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: who ordered the charge of the light brigade
sentences:
- >-
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.
- >-
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]
- >-
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: what part of the cow is the rib roast
sentences:
- >-
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.
- >-
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]
- "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: what is the current big bang theory season
sentences:
- >-
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.
- >-
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]
- >-
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: what happened to tates mom on days of our lives
sentences:
- >-
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.
- >-
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]
- >-
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: where does the last name francisco come from
sentences:
- >-
Francisco Francisco is the Spanish and Portuguese form of the masculine
given name Franciscus (corresponding to English Francis).
- >-
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]
- >-
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: 104.55706247798686
energy_consumed: 0.2689902868469595
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: 0.782
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: 0.22
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.52
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.62
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
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: 0.09833333333333333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23566666666666666
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.27899999999999997
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3256666666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.272752213638476
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35327777777777775
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22112124218381526
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.64
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: 0.64
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.46
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.44000000000000006
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.39000000000000007
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06726969384535866
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10829208895108451
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.16535944626004745
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2529406741716369
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4767334977280841
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7150238095238095
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.35379332004025005
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: 0.72
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.74
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.52
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15200000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.51
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.69
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.72
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.79
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6577792985828184
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6226904761904761
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6145173719999807
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: 0.3
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.36
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.42
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.11999999999999998
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.064
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08807936507936509
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15074603174603174
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.20407936507936508
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2524126984126984
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20563274962284733
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.26849999999999996
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.17422019443090375
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.58
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.68
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.58
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3
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: 0.29
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.45
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: 0.5242406054056967
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.643968253968254
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4580837397163866
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: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.14666666666666664
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.10000000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.068
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.22
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.44
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.68
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.43963932117536886
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3651269841269841
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3787220730332196
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.3
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2733333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.21600000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.17800000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.021273572702979568
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.05401978082802253
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.06417737946426297
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.09731580553141
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.22424563391154495
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3820476190476191
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.0898107385158962
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: 0.66
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.72
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: 0.136
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07400000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.41
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.56
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.65
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.69
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.558255406320814
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5235
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5201771704107404
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: 0.94
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: 0.3666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.23999999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13399999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7040000000000001
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8786666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9193333333333333
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.97
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8832492246317404
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8669999999999999
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8484825708061002
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: 0.44
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: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.192
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.136
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06766666666666668
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1366666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.19766666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2776666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2659892317280556
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.40594444444444444
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2092360599636062
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.2
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6
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: 0.2
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2
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: 0.2
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.6
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.72
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5055380364663926
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.40949999999999986
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4212792624042624
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.34
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.52
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.58
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.66
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.132
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.305
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.485
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.565
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.65
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.48695696351260737
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.44471428571428573
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4344481461926843
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.4897959183673469
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7755102040816326
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9387755102040817
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9795918367346939
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4897959183673469
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.44897959183673464
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.4448979591836735
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.37755102040816335
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.038721005631019066
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10614028485651965
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.16536659269196569
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2595083655687104
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.418741764200365
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6490038872691933
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.32735138614786596
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.4038304552590267
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5858084772370487
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6552904238618524
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7338147566718993
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4038304552590267
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2560753532182104
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.20191522762951336
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14288854003139717
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.23233412594297867
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.37655370664474297
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.43307559873043383
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5088854520782915
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4553656882249855
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5115613490817572
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.38855717506505477
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': True})
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/bert-base-nq")
# Run inference
sentences = [
'where does the last name francisco come from',
'Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
'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 | 0.22 | 0.64 | 0.52 | 0.2 | 0.58 | 0.22 | 0.3 | 0.42 | 0.8 | 0.32 | 0.2 | 0.34 | 0.4898 |
| cosine_accuracy@3 | 0.44 | 0.74 | 0.72 | 0.3 | 0.68 | 0.44 | 0.44 | 0.58 | 0.94 | 0.44 | 0.6 | 0.52 | 0.7755 |
| cosine_accuracy@5 | 0.52 | 0.84 | 0.74 | 0.36 | 0.7 | 0.5 | 0.48 | 0.66 | 0.96 | 0.52 | 0.72 | 0.58 | 0.9388 |
| cosine_accuracy@10 | 0.62 | 0.9 | 0.8 | 0.42 | 0.8 | 0.68 | 0.58 | 0.72 | 0.98 | 0.6 | 0.8 | 0.66 | 0.9796 |
| cosine_precision@1 | 0.22 | 0.64 | 0.52 | 0.2 | 0.58 | 0.22 | 0.3 | 0.42 | 0.8 | 0.32 | 0.2 | 0.34 | 0.4898 |
| cosine_precision@3 | 0.18 | 0.46 | 0.24 | 0.12 | 0.3 | 0.1467 | 0.2733 | 0.1933 | 0.3667 | 0.22 | 0.2 | 0.18 | 0.449 |
| cosine_precision@5 | 0.136 | 0.44 | 0.152 | 0.1 | 0.192 | 0.1 | 0.216 | 0.136 | 0.24 | 0.192 | 0.144 | 0.132 | 0.4449 |
| cosine_precision@10 | 0.082 | 0.39 | 0.084 | 0.064 | 0.114 | 0.068 | 0.178 | 0.074 | 0.134 | 0.136 | 0.08 | 0.076 | 0.3776 |
| cosine_recall@1 | 0.0983 | 0.0673 | 0.51 | 0.0881 | 0.29 | 0.22 | 0.0213 | 0.41 | 0.704 | 0.0677 | 0.2 | 0.305 | 0.0387 |
| cosine_recall@3 | 0.2357 | 0.1083 | 0.69 | 0.1507 | 0.45 | 0.44 | 0.054 | 0.56 | 0.8787 | 0.1367 | 0.6 | 0.485 | 0.1061 |
| cosine_recall@5 | 0.279 | 0.1654 | 0.72 | 0.2041 | 0.48 | 0.5 | 0.0642 | 0.65 | 0.9193 | 0.1977 | 0.72 | 0.565 | 0.1654 |
| cosine_recall@10 | 0.3257 | 0.2529 | 0.79 | 0.2524 | 0.57 | 0.68 | 0.0973 | 0.69 | 0.97 | 0.2777 | 0.8 | 0.65 | 0.2595 |
| cosine_ndcg@10 | 0.2728 | 0.4767 | 0.6578 | 0.2056 | 0.5242 | 0.4396 | 0.2242 | 0.5583 | 0.8832 | 0.266 | 0.5055 | 0.487 | 0.4187 |
| cosine_mrr@10 | 0.3533 | 0.715 | 0.6227 | 0.2685 | 0.644 | 0.3651 | 0.382 | 0.5235 | 0.867 | 0.4059 | 0.4095 | 0.4447 | 0.649 |
| cosine_map@100 | 0.2211 | 0.3538 | 0.6145 | 0.1742 | 0.4581 | 0.3787 | 0.0898 | 0.5202 | 0.8485 | 0.2092 | 0.4213 | 0.4344 | 0.3274 |
Nano BEIR
- Dataset:
NanoBEIR_mean - Evaluated with
NanoBEIREvaluator
| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.4038 |
| cosine_accuracy@3 | 0.5858 |
| cosine_accuracy@5 | 0.6553 |
| cosine_accuracy@10 | 0.7338 |
| cosine_precision@1 | 0.4038 |
| cosine_precision@3 | 0.2561 |
| cosine_precision@5 | 0.2019 |
| cosine_precision@10 | 0.1429 |
| cosine_recall@1 | 0.2323 |
| cosine_recall@3 | 0.3766 |
| cosine_recall@5 | 0.4331 |
| cosine_recall@10 | 0.5089 |
| cosine_ndcg@10 | 0.4554 |
| cosine_mrr@10 | 0.5116 |
| cosine_map@100 | 0.3886 |
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: 10 tokens
- mean: 11.74 tokens
- max: 24 tokens
- min: 15 tokens
- mean: 137.2 tokens
- max: 508 tokens
- Samples:
query answer who is required to report according to the hmdaHome 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]what is the definition of endoplasmic reticulum in biologyEndoplasmic 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 using elect...what does the ski mean in polish namesPolish 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: 10 tokens
- mean: 11.78 tokens
- max: 22 tokens
- min: 11 tokens
- mean: 135.64 tokens
- max: 512 tokens
- Samples:
query answer difference between russian blue and british blue catRussian 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.who played the little girl on mrs doubtfireMara 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.what year did the movie the sound of music come outThe 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: Truebatch_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: Nonebatch_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 | - | - | 0.1046 | 0.2182 | 0.1573 | 0.0575 | 0.2597 | 0.1602 | 0.0521 | 0.0493 | 0.7310 | 0.1320 | 0.2309 | 0.1240 | 0.0970 | 0.1826 |
| 0.0026 | 1 | 2.9906 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0129 | 5 | 3.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0258 | 10 | 2.715 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0387 | 15 | 2.1234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0515 | 20 | 1.5407 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0644 | 25 | 1.2437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0773 | 30 | 0.9972 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0902 | 35 | 0.7725 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1031 | 40 | 0.6277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1160 | 45 | 0.5025 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1289 | 50 | 0.3382 | 0.3345 | 0.2316 | 0.4851 | 0.6465 | 0.2296 | 0.4966 | 0.4263 | 0.2129 | 0.4993 | 0.8656 | 0.2590 | 0.4917 | 0.4712 | 0.4293 | 0.4419 |
| 0.1418 | 55 | 0.3324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1546 | 60 | 0.2682 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1675 | 65 | 0.2473 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1804 | 70 | 0.1919 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1933 | 75 | 0.2543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2062 | 80 | 0.2179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2191 | 85 | 0.2067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2320 | 90 | 0.2065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2448 | 95 | 0.2104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2577 | 100 | 0.2068 | 0.1892 | 0.2690 | 0.4640 | 0.6510 | 0.2334 | 0.4844 | 0.4357 | 0.2162 | 0.5012 | 0.8806 | 0.2673 | 0.5057 | 0.4322 | 0.4265 | 0.4436 |
| 0.2706 | 105 | 0.1864 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2835 | 110 | 0.1897 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2964 | 115 | 0.1828 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3093 | 120 | 0.1715 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3222 | 125 | 0.1786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3351 | 130 | 0.1505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3479 | 135 | 0.1672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3608 | 140 | 0.1742 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3737 | 145 | 0.1582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3866 | 150 | 0.1289 | 0.1575 | 0.2760 | 0.4735 | 0.6475 | 0.2177 | 0.5005 | 0.4452 | 0.2185 | 0.5302 | 0.8873 | 0.2687 | 0.4949 | 0.4475 | 0.4220 | 0.4484 |
| 0.3995 | 155 | 0.1566 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4124 | 160 | 0.1502 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4253 | 165 | 0.143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4381 | 170 | 0.1592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4510 | 175 | 0.1398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4639 | 180 | 0.1173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4768 | 185 | 0.123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4897 | 190 | 0.1752 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5026 | 195 | 0.1433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5155 | 200 | 0.1719 | 0.1433 | 0.2796 | 0.4769 | 0.6449 | 0.2145 | 0.5111 | 0.4484 | 0.2239 | 0.5568 | 0.8901 | 0.2709 | 0.4841 | 0.4647 | 0.4184 | 0.4526 |
| 0.5284 | 205 | 0.1596 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5412 | 210 | 0.145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5541 | 215 | 0.1654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5670 | 220 | 0.1477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5799 | 225 | 0.1559 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5928 | 230 | 0.147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6057 | 235 | 0.1645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6186 | 240 | 0.1504 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6314 | 245 | 0.1648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6443 | 250 | 0.1399 | 0.1345 | 0.2707 | 0.4727 | 0.6523 | 0.2101 | 0.5168 | 0.4432 | 0.2236 | 0.5542 | 0.8904 | 0.2637 | 0.5034 | 0.4737 | 0.4207 | 0.4535 |
| 0.6572 | 255 | 0.1562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6701 | 260 | 0.1532 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6830 | 265 | 0.1367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6959 | 270 | 0.1166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7088 | 275 | 0.1307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7216 | 280 | 0.1365 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7345 | 285 | 0.1279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7474 | 290 | 0.1338 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7603 | 295 | 0.1448 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7732 | 300 | 0.1313 | 0.1278 | 0.2737 | 0.4750 | 0.6552 | 0.2246 | 0.5280 | 0.4390 | 0.2247 | 0.5609 | 0.8832 | 0.2661 | 0.5092 | 0.4865 | 0.4224 | 0.4576 |
| 0.7861 | 305 | 0.1376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7990 | 310 | 0.106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8119 | 315 | 0.1331 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8247 | 320 | 0.1376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8376 | 325 | 0.1349 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8505 | 330 | 0.1392 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8634 | 335 | 0.1394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8763 | 340 | 0.1276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8892 | 345 | 0.1228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9021 | 350 | 0.112 | 0.1244 | 0.2807 | 0.4747 | 0.6564 | 0.2063 | 0.5249 | 0.4373 | 0.2238 | 0.5576 | 0.8832 | 0.2641 | 0.5038 | 0.5028 | 0.4181 | 0.4564 |
| 0.9149 | 355 | 0.1258 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9278 | 360 | 0.1304 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9407 | 365 | 0.1372 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9536 | 370 | 0.145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9665 | 375 | 0.1423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9794 | 380 | 0.1463 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9923 | 385 | 0.1101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 388 | - | - | 0.2728 | 0.4767 | 0.6578 | 0.2056 | 0.5242 | 0.4396 | 0.2242 | 0.5583 | 0.8832 | 0.2660 | 0.5055 | 0.4870 | 0.4187 | 0.4554 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Energy Consumed: 0.269 kWh
- Carbon Emitted: 0.105 kg of CO2
- Hours Used: 0.782 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}
}