metadata

language:
  - en
tags:
  - sentence-transformers
  - sparse-encoder
  - generated_from_trainer
  - dataset_size:99000
  - loss:SpladeLoss
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 maÃ®tre 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 â€“
        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 2008â€“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
        â€“ 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
model-index:
  - name: SparseEncoder
    results:
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.22
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.46
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.56
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.72
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.22
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.15999999999999998
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.124
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08599999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.06666666666666667
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.2383333333333333
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.27999999999999997
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.36999999999999994
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2677421246620843
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.3605714285714285
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.1850759743565379
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: dot_accuracy@1
            value: 0.66
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.76
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.84
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.94
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.66
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.5266666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.488
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.44800000000000006
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.06670257100262758
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.11974142991079241
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.1791957371678672
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.30349140489526194
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5343019203634498
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.735468253968254
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4049377767237549
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: dot_accuracy@1
            value: 0.48
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.82
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.88
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.92
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.48
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.28
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.17999999999999997
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09399999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.47
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.79
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.85
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.89
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.7040555094504362
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.6526666666666666
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.6419071669071669
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: dot_accuracy@1
            value: 0.38
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.52
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.6
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.7
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.23333333333333336
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.17599999999999993
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.10599999999999998
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.18391269841269842
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.30518253968253967
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.37670634920634916
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.47370634920634913
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.38850010242023175
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4707460317460317
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3248412848822558
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: dot_accuracy@1
            value: 0.74
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.8
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.88
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.92
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.74
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.37999999999999984
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.256
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.142
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.37
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.57
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.64
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.71
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6641341464635651
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7923333333333333
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.5975783218818589
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: dot_accuracy@1
            value: 0.32
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.82
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.32
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08199999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.32
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.6
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.7
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.82
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5580216131954555
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.47502380952380946
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.48536594051110177
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        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: 0.54
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.56
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.32666666666666666
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.3
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.23800000000000004
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.040931301515821
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.07053120918183034
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.09088994062974395
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.1156589880572893
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.2972257598049526
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.44519047619047614
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.12826395582499595
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: dot_accuracy@1
            value: 0.38
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.62
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.74
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.38
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.21333333333333332
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.15200000000000002
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08399999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.35
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.59
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.69
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.75
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5725986299850336
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5327142857142857
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.514071091036335
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: dot_accuracy@1
            value: 0.76
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.86
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.92
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 1
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.76
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.33333333333333326
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.21599999999999994
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.12799999999999997
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.6673333333333333
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.8079999999999999
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.8613333333333333
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.966
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.841867853215048
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.8272460317460317
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.7951434565434564
            name: Dot Map@100
      - 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: 0.62
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.66
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.84
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.36
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.28
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.236
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.174
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.07566666666666667
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.17266666666666666
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.24166666666666667
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.35666666666666663
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.33289046907153425
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5092936507936507
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.2530417035110073
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: dot_accuracy@1
            value: 0.22
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.56
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.82
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.22
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.18666666666666668
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.14
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.08199999999999999
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.22
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.56
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.7
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.82
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5149698384079933
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.4176904761904761
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.4241184371184371
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: dot_accuracy@1
            value: 0.44
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.66
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.72
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.44
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.2333333333333333
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.16
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.09
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.425
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.63
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.7
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.79
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.6152484517716852
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5620793650793651
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.563195436355978
            name: Dot Map@100
      - task:
          type: sparse-information-retrieval
          name: Sparse Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: dot_accuracy@1
            value: 0.6122448979591837
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.8571428571428571
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.8979591836734694
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.9591836734693877
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.6122448979591837
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.5918367346938777
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.5142857142857142
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.44285714285714284
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.043460752262081716
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.12859741651843118
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.17924500279536645
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.29401927639301495
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5011253143808486
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.7391399416909621
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.3876386317128788
            name: Dot Map@100
      - task:
          type: sparse-nano-beir
          name: Sparse Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: dot_accuracy@1
            value: 0.45786499215070653
            name: Dot Accuracy@1
          - type: dot_accuracy@3
            value: 0.6643956043956045
            name: Dot Accuracy@3
          - type: dot_accuracy@5
            value: 0.7413814756671899
            name: Dot Accuracy@5
          - type: dot_accuracy@10
            value: 0.8307064364207223
            name: Dot Accuracy@10
          - type: dot_precision@1
            value: 0.45786499215070653
            name: Dot Precision@1
          - type: dot_precision@3
            value: 0.30347462061747776
            name: Dot Precision@3
          - type: dot_precision@5
            value: 0.2370989010989011
            name: Dot Precision@5
          - type: dot_precision@10
            value: 0.16898901098901098
            name: Dot Precision@10
          - type: dot_recall@1
            value: 0.25382107614306887
            name: Dot Recall@1
          - type: dot_recall@3
            value: 0.42946558425335335
            name: Dot Recall@3
          - type: dot_recall@5
            value: 0.49915669459994827
            name: Dot Recall@5
          - type: dot_recall@10
            value: 0.5891955911706602
            name: Dot Recall@10
          - type: dot_ndcg@10
            value: 0.5225139794763322
            name: Dot Ndcg@10
          - type: dot_mrr@10
            value: 0.5784741347088285
            name: Dot Mrr@10
          - type: dot_map@100
            value: 0.43885993672044354
            name: Dot Map@100

SparseEncoder

This is a Sparse Encoder model trained on the natural-questions dataset using the sentence-transformers library. It maps sentences & paragraphs to a 50368-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Type: Sparse Encoder
Maximum Sequence Length: 512 tokens
Training Dataset:
- natural-questions
Language: en

Model Sources

Documentation: Sentence Transformers Documentation
Documentation: Sparse Encoder Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sparse Encoders on Hugging Face

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("sparse-embedding/splade-ModernBERT-nq-fresh-lq0.05-lc0.003_scale1_lr-1e-4_bs64")
# 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, 50368)

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Sparse Information Retrieval

Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with SparseInformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
dot_accuracy@1	0.22	0.66	0.48	0.38	0.74	0.32	0.38	0.38	0.76	0.36	0.22	0.44	0.6122
dot_accuracy@3	0.46	0.76	0.82	0.52	0.8	0.6	0.5	0.62	0.86	0.62	0.56	0.66	0.8571
dot_accuracy@5	0.56	0.84	0.88	0.6	0.88	0.7	0.54	0.74	0.92	0.66	0.7	0.72	0.898
dot_accuracy@10	0.72	0.94	0.92	0.7	0.92	0.82	0.56	0.8	1.0	0.84	0.82	0.8	0.9592
dot_precision@1	0.22	0.66	0.48	0.38	0.74	0.32	0.38	0.38	0.76	0.36	0.22	0.44	0.6122
dot_precision@3	0.16	0.5267	0.28	0.2333	0.38	0.2	0.3267	0.2133	0.3333	0.28	0.1867	0.2333	0.5918
dot_precision@5	0.124	0.488	0.18	0.176	0.256	0.14	0.3	0.152	0.216	0.236	0.14	0.16	0.5143
dot_precision@10	0.086	0.448	0.094	0.106	0.142	0.082	0.238	0.084	0.128	0.174	0.082	0.09	0.4429
dot_recall@1	0.0667	0.0667	0.47	0.1839	0.37	0.32	0.0409	0.35	0.6673	0.0757	0.22	0.425	0.0435
dot_recall@3	0.2383	0.1197	0.79	0.3052	0.57	0.6	0.0705	0.59	0.808	0.1727	0.56	0.63	0.1286
dot_recall@5	0.28	0.1792	0.85	0.3767	0.64	0.7	0.0909	0.69	0.8613	0.2417	0.7	0.7	0.1792
dot_recall@10	0.37	0.3035	0.89	0.4737	0.71	0.82	0.1157	0.75	0.966	0.3567	0.82	0.79	0.294
dot_ndcg@10	0.2677	0.5343	0.7041	0.3885	0.6641	0.558	0.2972	0.5726	0.8419	0.3329	0.515	0.6152	0.5011
dot_mrr@10	0.3606	0.7355	0.6527	0.4707	0.7923	0.475	0.4452	0.5327	0.8272	0.5093	0.4177	0.5621	0.7391
dot_map@100	0.1851	0.4049	0.6419	0.3248	0.5976	0.4854	0.1283	0.5141	0.7951	0.253	0.4241	0.5632	0.3876

Sparse Nano BEIR

Dataset: NanoBEIR_mean

Evaluated with SparseNanoBEIREvaluator with these parameters:

{
    "dataset_names": [
        "climatefever",
        "dbpedia",
        "fever",
        "fiqa2018",
        "hotpotqa",
        "msmarco",
        "nfcorpus",
        "nq",
        "quoraretrieval",
        "scidocs",
        "arguana",
        "scifact",
        "touche2020"
    ]
}

Metric	Value
dot_accuracy@1	0.4579
dot_accuracy@3	0.6644
dot_accuracy@5	0.7414
dot_accuracy@10	0.8307
dot_precision@1	0.4579
dot_precision@3	0.3035
dot_precision@5	0.2371
dot_precision@10	0.169
dot_recall@1	0.2538
dot_recall@3	0.4295
dot_recall@5	0.4992
dot_recall@10	0.5892
dot_ndcg@10	0.5225
dot_mrr@10	0.5785
dot_map@100	0.4389

Training Details

Training Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 99,000 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 29 characters
mean: 46.96 characters
max: 93 characters

min: 10 characters
mean: 582.13 characters
max: 2141 characters

	query	answer
type	string	string
details	min: 29 characters mean: 46.96 characters max: 93 characters	min: 10 characters mean: 582.13 characters max: 2141 characters

Samples:

query	answer
`who played the father in papa don't preach`	`Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.`
`where was the location of the battle of hastings`	`Battle 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 to`	`Canine 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: SpladeLoss with these parameters:

{'lambda_corpus': 0.003, 'lambda_query': 0.05, 'main_loss': SparseMultipleNegativesRankingLoss(
  (model): SparseEncoder(
    (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: ModernBertForMaskedLM 
    (1): SpladePooling({'pooling_strategy': 'max', 'word_embedding_dimension': None})
  )
  (cross_entropy_loss): CrossEntropyLoss()
)}

Evaluation Dataset

natural-questions

Dataset: natural-questions at f9e894e
Size: 1,000 evaluation samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 30 characters
mean: 47.2 characters
max: 96 characters

min: 58 characters
mean: 598.96 characters
max: 2480 characters

	query	answer
type	string	string
details	min: 30 characters mean: 47.2 characters max: 96 characters	min: 58 characters mean: 598.96 characters max: 2480 characters

Samples:

query	answer
`where is the tiber river located in italy`	Tiber 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 drive`	`Jay 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 you`	`I 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: SpladeLoss with these parameters:

{'lambda_corpus': 0.003, 'lambda_query': 0.05, 'main_loss': SparseMultipleNegativesRankingLoss(
  (model): SparseEncoder(
    (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: ModernBertForMaskedLM 
    (1): SpladePooling({'pooling_strategy': 'max', 'word_embedding_dimension': None})
  )
  (cross_entropy_loss): CrossEntropyLoss()
)}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 64
per_device_eval_batch_size: 64
learning_rate: 0.0001
num_train_epochs: 1
bf16: True
load_best_model_at_end: True
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: 64
per_device_eval_batch_size: 64
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: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 1
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: 42
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: True
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: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	NanoClimateFEVER_dot_ndcg@10	NanoDBPedia_dot_ndcg@10	NanoFEVER_dot_ndcg@10	NanoFiQA2018_dot_ndcg@10	NanoHotpotQA_dot_ndcg@10	NanoMSMARCO_dot_ndcg@10	NanoNFCorpus_dot_ndcg@10	NanoNQ_dot_ndcg@10	NanoQuoraRetrieval_dot_ndcg@10	NanoSCIDOCS_dot_ndcg@10	NanoArguAna_dot_ndcg@10	NanoSciFact_dot_ndcg@10	NanoTouche2020_dot_ndcg@10	NanoBEIR_mean_dot_ndcg@10
0.0065	10	7184.1445	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0129	20	1903.0385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0194	30	909.2298	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0259	40	316.7136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0323	50	244.4539	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0388	60	294.2571	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0452	70	353.676	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0517	80	152.3629	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0582	90	104.8372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0646	100	45.3187	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0711	110	22.4178	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0776	120	13.1608	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0840	130	8.5385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0905	140	6.384	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0970	150	4.6373	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1034	160	4.4281	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1099	170	3.8958	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1164	180	2.9138	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1228	190	2.0	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1293	200	1.3596	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1357	210	0.931	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1422	220	0.8304	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1487	230	0.5943	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1551	240	0.4164	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1616	250	0.3703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1681	260	0.3452	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1745	270	0.3224	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1810	280	0.2795	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1875	290	0.2597	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1939	300	0.3003	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1997	309	-	0.2614	0.2510	0.4642	0.7084	0.2488	0.5736	0.4460	0.2437	0.3954	0.6417	0.2529	0.3068	0.5133	0.3905	0.4182
0.2004	310	0.2549	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2069	320	0.2208	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2133	330	0.215	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2198	340	0.2113	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2262	350	0.198	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2327	360	0.2177	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2392	370	0.3034	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2456	380	0.2184	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2521	390	0.211	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2586	400	0.1726	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2650	410	0.1745	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2715	420	0.1978	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2780	430	0.1966	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2844	440	0.1961	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2909	450	0.1705	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2973	460	0.2358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3038	470	0.1643	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3103	480	0.1824	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3167	490	0.2357	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3232	500	0.1341	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3297	510	0.1786	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3361	520	0.1392	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3426	530	0.1434	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3491	540	0.1684	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3555	550	0.1827	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3620	560	0.1296	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3685	570	0.1731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3749	580	0.182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3814	590	0.1587	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3878	600	0.1519	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3943	610	0.1944	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3995	618	-	0.1768	0.2776	0.4818	0.7670	0.3444	0.5941	0.4696	0.2791	0.5422	0.7823	0.2977	0.4389	0.5579	0.4978	0.4869
0.4008	620	0.1778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4072	630	0.1595	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4137	640	0.1268	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4202	650	0.1361	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4266	660	0.1416	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4331	670	0.1139	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4396	680	0.1734	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4460	690	0.1082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4525	700	0.1198	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4590	710	0.0981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4654	720	0.0943	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4719	730	0.1421	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4783	740	0.0903	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4848	750	0.1339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4913	760	0.1109	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4977	770	0.1245	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5042	780	0.0949	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5107	790	0.0954	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5171	800	0.1136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5236	810	0.1206	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5301	820	0.101	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5365	830	0.1372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5430	840	0.1123	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5495	850	0.1358	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5559	860	0.1303	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5624	870	0.1339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5688	880	0.1096	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5753	890	0.079	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5818	900	0.0988	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5882	910	0.1042	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5947	920	0.0905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5992	927	-	0.1155	0.2490	0.5054	0.7070	0.3375	0.6075	0.4871	0.2843	0.5344	0.7616	0.3309	0.4869	0.6136	0.4983	0.4926
0.6012	930	0.0841	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6076	940	0.0946	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6141	950	0.086	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6206	960	0.118	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6270	970	0.0981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6335	980	0.117	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6399	990	0.0984	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6464	1000	0.1235	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6529	1010	0.1026	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6593	1020	0.0919	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6658	1030	0.0891	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6723	1040	0.1363	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6787	1050	0.0765	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6852	1060	0.0918	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6917	1070	0.1433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6981	1080	0.076	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7046	1090	0.0851	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7111	1100	0.0811	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7175	1110	0.0775	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7240	1120	0.1029	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7304	1130	0.104	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7369	1140	0.0961	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7434	1150	0.1159	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7498	1160	0.0919	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7563	1170	0.0849	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7628	1180	0.1021	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7692	1190	0.065	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7757	1200	0.0858	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7822	1210	0.0826	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7886	1220	0.069	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7951	1230	0.0718	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.799	1236	-	0.0956	0.2677	0.5343	0.7041	0.3885	0.6641	0.558	0.2972	0.5726	0.8419	0.3329	0.515	0.6152	0.5011	0.5225
0.8016	1240	0.076	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8080	1250	0.0703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8145	1260	0.0615	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8209	1270	0.0969	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8274	1280	0.104	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8339	1290	0.0616	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8403	1300	0.0752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8468	1310	0.0762	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8533	1320	0.0691	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8597	1330	0.102	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8662	1340	0.0778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8727	1350	0.0619	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8791	1360	0.0865	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8856	1370	0.0546	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8920	1380	0.0705	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8985	1390	0.0713	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9050	1400	0.0669	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9114	1410	0.0742	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9179	1420	0.0714	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9244	1430	0.0753	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9308	1440	0.0536	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9373	1450	0.0765	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9438	1460	0.0665	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9502	1470	0.0736	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9567	1480	0.0559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9632	1490	0.0587	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9696	1500	0.0798	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9761	1510	0.0819	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9825	1520	0.1039	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9890	1530	0.0617	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9955	1540	0.062	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9987	1545	-	0.0789	0.2818	0.5012	0.6833	0.3634	0.5937	0.5268	0.2913	0.5805	0.8237	0.3454	0.5020	0.6028	0.4961	0.5071
1	-1	-	-	0.2677	0.5343	0.7041	0.3885	0.6641	0.5580	0.2972	0.5726	0.8419	0.3329	0.5150	0.6152	0.5011	0.5225

The bold row denotes the saved checkpoint.

Framework Versions

Python: 3.12.10
Sentence Transformers: 4.2.0.dev0
Transformers: 4.48.3
PyTorch: 2.7.0+cu126
Accelerate: 1.6.0
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

@inproceedings{10.1145/3477495.3531857,
author = {Formal, Thibault and Lassance, Carlos and Piwowarski, Benjamin and Clinchant, St'{e}phane},
title = {From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3531857},
doi = {10.1145/3477495.3531857},
abstract = {Neural retrievers based on dense representations combined with Approximate Nearest Neighbors search have recently received a lot of attention, owing their success to distillation and/or better sampling of examples for training -- while still relying on the same backbone architecture. In the meantime, sparse representation learning fueled by traditional inverted indexing techniques has seen a growing interest, inheriting from desirable IR priors such as explicit lexical matching. While some architectural variants have been proposed, a lesser effort has been put in the training of such models. In this work, we build on SPLADE -- a sparse expansion-based retriever -- and show to which extent it is able to benefit from the same training improvements as dense models, by studying the effect of distillation, hard-negative mining as well as the Pre-trained Language Model initialization. We furthermore study the link between effectiveness and efficiency, on in-domain and zero-shot settings, leading to state-of-the-art results in both scenarios for sufficiently expressive models.},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {2353–2359},
numpages = {7},
keywords = {neural networks, indexing, sparse representations, regularization},
location = {Madrid, Spain},
series = {SIGIR '22}
}
}