metadata
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100231
- loss:CachedMultipleNegativesRankingLoss
base_model: google-bert/bert-base-uncased
widget:
- source_sentence: 'query: who ordered the charge of the light brigade'
sentences:
- >-
document: Charge of the Light Brigade The Charge of the Light Brigade
was a charge of British light cavalry led by Lord Cardigan against
Russian forces during the Battle of Balaclava on 25 October 1854 in the
Crimean War. Lord Raglan, overall commander of the British forces, had
intended to send the Light Brigade to prevent the Russians from removing
captured guns from overrun Turkish positions, a task well-suited to
light cavalry.
- >-
document: UNICEF The United Nations International Children's Emergency
Fund was created by the United Nations General Assembly on 11 December
1946, to provide emergency food and healthcare to children in countries
that had been devastated by World War II. The Polish physician Ludwik
Rajchman is widely regarded as the founder of UNICEF and served as its
first chairman from 1946. On Rajchman's suggestion, the American Maurice
Pate was appointed its first executive director, serving from 1947 until
his death in 1965.[5][6] In 1950, UNICEF's mandate was extended to
address the long-term needs of children and women in developing
countries everywhere. In 1953 it became a permanent part of the United
Nations System, and the words "international" and "emergency" were
dropped from the organization's name, making it simply the United
Nations Children's Fund, retaining the original acronym, "UNICEF".[3]
- >-
document: Marcus Jordan Marcus James Jordan (born December 24, 1990) is
an American former college basketball player who played for the UCF
Knights men's basketball team of Conference USA.[1] He is the son of
retired Hall of Fame basketball player Michael Jordan.
- source_sentence: 'query: what part of the cow is the rib roast'
sentences:
- >-
document: Standing rib roast A standing rib roast, also known as prime
rib, is a cut of beef from the primal rib, one of the nine primal cuts
of beef. While the entire rib section comprises ribs six through 12, a
standing rib roast may contain anywhere from two to seven ribs.
- >-
document: Blaine Anderson Kurt begins to mend their relationship in
"Thanksgiving", just before New Directions loses at Sectionals to the
Warblers, and they spend Christmas together in New York City.[29][30]
Though he and Kurt continue to be on good terms, Blaine finds himself
developing a crush on his best friend, Sam, which he knows will come to
nothing as he knows Sam is not gay; the two of them team up to find
evidence that the Warblers cheated at Sectionals, which means New
Directions will be competing at Regionals. He ends up going to the Sadie
Hawkins dance with Tina Cohen-Chang (Jenna Ushkowitz), who has developed
a crush on him, but as friends only.[31] When Kurt comes to Lima for the
wedding of glee club director Will (Matthew Morrison) and Emma (Jayma
Mays)—which Emma flees—he and Blaine make out beforehand, and sleep
together afterward, though they do not resume a permanent
relationship.[32]
- "document: Soviet Union The Soviet Union (Russian: Сове́тский Сою́з, tr. Sovétsky Soyúz, IPA:\_[sɐˈvʲɛt͡skʲɪj sɐˈjus]\_(\_listen)), officially the Union of Soviet Socialist Republics (Russian: Сою́з Сове́тских Социалисти́ческих Респу́блик, tr. Soyúz Sovétskikh Sotsialistícheskikh Respúblik, IPA:\_[sɐˈjus sɐˈvʲɛtskʲɪx sətsɨəlʲɪsˈtʲitɕɪskʲɪx rʲɪˈspublʲɪk]\_(\_listen)), abbreviated as the USSR (Russian: СССР, tr. SSSR), was a socialist state in Eurasia that existed from 1922 to 1991. Nominally a union of multiple national Soviet republics,[a] its government and economy were highly centralized. The country was a one-party state, governed by the Communist Party with Moscow as its capital in its largest republic, the Russian Soviet Federative Socialist Republic. The Russian nation had constitutionally equal status among the many nations of the union but exerted de facto dominance in various respects.[7] Other major urban centres were Leningrad, Kiev, Minsk, Alma-Ata and Novosibirsk. The Soviet Union was one of the five recognized nuclear weapons states and possessed the largest stockpile of weapons of mass destruction.[8] It was a founding permanent member of the United Nations Security Council, as well as a member of the Organization for Security and Co-operation in Europe (OSCE) and the leading member of the Council for Mutual Economic Assistance (CMEA) and the Warsaw Pact."
- source_sentence: 'query: what is the current big bang theory season'
sentences:
- >-
document: Byzantine army From the seventh to the 12th centuries, the
Byzantine army was among the most powerful and effective military forces
in the world – neither Middle Ages Europe nor (following its early
successes) the fracturing Caliphate could match the strategies and the
efficiency of the Byzantine army. Restricted to a largely defensive role
in the 7th to mid-9th centuries, the Byzantines developed the
theme-system to counter the more powerful Caliphate. From the mid-9th
century, however, they gradually went on the offensive, culminating in
the great conquests of the 10th century under a series of
soldier-emperors such as Nikephoros II Phokas, John Tzimiskes and Basil
II. The army they led was less reliant on the militia of the themes; it
was by now a largely professional force, with a strong and well-drilled
infantry at its core and augmented by a revived heavy cavalry arm. With
one of the most powerful economies in the world at the time, the Empire
had the resources to put to the field a powerful host when needed, in
order to reclaim its long-lost territories.
- >-
document: The Big Bang Theory The Big Bang Theory is an American
television sitcom created by Chuck Lorre and Bill Prady, both of whom
serve as executive producers on the series, along with Steven Molaro.
All three also serve as head writers. The show premiered on CBS on
September 24, 2007.[3] The series' tenth season premiered on September
19, 2016.[4] In March 2017, the series was renewed for two additional
seasons, bringing its total to twelve, and running through the 2018–19
television season. The eleventh season is set to premiere on September
25, 2017.[5]
- >-
document: 2016 NCAA Division I Softball Tournament The 2016 NCAA
Division I Softball Tournament was held from May 20 through June 8, 2016
as the final part of the 2016 NCAA Division I softball season. The 64
NCAA Division I college softball teams were to be selected out of an
eligible 293 teams on May 15, 2016. Thirty-two teams were awarded an
automatic bid as champions of their conference, and thirty-two teams
were selected at-large by the NCAA Division I softball selection
committee. The tournament culminated with eight teams playing in the
2016 Women's College World Series at ASA Hall of Fame Stadium in
Oklahoma City in which the Oklahoma Sooners were crowned the champions.
- source_sentence: 'query: what happened to tates mom on days of our lives'
sentences:
- >-
document: Paige O'Hara Donna Paige Helmintoller, better known as Paige
O'Hara (born May 10, 1956),[1] is an American actress, voice actress,
singer and painter. O'Hara began her career as a Broadway actress in
1983 when she portrayed Ellie May Chipley in the musical Showboat. In
1991, she made her motion picture debut in Disney's Beauty and the
Beast, in which she voiced the film's heroine, Belle. Following the
critical and commercial success of Beauty and the Beast, O'Hara reprised
her role as Belle in the film's two direct-to-video follow-ups, Beauty
and the Beast: The Enchanted Christmas and Belle's Magical World.
- >-
document: M. Shadows Matthew Charles Sanders (born July 31, 1981),
better known as M. Shadows, is an American singer, songwriter, and
musician. He is best known as the lead vocalist, songwriter, and a
founding member of the American heavy metal band Avenged Sevenfold. In
2017, he was voted 3rd in the list of Top 25 Greatest Modern Frontmen by
Ultimate Guitar.[1]
- >-
document: Theresa Donovan In July 2013, Jeannie returns to Salem, this
time going by her middle name, Theresa. Initially, she strikes up a
connection with resident bad boy JJ Deveraux (Casey Moss) while trying
to secure some pot.[28] During a confrontation with JJ and his mother
Jennifer Horton (Melissa Reeves) in her office, her aunt Kayla confirms
that Theresa is in fact Jeannie and that Jen promised to hire her as her
assistant, a promise she reluctantly agrees to. Kayla reminds Theresa it
is her last chance at a fresh start.[29] Theresa also strikes up a bad
first impression with Jennifer's daughter Abigail Deveraux (Kate Mansi)
when Abigail smells pot on Theresa in her mother's office.[30] To
continue to battle against Jennifer, she teams up with Anne Milbauer
(Meredith Scott Lynn) in hopes of exacting her perfect revenge. In a
ploy, Theresa reveals her intentions to hopefully woo Dr. Daniel Jonas
(Shawn Christian). After sleeping with JJ, Theresa overdoses on
marijuana and GHB. Upon hearing of their daughter's overdose and
continuing problems, Shane and Kimberly return to town in the hopes of
handling their daughter's problem, together. After believing that
Theresa has a handle on her addictions, Shane and Kimberly leave town
together. Theresa then teams up with hospital co-worker Anne Milbauer
(Meredith Scott Lynn) to conspire against Jennifer, using Daniel as a
way to hurt their relationship. In early 2014, following a Narcotics
Anonymous (NA) meeting, she begins a sexual and drugged-fused
relationship with Brady Black (Eric Martsolf). In 2015, after it is
found that Kristen DiMera (Eileen Davidson) stole Theresa's embryo and
carried it to term, Brady and Melanie Jonas return her son, Christopher,
to her and Brady, and the pair rename him Tate. When Theresa moves into
the Kiriakis mansion, tensions arise between her and Victor. She
eventually expresses her interest in purchasing Basic Black and running
it as her own fashion company, with financial backing from Maggie Horton
(Suzanne Rogers). In the hopes of finding the right partner, she teams
up with Kate Roberts (Lauren Koslow) and Nicole Walker (Arianne Zucker)
to achieve the goal of purchasing Basic Black, with Kate and Nicole's
business background and her own interest in fashion design. As she and
Brady share several instances of rekindling their romance, she is kicked
out of the mansion by Victor; as a result, Brady quits Titan and moves
in with Theresa and Tate, in their own penthouse.
- source_sentence: 'query: where does the last name francisco come from'
sentences:
- >-
document: Francisco Francisco is the Spanish and Portuguese form of the
masculine given name Franciscus (corresponding to English Francis).
- >-
document: Book of Esther The Book of Esther, also known in Hebrew as
"the Scroll" (Megillah), is a book in the third section (Ketuvim,
"Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian
Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew
Bible. It relates the story of a Hebrew woman in Persia, born as
Hadassah but known as Esther, who becomes queen of Persia and thwarts a
genocide of her people. The story forms the core of the Jewish festival
of Purim, during which it is read aloud twice: once in the evening and
again the following morning. The books of Esther and Song of Songs are
the only books in the Hebrew Bible that do not explicitly mention
God.[2]
- >-
document: Times Square Times Square is a major commercial intersection,
tourist destination, entertainment center and neighborhood in the
Midtown Manhattan section of New York City at the junction of Broadway
and Seventh Avenue. It stretches from West 42nd to West 47th Streets.[1]
Brightly adorned with billboards and advertisements, Times Square is
sometimes referred to as "The Crossroads of the World",[2] "The Center
of the Universe",[3] "the heart of The Great White Way",[4][5][6] and
the "heart of the world".[7] One of the world's busiest pedestrian
areas,[8] it is also the hub of the Broadway Theater District[9] and a
major center of the world's entertainment industry.[10] Times Square is
one of the world's most visited tourist attractions, drawing an
estimated 50 million visitors annually.[11] Approximately 330,000 people
pass through Times Square daily,[12] many of them tourists,[13] while
over 460,000 pedestrians walk through Times Square on its busiest
days.[7]
datasets:
- sentence-transformers/natural-questions
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
co2_eq_emissions:
emissions: 103.95223177174714
energy_consumed: 0.2674342601060636
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.776
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: BERT base trained on Natural Questions pairs
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.24
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.36
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.42
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.24
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.076
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08833333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1733333333333333
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.205
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.31066666666666665
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.23668411144897733
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.32507936507936497
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.18064440317511302
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.58
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.74
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.84
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.58
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.46
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.43200000000000005
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.4
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.060187987174836206
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10977424825151455
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.16707520990044147
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.24597415193723152
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4733134773883028
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6808571428571429
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.33434372400711937
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.52
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.66
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.52
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.5
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.63
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.67
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.75
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6250288470609421
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5971904761904763
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5841699073691555
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.14
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.3
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.4
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.44
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.064
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07933333333333334
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.16352380952380952
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.22846031746031745
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.27512698412698416
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2070483011862227
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.23955555555555555
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.17184447175268844
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.54
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.62
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.66
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.78
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.54
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.25333333333333335
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17199999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10799999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.27
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.38
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.43
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.54
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4758825161205549
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5948571428571429
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.403633154924419
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.2
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.62
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.13333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.096
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06200000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.48
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.62
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3929333444965005
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3225793650793651
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3345903944684922
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.52
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.22800000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.16999999999999996
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.023393732410294653
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.04028202721825723
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.05292320850853196
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.06512766188420571
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.21330057691798984
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.40985714285714286
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.07333772175450959
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.42
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.64
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.42
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.128
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.4
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.56
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.62
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.67
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5390417243338262
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5118333333333334
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5014983526115104
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.68
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.94
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.94
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.68
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3533333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.23599999999999993
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.12599999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6106666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8486666666666668
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9093333333333333
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9266666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8205618979026005
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7846666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.786847374847375
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17600000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.132
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.05566666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.14466666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1806666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.27066666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2517704665914677
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.36450000000000005
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.20084375671559634
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.14
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.58
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.14
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11600000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.14
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.58
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.76
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4417985537040473
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3413253968253968
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3506916603232609
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.38
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.54
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.56
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.19333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.128
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.345
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.51
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.545
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.59
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.48570181290684433
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.46035714285714285
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4539281050639794
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.5510204081632653
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7959183673469388
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9183673469387755
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9795918367346939
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5510204081632653
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.45578231292516996
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4326530612244897
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.37755102040816335
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.040936400203138934
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10543098224373823
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15289328979061165
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2540307547275961
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4244756661687274
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.689310009718173
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3161855102539037
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.38238618524332807
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5627629513343799
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6229513343799058
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7107378335949763
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38238618524332807
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24634222919937204
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19266562009419153
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13935007849293563
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.21642447075294383
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3512059795310759
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.40164246351230015
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.48294304251353987
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.42981086894053877
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.48630528768283876
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.36096604132824023
name: Cosine Map@100
BERT base trained on Natural Questions pairs
This is a sentence-transformers model finetuned from google-bert/bert-base-uncased on the natural-questions dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
This model was trained using the script from the Training with Prompts Sentence Transformers documentation.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: google-bert/bert-base-uncased
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
- Training Dataset:
- Language: en
- License: apache-2.0
Model Sources
- Documentation: Sentence Transformers Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sentence Transformers on Hugging Face
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': False})
)
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("tomaarsen/bert-base-nq-prompts-exclude-pooling-prompts")
# Run inference
sentences = [
'query: where does the last name francisco come from',
'document: Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
'document: Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Datasets:
NanoClimateFEVER,NanoDBPedia,NanoFEVER,NanoFiQA2018,NanoHotpotQA,NanoMSMARCO,NanoNFCorpus,NanoNQ,NanoQuoraRetrieval,NanoSCIDOCS,NanoArguAna,NanoSciFactandNanoTouche2020 - Evaluated with
InformationRetrievalEvaluator
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cosine_accuracy@1 | 0.24 | 0.58 | 0.52 | 0.14 | 0.54 | 0.2 | 0.32 | 0.42 | 0.68 | 0.26 | 0.14 | 0.38 | 0.551 |
| cosine_accuracy@3 | 0.36 | 0.74 | 0.66 | 0.3 | 0.62 | 0.4 | 0.46 | 0.58 | 0.9 | 0.46 | 0.5 | 0.54 | 0.7959 |
| cosine_accuracy@5 | 0.42 | 0.84 | 0.68 | 0.4 | 0.66 | 0.48 | 0.52 | 0.64 | 0.94 | 0.46 | 0.58 | 0.56 | 0.9184 |
| cosine_accuracy@10 | 0.6 | 0.9 | 0.76 | 0.44 | 0.78 | 0.62 | 0.58 | 0.7 | 0.94 | 0.58 | 0.76 | 0.6 | 0.9796 |
| cosine_precision@1 | 0.24 | 0.58 | 0.52 | 0.14 | 0.54 | 0.2 | 0.32 | 0.42 | 0.68 | 0.26 | 0.14 | 0.38 | 0.551 |
| cosine_precision@3 | 0.1467 | 0.46 | 0.22 | 0.1133 | 0.2533 | 0.1333 | 0.28 | 0.1933 | 0.3533 | 0.2333 | 0.1667 | 0.1933 | 0.4558 |
| cosine_precision@5 | 0.108 | 0.432 | 0.144 | 0.108 | 0.172 | 0.096 | 0.228 | 0.128 | 0.236 | 0.176 | 0.116 | 0.128 | 0.4327 |
| cosine_precision@10 | 0.076 | 0.4 | 0.08 | 0.064 | 0.108 | 0.062 | 0.17 | 0.07 | 0.126 | 0.132 | 0.076 | 0.07 | 0.3776 |
| cosine_recall@1 | 0.0883 | 0.0602 | 0.5 | 0.0793 | 0.27 | 0.2 | 0.0234 | 0.4 | 0.6107 | 0.0557 | 0.14 | 0.345 | 0.0409 |
| cosine_recall@3 | 0.1733 | 0.1098 | 0.63 | 0.1635 | 0.38 | 0.4 | 0.0403 | 0.56 | 0.8487 | 0.1447 | 0.5 | 0.51 | 0.1054 |
| cosine_recall@5 | 0.205 | 0.1671 | 0.67 | 0.2285 | 0.43 | 0.48 | 0.0529 | 0.62 | 0.9093 | 0.1807 | 0.58 | 0.545 | 0.1529 |
| cosine_recall@10 | 0.3107 | 0.246 | 0.75 | 0.2751 | 0.54 | 0.62 | 0.0651 | 0.67 | 0.9267 | 0.2707 | 0.76 | 0.59 | 0.254 |
| cosine_ndcg@10 | 0.2367 | 0.4733 | 0.625 | 0.207 | 0.4759 | 0.3929 | 0.2133 | 0.539 | 0.8206 | 0.2518 | 0.4418 | 0.4857 | 0.4245 |
| cosine_mrr@10 | 0.3251 | 0.6809 | 0.5972 | 0.2396 | 0.5949 | 0.3226 | 0.4099 | 0.5118 | 0.7847 | 0.3645 | 0.3413 | 0.4604 | 0.6893 |
| cosine_map@100 | 0.1806 | 0.3343 | 0.5842 | 0.1718 | 0.4036 | 0.3346 | 0.0733 | 0.5015 | 0.7868 | 0.2008 | 0.3507 | 0.4539 | 0.3162 |
Nano BEIR
- Dataset:
NanoBEIR_mean - Evaluated with
NanoBEIREvaluator
| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.3824 |
| cosine_accuracy@3 | 0.5628 |
| cosine_accuracy@5 | 0.623 |
| cosine_accuracy@10 | 0.7107 |
| cosine_precision@1 | 0.3824 |
| cosine_precision@3 | 0.2463 |
| cosine_precision@5 | 0.1927 |
| cosine_precision@10 | 0.1394 |
| cosine_recall@1 | 0.2164 |
| cosine_recall@3 | 0.3512 |
| cosine_recall@5 | 0.4016 |
| cosine_recall@10 | 0.4829 |
| cosine_ndcg@10 | 0.4298 |
| cosine_mrr@10 | 0.4863 |
| cosine_map@100 | 0.361 |
Training Details
Training Dataset
natural-questions
- Dataset: natural-questions at f9e894e
- Size: 100,231 training samples
- Columns:
queryandanswer - Approximate statistics based on the first 1000 samples:
query answer type string string details - min: 12 tokens
- mean: 13.74 tokens
- max: 26 tokens
- min: 17 tokens
- mean: 139.2 tokens
- max: 510 tokens
- Samples:
query answer query: who is required to report according to the hmdadocument: Home Mortgage Disclosure Act US financial institutions must report HMDA data to their regulator if they meet certain criteria, such as having assets above a specific threshold. The criteria is different for depository and non-depository institutions and are available on the FFIEC website.[4] In 2012, there were 7,400 institutions that reported a total of 18.7 million HMDA records.[5]query: what is the definition of endoplasmic reticulum in biologydocument: Endoplasmic reticulum The endoplasmic reticulum (ER) is a type of organelle in eukaryotic cells that forms an interconnected network of flattened, membrane-enclosed sacs or tube-like structures known as cisternae. The membranes of the ER are continuous with the outer nuclear membrane. The endoplasmic reticulum occurs in most types of eukaryotic cells, but is absent from red blood cells and spermatozoa. There are two types of endoplasmic reticulum: rough and smooth. The outer (cytosolic) face of the rough endoplasmic reticulum is studded with ribosomes that are the sites of protein synthesis. The rough endoplasmic reticulum is especially prominent in cells such as hepatocytes. The smooth endoplasmic reticulum lacks ribosomes and functions in lipid manufacture and metabolism, the production of steroid hormones, and detoxification.[1] The smooth ER is especially abundant in mammalian liver and gonad cells. The lacy membranes of the endoplasmic reticulum were first seen in 1945 u...query: what does the ski mean in polish namesdocument: Polish name Since the High Middle Ages, Polish-sounding surnames ending with the masculine -ski suffix, including -cki and -dzki, and the corresponding feminine suffix -ska/-cka/-dzka were associated with the nobility (Polish szlachta), which alone, in the early years, had such suffix distinctions.[1] They are widely popular today. - Loss:
CachedMultipleNegativesRankingLosswith these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Evaluation Dataset
natural-questions
- Dataset: natural-questions at f9e894e
- Size: 100,231 evaluation samples
- Columns:
queryandanswer - Approximate statistics based on the first 1000 samples:
query answer type string string details - min: 12 tokens
- mean: 13.78 tokens
- max: 24 tokens
- min: 13 tokens
- mean: 137.63 tokens
- max: 512 tokens
- Samples:
query answer query: difference between russian blue and british blue catdocument: Russian Blue The coat is known as a "double coat", with the undercoat being soft, downy and equal in length to the guard hairs, which are an even blue with silver tips. However, the tail may have a few very dull, almost unnoticeable stripes. The coat is described as thick, plush and soft to the touch. The feeling is softer than the softest silk. The silver tips give the coat a shimmering appearance. Its eyes are almost always a dark and vivid green. Any white patches of fur or yellow eyes in adulthood are seen as flaws in show cats.[3] Russian Blues should not be confused with British Blues (which are not a distinct breed, but rather a British Shorthair with a blue coat as the British Shorthair breed itself comes in a wide variety of colors and patterns), nor the Chartreux or Korat which are two other naturally occurring breeds of blue cats, although they have similar traits.query: who played the little girl on mrs doubtfiredocument: Mara Wilson Mara Elizabeth Wilson[2] (born July 24, 1987) is an American writer and former child actress. She is known for playing Natalie Hillard in Mrs. Doubtfire (1993), Susan Walker in Miracle on 34th Street (1994), Matilda Wormwood in Matilda (1996) and Lily Stone in Thomas and the Magic Railroad (2000). Since retiring from film acting, Wilson has focused on writing.query: what year did the movie the sound of music come outdocument: The Sound of Music (film) The film was released on March 2, 1965 in the United States, initially as a limited roadshow theatrical release. Although critical response to the film was widely mixed, the film was a major commercial success, becoming the number one box office movie after four weeks, and the highest-grossing film of 1965. By November 1966, The Sound of Music had become the highest-grossing film of all-time—surpassing Gone with the Wind—and held that distinction for five years. The film was just as popular throughout the world, breaking previous box-office records in twenty-nine countries. Following an initial theatrical release that lasted four and a half years, and two successful re-releases, the film sold 283 million admissions worldwide and earned a total worldwide gross of $286,000,000. - Loss:
CachedMultipleNegativesRankingLosswith these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 256per_device_eval_batch_size: 256learning_rate: 2e-05num_train_epochs: 1warmup_ratio: 0.1seed: 12bf16: Trueprompts: {'query': 'query: ', 'answer': 'document: '}batch_sampler: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 256per_device_eval_batch_size: 256per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 2e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1.0num_train_epochs: 1max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.1warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 12data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Truefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Falsehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseinclude_for_metrics: []eval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseuse_liger_kernel: Falseeval_use_gather_object: Falseaverage_tokens_across_devices: Falseprompts: {'query': 'query: ', 'answer': 'document: '}batch_sampler: no_duplicatesmulti_dataset_batch_sampler: proportional
Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | - | - | 0.1106 | 0.2356 | 0.1544 | 0.0809 | 0.2551 | 0.2289 | 0.0889 | 0.0875 | 0.7699 | 0.1312 | 0.2403 | 0.1457 | 0.1601 | 0.2068 |
| 0.0026 | 1 | 3.0398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0129 | 5 | 3.0734 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0258 | 10 | 2.8416 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0387 | 15 | 2.3639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0515 | 20 | 1.8224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0644 | 25 | 1.4264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0773 | 30 | 1.1915 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0902 | 35 | 1.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1031 | 40 | 0.8502 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1160 | 45 | 0.6719 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1289 | 50 | 0.517 | 0.4561 | 0.1696 | 0.4226 | 0.5939 | 0.1618 | 0.4108 | 0.3236 | 0.1649 | 0.4491 | 0.8389 | 0.2458 | 0.4394 | 0.4473 | 0.3660 | 0.3872 |
| 0.1418 | 55 | 0.4655 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1546 | 60 | 0.3677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1675 | 65 | 0.3677 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1804 | 70 | 0.2745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1933 | 75 | 0.3488 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2062 | 80 | 0.3043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2191 | 85 | 0.2866 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2320 | 90 | 0.2697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2448 | 95 | 0.2543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2577 | 100 | 0.2702 | 0.2429 | 0.2066 | 0.4474 | 0.6078 | 0.1928 | 0.4406 | 0.3904 | 0.2059 | 0.5030 | 0.8272 | 0.2647 | 0.4627 | 0.4723 | 0.3897 | 0.4162 |
| 0.2706 | 105 | 0.2493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2835 | 110 | 0.2636 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2964 | 115 | 0.2574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3093 | 120 | 0.2447 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3222 | 125 | 0.2639 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3351 | 130 | 0.2073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3479 | 135 | 0.2185 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3608 | 140 | 0.2413 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3737 | 145 | 0.2167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3866 | 150 | 0.1871 | 0.2020 | 0.2084 | 0.4588 | 0.6261 | 0.1931 | 0.4470 | 0.3937 | 0.2068 | 0.5154 | 0.8236 | 0.2570 | 0.4578 | 0.4640 | 0.3999 | 0.4194 |
| 0.3995 | 155 | 0.2143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4124 | 160 | 0.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4253 | 165 | 0.1852 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4381 | 170 | 0.1932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4510 | 175 | 0.1853 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4639 | 180 | 0.1612 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4768 | 185 | 0.1665 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4897 | 190 | 0.2422 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5026 | 195 | 0.1948 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5155 | 200 | 0.2277 | 0.1861 | 0.2178 | 0.4567 | 0.6168 | 0.2158 | 0.4684 | 0.3760 | 0.2088 | 0.5388 | 0.8247 | 0.2632 | 0.4582 | 0.4680 | 0.4249 | 0.4260 |
| 0.5284 | 205 | 0.2216 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5412 | 210 | 0.189 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5541 | 215 | 0.2094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5670 | 220 | 0.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5799 | 225 | 0.2145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5928 | 230 | 0.2033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6057 | 235 | 0.2355 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6186 | 240 | 0.2044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6314 | 245 | 0.2201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6443 | 250 | 0.1841 | 0.1760 | 0.2397 | 0.4601 | 0.6282 | 0.2002 | 0.4693 | 0.3899 | 0.2124 | 0.5446 | 0.8262 | 0.2568 | 0.4581 | 0.4835 | 0.4355 | 0.4311 |
| 0.6572 | 255 | 0.2144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6701 | 260 | 0.2123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6830 | 265 | 0.1824 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6959 | 270 | 0.1673 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7088 | 275 | 0.1663 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7216 | 280 | 0.1988 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7345 | 285 | 0.1727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7474 | 290 | 0.1851 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7603 | 295 | 0.2147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7732 | 300 | 0.1697 | 0.1688 | 0.2342 | 0.4741 | 0.6356 | 0.2060 | 0.4752 | 0.3947 | 0.2153 | 0.5443 | 0.8192 | 0.2547 | 0.4339 | 0.4818 | 0.4310 | 0.4308 |
| 0.7861 | 305 | 0.187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7990 | 310 | 0.1515 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8119 | 315 | 0.1703 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8247 | 320 | 0.1827 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8376 | 325 | 0.1881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8505 | 330 | 0.1792 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8634 | 335 | 0.1954 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8763 | 340 | 0.1772 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8892 | 345 | 0.1694 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9021 | 350 | 0.1727 | 0.1622 | 0.2394 | 0.4702 | 0.6247 | 0.2123 | 0.4772 | 0.3884 | 0.2152 | 0.5356 | 0.8199 | 0.2527 | 0.4351 | 0.4853 | 0.4245 | 0.4293 |
| 0.9149 | 355 | 0.1794 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9278 | 360 | 0.1816 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9407 | 365 | 0.1708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9536 | 370 | 0.202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9665 | 375 | 0.1854 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9794 | 380 | 0.1958 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9923 | 385 | 0.1561 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 388 | - | - | 0.2367 | 0.4733 | 0.6250 | 0.2070 | 0.4759 | 0.3929 | 0.2133 | 0.5390 | 0.8206 | 0.2518 | 0.4418 | 0.4857 | 0.4245 | 0.4298 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Energy Consumed: 0.267 kWh
- Carbon Emitted: 0.104 kg of CO2
- Hours Used: 0.776 hours
Training Hardware
- On Cloud: No
- GPU Model: 1 x NVIDIA GeForce RTX 3090
- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
- RAM Size: 31.78 GB
Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.3.0.dev0
- Transformers: 4.46.2
- PyTorch: 2.5.0+cu121
- Accelerate: 1.0.0
- Datasets: 2.20.0
- Tokenizers: 0.20.3
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",
}
CachedMultipleNegativesRankingLoss
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}