--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:99000 - loss:SpladeLoss - loss:SparseMultipleNegativesRankingLoss - loss:FlopsLoss base_model: distilbert/distilbert-base-uncased widget: - source_sentence: who are the dancers in the limp bizkit rollin video sentences: - Voting age Before the Second World War, the voting age in almost all countries was 21 years or higher. Czechoslovakia was the first to reduce the voting age to 20 years in 1946, and by 1968 a total of 17 countries had lowered their voting age.[1] Many countries, particularly in Western Europe, reduced their voting ages to 18 years during the 1970s, starting with the United Kingdom (1969),[2] with the United States (26th Amendment) (1971), Canada, West Germany (1972), Australia (1974), France (1974), and others following soon afterwards. By the end of the 20th century, 18 had become by far the most common voting age. However, a few countries maintain a voting age of 20 years or higher. It was argued that young men could be drafted to go to war at 18, and many people felt they should be able to vote at the age of 18.[3] - Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower of the former World Trade Center in New York City. The introduction features Ben Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles. The rest of the video has several cuts to Durst and his bandmates hanging out of the Bentley as they drive about Manhattan. The song Ben Stiller is playing at the beginning is "My Generation" from the same album. The video also features scenes of Fred Durst with five girls dancing in a room. The video was filmed around the same time as the film Zoolander, which explains Stiller and Dorff's appearance. Fred Durst has a small cameo in that film. - Eobard Thawne When Thawne reappears, he murders the revived Johnny Quick,[9] before proceeding to trap Barry and the revived Max Mercury inside the negative Speed Force. Thawne then attempts to kill Wally West's children through their connection to the Speed Force in front of Linda Park-West, only to be stopped by Jay Garrick and Bart Allen. Thawne defeats Jay and prepares to kill Bart, but Barry, Max, Wally, Jesse Quick, and Impulse arrive to prevent the villain from doing so.[8][10] In the ensuing fight, Thawne reveals that he is responsible for every tragedy that has occurred in Barry's life, including the death of his mother. Thawne then decides to destroy everything the Flash holds dear by killing Barry's wife, Iris, before they even met.[10] - source_sentence: who wins season 14 of hell's kitchen sentences: - Hell's Kitchen (U.S. season 14) Season 14 of the American competitive reality television series Hell's Kitchen premiered on March 3, 2015 on Fox. The prize is a head chef position at Gordon Ramsay Pub & Grill in Caesars Atlantic City.[1] Gordon Ramsay returned as head chef with Andi Van Willigan and James Avery returning as sous-chefs for both their respective kitchens as well as Marino Monferrato as the maître d'. Executive chef Meghan Gill from Roanoke, Virginia, won the competition, thus becoming the fourteenth winner of Hell's Kitchen. - 'Maze Runner: The Death Cure On April 22, 2017, the studio delayed the release date once again, to February 9, 2018, in order to allow more time for post-production; months later, on August 25, the studio moved the release forward two weeks.[17] The film will premiere on January 26, 2018 in 3D, IMAX and IMAX 3D.[18][19]' - North American Plate On its western edge, the Farallon Plate has been subducting under the North American Plate since the Jurassic Period. The Farallon Plate has almost completely subducted beneath the western portion of the North American Plate leaving that part of the North American Plate in contact with the Pacific Plate as the San Andreas Fault. The Juan de Fuca, Explorer, Gorda, Rivera, Cocos and Nazca plates are remnants of the Farallon Plate. - source_sentence: who played the dj in the movie the warriors sentences: - List of Arrow episodes As of May 17, 2018,[update] 138 episodes of Arrow have aired, concluding the sixth season. On April 2, 2018, the CW renewed the series for a seventh season.[1] - Lynne Thigpen Cherlynne Theresa "Lynne" Thigpen (December 22, 1948 – March 12, 2003) was an American actress, best known for her role as "The Chief" of ACME in the various Carmen Sandiego television series and computer games from 1991 to 1997. For her varied television work, Thigpen was nominated for six Daytime Emmy Awards; she won a Tony Award in 1997 for portraying Dr. Judith Kaufman in An American Daughter. - The Washington Post The Washington Post is an American daily newspaper. It is the most widely circulated newspaper published in Washington, D.C., and was founded on December 6, 1877,[7] making it the area's oldest extant newspaper. In February 2017, amid a barrage of criticism from President Donald Trump over the paper's coverage of his campaign and early presidency as well as concerns among the American press about Trump's criticism and threats against journalists who provide coverage he deems unfavorable, the Post adopted the slogan "Democracy Dies in Darkness".[8] - source_sentence: how old was messi when he started his career sentences: - Lionel Messi Born and raised in central Argentina, Messi was diagnosed with a growth hormone deficiency as a child. At age 13, he relocated to Spain to join Barcelona, who agreed to pay for his medical treatment. After a fast progression through Barcelona's youth academy, Messi made his competitive debut aged 17 in October 2004. Despite being injury-prone during his early career, he established himself as an integral player for the club within the next three years, finishing 2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year award, a feat he repeated the following year. His first uninterrupted campaign came in the 2008–09 season, during which he helped Barcelona achieve the first treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA World Player of the Year award by record voting margins. - We Are Marshall Filming of We Are Marshall commenced on April 3, 2006, in Huntington, West Virginia, and was completed in Atlanta, Georgia. The premiere for the film was held at the Keith Albee Theater on December 12, 2006, in Huntington; other special screenings were held at Pullman Square. The movie was released nationwide on December 22, 2006. - One Fish, Two Fish, Red Fish, Blue Fish One Fish, Two Fish, Red Fish, Blue Fish is a 1960 children's book by Dr. Seuss. It is a simple rhyming book for beginning readers, with a freewheeling plot about a boy and a girl named Jay and Kay and the many amazing creatures they have for friends and pets. Interspersed are some rather surreal and unrelated skits, such as a man named Ned whose feet stick out from his bed, and a creature who has a bird in his ear. As of 2001, over 6 million copies of the book had been sold, placing it 13th on a list of "All-Time Bestselling Children's Books" from Publishers Weekly.[1] Based on a 2007 online poll, the United States' National Education Association labor union named the book one of its "Teachers' Top 100 Books for Children."[2] - source_sentence: is send in the clowns from a musical sentences: - Money in the Bank ladder match The first match was contested in 2005 at WrestleMania 21, after being invented (in kayfabe) by Chris Jericho.[1] At the time, it was exclusive to wrestlers of the Raw brand, and Edge won the inaugural match.[1] From then until 2010, the Money in the Bank ladder match, now open to all WWE brands, became a WrestleMania mainstay. 2010 saw a second and third Money in the Bank ladder match when the Money in the Bank pay-per-view debuted in July. Unlike the matches at WrestleMania, this new event featured two such ladder matches – one each for a contract for the WWE Championship and World Heavyweight Championship, respectively. - The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1] - 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik''s young wife runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - row_non_zero_mean_query - row_sparsity_mean_query - row_non_zero_mean_corpus - row_sparsity_mean_corpus co2_eq_emissions: emissions: 32.749162711505036 energy_consumed: 0.08425262208968576 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.292 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: splade-distilbert-base-uncased trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.24 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.44 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.72 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.24 name: Dot Precision@1 - type: dot_precision@3 value: 0.14666666666666664 name: Dot Precision@3 - type: dot_precision@5 value: 0.12000000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.07200000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.24 name: Dot Recall@1 - type: dot_recall@3 value: 0.44 name: Dot Recall@3 - type: dot_recall@5 value: 0.6 name: Dot Recall@5 - type: dot_recall@10 value: 0.72 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.46533877878819696 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3856269841269841 name: Dot Mrr@10 - type: dot_map@100 value: 0.3974184036014145 name: Dot Map@100 - type: row_non_zero_mean_query value: 15.779999732971191 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9994829297065735 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 25.729328155517578 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9991570711135864 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.22 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.42 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.22 name: Dot Precision@1 - type: dot_precision@3 value: 0.13999999999999999 name: Dot Precision@3 - type: dot_precision@5 value: 0.12000000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.22 name: Dot Recall@1 - type: dot_recall@3 value: 0.42 name: Dot Recall@3 - type: dot_recall@5 value: 0.6 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.46328494594550307 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.37662698412698403 name: Dot Mrr@10 - type: dot_map@100 value: 0.3856610333651542 name: Dot Map@100 - type: row_non_zero_mean_query value: 15.380000114440918 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9994961023330688 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 26.596866607666016 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9991285800933838 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.42 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.56 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.2866666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.264 name: Dot Precision@5 - type: dot_precision@10 value: 0.214 name: Dot Precision@10 - type: dot_recall@1 value: 0.01879480879384032 name: Dot Recall@1 - type: dot_recall@3 value: 0.05027421919442009 name: Dot Recall@3 - type: dot_recall@5 value: 0.08706875727827264 name: Dot Recall@5 - type: dot_recall@10 value: 0.11178880663195827 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2582539565166507 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.38549999999999995 name: Dot Mrr@10 - type: dot_map@100 value: 0.1034946476704924 name: Dot Map@100 - type: row_non_zero_mean_query value: 20.18000030517578 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9993388652801514 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 30.07179069519043 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9990148544311523 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.34 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.34 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.288 name: Dot Precision@5 - type: dot_precision@10 value: 0.226 name: Dot Precision@10 - type: dot_recall@1 value: 0.021422381525060468 name: Dot Recall@1 - type: dot_recall@3 value: 0.0742401436593227 name: Dot Recall@3 - type: dot_recall@5 value: 0.08995450762658255 name: Dot Recall@5 - type: dot_recall@10 value: 0.11319066947710729 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.27630767880389084 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.42138888888888887 name: Dot Mrr@10 - type: dot_map@100 value: 0.11387493422516994 name: Dot Map@100 - type: row_non_zero_mean_query value: 18.81999969482422 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9993834495544434 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 30.65966796875 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9989954829216003 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.64 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666663 name: Dot Precision@3 - type: dot_precision@5 value: 0.11599999999999999 name: Dot Precision@5 - type: dot_precision@10 value: 0.064 name: Dot Precision@10 - type: dot_recall@1 value: 0.31 name: Dot Recall@1 - type: dot_recall@3 value: 0.49 name: Dot Recall@3 - type: dot_recall@5 value: 0.56 name: Dot Recall@5 - type: dot_recall@10 value: 0.61 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.46811217927927307 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.43099999999999994 name: Dot Mrr@10 - type: dot_map@100 value: 0.4334878570971412 name: Dot Map@100 - type: row_non_zero_mean_query value: 15.079999923706055 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9995059370994568 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 22.96107292175293 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.999247670173645 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666663 name: Dot Precision@3 - type: dot_precision@5 value: 0.124 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.29 name: Dot Recall@1 - type: dot_recall@3 value: 0.49 name: Dot Recall@3 - type: dot_recall@5 value: 0.6 name: Dot Recall@5 - type: dot_recall@10 value: 0.66 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4796509872234161 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.42804761904761895 name: Dot Mrr@10 - type: dot_map@100 value: 0.4288636915548681 name: Dot Map@100 - type: row_non_zero_mean_query value: 14.399999618530273 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.999528169631958 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 23.73485565185547 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9992223381996155 name: Row Sparsity Mean Corpus - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.2866666666666667 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.4533333333333333 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.5666666666666668 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.64 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.2866666666666667 name: Dot Precision@1 - type: dot_precision@3 value: 0.19999999999999998 name: Dot Precision@3 - type: dot_precision@5 value: 0.16666666666666666 name: Dot Precision@5 - type: dot_precision@10 value: 0.11666666666666668 name: Dot Precision@10 - type: dot_recall@1 value: 0.1895982695979468 name: Dot Recall@1 - type: dot_recall@3 value: 0.3267580730648067 name: Dot Recall@3 - type: dot_recall@5 value: 0.4156895857594242 name: Dot Recall@5 - type: dot_recall@10 value: 0.4805962688773194 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.39723497152804027 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.40070899470899474 name: Dot Mrr@10 - type: dot_map@100 value: 0.3114669694563494 name: Dot Map@100 - type: row_non_zero_mean_query value: 17.013333320617676 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9994425773620605 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 26.254063924153645 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9991398652394613 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.4023861852433281 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5827315541601256 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6721193092621665 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7583987441130299 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4023861852433281 name: Dot Precision@1 - type: dot_precision@3 value: 0.25922553636839346 name: Dot Precision@3 - type: dot_precision@5 value: 0.2099277864992151 name: Dot Precision@5 - type: dot_precision@10 value: 0.14982417582417581 name: Dot Precision@10 - type: dot_recall@1 value: 0.22672192221710946 name: Dot Recall@1 - type: dot_recall@3 value: 0.36838967779676207 name: Dot Recall@3 - type: dot_recall@5 value: 0.44570232082548333 name: Dot Recall@5 - type: dot_recall@10 value: 0.5264378082924004 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4631187549753249 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5167952081931673 name: Dot Mrr@10 - type: dot_map@100 value: 0.38677121563396466 name: Dot Map@100 - type: row_non_zero_mean_query value: 19.27265313955454 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9993685804880582 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 27.068602635310246 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9991131195655236 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.18 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.36 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.44 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.18 name: Dot Precision@1 - type: dot_precision@3 value: 0.13333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.1 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.07 name: Dot Recall@1 - type: dot_recall@3 value: 0.1733333333333333 name: Dot Recall@3 - type: dot_recall@5 value: 0.2033333333333333 name: Dot Recall@5 - type: dot_recall@10 value: 0.28 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.216118762316258 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.2994126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.16840852597130174 name: Dot Map@100 - type: row_non_zero_mean_query value: 25.020000457763672 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9991803169250488 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 27.777875900268555 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9990898966789246 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.6 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.82 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.86 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6 name: Dot Precision@1 - type: dot_precision@3 value: 0.48666666666666664 name: Dot Precision@3 - type: dot_precision@5 value: 0.4439999999999999 name: Dot Precision@5 - type: dot_precision@10 value: 0.4000000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.05376110547712118 name: Dot Recall@1 - type: dot_recall@3 value: 0.15092123200468407 name: Dot Recall@3 - type: dot_recall@5 value: 0.19238478534118364 name: Dot Recall@5 - type: dot_recall@10 value: 0.2793082705020891 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4933229100355268 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7174126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.3647742683351921 name: Dot Map@100 - type: row_non_zero_mean_query value: 14.34000015258789 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9995301961898804 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 22.812902450561523 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9992524981498718 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.62 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.82 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.62 name: Dot Precision@1 - type: dot_precision@3 value: 0.28 name: Dot Precision@3 - type: dot_precision@5 value: 0.184 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.61 name: Dot Recall@1 - type: dot_recall@3 value: 0.7866666666666666 name: Dot Recall@3 - type: dot_recall@5 value: 0.8566666666666666 name: Dot Recall@5 - type: dot_recall@10 value: 0.8566666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7518512751926597 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7293333333333335 name: Dot Mrr@10 - type: dot_map@100 value: 0.7119416486291485 name: Dot Map@100 - type: row_non_zero_mean_query value: 17.84000015258789 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9994155168533325 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 25.645116806030273 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9991597533226013 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.22 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.32 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.44 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.54 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.22 name: Dot Precision@1 - type: dot_precision@3 value: 0.1333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.11599999999999999 name: Dot Precision@5 - type: dot_precision@10 value: 0.07600000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.138 name: Dot Recall@1 - type: dot_recall@3 value: 0.25 name: Dot Recall@3 - type: dot_recall@5 value: 0.32938888888888884 name: Dot Recall@5 - type: dot_recall@10 value: 0.3908015873015873 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.29315131681028644 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.30430158730158724 name: Dot Mrr@10 - type: dot_map@100 value: 0.2444001739214205 name: Dot Map@100 - type: row_non_zero_mean_query value: 18.940000534057617 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9993795156478882 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 27.020782470703125 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9991146922111511 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.64 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.82 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.86 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.64 name: Dot Precision@1 - type: dot_precision@3 value: 0.37333333333333324 name: Dot Precision@3 - type: dot_precision@5 value: 0.23199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.132 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 name: Dot Recall@1 - type: dot_recall@3 value: 0.56 name: Dot Recall@3 - type: dot_recall@5 value: 0.58 name: Dot Recall@5 - type: dot_recall@10 value: 0.66 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.60467671511462 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7286666666666669 name: Dot Mrr@10 - type: dot_map@100 value: 0.5280557928272471 name: Dot Map@100 - type: row_non_zero_mean_query value: 18.799999237060547 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9993841648101807 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 24.752653121948242 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.999189019203186 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.64 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.84 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.88 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.64 name: Dot Precision@1 - type: dot_precision@3 value: 0.32 name: Dot Precision@3 - type: dot_precision@5 value: 0.21999999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.12399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.5740000000000001 name: Dot Recall@1 - type: dot_recall@3 value: 0.768 name: Dot Recall@3 - type: dot_recall@5 value: 0.8446666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9553333333333334 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7881541877243683 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7535238095238094 name: Dot Mrr@10 - type: dot_map@100 value: 0.727066872303161 name: Dot Map@100 - type: row_non_zero_mean_query value: 17.780000686645508 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9994174242019653 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 19.436979293823242 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9993631839752197 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.48 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.20400000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.154 name: Dot Precision@10 - type: dot_recall@1 value: 0.07666666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.13366666666666668 name: Dot Recall@3 - type: dot_recall@5 value: 0.21066666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.31666666666666665 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.29354115188538094 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4672380952380951 name: Dot Mrr@10 - type: dot_map@100 value: 0.21425734227573925 name: Dot Map@100 - type: row_non_zero_mean_query value: 24.84000015258789 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9991861581802368 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 34.34458923339844 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9988747239112854 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.18 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.4 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.54 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.18 name: Dot Precision@1 - type: dot_precision@3 value: 0.13333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.10800000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.18 name: Dot Recall@1 - type: dot_recall@3 value: 0.4 name: Dot Recall@3 - type: dot_recall@5 value: 0.54 name: Dot Recall@5 - type: dot_recall@10 value: 0.7 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4216491858751158 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.33469047619047615 name: Dot Mrr@10 - type: dot_map@100 value: 0.34714031247291627 name: Dot Map@100 - type: row_non_zero_mean_query value: 29.360000610351562 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9990381002426147 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 29.988996505737305 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9990174770355225 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.18 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.355 name: Dot Recall@1 - type: dot_recall@3 value: 0.475 name: Dot Recall@3 - type: dot_recall@5 value: 0.59 name: Dot Recall@5 - type: dot_recall@10 value: 0.64 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5021918146434317 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.467 name: Dot Mrr@10 - type: dot_map@100 value: 0.462876176092865 name: Dot Map@100 - type: row_non_zero_mean_query value: 19.799999237060547 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9993513226509094 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 27.219938278198242 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9991081357002258 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.5510204081632653 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7755102040816326 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8775510204081632 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9591836734693877 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5510204081632653 name: Dot Precision@1 - type: dot_precision@3 value: 0.4965986394557823 name: Dot Precision@3 - type: dot_precision@5 value: 0.4530612244897959 name: Dot Precision@5 - type: dot_precision@10 value: 0.3857142857142857 name: Dot Precision@10 - type: dot_recall@1 value: 0.038534835153574185 name: Dot Recall@1 - type: dot_recall@3 value: 0.1072377690272331 name: Dot Recall@3 - type: dot_recall@5 value: 0.15706865554129606 name: Dot Recall@5 - type: dot_recall@10 value: 0.25172431385375454 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4366428831087667 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6906948493683187 name: Dot Mrr@10 - type: dot_map@100 value: 0.33070503126735623 name: Dot Map@100 - type: row_non_zero_mean_query value: 15.22449016571045 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.99950110912323 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 31.900609970092773 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9989547729492188 name: Row Sparsity Mean Corpus --- # splade-distilbert-base-uncased trained on Natural Questions This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) 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 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** SPLADE Sparse Encoder - **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **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) ### Full Model Architecture ``` SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```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("tomaarsen/splade-distilbert-base-uncased-nq-e-3") # Run inference sentences = [ 'is send in the clowns from a musical', 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]', 'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]', ] embeddings = model.encode(sentences) print(embeddings.shape) # (3, 30522) # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:-------------------------|:------------|:-------------|:-----------|:-----------------|:------------|:-----------|:-------------|:-------------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.22 | 0.34 | 0.3 | 0.18 | 0.6 | 0.62 | 0.22 | 0.64 | 0.64 | 0.36 | 0.18 | 0.38 | 0.551 | | dot_accuracy@3 | 0.42 | 0.52 | 0.5 | 0.36 | 0.82 | 0.82 | 0.32 | 0.82 | 0.84 | 0.48 | 0.4 | 0.5 | 0.7755 | | dot_accuracy@5 | 0.6 | 0.52 | 0.62 | 0.44 | 0.86 | 0.9 | 0.44 | 0.82 | 0.88 | 0.62 | 0.54 | 0.62 | 0.8776 | | dot_accuracy@10 | 0.74 | 0.58 | 0.68 | 0.6 | 0.9 | 0.9 | 0.54 | 0.86 | 0.98 | 0.76 | 0.7 | 0.66 | 0.9592 | | dot_precision@1 | 0.22 | 0.34 | 0.3 | 0.18 | 0.6 | 0.62 | 0.22 | 0.64 | 0.64 | 0.36 | 0.18 | 0.38 | 0.551 | | dot_precision@3 | 0.14 | 0.3133 | 0.1667 | 0.1333 | 0.4867 | 0.28 | 0.1333 | 0.3733 | 0.32 | 0.2133 | 0.1333 | 0.18 | 0.4966 | | dot_precision@5 | 0.12 | 0.288 | 0.124 | 0.1 | 0.444 | 0.184 | 0.116 | 0.232 | 0.22 | 0.204 | 0.108 | 0.136 | 0.4531 | | dot_precision@10 | 0.074 | 0.226 | 0.07 | 0.07 | 0.4 | 0.092 | 0.076 | 0.132 | 0.124 | 0.154 | 0.07 | 0.074 | 0.3857 | | dot_recall@1 | 0.22 | 0.0214 | 0.29 | 0.07 | 0.0538 | 0.61 | 0.138 | 0.32 | 0.574 | 0.0767 | 0.18 | 0.355 | 0.0385 | | dot_recall@3 | 0.42 | 0.0742 | 0.49 | 0.1733 | 0.1509 | 0.7867 | 0.25 | 0.56 | 0.768 | 0.1337 | 0.4 | 0.475 | 0.1072 | | dot_recall@5 | 0.6 | 0.09 | 0.6 | 0.2033 | 0.1924 | 0.8567 | 0.3294 | 0.58 | 0.8447 | 0.2107 | 0.54 | 0.59 | 0.1571 | | dot_recall@10 | 0.74 | 0.1132 | 0.66 | 0.28 | 0.2793 | 0.8567 | 0.3908 | 0.66 | 0.9553 | 0.3167 | 0.7 | 0.64 | 0.2517 | | **dot_ndcg@10** | **0.4633** | **0.2763** | **0.4797** | **0.2161** | **0.4933** | **0.7519** | **0.2932** | **0.6047** | **0.7882** | **0.2935** | **0.4216** | **0.5022** | **0.4366** | | dot_mrr@10 | 0.3766 | 0.4214 | 0.428 | 0.2994 | 0.7174 | 0.7293 | 0.3043 | 0.7287 | 0.7535 | 0.4672 | 0.3347 | 0.467 | 0.6907 | | dot_map@100 | 0.3857 | 0.1139 | 0.4289 | 0.1684 | 0.3648 | 0.7119 | 0.2444 | 0.5281 | 0.7271 | 0.2143 | 0.3471 | 0.4629 | 0.3307 | | row_non_zero_mean_query | 15.38 | 18.82 | 14.4 | 25.02 | 14.34 | 17.84 | 18.94 | 18.8 | 17.78 | 24.84 | 29.36 | 19.8 | 15.2245 | | row_sparsity_mean_query | 0.9995 | 0.9994 | 0.9995 | 0.9992 | 0.9995 | 0.9994 | 0.9994 | 0.9994 | 0.9994 | 0.9992 | 0.999 | 0.9994 | 0.9995 | | row_non_zero_mean_corpus | 26.5969 | 30.6597 | 23.7349 | 27.7779 | 22.8129 | 25.6451 | 27.0208 | 24.7527 | 19.437 | 34.3446 | 29.989 | 27.2199 | 31.9006 | | row_sparsity_mean_corpus | 0.9991 | 0.999 | 0.9992 | 0.9991 | 0.9993 | 0.9992 | 0.9991 | 0.9992 | 0.9994 | 0.9989 | 0.999 | 0.9991 | 0.999 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:-------------------------|:-----------| | dot_accuracy@1 | 0.2867 | | dot_accuracy@3 | 0.4533 | | dot_accuracy@5 | 0.5667 | | dot_accuracy@10 | 0.64 | | dot_precision@1 | 0.2867 | | dot_precision@3 | 0.2 | | dot_precision@5 | 0.1667 | | dot_precision@10 | 0.1167 | | dot_recall@1 | 0.1896 | | dot_recall@3 | 0.3268 | | dot_recall@5 | 0.4157 | | dot_recall@10 | 0.4806 | | **dot_ndcg@10** | **0.3972** | | dot_mrr@10 | 0.4007 | | dot_map@100 | 0.3115 | | row_non_zero_mean_query | 17.0133 | | row_sparsity_mean_query | 0.9994 | | row_non_zero_mean_corpus | 26.2541 | | row_sparsity_mean_corpus | 0.9991 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [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 | 0.4024 | | dot_accuracy@3 | 0.5827 | | dot_accuracy@5 | 0.6721 | | dot_accuracy@10 | 0.7584 | | dot_precision@1 | 0.4024 | | dot_precision@3 | 0.2592 | | dot_precision@5 | 0.2099 | | dot_precision@10 | 0.1498 | | dot_recall@1 | 0.2267 | | dot_recall@3 | 0.3684 | | dot_recall@5 | 0.4457 | | dot_recall@10 | 0.5264 | | **dot_ndcg@10** | **0.4631** | | dot_mrr@10 | 0.5168 | | dot_map@100 | 0.3868 | | row_non_zero_mean_query | 19.2727 | | row_sparsity_mean_query | 0.9994 | | row_non_zero_mean_corpus | 27.0686 | | row_sparsity_mean_corpus | 0.9991 | ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](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 {'loss': SparseMultipleNegativesRankingLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) (cross_entropy_loss): CrossEntropyLoss() ), 'lambda_corpus': 0.003, 'lambda_query': 0.005} ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](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 {'loss': SparseMultipleNegativesRankingLoss( (model): SparseEncoder( (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': None}) ) (cross_entropy_loss): CrossEntropyLoss() ), 'lambda_corpus': 0.003, 'lambda_query': 0.005} ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 12 - `per_device_eval_batch_size`: 12 - `learning_rate`: 2e-05 - `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`: 12 - `per_device_eval_batch_size`: 12 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: 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 | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | |:-------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:| | 0.0242 | 200 | 4.6206 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0485 | 400 | 0.074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0727 | 600 | 0.0441 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0970 | 800 | 0.0288 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1212 | 1000 | 0.0395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1455 | 1200 | 0.0387 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1697 | 1400 | 0.039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1939 | 1600 | 0.0274 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2 | 1650 | - | 0.0425 | 0.4834 | 0.2578 | 0.4469 | 0.3960 | - | - | - | - | - | - | - | - | - | - | | 0.2182 | 1800 | 0.0317 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2424 | 2000 | 0.0563 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2667 | 2200 | 0.0521 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2909 | 2400 | 0.0481 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3152 | 2600 | 0.0562 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3394 | 2800 | 0.0524 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3636 | 3000 | 0.0477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3879 | 3200 | 0.0579 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4 | 3300 | - | 0.0544 | 0.4270 | 0.2376 | 0.4740 | 0.3795 | - | - | - | - | - | - | - | - | - | - | | 0.4121 | 3400 | 0.0458 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4364 | 3600 | 0.0477 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4606 | 3800 | 0.0479 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4848 | 4000 | 0.046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5091 | 4200 | 0.0382 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5333 | 4400 | 0.0442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5576 | 4600 | 0.0405 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 4800 | 0.0417 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6 | 4950 | - | 0.0416 | 0.4677 | 0.2401 | 0.4760 | 0.3946 | - | - | - | - | - | - | - | - | - | - | | 0.6061 | 5000 | 0.033 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6303 | 5200 | 0.0437 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6545 | 5400 | 0.0351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6788 | 5600 | 0.0387 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7030 | 5800 | 0.048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7273 | 6000 | 0.0498 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7515 | 6200 | 0.0442 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7758 | 6400 | 0.0359 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.8** | **6600** | **0.0398** | **0.0403** | **0.4633** | **0.2763** | **0.4797** | **0.4064** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.8242 | 6800 | 0.0364 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8485 | 7000 | 0.0363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8727 | 7200 | 0.0344 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8970 | 7400 | 0.0351 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9212 | 7600 | 0.0296 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9455 | 7800 | 0.0363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9697 | 8000 | 0.0387 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9939 | 8200 | 0.041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0 | 8250 | - | 0.0413 | 0.4653 | 0.2583 | 0.4681 | 0.3972 | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | 0.4633 | 0.2763 | 0.4797 | 0.4631 | 0.2161 | 0.4933 | 0.7519 | 0.2932 | 0.6047 | 0.7882 | 0.2935 | 0.4216 | 0.5022 | 0.4366 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.084 kWh - **Carbon Emitted**: 0.033 kg of CO2 - **Hours Used**: 0.292 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```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 @misc{formal2022distillationhardnegativesampling, title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, year={2022}, eprint={2205.04733}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2205.04733}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` #### FlopsLoss ```bibtex @article{paria2020minimizing, title={Minimizing flops to learn efficient sparse representations}, author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, journal={arXiv preprint arXiv:2004.05665}, year={2020} } ```