---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:100231
- loss:CachedMultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
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
model-index:
- name: mpnet-base trained with prompts on NQ (baseline)
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
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: 0.58
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.74
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.136
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.1
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1383333333333333
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.23
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.29
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40399999999999997
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.322743595300966
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4182460317460317
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2506792219407537
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.52
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.76
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.88
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.52
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4733333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.45600000000000007
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.382
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06092269601560222
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.1154586383509253
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17899708780601598
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.280248635883367
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4724594958870042
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6686904761904762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.35151457604730424
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.66
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.84
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16399999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.088
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.49
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.63
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.76
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.81
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6580372696463376
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6168571428571429
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6095550970534841
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
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: 0.52
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: 0.23333333333333336
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10199999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.1796904761904762
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.3189365079365079
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.34826984126984123
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.4812698412698412
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3826689512421986
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4447619047619047
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3224370987422607
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: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.74
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.58
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.184
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.102
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.29
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.44
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.46
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.51
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.49341624816448965
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6311666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4387176781413483
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.24
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.54
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.82
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.24
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08199999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.24
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.54
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.7
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.82
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5257140149140848
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4328888888888888
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4389275332182773
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: 0.46
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: 0.36
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.28
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.256
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.2
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.01238391750608928
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.04019755666315251
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.05578538536994838
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.08145961287191616
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.23562797863029503
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4232142857142857
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.07963346545764961
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: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.44
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14800000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.42
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.56
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.68
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.73
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5782914478750161
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5401904761904762
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5330545130864855
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.86
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.92
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.94
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.96
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.86
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.37999999999999995
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.23999999999999996
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.128
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7606666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8786666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9093333333333333
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9433333333333332
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8976712250359643
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8916666666666666
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8805309250136836
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.38
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: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2866666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.24400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.16799999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07966666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.17666666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2506666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3456666666666666
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.32851173377952236
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4970714285714286
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.25445383990026627
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.22
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.66
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.84
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.94
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.22
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16799999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.22
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.66
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.84
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.94
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5812935727911315
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4653888888888889
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4684587977034785
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.42
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.76
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.42
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.21333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.156
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.086
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.385
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.57
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.685
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.75
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5826136869806517
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.539079365079365
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5285677489177489
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: 0.8775510204081632
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9183673469387755
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9795918367346939
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4897959183673469
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.5306122448979591
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.49795918367346936
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.426530612244898
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03672769429875476
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11837375395207567
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.18205812553048406
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.28673154535406004
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4702222886113158
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6726190476190476
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3659914143985027
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.43459968602825744
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.636734693877551
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.7260282574568289
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8061224489795917
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.43459968602825744
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2828676085818943
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.22691993720565154
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.15681004709576138
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.25487626543673764
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.40602306078738426
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4877008030750992
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5679007411830141
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5022516545276137
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.557064713064713
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.42480937766317256
name: Cosine Map@100
---
# mpnet-base trained with prompts on NQ (baseline)
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) 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.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base)
- **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: MPNetModel
(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("din0s/mpnet-base-nq-prompts-cosine")
# Run inference
sentences = [
'query: where does the last name francisco come from',
'document: Francisco Francisco is the Spanish and Portuguese form of the masculine given name Franciscus (corresponding to English Francis).',
'document: Book of Esther The Book of Esther, also known in Hebrew as "the Scroll" (Megillah), is a book in the third section (Ketuvim, "Writings") of the Jewish Tanakh (the Hebrew Bible) and in the Christian Old Testament. It is one of the five Scrolls (Megillot) in the Hebrew Bible. It relates the story of a Hebrew woman in Persia, born as Hadassah but known as Esther, who becomes queen of Persia and thwarts a genocide of her people. The story forms the core of the Jewish festival of Purim, during which it is read aloud twice: once in the evening and again the following morning. The books of Esther and Song of Songs are the only books in the Hebrew Bible that do not explicitly mention God.[2]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [InformationRetrievalEvaluator](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 | 0.3 | 0.52 | 0.5 | 0.34 | 0.58 | 0.24 | 0.36 | 0.44 | 0.86 | 0.38 | 0.22 | 0.42 | 0.4898 |
| cosine_accuracy@3 | 0.44 | 0.76 | 0.66 | 0.52 | 0.68 | 0.54 | 0.46 | 0.58 | 0.92 | 0.58 | 0.66 | 0.6 | 0.8776 |
| cosine_accuracy@5 | 0.58 | 0.88 | 0.8 | 0.52 | 0.68 | 0.7 | 0.52 | 0.72 | 0.94 | 0.64 | 0.84 | 0.7 | 0.9184 |
| cosine_accuracy@10 | 0.74 | 0.92 | 0.84 | 0.66 | 0.74 | 0.82 | 0.6 | 0.76 | 0.96 | 0.76 | 0.94 | 0.76 | 0.9796 |
| cosine_precision@1 | 0.3 | 0.52 | 0.5 | 0.34 | 0.58 | 0.24 | 0.36 | 0.44 | 0.86 | 0.38 | 0.22 | 0.42 | 0.4898 |
| cosine_precision@3 | 0.1667 | 0.4733 | 0.22 | 0.2333 | 0.2933 | 0.18 | 0.28 | 0.2 | 0.38 | 0.2867 | 0.22 | 0.2133 | 0.5306 |
| cosine_precision@5 | 0.136 | 0.456 | 0.164 | 0.16 | 0.184 | 0.14 | 0.256 | 0.148 | 0.24 | 0.244 | 0.168 | 0.156 | 0.498 |
| cosine_precision@10 | 0.1 | 0.382 | 0.088 | 0.102 | 0.102 | 0.082 | 0.2 | 0.08 | 0.128 | 0.168 | 0.094 | 0.086 | 0.4265 |
| cosine_recall@1 | 0.1383 | 0.0609 | 0.49 | 0.1797 | 0.29 | 0.24 | 0.0124 | 0.42 | 0.7607 | 0.0797 | 0.22 | 0.385 | 0.0367 |
| cosine_recall@3 | 0.23 | 0.1155 | 0.63 | 0.3189 | 0.44 | 0.54 | 0.0402 | 0.56 | 0.8787 | 0.1767 | 0.66 | 0.57 | 0.1184 |
| cosine_recall@5 | 0.29 | 0.179 | 0.76 | 0.3483 | 0.46 | 0.7 | 0.0558 | 0.68 | 0.9093 | 0.2507 | 0.84 | 0.685 | 0.1821 |
| cosine_recall@10 | 0.404 | 0.2802 | 0.81 | 0.4813 | 0.51 | 0.82 | 0.0815 | 0.73 | 0.9433 | 0.3457 | 0.94 | 0.75 | 0.2867 |
| **cosine_ndcg@10** | **0.3227** | **0.4725** | **0.658** | **0.3827** | **0.4934** | **0.5257** | **0.2356** | **0.5783** | **0.8977** | **0.3285** | **0.5813** | **0.5826** | **0.4702** |
| cosine_mrr@10 | 0.4182 | 0.6687 | 0.6169 | 0.4448 | 0.6312 | 0.4329 | 0.4232 | 0.5402 | 0.8917 | 0.4971 | 0.4654 | 0.5391 | 0.6726 |
| cosine_map@100 | 0.2507 | 0.3515 | 0.6096 | 0.3224 | 0.4387 | 0.4389 | 0.0796 | 0.5331 | 0.8805 | 0.2545 | 0.4685 | 0.5286 | 0.366 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.4346 |
| cosine_accuracy@3 | 0.6367 |
| cosine_accuracy@5 | 0.726 |
| cosine_accuracy@10 | 0.8061 |
| cosine_precision@1 | 0.4346 |
| cosine_precision@3 | 0.2829 |
| cosine_precision@5 | 0.2269 |
| cosine_precision@10 | 0.1568 |
| cosine_recall@1 | 0.2549 |
| cosine_recall@3 | 0.406 |
| cosine_recall@5 | 0.4877 |
| cosine_recall@10 | 0.5679 |
| **cosine_ndcg@10** | **0.5023** |
| cosine_mrr@10 | 0.5571 |
| cosine_map@100 | 0.4248 |
## 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: query and answer
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 12 tokens
- mean: 13.74 tokens
- max: 26 tokens
| - min: 17 tokens
- mean: 139.2 tokens
- max: 510 tokens
|
* Samples:
| query | answer |
|:-------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| query: who is required to report according to the hmda | 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] |
| query: what is the definition of endoplasmic reticulum in biology | 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... |
| query: what does the ski mean in polish names | 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. |
* Loss: [CachedMultipleNegativesRankingLoss](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: query and answer
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 12 tokens
- mean: 13.78 tokens
- max: 24 tokens
| - min: 13 tokens
- mean: 137.63 tokens
- max: 512 tokens
|
* Samples:
| query | answer |
|:-------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| query: difference between russian blue and british blue cat | 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. |
| query: who played the little girl on mrs doubtfire | 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. |
| query: what year did the movie the sound of music come out | 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. |
* Loss: [CachedMultipleNegativesRankingLoss](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
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `seed`: 12
- `bf16`: True
- `prompts`: {'query': 'query: ', 'answer': 'document: '}
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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`: cosine
- `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`: None
- `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