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---
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: 104.37144965279943
energy_consumed: 0.2685127672427706
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: 0.777
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: 0.28
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.52
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.136
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1433333333333333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22833333333333336
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.27566666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.32066666666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2878211790555906
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3808809523809524
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24287067974898857
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.62
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.78
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.62
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4866666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4360000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.40800000000000003
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06535268004155241
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1216287887586731
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15858900059934192
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.26908746011644075
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4934348491212812
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7205238095238095
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3576283593370125
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: 0.68
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.78
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.52
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22666666666666668
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.136
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08199999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.51
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.65
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.65
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.77
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.633022394505949
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5984682539682539
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5919427742787183
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.2
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.32
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.38
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.44
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.12666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11200000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.10307936507936509
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.16074603174603175
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21974603174603177
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.28174603174603174
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.22658852595790738
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2786031746031746
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1922135585498111
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: 0.68
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.54
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2733333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19199999999999995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.27
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.41
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.48
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.57
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5034154059201228
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6131269841269841
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4305742392442027
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.22
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.72
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15999999999999998
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07200000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.22
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.48
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.54
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.72
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.45705356713588047
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.375611111111111
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3857851144021
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.36
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.36
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.204
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.174
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.024407479336886202
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07046144749468919
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.07850608191050495
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.09381721892145363
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.22797188414421854
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4163888888888889
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08839180108803671
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.44
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: 0.72
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.44
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: 0.07400000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.43
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: 0.7
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5663654982326838
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5321904761904762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.528801111695453
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.8
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.92
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.96
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.98
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.35999999999999993
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.23199999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13199999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7040000000000001
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8686666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9059999999999999
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9666666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8785310313702681
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8620000000000002
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8450119047619047
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.64
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.196
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.15
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06766666666666668
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.14566666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.20266666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3096666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.28426149306595105
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4186904761904761
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2157083483971642
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.62
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.86
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.20666666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08599999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.62
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.72
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.86
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5152276284094561
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.404968253968254
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.41307511335008557
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: 0.124
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
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: 0.54
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.585
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4801616550400968
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4569999999999999
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.44944174627586286
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.46938775510204084
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7755102040816326
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8979591836734694
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9795918367346939
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46938775510204084
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.44217687074829937
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.44081632653061226
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.37959183673469393
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.036426985042621235
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10277533850047522
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1638463922070553
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2589397020790032
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4199564938511377
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6389455782312925
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3257573592189866
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.4099529042386185
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5935007849293564
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6521507064364207
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7399686028257457
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4099529042386185
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2576033490319205
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1991397174254317
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14535321821036107
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.23840511611541731
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3790983287051181
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.42730929536894363
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5158146471433024
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4595239696777341
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5151844583987442
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.389784777719102
name: Cosine Map@100
---
# BERT base trained on Natural Questions pairs
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/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](https://sbert.net/examples/training/prompts/README.html) Sentence Transformers documentation.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### 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': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/bert-base-nq-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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1 | 0.28 | 0.62 | 0.52 | 0.2 | 0.54 | 0.22 | 0.36 | 0.44 | 0.8 | 0.32 | 0.18 | 0.38 | 0.4694 |
| cosine_accuracy@3 | 0.42 | 0.78 | 0.68 | 0.32 | 0.68 | 0.48 | 0.44 | 0.58 | 0.92 | 0.48 | 0.62 | 0.54 | 0.7755 |
| cosine_accuracy@5 | 0.52 | 0.82 | 0.68 | 0.38 | 0.72 | 0.54 | 0.5 | 0.64 | 0.96 | 0.54 | 0.72 | 0.56 | 0.898 |
| cosine_accuracy@10 | 0.6 | 0.92 | 0.78 | 0.44 | 0.8 | 0.72 | 0.58 | 0.72 | 0.98 | 0.64 | 0.86 | 0.6 | 0.9796 |
| cosine_precision@1 | 0.28 | 0.62 | 0.52 | 0.2 | 0.54 | 0.22 | 0.36 | 0.44 | 0.8 | 0.32 | 0.18 | 0.38 | 0.4694 |
| cosine_precision@3 | 0.1733 | 0.4867 | 0.2267 | 0.1267 | 0.2733 | 0.16 | 0.2733 | 0.1933 | 0.36 | 0.2333 | 0.2067 | 0.1933 | 0.4422 |
| cosine_precision@5 | 0.136 | 0.436 | 0.136 | 0.112 | 0.192 | 0.108 | 0.204 | 0.128 | 0.232 | 0.196 | 0.144 | 0.124 | 0.4408 |
| cosine_precision@10 | 0.08 | 0.408 | 0.082 | 0.07 | 0.114 | 0.072 | 0.174 | 0.074 | 0.132 | 0.15 | 0.086 | 0.068 | 0.3796 |
| cosine_recall@1 | 0.1433 | 0.0654 | 0.51 | 0.1031 | 0.27 | 0.22 | 0.0244 | 0.43 | 0.704 | 0.0677 | 0.18 | 0.345 | 0.0364 |
| cosine_recall@3 | 0.2283 | 0.1216 | 0.65 | 0.1607 | 0.41 | 0.48 | 0.0705 | 0.56 | 0.8687 | 0.1457 | 0.62 | 0.51 | 0.1028 |
| cosine_recall@5 | 0.2757 | 0.1586 | 0.65 | 0.2197 | 0.48 | 0.54 | 0.0785 | 0.62 | 0.906 | 0.2027 | 0.72 | 0.54 | 0.1638 |
| cosine_recall@10 | 0.3207 | 0.2691 | 0.77 | 0.2817 | 0.57 | 0.72 | 0.0938 | 0.7 | 0.9667 | 0.3097 | 0.86 | 0.585 | 0.2589 |
| **cosine_ndcg@10** | **0.2878** | **0.4934** | **0.633** | **0.2266** | **0.5034** | **0.4571** | **0.228** | **0.5664** | **0.8785** | **0.2843** | **0.5152** | **0.4802** | **0.42** |
| cosine_mrr@10 | 0.3809 | 0.7205 | 0.5985 | 0.2786 | 0.6131 | 0.3756 | 0.4164 | 0.5322 | 0.862 | 0.4187 | 0.405 | 0.457 | 0.6389 |
| cosine_map@100 | 0.2429 | 0.3576 | 0.5919 | 0.1922 | 0.4306 | 0.3858 | 0.0884 | 0.5288 | 0.845 | 0.2157 | 0.4131 | 0.4494 | 0.3258 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.41 |
| cosine_accuracy@3 | 0.5935 |
| cosine_accuracy@5 | 0.6522 |
| cosine_accuracy@10 | 0.74 |
| cosine_precision@1 | 0.41 |
| cosine_precision@3 | 0.2576 |
| cosine_precision@5 | 0.1991 |
| cosine_precision@10 | 0.1454 |
| cosine_recall@1 | 0.2384 |
| cosine_recall@3 | 0.3791 |
| cosine_recall@5 | 0.4273 |
| cosine_recall@10 | 0.5158 |
| **cosine_ndcg@10** | **0.4595** |
| cosine_mrr@10 | 0.5152 |
| cosine_map@100 | 0.3898 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 100,231 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 13.74 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 139.2 tokens</li><li>max: 510 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>query: who is required to report according to the hmda</code> | <code>document: 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]</code> |
| <code>query: what is the definition of endoplasmic reticulum in biology</code> | <code>document: 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...</code> |
| <code>query: what does the ski mean in polish names</code> | <code>document: 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.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 100,231 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 12 tokens</li><li>mean: 13.78 tokens</li><li>max: 24 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 137.63 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>query: difference between russian blue and british blue cat</code> | <code>document: 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.</code> |
| <code>query: who played the little girl on mrs doubtfire</code> | <code>document: 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.</code> |
| <code>query: what year did the movie the sound of music come out</code> | <code>document: 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.</code> |
* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `prompts`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `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`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `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`: 12
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `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`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### 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 | - | - | 0.1042 | 0.1641 | 0.1239 | 0.0397 | 0.2320 | 0.1682 | 0.0526 | 0.0678 | 0.7440 | 0.1153 | 0.2443 | 0.1516 | 0.1010 | 0.1776 |
| 0.0026 | 1 | 3.2174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0129 | 5 | 3.2181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0258 | 10 | 2.9101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0387 | 15 | 2.2308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0515 | 20 | 1.5687 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0644 | 25 | 1.1955 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0773 | 30 | 0.9679 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0902 | 35 | 0.787 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1031 | 40 | 0.6266 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1160 | 45 | 0.4877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1289 | 50 | 0.344 | 0.3217 | 0.2374 | 0.4663 | 0.6383 | 0.2397 | 0.4848 | 0.4183 | 0.2096 | 0.4839 | 0.8519 | 0.2619 | 0.4823 | 0.4781 | 0.4308 | 0.4372 |
| 0.1418 | 55 | 0.3294 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1546 | 60 | 0.2493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1675 | 65 | 0.257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1804 | 70 | 0.1839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1933 | 75 | 0.2339 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2062 | 80 | 0.2095 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2191 | 85 | 0.2052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2320 | 90 | 0.199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2448 | 95 | 0.1867 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2577 | 100 | 0.1959 | 0.1771 | 0.2796 | 0.4858 | 0.6150 | 0.2331 | 0.4745 | 0.4345 | 0.2158 | 0.5154 | 0.8756 | 0.2827 | 0.5131 | 0.4839 | 0.4315 | 0.4493 |
| 0.2706 | 105 | 0.1759 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2835 | 110 | 0.1727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2964 | 115 | 0.1773 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3093 | 120 | 0.1708 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3222 | 125 | 0.1881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3351 | 130 | 0.1465 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3479 | 135 | 0.1583 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3608 | 140 | 0.1658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3737 | 145 | 0.1547 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3866 | 150 | 0.1262 | 0.1482 | 0.2755 | 0.4955 | 0.6403 | 0.2358 | 0.4871 | 0.4548 | 0.2329 | 0.5372 | 0.8873 | 0.2821 | 0.5173 | 0.4658 | 0.4217 | 0.4564 |
| 0.3995 | 155 | 0.1522 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4124 | 160 | 0.1486 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4253 | 165 | 0.1277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4381 | 170 | 0.1491 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4510 | 175 | 0.1308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4639 | 180 | 0.102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4768 | 185 | 0.117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4897 | 190 | 0.1748 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5026 | 195 | 0.1431 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5155 | 200 | 0.1684 | 0.1378 | 0.3042 | 0.4804 | 0.6335 | 0.2329 | 0.5004 | 0.4184 | 0.2284 | 0.5609 | 0.8885 | 0.2742 | 0.5192 | 0.4788 | 0.4193 | 0.4569 |
| 0.5284 | 205 | 0.1593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5412 | 210 | 0.1331 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5541 | 215 | 0.1498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5670 | 220 | 0.1467 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5799 | 225 | 0.139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5928 | 230 | 0.1346 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6057 | 235 | 0.1738 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6186 | 240 | 0.146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6314 | 245 | 0.1685 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6443 | 250 | 0.1327 | 0.1318 | 0.2967 | 0.4921 | 0.6348 | 0.2225 | 0.4917 | 0.4437 | 0.2301 | 0.5628 | 0.8889 | 0.2769 | 0.5166 | 0.4754 | 0.4135 | 0.4573 |
| 0.6572 | 255 | 0.1517 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6701 | 260 | 0.1521 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6830 | 265 | 0.1349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6959 | 270 | 0.1127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7088 | 275 | 0.1141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7216 | 280 | 0.1273 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7345 | 285 | 0.1168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7474 | 290 | 0.1223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7603 | 295 | 0.1444 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7732 | 300 | 0.1153 | 0.1242 | 0.2892 | 0.4960 | 0.6431 | 0.2189 | 0.5059 | 0.4589 | 0.2280 | 0.5635 | 0.8784 | 0.2847 | 0.5048 | 0.4788 | 0.4157 | 0.4589 |
| 0.7861 | 305 | 0.1337 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7990 | 310 | 0.0992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8119 | 315 | 0.1206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8247 | 320 | 0.1272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8376 | 325 | 0.1354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8505 | 330 | 0.1298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8634 | 335 | 0.1289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8763 | 340 | 0.1291 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8892 | 345 | 0.1187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9021 | 350 | 0.1173 | 0.1196 | 0.2891 | 0.4945 | 0.6421 | 0.2191 | 0.5113 | 0.4600 | 0.2289 | 0.5667 | 0.8785 | 0.2835 | 0.5134 | 0.4804 | 0.4201 | 0.4606 |
| 0.9149 | 355 | 0.1197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9278 | 360 | 0.1257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9407 | 365 | 0.1242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9536 | 370 | 0.1479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9665 | 375 | 0.1298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9794 | 380 | 0.143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9923 | 385 | 0.1026 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 388 | - | - | 0.2878 | 0.4934 | 0.6330 | 0.2266 | 0.5034 | 0.4571 | 0.2280 | 0.5664 | 0.8785 | 0.2843 | 0.5152 | 0.4802 | 0.4200 | 0.4595 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.269 kWh
- **Carbon Emitted**: 0.104 kg of CO2
- **Hours Used**: 0.777 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
```bibtex
@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
```bibtex
@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}
}
```
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