--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:60315 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-base-en-v1.5 widget: - source_sentence: Which university did Cheryl Miller attend? sentences: - Cheryl Miller male or female, to be named an All-American by "Parade" magazine four times. Averaging 32.8 points and 15.0 rebounds a game, Miller was Street & Smith's national High School Player of the Year in both 1981 and 1982. In her senior year she scored 105 points in a game against Norte Vista High School. She set California state records for points scored in a single season (1156), and points scored in a high school career (3405). At the University of Southern California (USC), the 6 ft. 2 in. (1.87 m) Miller played the forward position. She was a four-year letter - 1979 Formula One season 1979 Formula One season The 1979 Formula One season was the 33rd season of FIA Formula One motor racing. It featured the 1979 World Championship of F1 Drivers and the 1979 International Cup for F1 Constructors which were contested concurrently over a fifteen-round series which commenced on 21 January 1979, and ended on 7 October. The season also included three non-championship Formula One races. Jody Scheckter of Scuderia Ferrari won the 1979 World Championship of F1 Drivers while Scuderia Ferrari won 1979 International Cup for F1 Constructors. Gilles Villeneuve made it a 1–2 for Ferrari in the championship, concluding a - Cheryl Miller April 30, 2014, she was named the women's basketball coach at Langston University by athletic director Mike Garrett. On May 26, 2016, she was named the women's basketball coach at California State Los Angeles by athletic director Mike Garrett. Cheryl Miller serves as a sideline reporter for the "NBA on TNT"’s Thursday night doubleheader coverage for TNT Sports. She also made appearances on NBA TV during the 2008-09 NBA season as a reporter and analyst. Miller joined Turner Sports in September 1995 as an analyst and reporter for the "NBA on TBS" and TNT. She did make occasional appearances as - source_sentence: For what did Georgie O'Keefe become famous? sentences: - The Day the Earth Stood Still The Day the Earth Stood Still The Day the Earth Stood Still (a.k.a. Farewell to the Master and Journey to the World) is a 1951 American black-and-white science fiction film from 20th Century Fox, produced by Julian Blaustein and directed by Robert Wise. The film stars Michael Rennie, Patricia Neal, Billy Gray, Hugh Marlowe, and Sam Jaffe. The screenplay was written by Edmund H. North, based on the 1940 science fiction short story "Farewell to the Master" by Harry Bates, and the film score was composed by Bernard Herrmann. The storyline for "The Day the Earth Stood Still" involves a - Brian Keefe Bryant University in Smithfield, R.I. for four seasons (2001-05). In his final season, he helped the Bryant Bulldogs earn a trip to the Division II Championship in 2005. NBA Career Keefe started his career in professional basketball at the San Antonio Spurs where he served as video coordinator under head coach Gregg Popovich, and won a ring as part of the Spur’s 2007 championship in his second season. Keefe was selected by former Spurs assistant GM Sam Presti and former Spurs assistant coach PJ Carlesimo to join them in laying the groundwork for what would become the Oklahoma City Thunder. - What Have We Become? playlist in April 2014. The cover painting is by David Storey. "What Have We Become?" received generally positive reviews from music critics. The album received an average score of 76/100 from 14 reviews on Metacritic, indicating "generally favorable reviews". In his review for AllMusic, David Jeffries wrote that, "Anyone who enjoys their pop with extra wry and some sobering awareness should love What Have We Become?, but it's the Beautiful South faithful who will rightfully gush over the release, as these antiheroes have lost none of their touch or fatalistic flair." What Have We Become? What Have We Become? is - source_sentence: How much time did Jonah spend in the belly of the whale? sentences: - Book of Jonah all their efforts fail and they are eventually forced to throw Jonah overboard. As a result, the storm calms and the sailors then offer sacrifices to God. Jonah is miraculously saved by being swallowed by a large fish, in whose belly he spends three days and three nights. While in the great fish, Jonah prays to God in his affliction and commits to thanksgiving and to paying what he has vowed. God then commands the fish to vomit Jonah out. God again commands Jonah to travel to Nineveh and prophesy to its inhabitants. This time he goes and enters the - Jonah Who Lived in the Whale Jonah Who Lived in the Whale Jonah Who Lived in the Whale (), in the United States released as (Look to the Sky) is a 1993 Italian-French drama film directed by Roberto Faenza, based on the autobiographical novel by the writer Jona Oberski entitled "Childhood", focused on the drama of the Holocaust. It was entered into the 18th Moscow International Film Festival, where it won the Prix of Ecumenical Jury. Jonah is a four-year-old Dutch boy who lives in Amsterdam during the Second World War. After the occupation of the city by the Germans, he was deported to the concentration - Rain Man Rain Man Rain Man is a 1988 American comedy-drama road movie directed by Barry Levinson and written by Barry Morrow and Ronald Bass. It tells the story of an abrasive, selfish young wheeler-dealer Charlie Babbitt (Tom Cruise), who discovers that his estranged father has died and bequeathed all of his multimillion-dollar estate to his other son, Raymond (Dustin Hoffman), an autistic savant, of whose existence Charlie was unaware. Charlie is left with only his father's car and collection of rose bushes. In addition to the two leads, Valeria Golino stars as Charlie's girlfriend, Susanna. Morrow created the character of Raymond - source_sentence: In which country are Tangier and Casablanca? sentences: - Casablanca–Tangier high-speed rail line by a new high-speed right of way, with construction scheduled to begin in 2020. Two electrification types are used—from Tangier to Kenitra the new trackage was built with 25 kV at 50 Hz, while the line from Kenitra to Casablanca retained the existing 3 kV DC catenary. The ETCS-type signal system was installed by Ansaldo STS and Cofely Ineo. At the launch of service in 2018, the travel time between Casablanca and Tangier was reduced from 4 hours and 45 minutes to 2 hours and 10 minutes. The completion of dedicated high-speed trackage into Casablanca would further reduce the end-to-end - 'Maybellene Maybellene "Maybellene" is one of the first rock and roll songs. It was written and recorded in 1955 by Chuck Berry, and inspired/adapted from the Western Swing fiddle tune "Ida Red", which was recorded in 1938 by Bob Wills and his Texas Playboys. Berry''s song tells the story of a hot rod race and a broken romance. It was released in July 1955 as a single by Chess Records, of Chicago, Illinois. It was Berry''s first single and his first hit. "Maybellene" is considered one of the pioneering rock songs: "Rolling Stone" magazine wrote, "Rock & roll guitar starts here."' - Casablanca–Tangier high-speed rail line travel time to 1 hour and 30 minutes. The 12 Alstom Euroduplex trainsets operating on the line are bilevel trains, each comprised two power cars and eight passenger cars. The passenger capacity is 533 across two first-class cars, five second-class cars, and a food-service car. Casablanca–Tangier high-speed rail line The Casablanca—Tangier high-speed rail line is a high-speed rail line in Morocco that is the first on the African continent. The line was inaugurated on 15 November 2018 by King Mohammed VI of Morocco following over a decade of planning and construction by Moroccan national railway company ONCF. It is the - source_sentence: Where in Australia was swashbuckling Errol Flynn born? sentences: - 'Errol Flynn early in his career: Errol Flynn Errol Leslie Thomson Flynn (20 June 1909 – 14 October 1959) was an Australian-born American actor during the Golden Age of Hollywood. Considered the natural successor to Douglas Fairbanks, he achieved worldwide fame for his romantic swashbuckler roles in Hollywood films, as well as frequent partnerships with Olivia de Havilland. He was best known for his role as Robin Hood in "The Adventures of Robin Hood" (1938); his portrayal of the character was named by the American Film Institute as the 18th greatest hero in American film history. His other famous roles included the' - Phillips Recording Phillips Recording Phillips Recording is the short name widely used to refer to the Sam C. Phillips Recording Studio opened at 639 Madison Avenue in Memphis, Tennessee, by Sam Phillips in 1960. Internationally regarded at that time as a state-of-the-art facility, it was built to fill the needs of the Sun Records recording label that the older, smaller Sun Records Studio was no longer able to handle. This Memphis studio was originally a division of a larger corporation, Sam Phillips Recording Service, Inc., which also briefly included under its umbrella a Nashville studio, where famed CBS Records producer Billy Sherrill - Errol Flynn Blood" (1935), Major Geoffrey Vickers in "The Charge of the Light Brigade" (1936), as well as a number of Westerns, such as "Dodge City" (1939), "Santa Fe Trail" (1940), and "San Antonio" (1945). Errol Leslie Flynn was born on 20 June 1909 in Battery Point, a suburb of Hobart, Tasmania, Australia. His father, Theodore Thomson Flynn, was a lecturer (1909) and later professor (1911) of biology at the University of Tasmania. His mother was born Lily Mary Young, but shortly after marrying Theodore at St John's Church of England, Birchgrove, Sydney, on 23 January 1909, she changed her first name datasets: - sentence-transformers/trivia-qa-triplet pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: SentenceTransformer based on BAAI/bge-base-en-v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: cosine_accuracy@1 value: 0.28 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.28 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.132 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08800000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.12166666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.22666666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.2773333333333333 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.349 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2921247797723984 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3988253968253968 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.23009905552923093 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: cosine_accuracy@1 value: 0.6 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.78 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.84 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.52 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.496 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.4320000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.06294234345262387 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.13008183594343983 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1826677141588478 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.28710629918570024 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5202322797992843 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7049126984126982 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3763292112580843 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: cosine_accuracy@1 value: 0.64 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.9 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.92 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.94 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.64 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.18799999999999997 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09599999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6166666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.8566666666666666 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8766666666666666 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8966666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7846547160527625 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7672222222222221 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.740638888888889 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: cosine_accuracy@1 value: 0.3 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.38 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.42 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.48 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.3 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.128 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07600000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.15541269841269842 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.22260317460317464 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.26460317460317456 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3058253968253968 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2790870219513927 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.35669047619047617 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.24703484886940344 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: cosine_accuracy@1 value: 0.66 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.78 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.82 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.92 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.66 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3466666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.244 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.142 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.33 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.52 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.61 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.71 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6313501479198645 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7391587301587301 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5492849385578099 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: cosine_accuracy@1 value: 0.38 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.74 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.38 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666669 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12000000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07400000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.38 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.5 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.6 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.74 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5328147829793286 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4702142857142857 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4805827799103662 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: cosine_accuracy@1 value: 0.34 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.5 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.56 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.34 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.30000000000000004 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.3 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.244 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.01210979765940875 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.040583707862991376 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.05871448569598863 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.08206726954742757 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.26991337113740815 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.428 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.09944171039889445 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: cosine_accuracy@1 value: 0.26 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.64 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.72 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.26 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15999999999999998 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.132 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07600000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.25 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.46 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.61 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.69 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.46501655674505726 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.39988888888888885 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3974487222592024 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: cosine_accuracy@1 value: 0.86 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.96 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.96 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 1.0 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.86 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.4 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.25199999999999995 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.13599999999999998 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.7473333333333332 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.9253333333333333 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.9420000000000001 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.9933333333333334 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.9203896722112936 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9073333333333332 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.8924516594516595 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: cosine_accuracy@1 value: 0.34 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.48 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.34 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.24666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.204 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.132 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.07366666666666667 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.15466666666666667 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.21166666666666664 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.27266666666666667 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2719940457772305 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4480555555555556 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.22300523301536854 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.52 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.64 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.17333333333333337 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.128 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.52 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.64 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4849234061490301 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3843809523809524 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3930335420922445 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: cosine_accuracy@1 value: 0.56 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.66 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.84 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.56 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09599999999999997 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.54 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.64 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.66 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.84 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6789363300745337 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6334285714285713 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6282075055376187 name: Cosine Map@100 - task: type: information-retrieval name: Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: cosine_accuracy@1 value: 0.46938775510204084 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.7346938775510204 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8163265306122449 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.9183673469387755 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.46938775510204084 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.3945578231292517 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.38775510204081626 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.32653061224489793 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.03566240843889317 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.08551618356765243 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.1405258525735832 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.2203603523232973 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.36905892568943005 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6109653385163588 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2939021691226414 name: Cosine Map@100 - task: type: nano-beir name: Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: cosine_accuracy@1 value: 0.4514913657770801 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6257456828885399 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.6951020408163264 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.789105180533752 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.4514913657770801 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.27701726844583985 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.21967346938775512 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.15373312401883832 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.26965081394591983 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.40631678733158394 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.4672444533614047 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.552848152657576 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5000381566353087 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5576212653559591 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.42703540499164716 name: Cosine Map@100 --- # SentenceTransformer based on BAAI/bge-base-en-v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [trivia](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - **Maximum Sequence Length:** 196 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [trivia](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet) - **Language:** en ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 196, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("bwang0911/bge-int8") # Run inference sentences = [ 'Where in Australia was swashbuckling Errol Flynn born?', 'Errol Flynn Blood" (1935), Major Geoffrey Vickers in "The Charge of the Light Brigade" (1936), as well as a number of Westerns, such as "Dodge City" (1939), "Santa Fe Trail" (1940), and "San Antonio" (1945). Errol Leslie Flynn was born on 20 June 1909 in Battery Point, a suburb of Hobart, Tasmania, Australia. His father, Theodore Thomson Flynn, was a lecturer (1909) and later professor (1911) of biology at the University of Tasmania. His mother was born Lily Mary Young, but shortly after marrying Theodore at St John\'s Church of England, Birchgrove, Sydney, on 23 January 1909, she changed her first name', 'Errol Flynn early in his career: Errol Flynn Errol Leslie Thomson Flynn (20 June 1909 – 14 October 1959) was an Australian-born American actor during the Golden Age of Hollywood. Considered the natural successor to Douglas Fairbanks, he achieved worldwide fame for his romantic swashbuckler roles in Hollywood films, as well as frequent partnerships with Olivia de Havilland. He was best known for his role as Robin Hood in "The Adventures of Robin Hood" (1938); his portrayal of the character was named by the American Film Institute as the 18th greatest hero in American film history. His other famous roles included the', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:----------|:-------------------|:------------|:------------|:------------|:---------------| | cosine_accuracy@1 | 0.28 | 0.6 | 0.64 | 0.3 | 0.66 | 0.38 | 0.34 | 0.26 | 0.86 | 0.34 | 0.18 | 0.56 | 0.4694 | | cosine_accuracy@3 | 0.46 | 0.78 | 0.9 | 0.38 | 0.78 | 0.5 | 0.5 | 0.48 | 0.96 | 0.48 | 0.52 | 0.66 | 0.7347 | | cosine_accuracy@5 | 0.54 | 0.84 | 0.92 | 0.42 | 0.82 | 0.6 | 0.56 | 0.64 | 0.96 | 0.6 | 0.64 | 0.68 | 0.8163 | | cosine_accuracy@10 | 0.68 | 0.92 | 0.94 | 0.48 | 0.92 | 0.74 | 0.6 | 0.72 | 1.0 | 0.7 | 0.8 | 0.84 | 0.9184 | | cosine_precision@1 | 0.28 | 0.6 | 0.64 | 0.3 | 0.66 | 0.38 | 0.34 | 0.26 | 0.86 | 0.34 | 0.18 | 0.56 | 0.4694 | | cosine_precision@3 | 0.18 | 0.52 | 0.3 | 0.18 | 0.3467 | 0.1667 | 0.3 | 0.16 | 0.4 | 0.2467 | 0.1733 | 0.2333 | 0.3946 | | cosine_precision@5 | 0.132 | 0.496 | 0.188 | 0.128 | 0.244 | 0.12 | 0.3 | 0.132 | 0.252 | 0.204 | 0.128 | 0.144 | 0.3878 | | cosine_precision@10 | 0.088 | 0.432 | 0.096 | 0.076 | 0.142 | 0.074 | 0.244 | 0.076 | 0.136 | 0.132 | 0.08 | 0.096 | 0.3265 | | cosine_recall@1 | 0.1217 | 0.0629 | 0.6167 | 0.1554 | 0.33 | 0.38 | 0.0121 | 0.25 | 0.7473 | 0.0737 | 0.18 | 0.54 | 0.0357 | | cosine_recall@3 | 0.2267 | 0.1301 | 0.8567 | 0.2226 | 0.52 | 0.5 | 0.0406 | 0.46 | 0.9253 | 0.1547 | 0.52 | 0.64 | 0.0855 | | cosine_recall@5 | 0.2773 | 0.1827 | 0.8767 | 0.2646 | 0.61 | 0.6 | 0.0587 | 0.61 | 0.942 | 0.2117 | 0.64 | 0.66 | 0.1405 | | cosine_recall@10 | 0.349 | 0.2871 | 0.8967 | 0.3058 | 0.71 | 0.74 | 0.0821 | 0.69 | 0.9933 | 0.2727 | 0.8 | 0.84 | 0.2204 | | **cosine_ndcg@10** | **0.2921** | **0.5202** | **0.7847** | **0.2791** | **0.6314** | **0.5328** | **0.2699** | **0.465** | **0.9204** | **0.272** | **0.4849** | **0.6789** | **0.3691** | | cosine_mrr@10 | 0.3988 | 0.7049 | 0.7672 | 0.3567 | 0.7392 | 0.4702 | 0.428 | 0.3999 | 0.9073 | 0.4481 | 0.3844 | 0.6334 | 0.611 | | cosine_map@100 | 0.2301 | 0.3763 | 0.7406 | 0.247 | 0.5493 | 0.4806 | 0.0994 | 0.3974 | 0.8925 | 0.223 | 0.393 | 0.6282 | 0.2939 | #### Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [NanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] } ``` | Metric | Value | |:--------------------|:--------| | cosine_accuracy@1 | 0.4515 | | cosine_accuracy@3 | 0.6257 | | cosine_accuracy@5 | 0.6951 | | cosine_accuracy@10 | 0.7891 | | cosine_precision@1 | 0.4515 | | cosine_precision@3 | 0.277 | | cosine_precision@5 | 0.2197 | | cosine_precision@10 | 0.1537 | | cosine_recall@1 | 0.2697 | | cosine_recall@3 | 0.4063 | | cosine_recall@5 | 0.4672 | | cosine_recall@10 | 0.5528 | | **cosine_ndcg@10** | **0.5** | | cosine_mrr@10 | 0.5576 | | cosine_map@100 | 0.427 | ## Training Details ### Training Dataset #### trivia * Dataset: [trivia](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet) at [bfe9460](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet/tree/bfe94607eb149a89fe8107e8c8d187977e587a7d) * Size: 60,315 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Which American-born Sinclair won the Nobel Prize for Literature in 1930? | Sinclair Lewis Sinclair Lewis Harry Sinclair Lewis (February 7, 1885 – January 10, 1951) was an American novelist, short-story writer, and playwright. In 1930, he became the first writer from the United States to receive the Nobel Prize in Literature, which was awarded "for his vigorous and graphic art of description and his ability to create, with wit and humor, new types of characters." His works are known for their insightful and critical views of American capitalism and materialism between the wars. He is also respected for his strong characterizations of modern working women. H. L. Mencken wrote of him, "[If] there | Nobel Prize in Literature analyze its importance on potential future Nobel Prize in Literature laureates. Only Alice Munro (2009) has been awarded with both. The Neustadt International Prize for Literature is regarded as one of the most prestigious international literary prizes, often referred to as the American equivalent to the Nobel Prize. Like the Nobel or the Man Booker International Prize, it is awarded not for any one work, but for an entire body of work. It is frequently seen as an indicator of who may be awarded the Nobel Prize in Literature. Gabriel García Márquez (1972 Neustadt, 1982 Nobel), Czesław Miłosz (1978 Neustadt, | | Where in England was Dame Judi Dench born? | Judi Dench regular contact with the theatre. Her father, a physician, was also the GP for the York theatre, and her mother was its wardrobe mistress. Actors often stayed in the Dench household. During these years, Judi Dench was involved on a non-professional basis in the first three productions of the modern revival of the York Mystery Plays in 1951, 1954 and 1957. In the third production she played the role of the Virgin Mary, performed on a fixed stage in the Museum Gardens. Though she initially trained as a set designer, she became interested in drama school as her brother Jeff | Judi Dench to independence, published in August 2014, a few weeks before the Scottish referendum. In September 2018, Dench criticized the response to the sexual misconduct allegations made against actor Kevin Spacey, referring to him as a "good friend". Judi Dench Dame Judith Olivia Dench (born 9 December 1934) is an English actress. Dench made her professional debut in 1957 with the Old Vic Company. Over the following few years, she performed in several of Shakespeare's plays, in such roles as Ophelia in "Hamlet", Juliet in "Romeo and Juliet", and Lady Macbeth in "Macbeth". Although most of her work during this period | | From which country did Angola achieve independence in 1975? | Corruption in Angola they really are. Angola's colonial era ended with the Angolan War of Independence against Portugal occurred between 1970 and 1975. Independence did not produce a unified Angola, however; the country plunged into years of civil war between the National Union for the Total Independence of Angola (UNITA) and the governing Popular Movement for the Liberation of Angola (MPLA). 30 years of war would produce historical legacies that combine to allow for the persistence of a highly corrupt government system. The Angolan civil war was fought between the pro-western UNITA and the communist MPLA and had the characteristics typical of a | Cuban intervention in Angola Cuban intervention in Angola In November 1975, on the eve of Angola's independence, Cuba launched a large-scale military intervention in support of the leftist People's Movement for the Liberation of Angola (MPLA) against United States-backed interventions by South Africa and Zaire in support of two right-wing independence movements competing for power in the country, the National Liberation Front of Angola (FNLA) and the National Union for the Total Independence of Angola (UNITA). By the end of 1975 the Cuban military in Angola numbered more than 25,000 troops. Following the withdrawal of Zaire and South Africa, Cuban forces remained in Angola | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 128 - `learning_rate`: 1e-05 - `warmup_ratio`: 0.1 - `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`: 128 - `per_device_eval_batch_size`: 8 - `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`: 1e-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`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `tp_size`: 0 - `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 - `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 | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 | |:------:|:----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:| | 0.0212 | 10 | 2.7514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0424 | 20 | 2.7415 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0636 | 30 | 2.5319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0847 | 40 | 2.3283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1059 | 50 | 2.0535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1271 | 60 | 1.8257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1483 | 70 | 1.6569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1695 | 80 | 1.5127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1907 | 90 | 1.3586 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2119 | 100 | 1.3002 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2331 | 110 | 1.2825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2542 | 120 | 1.1649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2754 | 130 | 1.1589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2966 | 140 | 1.1404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3178 | 150 | 1.1462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3390 | 160 | 1.1297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3602 | 170 | 1.0774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3814 | 180 | 1.0845 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4025 | 190 | 1.0574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4237 | 200 | 1.1048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4449 | 210 | 1.0817 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4661 | 220 | 1.0603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4873 | 230 | 1.0383 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5085 | 240 | 1.0197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5297 | 250 | 1.0979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5508 | 260 | 1.0303 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5720 | 270 | 1.0363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5932 | 280 | 1.0433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6144 | 290 | 0.98 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6356 | 300 | 1.0272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6568 | 310 | 1.054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6780 | 320 | 1.0213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6992 | 330 | 1.0111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7203 | 340 | 0.9849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7415 | 350 | 1.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7627 | 360 | 0.9998 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7839 | 370 | 0.9871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8051 | 380 | 1.0223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8263 | 390 | 0.9592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8475 | 400 | 0.9736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8686 | 410 | 0.9653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8898 | 420 | 0.9856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9110 | 430 | 1.0445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9322 | 440 | 0.9818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9534 | 450 | 0.9937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9746 | 460 | 0.9818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9958 | 470 | 0.9799 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0169 | 480 | 0.908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0381 | 490 | 0.9568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0593 | 500 | 0.9887 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.0805 | 510 | 0.9401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1017 | 520 | 0.934 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1229 | 530 | 0.9245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1441 | 540 | 0.9329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1653 | 550 | 0.9985 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.1864 | 560 | 0.9591 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2076 | 570 | 0.9433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2288 | 580 | 0.9645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.25 | 590 | 0.9682 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2712 | 600 | 0.9385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.2924 | 610 | 0.8819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3136 | 620 | 0.9471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3347 | 630 | 0.919 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3559 | 640 | 0.9523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3771 | 650 | 0.9248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.3983 | 660 | 0.9784 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4195 | 670 | 0.9003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4407 | 680 | 0.9652 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4619 | 690 | 0.9286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.4831 | 700 | 0.8873 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5042 | 710 | 0.9252 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5254 | 720 | 0.938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5466 | 730 | 0.9394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5678 | 740 | 0.9224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.5890 | 750 | 0.9128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6102 | 760 | 0.9367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6314 | 770 | 0.9664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6525 | 780 | 0.9307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6737 | 790 | 0.8823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.6949 | 800 | 0.9306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7161 | 810 | 0.8754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7373 | 820 | 0.9376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7585 | 830 | 0.8803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.7797 | 840 | 0.9254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8008 | 850 | 0.9282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8220 | 860 | 0.9175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8432 | 870 | 0.9482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8644 | 880 | 0.9289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.8856 | 890 | 0.9354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9068 | 900 | 0.9253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9280 | 910 | 0.9363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9492 | 920 | 1.0037 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9703 | 930 | 0.8552 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 1.9915 | 940 | 0.9267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0127 | 950 | 0.9043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0339 | 960 | 0.8859 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0551 | 970 | 0.9149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0763 | 980 | 0.917 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.0975 | 990 | 0.8839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1186 | 1000 | 0.9502 | 0.2921 | 0.5202 | 0.7847 | 0.2791 | 0.6314 | 0.5328 | 0.2699 | 0.4650 | 0.9204 | 0.2720 | 0.4849 | 0.6789 | 0.3691 | 0.5000 | | 2.1398 | 1010 | 0.9131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1610 | 1020 | 0.9191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.1822 | 1030 | 0.8992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2034 | 1040 | 0.913 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2246 | 1050 | 0.871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2458 | 1060 | 0.9336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2669 | 1070 | 0.903 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.2881 | 1080 | 0.8995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3093 | 1090 | 0.9018 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3305 | 1100 | 0.861 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3517 | 1110 | 0.8548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3729 | 1120 | 0.8928 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.3941 | 1130 | 0.9606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4153 | 1140 | 0.8921 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4364 | 1150 | 0.8511 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4576 | 1160 | 0.8977 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.4788 | 1170 | 0.8894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5 | 1180 | 0.8647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5212 | 1190 | 0.8421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5424 | 1200 | 0.8654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5636 | 1210 | 0.926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.5847 | 1220 | 0.8911 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6059 | 1230 | 0.9191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6271 | 1240 | 0.8731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6483 | 1250 | 0.8757 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6695 | 1260 | 0.8825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.6907 | 1270 | 0.8881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7119 | 1280 | 0.8745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7331 | 1290 | 0.8404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7542 | 1300 | 0.9377 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7754 | 1310 | 0.9149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.7966 | 1320 | 0.8881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8178 | 1330 | 0.8889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8390 | 1340 | 0.9289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8602 | 1350 | 0.9169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.8814 | 1360 | 0.8803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9025 | 1370 | 0.8398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9237 | 1380 | 0.8716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9449 | 1390 | 0.8912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9661 | 1400 | 0.8471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 2.9873 | 1410 | 0.9158 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 4.1.0 - Transformers: 4.51.3 - PyTorch: 2.7.0+cu126 - Accelerate: 1.6.0 - Datasets: 3.6.0 - 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", } ``` #### MultipleNegativesRankingLoss ```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} } ```