metadata
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: distilbert/distilbert-base-uncased
widget:
- source_sentence: who are the dancers in the limp bizkit rollin video
sentences:
- >-
Voting age Before the Second World War, the voting age in almost all
countries was 21 years or higher. Czechoslovakia was the first to reduce
the voting age to 20 years in 1946, and by 1968 a total of 17 countries
had lowered their voting age.[1] Many countries, particularly in Western
Europe, reduced their voting ages to 18 years during the 1970s, starting
with the United Kingdom (1969),[2] with the United States (26th
Amendment) (1971), Canada, West Germany (1972), Australia (1974), France
(1974), and others following soon afterwards. By the end of the 20th
century, 18 had become by far the most common voting age. However, a few
countries maintain a voting age of 20 years or higher. It was argued
that young men could be drafted to go to war at 18, and many people felt
they should be able to vote at the age of 18.[3]
- >-
Rollin' (Limp Bizkit song) The music video was filmed atop the South
Tower of the former World Trade Center in New York City. The
introduction features Ben Stiller and Stephen Dorff mistaking Fred Durst
for the valet and giving him the keys to their Bentley Azure. Also
making a cameo is break dancer Mr. Wiggles. The rest of the video has
several cuts to Durst and his bandmates hanging out of the Bentley as
they drive about Manhattan. The song Ben Stiller is playing at the
beginning is "My Generation" from the same album. The video also
features scenes of Fred Durst with five girls dancing in a room. The
video was filmed around the same time as the film Zoolander, which
explains Stiller and Dorff's appearance. Fred Durst has a small cameo in
that film.
- >-
Eobard Thawne When Thawne reappears, he murders the revived Johnny
Quick,[9] before proceeding to trap Barry and the revived Max Mercury
inside the negative Speed Force. Thawne then attempts to kill Wally
West's children through their connection to the Speed Force in front of
Linda Park-West, only to be stopped by Jay Garrick and Bart Allen.
Thawne defeats Jay and prepares to kill Bart, but Barry, Max, Wally,
Jesse Quick, and Impulse arrive to prevent the villain from doing
so.[8][10] In the ensuing fight, Thawne reveals that he is responsible
for every tragedy that has occurred in Barry's life, including the death
of his mother. Thawne then decides to destroy everything the Flash holds
dear by killing Barry's wife, Iris, before they even met.[10]
- source_sentence: who wins season 14 of hell's kitchen
sentences:
- >-
Hell's Kitchen (U.S. season 14) Season 14 of the American competitive
reality television series Hell's Kitchen premiered on March 3, 2015 on
Fox. The prize is a head chef position at Gordon Ramsay Pub & Grill in
Caesars Atlantic City.[1] Gordon Ramsay returned as head chef with Andi
Van Willigan and James Avery returning as sous-chefs for both their
respective kitchens as well as Marino Monferrato as the maître d'.
Executive chef Meghan Gill from Roanoke, Virginia, won the competition,
thus becoming the fourteenth winner of Hell's Kitchen.
- >-
Maze Runner: The Death Cure On April 22, 2017, the studio delayed the
release date once again, to February 9, 2018, in order to allow more
time for post-production; months later, on August 25, the studio moved
the release forward two weeks.[17] The film will premiere on January 26,
2018 in 3D, IMAX and IMAX 3D.[18][19]
- >-
North American Plate On its western edge, the Farallon Plate has been
subducting under the North American Plate since the Jurassic Period. The
Farallon Plate has almost completely subducted beneath the western
portion of the North American Plate leaving that part of the North
American Plate in contact with the Pacific Plate as the San Andreas
Fault. The Juan de Fuca, Explorer, Gorda, Rivera, Cocos and Nazca plates
are remnants of the Farallon Plate.
- source_sentence: who played the dj in the movie the warriors
sentences:
- "List of Arrow episodes As of May\_17, 2018,[update] 138 episodes of Arrow\_have aired, concluding the\_sixth season. On April 2, 2018, the CW renewed the series for a seventh season.[1]"
- >-
Lynne Thigpen Cherlynne Theresa "Lynne" Thigpen (December 22, 1948 –
March 12, 2003) was an American actress, best known for her role as "The
Chief" of ACME in the various Carmen Sandiego television series and
computer games from 1991 to 1997. For her varied television work,
Thigpen was nominated for six Daytime Emmy Awards; she won a Tony Award
in 1997 for portraying Dr. Judith Kaufman in An American Daughter.
- >-
The Washington Post The Washington Post is an American daily newspaper.
It is the most widely circulated newspaper published in Washington,
D.C., and was founded on December 6, 1877,[7] making it the area's
oldest extant newspaper. In February 2017, amid a barrage of criticism
from President Donald Trump over the paper's coverage of his campaign
and early presidency as well as concerns among the American press about
Trump's criticism and threats against journalists who provide coverage
he deems unfavorable, the Post adopted the slogan "Democracy Dies in
Darkness".[8]
- source_sentence: how old was messi when he started his career
sentences:
- >-
Lionel Messi Born and raised in central Argentina, Messi was diagnosed
with a growth hormone deficiency as a child. At age 13, he relocated to
Spain to join Barcelona, who agreed to pay for his medical treatment.
After a fast progression through Barcelona's youth academy, Messi made
his competitive debut aged 17 in October 2004. Despite being
injury-prone during his early career, he established himself as an
integral player for the club within the next three years, finishing 2007
as a finalist for both the Ballon d'Or and FIFA World Player of the Year
award, a feat he repeated the following year. His first uninterrupted
campaign came in the 2008–09 season, during which he helped Barcelona
achieve the first treble in Spanish football. At 22 years old, Messi won
the Ballon d'Or and FIFA World Player of the Year award by record voting
margins.
- >-
We Are Marshall Filming of We Are Marshall commenced on April 3, 2006,
in Huntington, West Virginia, and was completed in Atlanta, Georgia. The
premiere for the film was held at the Keith Albee Theater on December
12, 2006, in Huntington; other special screenings were held at Pullman
Square. The movie was released nationwide on December 22, 2006.
- >-
One Fish, Two Fish, Red Fish, Blue Fish One Fish, Two Fish, Red Fish,
Blue Fish is a 1960 children's book by Dr. Seuss. It is a simple rhyming
book for beginning readers, with a freewheeling plot about a boy and a
girl named Jay and Kay and the many amazing creatures they have for
friends and pets. Interspersed are some rather surreal and unrelated
skits, such as a man named Ned whose feet stick out from his bed, and a
creature who has a bird in his ear. As of 2001, over 6 million copies of
the book had been sold, placing it 13th on a list of "All-Time
Bestselling Children's Books" from Publishers Weekly.[1] Based on a 2007
online poll, the United States' National Education Association labor
union named the book one of its "Teachers' Top 100 Books for
Children."[2]
- source_sentence: is send in the clowns from a musical
sentences:
- >-
Money in the Bank ladder match The first match was contested in 2005 at
WrestleMania 21, after being invented (in kayfabe) by Chris Jericho.[1]
At the time, it was exclusive to wrestlers of the Raw brand, and Edge
won the inaugural match.[1] From then until 2010, the Money in the Bank
ladder match, now open to all WWE brands, became a WrestleMania
mainstay. 2010 saw a second and third Money in the Bank ladder match
when the Money in the Bank pay-per-view debuted in July. Unlike the
matches at WrestleMania, this new event featured two such ladder matches
– one each for a contract for the WWE Championship and World
Heavyweight Championship, respectively.
- >-
The Suite Life on Deck The Suite Life on Deck is an American sitcom that
aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a
sequel/spin-off of the Disney Channel Original Series The Suite Life of
Zack & Cody. The series follows twin brothers Zack and Cody Martin and
hotel heiress London Tipton in a new setting, the SS Tipton, where they
attend classes at "Seven Seas High School" and meet Bailey Pickett while
Mr. Moseby manages the ship. The ship travels around the world to
nations such as Italy, France, Greece, India, Sweden and the United
Kingdom where the characters experience different cultures, adventures,
and situations.[1]
- >-
Send In the Clowns "Send In the Clowns" is a song written by Stephen
Sondheim for the 1973 musical A Little Night Music, an adaptation of
Ingmar Bergman's film Smiles of a Summer Night. It is a ballad from Act
Two, in which the character Desirée reflects on the ironies and
disappointments of her life. Among other things, she looks back on an
affair years earlier with the lawyer Fredrik, who was deeply in love
with her but whose marriage proposals she had rejected. Meeting him
after so long, she realizes she is in love with him and finally ready to
marry him, but now it is he who rejects her: he is in an unconsummated
marriage with a much younger woman. Desirée proposes marriage to rescue
him from this situation, but he declines, citing his dedication to his
bride. Reacting to his rejection, Desirée sings this song. The song is
later reprised as a coda after Fredrik's young wife runs away with his
son, and Fredrik is finally free to accept Desirée's offer.[1]
datasets:
- sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- row_non_zero_mean_query
- row_sparsity_mean_query
- row_non_zero_mean_corpus
- row_sparsity_mean_corpus
co2_eq_emissions:
emissions: 32.749162711505036
energy_consumed: 0.08425262208968576
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: 0.292
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: splade-distilbert-base-uncased trained on Natural Questions
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.24
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.44
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.24
name: Dot Precision@1
- type: dot_precision@3
value: 0.14666666666666664
name: Dot Precision@3
- type: dot_precision@5
value: 0.12000000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.07200000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.24
name: Dot Recall@1
- type: dot_recall@3
value: 0.44
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.72
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.46533877878819696
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3856269841269841
name: Dot Mrr@10
- type: dot_map@100
value: 0.3974184036014145
name: Dot Map@100
- type: row_non_zero_mean_query
value: 15.779999732971191
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9994829297065735
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 25.729328155517578
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9991570711135864
name: Row Sparsity Mean Corpus
- type: dot_accuracy@1
value: 0.22
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.22
name: Dot Precision@1
- type: dot_precision@3
value: 0.13999999999999999
name: Dot Precision@3
- type: dot_precision@5
value: 0.12000000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.22
name: Dot Recall@1
- type: dot_recall@3
value: 0.42
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.46328494594550307
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.37662698412698403
name: Dot Mrr@10
- type: dot_map@100
value: 0.3856610333651542
name: Dot Map@100
- type: row_non_zero_mean_query
value: 15.380000114440918
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9994961023330688
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 26.596866607666016
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9991285800933838
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.42
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.56
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.2866666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.264
name: Dot Precision@5
- type: dot_precision@10
value: 0.214
name: Dot Precision@10
- type: dot_recall@1
value: 0.01879480879384032
name: Dot Recall@1
- type: dot_recall@3
value: 0.05027421919442009
name: Dot Recall@3
- type: dot_recall@5
value: 0.08706875727827264
name: Dot Recall@5
- type: dot_recall@10
value: 0.11178880663195827
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2582539565166507
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.38549999999999995
name: Dot Mrr@10
- type: dot_map@100
value: 0.1034946476704924
name: Dot Map@100
- type: row_non_zero_mean_query
value: 20.18000030517578
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9993388652801514
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 30.07179069519043
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9990148544311523
name: Row Sparsity Mean Corpus
- type: dot_accuracy@1
value: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.52
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.58
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.3133333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.288
name: Dot Precision@5
- type: dot_precision@10
value: 0.226
name: Dot Precision@10
- type: dot_recall@1
value: 0.021422381525060468
name: Dot Recall@1
- type: dot_recall@3
value: 0.0742401436593227
name: Dot Recall@3
- type: dot_recall@5
value: 0.08995450762658255
name: Dot Recall@5
- type: dot_recall@10
value: 0.11319066947710729
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.27630767880389084
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.42138888888888887
name: Dot Mrr@10
- type: dot_map@100
value: 0.11387493422516994
name: Dot Map@100
- type: row_non_zero_mean_query
value: 18.81999969482422
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9993834495544434
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 30.65966796875
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9989954829216003
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.32
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.64
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.32
name: Dot Precision@1
- type: dot_precision@3
value: 0.16666666666666663
name: Dot Precision@3
- type: dot_precision@5
value: 0.11599999999999999
name: Dot Precision@5
- type: dot_precision@10
value: 0.064
name: Dot Precision@10
- type: dot_recall@1
value: 0.31
name: Dot Recall@1
- type: dot_recall@3
value: 0.49
name: Dot Recall@3
- type: dot_recall@5
value: 0.56
name: Dot Recall@5
- type: dot_recall@10
value: 0.61
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.46811217927927307
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.43099999999999994
name: Dot Mrr@10
- type: dot_map@100
value: 0.4334878570971412
name: Dot Map@100
- type: row_non_zero_mean_query
value: 15.079999923706055
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9995059370994568
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 22.96107292175293
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.999247670173645
name: Row Sparsity Mean Corpus
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.16666666666666663
name: Dot Precision@3
- type: dot_precision@5
value: 0.124
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.29
name: Dot Recall@1
- type: dot_recall@3
value: 0.49
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.66
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4796509872234161
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.42804761904761895
name: Dot Mrr@10
- type: dot_map@100
value: 0.4288636915548681
name: Dot Map@100
- type: row_non_zero_mean_query
value: 14.399999618530273
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.999528169631958
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 23.73485565185547
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9992223381996155
name: Row Sparsity Mean Corpus
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.2866666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.4533333333333333
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.5666666666666668
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.64
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.2866666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.19999999999999998
name: Dot Precision@3
- type: dot_precision@5
value: 0.16666666666666666
name: Dot Precision@5
- type: dot_precision@10
value: 0.11666666666666668
name: Dot Precision@10
- type: dot_recall@1
value: 0.1895982695979468
name: Dot Recall@1
- type: dot_recall@3
value: 0.3267580730648067
name: Dot Recall@3
- type: dot_recall@5
value: 0.4156895857594242
name: Dot Recall@5
- type: dot_recall@10
value: 0.4805962688773194
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.39723497152804027
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.40070899470899474
name: Dot Mrr@10
- type: dot_map@100
value: 0.3114669694563494
name: Dot Map@100
- type: row_non_zero_mean_query
value: 17.013333320617676
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9994425773620605
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 26.254063924153645
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9991398652394613
name: Row Sparsity Mean Corpus
- type: dot_accuracy@1
value: 0.4023861852433281
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5827315541601256
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6721193092621665
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7583987441130299
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4023861852433281
name: Dot Precision@1
- type: dot_precision@3
value: 0.25922553636839346
name: Dot Precision@3
- type: dot_precision@5
value: 0.2099277864992151
name: Dot Precision@5
- type: dot_precision@10
value: 0.14982417582417581
name: Dot Precision@10
- type: dot_recall@1
value: 0.22672192221710946
name: Dot Recall@1
- type: dot_recall@3
value: 0.36838967779676207
name: Dot Recall@3
- type: dot_recall@5
value: 0.44570232082548333
name: Dot Recall@5
- type: dot_recall@10
value: 0.5264378082924004
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4631187549753249
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5167952081931673
name: Dot Mrr@10
- type: dot_map@100
value: 0.38677121563396466
name: Dot Map@100
- type: row_non_zero_mean_query
value: 19.27265313955454
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9993685804880582
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 27.068602635310246
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9991131195655236
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: dot_accuracy@1
value: 0.18
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.36
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.44
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.18
name: Dot Precision@1
- type: dot_precision@3
value: 0.13333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.1
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.07
name: Dot Recall@1
- type: dot_recall@3
value: 0.1733333333333333
name: Dot Recall@3
- type: dot_recall@5
value: 0.2033333333333333
name: Dot Recall@5
- type: dot_recall@10
value: 0.28
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.216118762316258
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.2994126984126984
name: Dot Mrr@10
- type: dot_map@100
value: 0.16840852597130174
name: Dot Map@100
- type: row_non_zero_mean_query
value: 25.020000457763672
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9991803169250488
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 27.777875900268555
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9990898966789246
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: dot_accuracy@1
value: 0.6
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.82
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.86
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.6
name: Dot Precision@1
- type: dot_precision@3
value: 0.48666666666666664
name: Dot Precision@3
- type: dot_precision@5
value: 0.4439999999999999
name: Dot Precision@5
- type: dot_precision@10
value: 0.4000000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.05376110547712118
name: Dot Recall@1
- type: dot_recall@3
value: 0.15092123200468407
name: Dot Recall@3
- type: dot_recall@5
value: 0.19238478534118364
name: Dot Recall@5
- type: dot_recall@10
value: 0.2793082705020891
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4933229100355268
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7174126984126984
name: Dot Mrr@10
- type: dot_map@100
value: 0.3647742683351921
name: Dot Map@100
- type: row_non_zero_mean_query
value: 14.34000015258789
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9995301961898804
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 22.812902450561523
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9992524981498718
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: dot_accuracy@1
value: 0.62
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.82
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.62
name: Dot Precision@1
- type: dot_precision@3
value: 0.28
name: Dot Precision@3
- type: dot_precision@5
value: 0.184
name: Dot Precision@5
- type: dot_precision@10
value: 0.092
name: Dot Precision@10
- type: dot_recall@1
value: 0.61
name: Dot Recall@1
- type: dot_recall@3
value: 0.7866666666666666
name: Dot Recall@3
- type: dot_recall@5
value: 0.8566666666666666
name: Dot Recall@5
- type: dot_recall@10
value: 0.8566666666666666
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7518512751926597
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7293333333333335
name: Dot Mrr@10
- type: dot_map@100
value: 0.7119416486291485
name: Dot Map@100
- type: row_non_zero_mean_query
value: 17.84000015258789
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9994155168533325
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 25.645116806030273
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9991597533226013
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: dot_accuracy@1
value: 0.22
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.32
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.44
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.54
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.22
name: Dot Precision@1
- type: dot_precision@3
value: 0.1333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.11599999999999999
name: Dot Precision@5
- type: dot_precision@10
value: 0.07600000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.138
name: Dot Recall@1
- type: dot_recall@3
value: 0.25
name: Dot Recall@3
- type: dot_recall@5
value: 0.32938888888888884
name: Dot Recall@5
- type: dot_recall@10
value: 0.3908015873015873
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.29315131681028644
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.30430158730158724
name: Dot Mrr@10
- type: dot_map@100
value: 0.2444001739214205
name: Dot Map@100
- type: row_non_zero_mean_query
value: 18.940000534057617
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9993795156478882
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 27.020782470703125
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9991146922111511
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: dot_accuracy@1
value: 0.64
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.82
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.82
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.86
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.64
name: Dot Precision@1
- type: dot_precision@3
value: 0.37333333333333324
name: Dot Precision@3
- type: dot_precision@5
value: 0.23199999999999996
name: Dot Precision@5
- type: dot_precision@10
value: 0.132
name: Dot Precision@10
- type: dot_recall@1
value: 0.32
name: Dot Recall@1
- type: dot_recall@3
value: 0.56
name: Dot Recall@3
- type: dot_recall@5
value: 0.58
name: Dot Recall@5
- type: dot_recall@10
value: 0.66
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.60467671511462
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7286666666666669
name: Dot Mrr@10
- type: dot_map@100
value: 0.5280557928272471
name: Dot Map@100
- type: row_non_zero_mean_query
value: 18.799999237060547
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9993841648101807
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 24.752653121948242
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.999189019203186
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: dot_accuracy@1
value: 0.64
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.84
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.88
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.98
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.64
name: Dot Precision@1
- type: dot_precision@3
value: 0.32
name: Dot Precision@3
- type: dot_precision@5
value: 0.21999999999999997
name: Dot Precision@5
- type: dot_precision@10
value: 0.12399999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.5740000000000001
name: Dot Recall@1
- type: dot_recall@3
value: 0.768
name: Dot Recall@3
- type: dot_recall@5
value: 0.8446666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 0.9553333333333334
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7881541877243683
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7535238095238094
name: Dot Mrr@10
- type: dot_map@100
value: 0.727066872303161
name: Dot Map@100
- type: row_non_zero_mean_query
value: 17.780000686645508
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9994174242019653
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 19.436979293823242
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9993631839752197
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: dot_accuracy@1
value: 0.36
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.48
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.36
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.20400000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.154
name: Dot Precision@10
- type: dot_recall@1
value: 0.07666666666666667
name: Dot Recall@1
- type: dot_recall@3
value: 0.13366666666666668
name: Dot Recall@3
- type: dot_recall@5
value: 0.21066666666666667
name: Dot Recall@5
- type: dot_recall@10
value: 0.31666666666666665
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.29354115188538094
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4672380952380951
name: Dot Mrr@10
- type: dot_map@100
value: 0.21425734227573925
name: Dot Map@100
- type: row_non_zero_mean_query
value: 24.84000015258789
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9991861581802368
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 34.34458923339844
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9988747239112854
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: dot_accuracy@1
value: 0.18
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.4
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.54
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.18
name: Dot Precision@1
- type: dot_precision@3
value: 0.13333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.10800000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.18
name: Dot Recall@1
- type: dot_recall@3
value: 0.4
name: Dot Recall@3
- type: dot_recall@5
value: 0.54
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4216491858751158
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.33469047619047615
name: Dot Mrr@10
- type: dot_map@100
value: 0.34714031247291627
name: Dot Map@100
- type: row_non_zero_mean_query
value: 29.360000610351562
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9990381002426147
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 29.988996505737305
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9990174770355225
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.66
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.18
name: Dot Precision@3
- type: dot_precision@5
value: 0.136
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.355
name: Dot Recall@1
- type: dot_recall@3
value: 0.475
name: Dot Recall@3
- type: dot_recall@5
value: 0.59
name: Dot Recall@5
- type: dot_recall@10
value: 0.64
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5021918146434317
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.467
name: Dot Mrr@10
- type: dot_map@100
value: 0.462876176092865
name: Dot Map@100
- type: row_non_zero_mean_query
value: 19.799999237060547
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.9993513226509094
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 27.219938278198242
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9991081357002258
name: Row Sparsity Mean Corpus
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: dot_accuracy@1
value: 0.5510204081632653
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.7755102040816326
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.8775510204081632
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9591836734693877
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.5510204081632653
name: Dot Precision@1
- type: dot_precision@3
value: 0.4965986394557823
name: Dot Precision@3
- type: dot_precision@5
value: 0.4530612244897959
name: Dot Precision@5
- type: dot_precision@10
value: 0.3857142857142857
name: Dot Precision@10
- type: dot_recall@1
value: 0.038534835153574185
name: Dot Recall@1
- type: dot_recall@3
value: 0.1072377690272331
name: Dot Recall@3
- type: dot_recall@5
value: 0.15706865554129606
name: Dot Recall@5
- type: dot_recall@10
value: 0.25172431385375454
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4366428831087667
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.6906948493683187
name: Dot Mrr@10
- type: dot_map@100
value: 0.33070503126735623
name: Dot Map@100
- type: row_non_zero_mean_query
value: 15.22449016571045
name: Row Non Zero Mean Query
- type: row_sparsity_mean_query
value: 0.99950110912323
name: Row Sparsity Mean Query
- type: row_non_zero_mean_corpus
value: 31.900609970092773
name: Row Non Zero Mean Corpus
- type: row_sparsity_mean_corpus
value: 0.9989547729492188
name: Row Sparsity Mean Corpus
splade-distilbert-base-uncased trained on Natural Questions
This is a SPLADE Sparse Encoder model finetuned from distilbert/distilbert-base-uncased on the natural-questions dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
Model Details
Model Description
- Model Type: SPLADE Sparse Encoder
- Base model: distilbert/distilbert-base-uncased
- Maximum Sequence Length: 256 tokens
- Output Dimensionality: 30522 dimensions
- Similarity Function: Dot Product
- Training Dataset:
- Language: en
- License: apache-2.0
Model Sources
- Documentation: Sentence Transformers Documentation
- Documentation: Sparse Encoder Documentation
- Repository: Sentence Transformers on GitHub
- Hugging Face: Sparse Encoders on Hugging Face
Full Model Architecture
SparseEncoder(
(0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
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 SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-nq-e-3")
# Run inference
sentences = [
'is send in the clowns from a musical',
'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 30522)
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Sparse Information Retrieval
- Datasets:
NanoMSMARCO,NanoNFCorpus,NanoNQ,NanoClimateFEVER,NanoDBPedia,NanoFEVER,NanoFiQA2018,NanoHotpotQA,NanoMSMARCO,NanoNFCorpus,NanoNQ,NanoQuoraRetrieval,NanoSCIDOCS,NanoArguAna,NanoSciFactandNanoTouche2020 - Evaluated with
SparseInformationRetrievalEvaluator
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| dot_accuracy@1 | 0.22 | 0.34 | 0.3 | 0.18 | 0.6 | 0.62 | 0.22 | 0.64 | 0.64 | 0.36 | 0.18 | 0.38 | 0.551 |
| dot_accuracy@3 | 0.42 | 0.52 | 0.5 | 0.36 | 0.82 | 0.82 | 0.32 | 0.82 | 0.84 | 0.48 | 0.4 | 0.5 | 0.7755 |
| dot_accuracy@5 | 0.6 | 0.52 | 0.62 | 0.44 | 0.86 | 0.9 | 0.44 | 0.82 | 0.88 | 0.62 | 0.54 | 0.62 | 0.8776 |
| dot_accuracy@10 | 0.74 | 0.58 | 0.68 | 0.6 | 0.9 | 0.9 | 0.54 | 0.86 | 0.98 | 0.76 | 0.7 | 0.66 | 0.9592 |
| dot_precision@1 | 0.22 | 0.34 | 0.3 | 0.18 | 0.6 | 0.62 | 0.22 | 0.64 | 0.64 | 0.36 | 0.18 | 0.38 | 0.551 |
| dot_precision@3 | 0.14 | 0.3133 | 0.1667 | 0.1333 | 0.4867 | 0.28 | 0.1333 | 0.3733 | 0.32 | 0.2133 | 0.1333 | 0.18 | 0.4966 |
| dot_precision@5 | 0.12 | 0.288 | 0.124 | 0.1 | 0.444 | 0.184 | 0.116 | 0.232 | 0.22 | 0.204 | 0.108 | 0.136 | 0.4531 |
| dot_precision@10 | 0.074 | 0.226 | 0.07 | 0.07 | 0.4 | 0.092 | 0.076 | 0.132 | 0.124 | 0.154 | 0.07 | 0.074 | 0.3857 |
| dot_recall@1 | 0.22 | 0.0214 | 0.29 | 0.07 | 0.0538 | 0.61 | 0.138 | 0.32 | 0.574 | 0.0767 | 0.18 | 0.355 | 0.0385 |
| dot_recall@3 | 0.42 | 0.0742 | 0.49 | 0.1733 | 0.1509 | 0.7867 | 0.25 | 0.56 | 0.768 | 0.1337 | 0.4 | 0.475 | 0.1072 |
| dot_recall@5 | 0.6 | 0.09 | 0.6 | 0.2033 | 0.1924 | 0.8567 | 0.3294 | 0.58 | 0.8447 | 0.2107 | 0.54 | 0.59 | 0.1571 |
| dot_recall@10 | 0.74 | 0.1132 | 0.66 | 0.28 | 0.2793 | 0.8567 | 0.3908 | 0.66 | 0.9553 | 0.3167 | 0.7 | 0.64 | 0.2517 |
| dot_ndcg@10 | 0.4633 | 0.2763 | 0.4797 | 0.2161 | 0.4933 | 0.7519 | 0.2932 | 0.6047 | 0.7882 | 0.2935 | 0.4216 | 0.5022 | 0.4366 |
| dot_mrr@10 | 0.3766 | 0.4214 | 0.428 | 0.2994 | 0.7174 | 0.7293 | 0.3043 | 0.7287 | 0.7535 | 0.4672 | 0.3347 | 0.467 | 0.6907 |
| dot_map@100 | 0.3857 | 0.1139 | 0.4289 | 0.1684 | 0.3648 | 0.7119 | 0.2444 | 0.5281 | 0.7271 | 0.2143 | 0.3471 | 0.4629 | 0.3307 |
| row_non_zero_mean_query | 15.38 | 18.82 | 14.4 | 25.02 | 14.34 | 17.84 | 18.94 | 18.8 | 17.78 | 24.84 | 29.36 | 19.8 | 15.2245 |
| row_sparsity_mean_query | 0.9995 | 0.9994 | 0.9995 | 0.9992 | 0.9995 | 0.9994 | 0.9994 | 0.9994 | 0.9994 | 0.9992 | 0.999 | 0.9994 | 0.9995 |
| row_non_zero_mean_corpus | 26.5969 | 30.6597 | 23.7349 | 27.7779 | 22.8129 | 25.6451 | 27.0208 | 24.7527 | 19.437 | 34.3446 | 29.989 | 27.2199 | 31.9006 |
| row_sparsity_mean_corpus | 0.9991 | 0.999 | 0.9992 | 0.9991 | 0.9993 | 0.9992 | 0.9991 | 0.9992 | 0.9994 | 0.9989 | 0.999 | 0.9991 | 0.999 |
Sparse Nano BEIR
- Dataset:
NanoBEIR_mean - Evaluated with
SparseNanoBEIREvaluatorwith these parameters:{ "dataset_names": [ "msmarco", "nfcorpus", "nq" ] }
| Metric | Value |
|---|---|
| dot_accuracy@1 | 0.2867 |
| dot_accuracy@3 | 0.4533 |
| dot_accuracy@5 | 0.5667 |
| dot_accuracy@10 | 0.64 |
| dot_precision@1 | 0.2867 |
| dot_precision@3 | 0.2 |
| dot_precision@5 | 0.1667 |
| dot_precision@10 | 0.1167 |
| dot_recall@1 | 0.1896 |
| dot_recall@3 | 0.3268 |
| dot_recall@5 | 0.4157 |
| dot_recall@10 | 0.4806 |
| dot_ndcg@10 | 0.3972 |
| dot_mrr@10 | 0.4007 |
| dot_map@100 | 0.3115 |
| row_non_zero_mean_query | 17.0133 |
| row_sparsity_mean_query | 0.9994 |
| row_non_zero_mean_corpus | 26.2541 |
| row_sparsity_mean_corpus | 0.9991 |
Sparse Nano BEIR
- Dataset:
NanoBEIR_mean - Evaluated with
SparseNanoBEIREvaluatorwith these parameters:{ "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] }
| Metric | Value |
|---|---|
| dot_accuracy@1 | 0.4024 |
| dot_accuracy@3 | 0.5827 |
| dot_accuracy@5 | 0.6721 |
| dot_accuracy@10 | 0.7584 |
| dot_precision@1 | 0.4024 |
| dot_precision@3 | 0.2592 |
| dot_precision@5 | 0.2099 |
| dot_precision@10 | 0.1498 |
| dot_recall@1 | 0.2267 |
| dot_recall@3 | 0.3684 |
| dot_recall@5 | 0.4457 |
| dot_recall@10 | 0.5264 |
| dot_ndcg@10 | 0.4631 |
| dot_mrr@10 | 0.5168 |
| dot_map@100 | 0.3868 |
| row_non_zero_mean_query | 19.2727 |
| row_sparsity_mean_query | 0.9994 |
| row_non_zero_mean_corpus | 27.0686 |
| row_sparsity_mean_corpus | 0.9991 |
Training Details
Training Dataset
natural-questions
- Dataset: natural-questions at f9e894e
- Size: 99,000 training samples
- Columns:
queryandanswer - Approximate statistics based on the first 1000 samples:
query answer type string string details - min: 10 tokens
- mean: 11.71 tokens
- max: 26 tokens
- min: 4 tokens
- mean: 131.81 tokens
- max: 450 tokens
- Samples:
query answer who played the father in papa don't preachAlex McArthur Alex McArthur (born March 6, 1957) is an American actor.where was the location of the battle of hastingsBattle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.how many puppies can a dog give birth toCanine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22] - Loss:
SpladeLosswith these parameters:{'loss': SparseMultipleNegativesRankingLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) (cross_entropy_loss): CrossEntropyLoss() ), 'lambda_corpus': 0.003, 'lambda_query': 0.005}
Evaluation Dataset
natural-questions
- Dataset: natural-questions at f9e894e
- Size: 1,000 evaluation samples
- Columns:
queryandanswer - Approximate statistics based on the first 1000 samples:
query answer type string string details - min: 10 tokens
- mean: 11.69 tokens
- max: 23 tokens
- min: 15 tokens
- mean: 134.01 tokens
- max: 512 tokens
- Samples:
query answer where is the tiber river located in italyTiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.what kind of car does jay gatsby driveJay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.who sings if i can dream about youI Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1] - Loss:
SpladeLosswith these parameters:{'loss': SparseMultipleNegativesRankingLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) (cross_entropy_loss): CrossEntropyLoss() ), 'lambda_corpus': 0.003, 'lambda_query': 0.005}
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 12per_device_eval_batch_size: 12learning_rate: 2e-05num_train_epochs: 1bf16: Trueload_best_model_at_end: Truebatch_sampler: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 12per_device_eval_batch_size: 12per_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: 0.0warmup_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: 42data_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: Trueignore_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: Nonehub_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 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0242 | 200 | 4.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0485 | 400 | 0.074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0727 | 600 | 0.0441 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0970 | 800 | 0.0288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1212 | 1000 | 0.0395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1455 | 1200 | 0.0387 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1697 | 1400 | 0.039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1939 | 1600 | 0.0274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2 | 1650 | - | 0.0425 | 0.4834 | 0.2578 | 0.4469 | 0.3960 | - | - | - | - | - | - | - | - | - | - |
| 0.2182 | 1800 | 0.0317 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2424 | 2000 | 0.0563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2667 | 2200 | 0.0521 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2909 | 2400 | 0.0481 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3152 | 2600 | 0.0562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3394 | 2800 | 0.0524 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3636 | 3000 | 0.0477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3879 | 3200 | 0.0579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4 | 3300 | - | 0.0544 | 0.4270 | 0.2376 | 0.4740 | 0.3795 | - | - | - | - | - | - | - | - | - | - |
| 0.4121 | 3400 | 0.0458 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4364 | 3600 | 0.0477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4606 | 3800 | 0.0479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4848 | 4000 | 0.046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5091 | 4200 | 0.0382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5333 | 4400 | 0.0442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5576 | 4600 | 0.0405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5818 | 4800 | 0.0417 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6 | 4950 | - | 0.0416 | 0.4677 | 0.2401 | 0.4760 | 0.3946 | - | - | - | - | - | - | - | - | - | - |
| 0.6061 | 5000 | 0.033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6303 | 5200 | 0.0437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6545 | 5400 | 0.0351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6788 | 5600 | 0.0387 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7030 | 5800 | 0.048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7273 | 6000 | 0.0498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7515 | 6200 | 0.0442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7758 | 6400 | 0.0359 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8 | 6600 | 0.0398 | 0.0403 | 0.4633 | 0.2763 | 0.4797 | 0.4064 | - | - | - | - | - | - | - | - | - | - |
| 0.8242 | 6800 | 0.0364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8485 | 7000 | 0.0363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8727 | 7200 | 0.0344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8970 | 7400 | 0.0351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9212 | 7600 | 0.0296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9455 | 7800 | 0.0363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9697 | 8000 | 0.0387 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9939 | 8200 | 0.041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 8250 | - | 0.0413 | 0.4653 | 0.2583 | 0.4681 | 0.3972 | - | - | - | - | - | - | - | - | - | - |
| -1 | -1 | - | - | 0.4633 | 0.2763 | 0.4797 | 0.4631 | 0.2161 | 0.4933 | 0.7519 | 0.2932 | 0.6047 | 0.7882 | 0.2935 | 0.4216 | 0.5022 | 0.4366 |
- The bold row denotes the saved checkpoint.
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Energy Consumed: 0.084 kWh
- Carbon Emitted: 0.033 kg of CO2
- Hours Used: 0.292 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: 4.2.0.dev0
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
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",
}
SpladeLoss
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
SparseMultipleNegativesRankingLoss
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
FlopsLoss
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}