--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:99000 - loss:CSRLoss base_model: microsoft/mpnet-base widget: - source_sentence: what is the difference between uae and saudi arabia sentences: - 'Monopoly Junior Players take turns in order, with the initial player determined by age before the game: the youngest player goes first. Players are dealt an initial amount Monopoly money depending on the total number of players playing: 20 in a two-player game, 18 in a three-player game or 16 in a four-player game. A typical turn begins with the rolling of the die and the player advancing their token clockwise around the board the corresponding number of spaces. When the player lands on an unowned space they must purchase the space from the bank for the amount indicated on the board, and places a sold sign on the coloured band at the top of the space to denote ownership. If a player lands on a space owned by an opponent the player pays the opponent rent in the amount written on the board. If the opponent owns both properties of the same colour the rent is doubled.' - Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4] - Governors of states of India The governors and lieutenant-governors are appointed by the President for a term of five years. - source_sentence: who came up with the seperation of powers sentences: - Separation of powers Aristotle first mentioned the idea of a "mixed government" or hybrid government in his work Politics where he drew upon many of the constitutional forms in the city-states of Ancient Greece. In the Roman Republic, the Roman Senate, Consuls and the Assemblies showed an example of a mixed government according to Polybius (Histories, Book 6, 11–13). - Economy of New Zealand New Zealand's diverse market economy has a sizable service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing industries include aluminium production, food processing, metal fabrication, wood and paper products. Mining, manufacturing, electricity, gas, water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary sector continues to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17] - John Dalton John Dalton FRS (/ˈdɔːltən/; 6 September 1766 – 27 July 1844) was an English chemist, physicist, and meteorologist. He is best known for proposing the modern atomic theory and for his research into colour blindness, sometimes referred to as Daltonism in his honour. - source_sentence: who was the first president of indian science congress meeting held in kolkata in 1914 sentences: - Nobody to Blame "Nobody to Blame" is a song recorded by American country music artist Chris Stapleton. The song was released in November 2015 as the singer's third single overall. Stapleton co-wrote the song with Barry Bales and Ronnie Bowman. It became Stapleton's first top 10 single on the US Country Airplay chart.[2] "Nobody to Blame" won Song of the Year at the ACM Awards.[3] - Indian Science Congress Association The first meeting of the congress was held from 15–17 January 1914 at the premises of the Asiatic Society, Calcutta. Honorable justice Sir Ashutosh Mukherjee, the then Vice Chancellor of the University of Calcutta presided over the Congress. One hundred and five scientists from different parts of India and abroad attended it. Altogether 35 papers under 6 different sections, namely Botany, Chemistry, Ethnography, Geology, Physics and Zoology were presented. - New Soul "New Soul" is a song by the French-Israeli R&B/soul singer Yael Naïm, from her self-titled second album. The song gained popularity in the United States following its use by Apple in an advertisement for their MacBook Air laptop. In the song Naïm sings of being a new soul who has come into the world to learn "a bit 'bout how to give and take." However, she finds that things are harder than they seem. The song, also featured in the films The House Bunny and Wild Target, features a prominent "la la la la" section as its hook. It remains Naïm's biggest hit single in the U.S. to date, and her only one to reach the Top 40 of the Billboard Hot 100. - source_sentence: who wrote get over it by the eagles sentences: - Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a single after a fourteen-year breakup. It was also the first song written by bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live for the first time during their Hell Freezes Over tour in 1994. It returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song was not played live by the Eagles after the "Hell Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S. - Pokhran-II In 1980, the general elections marked the return of Indira Gandhi and the nuclear program began to gain momentum under Ramanna in 1981. Requests for additional nuclear tests were continued to be denied by the government when Prime Minister Indira Gandhi saw Pakistan began exercising the brinkmanship, though the nuclear program continued to advance.[7] Initiation towards hydrogen bomb began as well as the launch of the missile programme began under Late president Dr. Abdul Kalam, who was then an aerospace engineer.[7] - R. Budd Dwyer Robert Budd Dwyer (November 21, 1939 – January 22, 1987) was the 30th State Treasurer of the Commonwealth of Pennsylvania. He served from 1971 to 1981 as a Republican member of the Pennsylvania State Senate representing the state's 50th district. He then served as the 30th Treasurer of Pennsylvania from January 20, 1981, until his death. On January 22, 1987, Dwyer called a news conference in the Pennsylvania state capital of Harrisburg where he killed himself in front of the gathered reporters, by shooting himself in the mouth with a .357 Magnum revolver.[4] Dwyer's suicide was broadcast later that day to a wide television audience across Pennsylvania. - source_sentence: who is cornelius in the book of acts sentences: - Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton's 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman. - Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1] - 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.' datasets: - sentence-transformers/natural-questions 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 co2_eq_emissions: emissions: 113.44094173179047 energy_consumed: 0.29184553136281904 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: 0.773 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: SparseEncoder based on microsoft/mpnet-base results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 16 type: NanoMSMARCO_16 metrics: - type: cosine_accuracy@1 value: 0.1 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.36 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.1 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07200000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05000000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.26 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.36 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.5 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.272077335852507 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.20234920634920633 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.21758364304569 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 16 type: NanoNFCorpus_16 metrics: - type: cosine_accuracy@1 value: 0.08 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.14 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.24 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.32 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.08 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.05999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.08 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.005993249911183041 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.009403252754209558 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.013285393478414642 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.01646720008819819 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.06095056479011788 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.14072222222222222 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.015310893897400863 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 16 type: NanoNQ_16 metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.42 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.54 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.64 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.13999999999999999 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.10800000000000003 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.064 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.4 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.5 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3867151912670764 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3266904761904762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.3250246379519026 name: Cosine Map@100 - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 16 type: NanoBEIR_mean_16 metrics: - type: cosine_accuracy@1 value: 0.12 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.2733333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.38000000000000006 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.48666666666666664 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.12 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.09555555555555555 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.08666666666666668 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05466666666666667 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.09533108330372768 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2231344175847365 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.29109513115947155 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.3721557333627327 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.2399143639699004 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.22325396825396826 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.18597305829833113 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 32 type: NanoMSMARCO_32 metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.36 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.56 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.08666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.07200000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.05600000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.18 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.26 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.36 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.56 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.33109644128066057 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2634444444444444 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.27935469743863556 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 32 type: NanoNFCorpus_32 metrics: - type: cosine_accuracy@1 value: 0.14 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.28 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.34 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.11333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.09 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.007695869325666863 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.012313937822266688 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.01702903494334016 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.024165659145052122 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.10225707780728845 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2055238095238095 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.022577551502700435 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 32 type: NanoNQ_32 metrics: - type: cosine_accuracy@1 value: 0.32 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.46 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.58 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.32 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11600000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.31 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.42 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.53 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.63 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4603957123337682 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.4211904761904762 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.41127594932176303 name: Cosine Map@100 - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 32 type: NanoBEIR_mean_32 metrics: - type: cosine_accuracy@1 value: 0.21333333333333335 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.32666666666666666 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.4066666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5266666666666667 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.21333333333333335 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.11777777777777776 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09466666666666668 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07133333333333335 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.16589862310855563 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.23077131260742223 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.30234301164778005 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4047218863816841 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.29791641047390577 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2967195767195767 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.23773606608769968 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 64 type: NanoMSMARCO_64 metrics: - type: cosine_accuracy@1 value: 0.16 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.38 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.46 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.16 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09200000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.06 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.16 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.38 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.46 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.6 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3545165496884908 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.27796031746031746 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.29572845389453484 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 64 type: NanoNFCorpus_64 metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.26 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.32 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.4 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.12666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.088 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.009483451025013268 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.012904129822135095 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.036867855927155205 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.04756198673273659 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.11496239522394665 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.24210317460317454 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.0318282871881163 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 64 type: NanoNQ_64 metrics: - type: cosine_accuracy@1 value: 0.44 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.62 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.72 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.44 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.20666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07400000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.42 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.58 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.64 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.68 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.561884513825323 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5395555555555555 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5268055680783221 name: Cosine Map@100 - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 64 type: NanoBEIR_mean_64 metrics: - type: cosine_accuracy@1 value: 0.26 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.42 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.48666666666666664 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5733333333333334 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.26 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15333333333333332 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.11733333333333335 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.074 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.19649448367500444 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.32430137660737834 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3789559519757184 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4425206622442455 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3437878195792535 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.35320634920634914 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2847874363869911 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 128 type: NanoMSMARCO_128 metrics: - type: cosine_accuracy@1 value: 0.2 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.34 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.46 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.11333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.09200000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.2 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.34 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.46 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.68 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4022072447482653 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.31815873015873014 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.33230553462724927 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 128 type: NanoNFCorpus_128 metrics: - type: cosine_accuracy@1 value: 0.14 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.34 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.38 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.52 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.14 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666663 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.128 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.11399999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.0036955722371344803 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.021194355136532755 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.024553995602026958 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.043293677887263404 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.12666378888376595 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2537936507936508 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.03330968914510828 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 128 type: NanoNQ_128 metrics: - type: cosine_accuracy@1 value: 0.38 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.56 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.38 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.18666666666666665 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14400000000000002 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08199999999999999 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.35 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.53 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.66 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.76 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5527057053472701 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5072460317460317 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.4846991157483792 name: Cosine Map@100 - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 128 type: NanoBEIR_mean_128 metrics: - type: cosine_accuracy@1 value: 0.24 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4133333333333334 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5133333333333333 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6666666666666666 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.24 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.15555555555555553 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.12133333333333335 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08800000000000001 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.1845651907457115 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.2970647850455109 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.381517998534009 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.4944312259624211 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3605255796597671 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.35973280423280424 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.2834381131735789 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 256 type: NanoMSMARCO_256 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: 0.52 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.68 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: 0.10400000000000001 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.068 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.26 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.48 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.52 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.68 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.4651758219790261 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.39804761904761904 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.412474140043243 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 256 type: NanoNFCorpus_256 metrics: - type: cosine_accuracy@1 value: 0.18 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.28 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.38 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.5 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.18 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.14666666666666667 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.114 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.005516710448516594 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.011401609103753301 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.021271103372355084 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.0347182833647384 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.12628863554710404 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.2575 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.033728487141126466 name: Cosine Map@100 - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 256 type: NanoNQ_256 metrics: - type: cosine_accuracy@1 value: 0.42 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.58 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.68 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.76 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.42 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.19333333333333333 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.14 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.4 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.54 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.64 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.73 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.5611650669716552 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.5226904761904763 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.5086922580864135 name: Cosine Map@100 - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: cosine_accuracy@1 value: 0.2866666666666667 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.4466666666666666 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.5266666666666667 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.6466666666666667 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.2866666666666667 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.16666666666666666 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.128 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08733333333333333 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.22183890348283888 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.3438005363679178 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.3937570344574517 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.48157276112157943 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.3842098414992618 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.3927460317460318 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.31829829509026103 name: Cosine Map@100 --- # SparseEncoder based on microsoft/mpnet-base This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) 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. See [train_nq.py](train_nq.py) for the training script used for this model. > [!WARNING] > Warning: > Sparse models in Sentence Transformers are still quite experimental. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **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 ``` SparseEncoder( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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): CSRSparsity({'input_dim': 768, 'hidden_dim': 3072, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30}) ) ``` ## 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("tomaarsen/sparse-mpnet-base-nq-fresh") # Run inference sentences = [ 'who is cornelius in the book of acts', 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.', "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]", ] 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 #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_16`, `NanoNFCorpus_16` and `NanoNQ_16` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 16 } ``` | Metric | NanoMSMARCO_16 | NanoNFCorpus_16 | NanoNQ_16 | |:--------------------|:---------------|:----------------|:-----------| | cosine_accuracy@1 | 0.1 | 0.08 | 0.18 | | cosine_accuracy@3 | 0.26 | 0.14 | 0.42 | | cosine_accuracy@5 | 0.36 | 0.24 | 0.54 | | cosine_accuracy@10 | 0.5 | 0.32 | 0.64 | | cosine_precision@1 | 0.1 | 0.08 | 0.18 | | cosine_precision@3 | 0.0867 | 0.06 | 0.14 | | cosine_precision@5 | 0.072 | 0.08 | 0.108 | | cosine_precision@10 | 0.05 | 0.05 | 0.064 | | cosine_recall@1 | 0.1 | 0.006 | 0.18 | | cosine_recall@3 | 0.26 | 0.0094 | 0.4 | | cosine_recall@5 | 0.36 | 0.0133 | 0.5 | | cosine_recall@10 | 0.5 | 0.0165 | 0.6 | | **cosine_ndcg@10** | **0.2721** | **0.061** | **0.3867** | | cosine_mrr@10 | 0.2023 | 0.1407 | 0.3267 | | cosine_map@100 | 0.2176 | 0.0153 | 0.325 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_16` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "truncate_dim": 16 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.12 | | cosine_accuracy@3 | 0.2733 | | cosine_accuracy@5 | 0.38 | | cosine_accuracy@10 | 0.4867 | | cosine_precision@1 | 0.12 | | cosine_precision@3 | 0.0956 | | cosine_precision@5 | 0.0867 | | cosine_precision@10 | 0.0547 | | cosine_recall@1 | 0.0953 | | cosine_recall@3 | 0.2231 | | cosine_recall@5 | 0.2911 | | cosine_recall@10 | 0.3722 | | **cosine_ndcg@10** | **0.2399** | | cosine_mrr@10 | 0.2233 | | cosine_map@100 | 0.186 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_32`, `NanoNFCorpus_32` and `NanoNQ_32` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 32 } ``` | Metric | NanoMSMARCO_32 | NanoNFCorpus_32 | NanoNQ_32 | |:--------------------|:---------------|:----------------|:-----------| | cosine_accuracy@1 | 0.18 | 0.14 | 0.32 | | cosine_accuracy@3 | 0.26 | 0.26 | 0.46 | | cosine_accuracy@5 | 0.36 | 0.28 | 0.58 | | cosine_accuracy@10 | 0.56 | 0.34 | 0.68 | | cosine_precision@1 | 0.18 | 0.14 | 0.32 | | cosine_precision@3 | 0.0867 | 0.1133 | 0.1533 | | cosine_precision@5 | 0.072 | 0.096 | 0.116 | | cosine_precision@10 | 0.056 | 0.09 | 0.068 | | cosine_recall@1 | 0.18 | 0.0077 | 0.31 | | cosine_recall@3 | 0.26 | 0.0123 | 0.42 | | cosine_recall@5 | 0.36 | 0.017 | 0.53 | | cosine_recall@10 | 0.56 | 0.0242 | 0.63 | | **cosine_ndcg@10** | **0.3311** | **0.1023** | **0.4604** | | cosine_mrr@10 | 0.2634 | 0.2055 | 0.4212 | | cosine_map@100 | 0.2794 | 0.0226 | 0.4113 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_32` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "truncate_dim": 32 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.2133 | | cosine_accuracy@3 | 0.3267 | | cosine_accuracy@5 | 0.4067 | | cosine_accuracy@10 | 0.5267 | | cosine_precision@1 | 0.2133 | | cosine_precision@3 | 0.1178 | | cosine_precision@5 | 0.0947 | | cosine_precision@10 | 0.0713 | | cosine_recall@1 | 0.1659 | | cosine_recall@3 | 0.2308 | | cosine_recall@5 | 0.3023 | | cosine_recall@10 | 0.4047 | | **cosine_ndcg@10** | **0.2979** | | cosine_mrr@10 | 0.2967 | | cosine_map@100 | 0.2377 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_64`, `NanoNFCorpus_64` and `NanoNQ_64` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 64 } ``` | Metric | NanoMSMARCO_64 | NanoNFCorpus_64 | NanoNQ_64 | |:--------------------|:---------------|:----------------|:-----------| | cosine_accuracy@1 | 0.16 | 0.18 | 0.44 | | cosine_accuracy@3 | 0.38 | 0.26 | 0.62 | | cosine_accuracy@5 | 0.46 | 0.32 | 0.68 | | cosine_accuracy@10 | 0.6 | 0.4 | 0.72 | | cosine_precision@1 | 0.16 | 0.18 | 0.44 | | cosine_precision@3 | 0.1267 | 0.1267 | 0.2067 | | cosine_precision@5 | 0.092 | 0.12 | 0.14 | | cosine_precision@10 | 0.06 | 0.088 | 0.074 | | cosine_recall@1 | 0.16 | 0.0095 | 0.42 | | cosine_recall@3 | 0.38 | 0.0129 | 0.58 | | cosine_recall@5 | 0.46 | 0.0369 | 0.64 | | cosine_recall@10 | 0.6 | 0.0476 | 0.68 | | **cosine_ndcg@10** | **0.3545** | **0.115** | **0.5619** | | cosine_mrr@10 | 0.278 | 0.2421 | 0.5396 | | cosine_map@100 | 0.2957 | 0.0318 | 0.5268 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_64` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "truncate_dim": 64 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.26 | | cosine_accuracy@3 | 0.42 | | cosine_accuracy@5 | 0.4867 | | cosine_accuracy@10 | 0.5733 | | cosine_precision@1 | 0.26 | | cosine_precision@3 | 0.1533 | | cosine_precision@5 | 0.1173 | | cosine_precision@10 | 0.074 | | cosine_recall@1 | 0.1965 | | cosine_recall@3 | 0.3243 | | cosine_recall@5 | 0.379 | | cosine_recall@10 | 0.4425 | | **cosine_ndcg@10** | **0.3438** | | cosine_mrr@10 | 0.3532 | | cosine_map@100 | 0.2848 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 128 } ``` | Metric | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 | |:--------------------|:----------------|:-----------------|:-----------| | cosine_accuracy@1 | 0.2 | 0.14 | 0.38 | | cosine_accuracy@3 | 0.34 | 0.34 | 0.56 | | cosine_accuracy@5 | 0.46 | 0.38 | 0.7 | | cosine_accuracy@10 | 0.68 | 0.52 | 0.8 | | cosine_precision@1 | 0.2 | 0.14 | 0.38 | | cosine_precision@3 | 0.1133 | 0.1667 | 0.1867 | | cosine_precision@5 | 0.092 | 0.128 | 0.144 | | cosine_precision@10 | 0.068 | 0.114 | 0.082 | | cosine_recall@1 | 0.2 | 0.0037 | 0.35 | | cosine_recall@3 | 0.34 | 0.0212 | 0.53 | | cosine_recall@5 | 0.46 | 0.0246 | 0.66 | | cosine_recall@10 | 0.68 | 0.0433 | 0.76 | | **cosine_ndcg@10** | **0.4022** | **0.1267** | **0.5527** | | cosine_mrr@10 | 0.3182 | 0.2538 | 0.5072 | | cosine_map@100 | 0.3323 | 0.0333 | 0.4847 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_128` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "truncate_dim": 128 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.24 | | cosine_accuracy@3 | 0.4133 | | cosine_accuracy@5 | 0.5133 | | cosine_accuracy@10 | 0.6667 | | cosine_precision@1 | 0.24 | | cosine_precision@3 | 0.1556 | | cosine_precision@5 | 0.1213 | | cosine_precision@10 | 0.088 | | cosine_recall@1 | 0.1846 | | cosine_recall@3 | 0.2971 | | cosine_recall@5 | 0.3815 | | cosine_recall@10 | 0.4944 | | **cosine_ndcg@10** | **0.3605** | | cosine_mrr@10 | 0.3597 | | cosine_map@100 | 0.2834 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 256 } ``` | Metric | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 | |:--------------------|:----------------|:-----------------|:-----------| | cosine_accuracy@1 | 0.26 | 0.18 | 0.42 | | cosine_accuracy@3 | 0.48 | 0.28 | 0.58 | | cosine_accuracy@5 | 0.52 | 0.38 | 0.68 | | cosine_accuracy@10 | 0.68 | 0.5 | 0.76 | | cosine_precision@1 | 0.26 | 0.18 | 0.42 | | cosine_precision@3 | 0.16 | 0.1467 | 0.1933 | | cosine_precision@5 | 0.104 | 0.14 | 0.14 | | cosine_precision@10 | 0.068 | 0.114 | 0.08 | | cosine_recall@1 | 0.26 | 0.0055 | 0.4 | | cosine_recall@3 | 0.48 | 0.0114 | 0.54 | | cosine_recall@5 | 0.52 | 0.0213 | 0.64 | | cosine_recall@10 | 0.68 | 0.0347 | 0.73 | | **cosine_ndcg@10** | **0.4652** | **0.1263** | **0.5612** | | cosine_mrr@10 | 0.398 | 0.2575 | 0.5227 | | cosine_map@100 | 0.4125 | 0.0337 | 0.5087 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_256` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "truncate_dim": 256 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.2867 | | cosine_accuracy@3 | 0.4467 | | cosine_accuracy@5 | 0.5267 | | cosine_accuracy@10 | 0.6467 | | cosine_precision@1 | 0.2867 | | cosine_precision@3 | 0.1667 | | cosine_precision@5 | 0.128 | | cosine_precision@10 | 0.0873 | | cosine_recall@1 | 0.2218 | | cosine_recall@3 | 0.3438 | | cosine_recall@5 | 0.3938 | | cosine_recall@10 | 0.4816 | | **cosine_ndcg@10** | **0.3842** | | cosine_mrr@10 | 0.3927 | | cosine_map@100 | 0.3183 | ## 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: [CSRLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1, "scale": 20.0 } ``` ### 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: [CSRLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1, "scale": 20.0 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 32 - `learning_rate`: 4e-05 - `weight_decay`: 0.0001 - `adam_epsilon`: 6.25e-10 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: 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`: 32 - `per_device_eval_batch_size`: 32 - `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`: 4e-05 - `weight_decay`: 0.0001 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 6.25e-10 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `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`: 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`: 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 | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_16_cosine_ndcg@10 | NanoNFCorpus_16_cosine_ndcg@10 | NanoNQ_16_cosine_ndcg@10 | NanoBEIR_mean_16_cosine_ndcg@10 | NanoMSMARCO_32_cosine_ndcg@10 | NanoNFCorpus_32_cosine_ndcg@10 | NanoNQ_32_cosine_ndcg@10 | NanoBEIR_mean_32_cosine_ndcg@10 | NanoMSMARCO_64_cosine_ndcg@10 | NanoNFCorpus_64_cosine_ndcg@10 | NanoNQ_64_cosine_ndcg@10 | NanoBEIR_mean_64_cosine_ndcg@10 | NanoMSMARCO_128_cosine_ndcg@10 | NanoNFCorpus_128_cosine_ndcg@10 | NanoNQ_128_cosine_ndcg@10 | NanoBEIR_mean_128_cosine_ndcg@10 | NanoMSMARCO_256_cosine_ndcg@10 | NanoNFCorpus_256_cosine_ndcg@10 | NanoNQ_256_cosine_ndcg@10 | NanoBEIR_mean_256_cosine_ndcg@10 | |:------:|:----:|:-------------:|:---------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:-----------------------------:|:------------------------------:|:------------------------:|:-------------------------------:|:------------------------------:|:-------------------------------:|:-------------------------:|:--------------------------------:|:------------------------------:|:-------------------------------:|:-------------------------:|:--------------------------------:| | -1 | -1 | - | - | 0.0318 | 0.0148 | 0.0149 | 0.0205 | 0.0794 | 0.0234 | 0.0102 | 0.0377 | 0.0855 | 0.0195 | 0.0508 | 0.0519 | 0.1081 | 0.0246 | 0.0264 | 0.0530 | 0.1006 | 0.0249 | 0.0388 | 0.0547 | | 0.0646 | 200 | 0.7332 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 400 | 0.2606 | 0.1970 | 0.2845 | 0.0970 | 0.3546 | 0.2454 | 0.3778 | 0.1358 | 0.3455 | 0.2864 | 0.3868 | 0.1563 | 0.3806 | 0.3079 | 0.3988 | 0.1664 | 0.4035 | 0.3229 | 0.4020 | 0.1782 | 0.4181 | 0.3327 | | 0.1939 | 600 | 0.2247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2586 | 800 | 0.1983 | 0.1750 | 0.2908 | 0.0866 | 0.3730 | 0.2502 | 0.3324 | 0.1155 | 0.4275 | 0.2918 | 0.3511 | 0.1621 | 0.4998 | 0.3377 | 0.3920 | 0.1563 | 0.5174 | 0.3553 | 0.4152 | 0.1555 | 0.5153 | 0.3620 | | 0.3232 | 1000 | 0.1822 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 1200 | 0.1846 | 0.1594 | 0.2775 | 0.0785 | 0.3723 | 0.2428 | 0.2642 | 0.1076 | 0.4389 | 0.2702 | 0.3865 | 0.1328 | 0.4329 | 0.3174 | 0.3883 | 0.1446 | 0.5040 | 0.3456 | 0.3638 | 0.1529 | 0.4939 | 0.3369 | | 0.4525 | 1400 | 0.1669 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 1600 | 0.1573 | 0.1452 | 0.2740 | 0.0624 | 0.3670 | 0.2345 | 0.3557 | 0.0855 | 0.4188 | 0.2867 | 0.4094 | 0.1099 | 0.5027 | 0.3407 | 0.3885 | 0.1340 | 0.4990 | 0.3405 | 0.4820 | 0.1577 | 0.5453 | 0.3950 | | 0.5818 | 1800 | 0.1502 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 2000 | 0.1375 | 0.1255 | 0.2307 | 0.0685 | 0.3801 | 0.2264 | 0.2529 | 0.0815 | 0.4335 | 0.2560 | 0.3509 | 0.0955 | 0.4611 | 0.3025 | 0.3932 | 0.1339 | 0.4875 | 0.3382 | 0.4184 | 0.1483 | 0.4904 | 0.3523 | | 0.7111 | 2200 | 0.1359 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 2400 | 0.1288 | 0.1184 | 0.2737 | 0.0703 | 0.3419 | 0.2286 | 0.3765 | 0.0843 | 0.4440 | 0.3016 | 0.3927 | 0.1247 | 0.5285 | 0.3486 | 0.3726 | 0.1203 | 0.5153 | 0.3361 | 0.4676 | 0.1343 | 0.5523 | 0.3847 | | 0.8403 | 2600 | 0.1235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 2800 | 0.1168 | 0.1094 | 0.2751 | 0.0710 | 0.3602 | 0.2354 | 0.3227 | 0.0966 | 0.5046 | 0.3080 | 0.4112 | 0.1129 | 0.5268 | 0.3503 | 0.4077 | 0.1259 | 0.5253 | 0.3530 | 0.4642 | 0.1238 | 0.5726 | 0.3869 | | 0.9696 | 3000 | 0.1187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | 0.2721 | 0.0610 | 0.3867 | 0.2399 | 0.3311 | 0.1023 | 0.4604 | 0.2979 | 0.3545 | 0.1150 | 0.5619 | 0.3438 | 0.4022 | 0.1267 | 0.5527 | 0.3605 | 0.4652 | 0.1263 | 0.5612 | 0.3842 | ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.292 kWh - **Carbon Emitted**: 0.113 kg of CO2 - **Hours Used**: 0.773 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: 4.1.0.dev0 - Transformers: 4.52.0.dev0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 3.3.2 - 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", } ```