-	-	-	-	-	-	-	-	-
2.7959	1096	0.0147	-	-	-	-	-	-	-	-	-
2.7985	1097	0.0135	-	-	-	-	-	-	-	-	-
2.8010	1098	0.0197	-	-	-	-	-	-	-	-	-
2.8036	1099	0.0685	-	-	-	-	-	-	-	-	-
2.8061	1100	0.1037	-	-	-	-	-	-	-	-	-
2.8087	1101	0.0142	-	-	-	-	-	-	-	-	-
2.8112	1102	0.0921	-	-	-	-	-	-	-	-	-
2.8138	1103	0.0312	-	-	-	-	-	-	-	-	-
2.8163	1104	0.0573	-	-	-	-	-	-	-	-	-
2.8189	1105	0.0149	-	-	-	-	-	-	-	-	-
2.8214	1106	0.0215	-	-	-	-	-	-	-	-	-
2.8240	1107	0.0393	-	-	-	-	-	-	-	-	-
2.8265	1108	0.0358	-	-	-	-	-	-	-	-	-
2.8291	1109	0.1027	-	-	-	-	-	-	-	-	-
2.8316	1110	0.024	-	-	-	-	-	-	-	-	-
2.8342	1111	0.0255	-	-	-	-	-	-	-	-	-
2.8367	1112	0.0442	-	-	-	-	-	-	-	-	-
2.8393	1113	0.0238	-	-	-	-	-	-	-	-	-
2.8418	1114	0.0607	-	-	-	-	-	-	-	-	-
2.8444	1115	0.0553	-	-	-	-	-	-	-	-	-
2.8469	1116	0.0618	-	-	-	-	-	-	-	-	-
2.8495	1117	0.0078	-	-	-	-	-	-	-	-	-
2.8520	1118	0.0207	-	-	-	-	-	-	-	-	-
2.8546	1119	0.0135	-	-	-	-	-	-	-	-	-
2.8571	1120	0.0267	-	-	-	-	-	-	-	-	-
2.8597	1121	0.0648	0.5748	0.2996	1.5611	0.1337	0.0214	0.4844	0.9197	0.6622	0.7131
2.8622	1122	0.0246	-	-	-	-	-	-	-	-	-
2.8648	1123	0.0319	-	-	-	-	-	-	-	-	-
2.8673	1124	0.0593	-	-	-	-	-	-	-	-	-
2.8699	1125	0.034	-	-	-	-	-	-	-	-	-
2.8724	1126	0.0198	-	-	-	-	-	-	-	-	-
2.875	1127	0.1633	-	-	-	-	-	-	-	-	-
2.8776	1128	0.0295	-	-	-	-	-	-	-	-	-
2.8801	1129	0.0392	-	-	-	-	-	-	-	-	-
2.8827	1130	0.0122	-	-	-	-	-	-	-	-	-
2.8852	1131	0.048	-	-	-	-	-	-	-	-	-
2.8878	1132	0.0754	-	-	-	-	-	-	-	-	-
2.8903	1133	0.0104	-	-	-	-	-	-	-	-	-
2.8929	1134	0.0894	-	-	-	-	-	-	-	-	-
2.8954	1135	0.0724	-	-	-	-	-	-	-	-	-
2.8980	1136	0.052	-	-	-	-	-	-	-	-	-
2.9005	1137	0.0661	-	-	-	-	-	-	-	-	-
2.9031	1138	0.0436	-	-	-	-	-	-	-	-	-
2.9056	1139	0.0499	-	-	-	-	-	-	-	-	-
2.9082	1140	0.0841	-	-	-	-	-	-	-	-	-
2.9107	1141	0.015	-	-	-	-	-	-	-	-	-
2.9133	1142	0.016	-	-	-	-	-	-	-	-	-
2.9158	1143	0.0436	-	-	-	-	-	-	-	-	-
2.9184	1144	0.0148	-	-	-	-	-	-	-	-	-
2.9209	1145	0.0391	-	-	-	-	-	-	-	-	-
2.9235	1146	0.018	-	-	-	-	-	-	-	-	-
2.9260	1147	0.0864	-	-	-	-	-	-	-	-	-
2.9286	1148	0.0626	-	-	-	-	-	-	-	-	-
2.9311	1149	0.062	-	-	-	-	-	-	-	-	-
2.9337	1150	0.0546	-	-	-	-	-	-	-	-	-
2.9362	1151	0.0266	-	-	-	-	-	-	-	-	-
2.9388	1152	0.0698	-	-	-	-	-	-	-	-	-
2.9413	1153	0.2801	-	-	-	-	-	-	-	-	-
2.9439	1154	0.0353	-	-	-	-	-	-	-	-	-
2.9464	1155	0.0549	-	-	-	-	-	-	-	-	-
2.9490	1156	0.0262	-	-	-	-	-	-	-	-	-
2.9515	1157	0.0424	-	-	-	-	-	-	-	-	-
2.9541	1158	0.0253	-	-	-	-	-	-	-	-	-
2.9566	1159	0.0613	-	-	-	-	-	-	-	-	-
2.9592	1160	0.0124	-	-	-	-	-	-	-	-	-
2.9617	1161	0.0148	-	-	-	-	-	-	-	-	-
2.9643	1162	0.0522	-	-	-	-	-	-	-	-	-
2.9668	1163	0.0323	-	-	-	-	-	-	-	-	-
2.9694	1164	0.039	-	-	-	-	-	-	-	-	-
2.9719	1165	0.0582	-	-	-	-	-	-	-	-	-
2.9745	1166	0.0497	-	-	-	-	-	-	-	-	-
2.9770	1167	0.0476	-	-	-	-	-	-	-	-	-
2.9796	1168	0.0586	-	-	-	-	-	-	-	-	-
2.9821	1169	0.0578	-	-	-	-	-	-	-	-	-
2.9847	1170	0.07	-	-	-	-	-	-	-	-	-
2.9872	1171	0.0403	-	-	-	-	-	-	-	-	-
2.9898	1172	0.0802	-	-	-	-	-	-	-	-	-
2.9923	1173	0.0348	-	-	-	-	-	-	-	-	-
2.9949	1174	0.0674	-	-	-	-	-	-	-	-	-
2.9974	1175	0.0505	-	-	-	-	-	-	-	-	-
3.0	1176	0.0	0.5673	0.3056	1.5746	0.1307	0.0215	0.4867	0.9187	0.6600	0.7170
-1	-1	-	-	-	-	-	-	-	0.9187	0.6600	0.7170

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",
}