--- 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 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 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: 1.0 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](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model trained on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) 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](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import 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](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.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](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "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](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | | | * 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](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json {'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](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: query and answer * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:----------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------| | type | string | string | | details | | | * 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](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json {'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 ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### SpladeLoss ```bibtex @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} } } ```