--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - csr - generated_from_trainer - dataset_size:99000 - loss:CSRLoss - loss:SparseMultipleNegativesRankingLoss base_model: mixedbread-ai/mxbai-embed-large-v1 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: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - row_non_zero_mean_query - row_sparsity_mean_query - row_non_zero_mean_corpus - row_sparsity_mean_corpus co2_eq_emissions: emissions: 53.0254354591015 energy_consumed: 0.1364166777096632 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: 0.398 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Sparse CSR model trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 128 type: NanoMSMARCO_128 metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.66 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.132 name: Dot Precision@5 - type: dot_precision@10 value: 0.07800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.66 name: Dot Recall@5 - type: dot_recall@10 value: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5700548121129412 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5031904761904762 name: Dot Mrr@10 - type: dot_map@100 value: 0.514501390584724 name: Dot Map@100 - type: row_non_zero_mean_query value: 128.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.96875 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 128.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.96875 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 128 type: NanoNFCorpus_128 metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.32 name: Dot Precision@3 - type: dot_precision@5 value: 0.28400000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.22399999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.020619614054435857 name: Dot Recall@1 - type: dot_recall@3 value: 0.07638129396550794 name: Dot Recall@3 - type: dot_recall@5 value: 0.09086567610708625 name: Dot Recall@5 - type: dot_recall@10 value: 0.10949508245462748 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2705576989448532 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.43883333333333324 name: Dot Mrr@10 - type: dot_map@100 value: 0.11570301194076318 name: Dot Map@100 - type: row_non_zero_mean_query value: 128.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.96875 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 128.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.96875 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 128 type: NanoNQ_128 metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.66 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.19333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.132 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.43 name: Dot Recall@1 - type: dot_recall@3 value: 0.54 name: Dot Recall@3 - type: dot_recall@5 value: 0.6 name: Dot Recall@5 - type: dot_recall@10 value: 0.73 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5760476804950475 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5402222222222222 name: Dot Mrr@10 - type: dot_map@100 value: 0.5348788301685897 name: Dot Map@100 - type: row_non_zero_mean_query value: 128.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.96875 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 128.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.96875 name: Row Sparsity Mean Corpus - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 128 type: NanoBEIR_mean_128 metrics: - type: dot_accuracy@1 value: 0.36666666666666664 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5866666666666666 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6533333333333333 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7333333333333334 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36666666666666664 name: Dot Precision@1 - type: dot_precision@3 value: 0.23777777777777778 name: Dot Precision@3 - type: dot_precision@5 value: 0.18266666666666667 name: Dot Precision@5 - type: dot_precision@10 value: 0.128 name: Dot Precision@10 - type: dot_recall@1 value: 0.27020653801814526 name: Dot Recall@1 - type: dot_recall@3 value: 0.405460431321836 name: Dot Recall@3 - type: dot_recall@5 value: 0.4502885587023621 name: Dot Recall@5 - type: dot_recall@10 value: 0.5398316941515425 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4722200638509473 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4940820105820105 name: Dot Mrr@10 - type: dot_map@100 value: 0.38836107756469235 name: Dot Map@100 - type: row_non_zero_mean_query value: 128.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.96875 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 128.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.96875 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 256 type: NanoMSMARCO_256 metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.76 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6020044872439759 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5252142857142856 name: Dot Mrr@10 - type: dot_map@100 value: 0.5321764898130005 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 256 type: NanoNFCorpus_256 metrics: - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.34666666666666657 name: Dot Precision@3 - type: dot_precision@5 value: 0.316 name: Dot Precision@5 - type: dot_precision@10 value: 0.27 name: Dot Precision@10 - type: dot_recall@1 value: 0.023916206387792894 name: Dot Recall@1 - type: dot_recall@3 value: 0.060605496737713836 name: Dot Recall@3 - type: dot_recall@5 value: 0.08375989700258081 name: Dot Recall@5 - type: dot_recall@10 value: 0.14574397353137197 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3186443185167164 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5101904761904763 name: Dot Mrr@10 - type: dot_map@100 value: 0.1354214218643388 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 256 type: NanoNQ_256 metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.23333333333333336 name: Dot Precision@3 - type: dot_precision@5 value: 0.15600000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.088 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.71 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6113177400510434 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5685238095238094 name: Dot Mrr@10 - type: dot_map@100 value: 0.5538446726220486 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: dot_accuracy@1 value: 0.39999999999999997 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6333333333333334 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7200000000000001 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8066666666666666 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.39999999999999997 name: Dot Precision@1 - type: dot_precision@3 value: 0.2644444444444444 name: Dot Precision@3 - type: dot_precision@5 value: 0.20800000000000005 name: Dot Precision@5 - type: dot_precision@10 value: 0.14733333333333332 name: Dot Precision@10 - type: dot_recall@1 value: 0.267972068795931 name: Dot Recall@1 - type: dot_recall@3 value: 0.45353516557923795 name: Dot Recall@3 - type: dot_recall@5 value: 0.517919965667527 name: Dot Recall@5 - type: dot_recall@10 value: 0.5919146578437907 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5106555152705786 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5346428571428571 name: Dot Mrr@10 - type: dot_map@100 value: 0.407147528099796 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.44 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.16 name: Dot Precision@3 - type: dot_precision@5 value: 0.128 name: Dot Precision@5 - type: dot_precision@10 value: 0.1 name: Dot Precision@10 - type: dot_recall@1 value: 0.16 name: Dot Recall@1 - type: dot_recall@3 value: 0.205 name: Dot Recall@3 - type: dot_recall@5 value: 0.255 name: Dot Recall@5 - type: dot_recall@10 value: 0.3833333333333333 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.31822361752418216 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.41229365079365077 name: Dot Mrr@10 - type: dot_map@100 value: 0.2533758500528694 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.66 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.88 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.94 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.66 name: Dot Precision@1 - type: dot_precision@3 value: 0.6466666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.6040000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.49 name: Dot Precision@10 - type: dot_recall@1 value: 0.06909677601128397 name: Dot Recall@1 - type: dot_recall@3 value: 0.17837135105230828 name: Dot Recall@3 - type: dot_recall@5 value: 0.26249987826636084 name: Dot Recall@5 - type: dot_recall@10 value: 0.35073086886185734 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6034573399856589 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7786666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.44336900502358395 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.78 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.94 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.78 name: Dot Precision@1 - type: dot_precision@3 value: 0.3066666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.19599999999999995 name: Dot Precision@5 - type: dot_precision@10 value: 0.09999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7266666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.8566666666666666 name: Dot Recall@3 - type: dot_recall@5 value: 0.9066666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9266666666666667 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8474860667472335 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8490000000000001 name: Dot Mrr@10 - type: dot_map@100 value: 0.8124727372162975 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.28 name: Dot Precision@3 - type: dot_precision@5 value: 0.2 name: Dot Precision@5 - type: dot_precision@10 value: 0.12399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.1779126984126984 name: Dot Recall@1 - type: dot_recall@3 value: 0.3990714285714285 name: Dot Recall@3 - type: dot_recall@5 value: 0.45465079365079364 name: Dot Recall@5 - type: dot_recall@10 value: 0.5628412698412698 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.44217413756349744 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.503095238095238 name: Dot Mrr@10 - type: dot_map@100 value: 0.3726712950424665 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.78 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.94 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.78 name: Dot Precision@1 - type: dot_precision@3 value: 0.5 name: Dot Precision@3 - type: dot_precision@5 value: 0.33599999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.17599999999999993 name: Dot Precision@10 - type: dot_recall@1 value: 0.39 name: Dot Recall@1 - type: dot_recall@3 value: 0.75 name: Dot Recall@3 - type: dot_recall@5 value: 0.84 name: Dot Recall@5 - type: dot_recall@10 value: 0.88 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8076193908022954 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8510000000000001 name: Dot Mrr@10 - type: dot_map@100 value: 0.7532702446589332 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.2333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.7 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6105756359135982 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5418571428571429 name: Dot Mrr@10 - type: dot_map@100 value: 0.5510242257742258 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.72 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.36 name: Dot Precision@3 - type: dot_precision@5 value: 0.32 name: Dot Precision@5 - type: dot_precision@10 value: 0.258 name: Dot Precision@10 - type: dot_recall@1 value: 0.04221121382565747 name: Dot Recall@1 - type: dot_recall@3 value: 0.07831185452988602 name: Dot Recall@3 - type: dot_recall@5 value: 0.09530060099380368 name: Dot Recall@5 - type: dot_recall@10 value: 0.12471139152233171 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.31877595732776315 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.49883333333333324 name: Dot Mrr@10 - type: dot_map@100 value: 0.14727865014045124 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.52 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.52 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.51 name: Dot Recall@1 - type: dot_recall@3 value: 0.67 name: Dot Recall@3 - type: dot_recall@5 value: 0.7 name: Dot Recall@5 - type: dot_recall@10 value: 0.77 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6459385405932947 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6175238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.6086016240895907 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.9 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.98 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.98 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9 name: Dot Precision@1 - type: dot_precision@3 value: 0.4 name: Dot Precision@3 - type: dot_precision@5 value: 0.25999999999999995 name: Dot Precision@5 - type: dot_precision@10 value: 0.13999999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.7906666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.9353333333333333 name: Dot Recall@3 - type: dot_recall@5 value: 0.966 name: Dot Recall@5 - type: dot_recall@10 value: 1.0 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9507875725473174 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9395238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.9286047619047619 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.68 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.3466666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.28800000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.20800000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.09466666666666669 name: Dot Recall@1 - type: dot_recall@3 value: 0.21566666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.29766666666666663 name: Dot Recall@5 - type: dot_recall@10 value: 0.4266666666666667 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4003633964698161 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5814126984126983 name: Dot Mrr@10 - type: dot_map@100 value: 0.3235984936558747 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.86 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.26666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.17199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 name: Dot Recall@1 - type: dot_recall@3 value: 0.8 name: Dot Recall@3 - type: dot_recall@5 value: 0.86 name: Dot Recall@5 - type: dot_recall@10 value: 0.94 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6564774175565204 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.562579365079365 name: Dot Mrr@10 - type: dot_map@100 value: 0.56506105006105 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.64 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.64 name: Dot Precision@1 - type: dot_precision@3 value: 0.24666666666666665 name: Dot Precision@3 - type: dot_precision@5 value: 0.17199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.615 name: Dot Recall@1 - type: dot_recall@3 value: 0.69 name: Dot Recall@3 - type: dot_recall@5 value: 0.775 name: Dot Recall@5 - type: dot_recall@10 value: 0.83 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7238166818627989 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.696888888888889 name: Dot Mrr@10 - type: dot_map@100 value: 0.6903857890475537 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.5714285714285714 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8367346938775511 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8979591836734694 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5714285714285714 name: Dot Precision@1 - type: dot_precision@3 value: 0.5374149659863945 name: Dot Precision@3 - type: dot_precision@5 value: 0.5306122448979592 name: Dot Precision@5 - type: dot_precision@10 value: 0.43061224489795913 name: Dot Precision@10 - type: dot_recall@1 value: 0.03877084212205675 name: Dot Recall@1 - type: dot_recall@3 value: 0.10977308661269546 name: Dot Recall@3 - type: dot_recall@5 value: 0.1862486001524683 name: Dot Recall@5 - type: dot_recall@10 value: 0.2846992525980098 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4824099438070076 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7269517330741821 name: Dot Mrr@10 - type: dot_map@100 value: 0.34839943189604633 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.5470329670329671 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7474411302982732 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8013814756671901 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8676923076923077 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5470329670329671 name: Dot Precision@1 - type: dot_precision@3 value: 0.34800627943485085 name: Dot Precision@3 - type: dot_precision@5 value: 0.2697394034536892 name: Dot Precision@5 - type: dot_precision@10 value: 0.1832778649921507 name: Dot Precision@10 - type: dot_recall@1 value: 0.33192242541320743 name: Dot Recall@1 - type: dot_recall@3 value: 0.506784183648691 name: Dot Recall@3 - type: dot_recall@5 value: 0.5645410158766739 name: Dot Recall@5 - type: dot_recall@10 value: 0.6384345730377028 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.600623515284691 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6584327950960606 name: Dot Mrr@10 - type: dot_map@100 value: 0.5229317814279774 name: Dot Map@100 - type: row_non_zero_mean_query value: 256.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 256.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9375 name: Row Sparsity Mean Corpus --- # Sparse CSR model trained on Natural Questions This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** CSR Sparse Encoder - **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 4096 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, '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): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, '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 SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq") # 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, 4096) # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 128 } ``` | Metric | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 | |:-------------------------|:----------------|:-----------------|:-----------| | dot_accuracy@1 | 0.36 | 0.3 | 0.44 | | dot_accuracy@3 | 0.6 | 0.58 | 0.58 | | dot_accuracy@5 | 0.66 | 0.64 | 0.66 | | dot_accuracy@10 | 0.78 | 0.66 | 0.76 | | dot_precision@1 | 0.36 | 0.3 | 0.44 | | dot_precision@3 | 0.2 | 0.32 | 0.1933 | | dot_precision@5 | 0.132 | 0.284 | 0.132 | | dot_precision@10 | 0.078 | 0.224 | 0.082 | | dot_recall@1 | 0.36 | 0.0206 | 0.43 | | dot_recall@3 | 0.6 | 0.0764 | 0.54 | | dot_recall@5 | 0.66 | 0.0909 | 0.6 | | dot_recall@10 | 0.78 | 0.1095 | 0.73 | | **dot_ndcg@10** | **0.5701** | **0.2706** | **0.576** | | dot_mrr@10 | 0.5032 | 0.4388 | 0.5402 | | dot_map@100 | 0.5145 | 0.1157 | 0.5349 | | row_non_zero_mean_query | 128.0 | 128.0 | 128.0 | | row_sparsity_mean_query | 0.9688 | 0.9688 | 0.9688 | | row_non_zero_mean_corpus | 128.0 | 128.0 | 128.0 | | row_sparsity_mean_corpus | 0.9688 | 0.9688 | 0.9688 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_128` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "max_active_dims": 128 } ``` | Metric | Value | |:-------------------------|:-----------| | dot_accuracy@1 | 0.3667 | | dot_accuracy@3 | 0.5867 | | dot_accuracy@5 | 0.6533 | | dot_accuracy@10 | 0.7333 | | dot_precision@1 | 0.3667 | | dot_precision@3 | 0.2378 | | dot_precision@5 | 0.1827 | | dot_precision@10 | 0.128 | | dot_recall@1 | 0.2702 | | dot_recall@3 | 0.4055 | | dot_recall@5 | 0.4503 | | dot_recall@10 | 0.5398 | | **dot_ndcg@10** | **0.4722** | | dot_mrr@10 | 0.4941 | | dot_map@100 | 0.3884 | | row_non_zero_mean_query | 128.0 | | row_sparsity_mean_query | 0.9688 | | row_non_zero_mean_corpus | 128.0 | | row_sparsity_mean_corpus | 0.9688 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 256 } ``` | Metric | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 | |:-------------------------|:----------------|:-----------------|:-----------| | dot_accuracy@1 | 0.36 | 0.4 | 0.44 | | dot_accuracy@3 | 0.64 | 0.56 | 0.7 | | dot_accuracy@5 | 0.76 | 0.64 | 0.76 | | dot_accuracy@10 | 0.84 | 0.76 | 0.82 | | dot_precision@1 | 0.36 | 0.4 | 0.44 | | dot_precision@3 | 0.2133 | 0.3467 | 0.2333 | | dot_precision@5 | 0.152 | 0.316 | 0.156 | | dot_precision@10 | 0.084 | 0.27 | 0.088 | | dot_recall@1 | 0.36 | 0.0239 | 0.42 | | dot_recall@3 | 0.64 | 0.0606 | 0.66 | | dot_recall@5 | 0.76 | 0.0838 | 0.71 | | dot_recall@10 | 0.84 | 0.1457 | 0.79 | | **dot_ndcg@10** | **0.602** | **0.3186** | **0.6113** | | dot_mrr@10 | 0.5252 | 0.5102 | 0.5685 | | dot_map@100 | 0.5322 | 0.1354 | 0.5538 | | row_non_zero_mean_query | 256.0 | 256.0 | 256.0 | | row_sparsity_mean_query | 0.9375 | 0.9375 | 0.9375 | | row_non_zero_mean_corpus | 256.0 | 256.0 | 256.0 | | row_sparsity_mean_corpus | 0.9375 | 0.9375 | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_256` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "max_active_dims": 256 } ``` | Metric | Value | |:-------------------------|:-----------| | dot_accuracy@1 | 0.4 | | dot_accuracy@3 | 0.6333 | | dot_accuracy@5 | 0.72 | | dot_accuracy@10 | 0.8067 | | dot_precision@1 | 0.4 | | dot_precision@3 | 0.2644 | | dot_precision@5 | 0.208 | | dot_precision@10 | 0.1473 | | dot_recall@1 | 0.268 | | dot_recall@3 | 0.4535 | | dot_recall@5 | 0.5179 | | dot_recall@10 | 0.5919 | | **dot_ndcg@10** | **0.5107** | | dot_mrr@10 | 0.5346 | | dot_map@100 | 0.4071 | | row_non_zero_mean_query | 256.0 | | row_sparsity_mean_query | 0.9375 | | row_non_zero_mean_corpus | 256.0 | | row_sparsity_mean_corpus | 0.9375 | #### Sparse Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:-------------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.32 | 0.66 | 0.78 | 0.4 | 0.78 | 0.38 | 0.4 | 0.52 | 0.9 | 0.44 | 0.32 | 0.64 | 0.5714 | | dot_accuracy@3 | 0.44 | 0.88 | 0.9 | 0.58 | 0.9 | 0.7 | 0.58 | 0.72 | 0.98 | 0.68 | 0.8 | 0.72 | 0.8367 | | dot_accuracy@5 | 0.52 | 0.94 | 0.94 | 0.62 | 0.94 | 0.74 | 0.64 | 0.76 | 0.98 | 0.78 | 0.86 | 0.8 | 0.898 | | dot_accuracy@10 | 0.66 | 0.94 | 0.96 | 0.76 | 1.0 | 0.82 | 0.72 | 0.8 | 1.0 | 0.84 | 0.94 | 0.84 | 1.0 | | dot_precision@1 | 0.32 | 0.66 | 0.78 | 0.4 | 0.78 | 0.38 | 0.4 | 0.52 | 0.9 | 0.44 | 0.32 | 0.64 | 0.5714 | | dot_precision@3 | 0.16 | 0.6467 | 0.3067 | 0.28 | 0.5 | 0.2333 | 0.36 | 0.24 | 0.4 | 0.3467 | 0.2667 | 0.2467 | 0.5374 | | dot_precision@5 | 0.128 | 0.604 | 0.196 | 0.2 | 0.336 | 0.148 | 0.32 | 0.152 | 0.26 | 0.288 | 0.172 | 0.172 | 0.5306 | | dot_precision@10 | 0.1 | 0.49 | 0.1 | 0.124 | 0.176 | 0.082 | 0.258 | 0.086 | 0.14 | 0.208 | 0.094 | 0.094 | 0.4306 | | dot_recall@1 | 0.16 | 0.0691 | 0.7267 | 0.1779 | 0.39 | 0.38 | 0.0422 | 0.51 | 0.7907 | 0.0947 | 0.32 | 0.615 | 0.0388 | | dot_recall@3 | 0.205 | 0.1784 | 0.8567 | 0.3991 | 0.75 | 0.7 | 0.0783 | 0.67 | 0.9353 | 0.2157 | 0.8 | 0.69 | 0.1098 | | dot_recall@5 | 0.255 | 0.2625 | 0.9067 | 0.4547 | 0.84 | 0.74 | 0.0953 | 0.7 | 0.966 | 0.2977 | 0.86 | 0.775 | 0.1862 | | dot_recall@10 | 0.3833 | 0.3507 | 0.9267 | 0.5628 | 0.88 | 0.82 | 0.1247 | 0.77 | 1.0 | 0.4267 | 0.94 | 0.83 | 0.2847 | | **dot_ndcg@10** | **0.3182** | **0.6035** | **0.8475** | **0.4422** | **0.8076** | **0.6106** | **0.3188** | **0.6459** | **0.9508** | **0.4004** | **0.6565** | **0.7238** | **0.4824** | | dot_mrr@10 | 0.4123 | 0.7787 | 0.849 | 0.5031 | 0.851 | 0.5419 | 0.4988 | 0.6175 | 0.9395 | 0.5814 | 0.5626 | 0.6969 | 0.727 | | dot_map@100 | 0.2534 | 0.4434 | 0.8125 | 0.3727 | 0.7533 | 0.551 | 0.1473 | 0.6086 | 0.9286 | 0.3236 | 0.5651 | 0.6904 | 0.3484 | | row_non_zero_mean_query | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | | row_sparsity_mean_query | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | | row_non_zero_mean_corpus | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | | row_sparsity_mean_corpus | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] } ``` | Metric | Value | |:-------------------------|:-----------| | dot_accuracy@1 | 0.547 | | dot_accuracy@3 | 0.7474 | | dot_accuracy@5 | 0.8014 | | dot_accuracy@10 | 0.8677 | | dot_precision@1 | 0.547 | | dot_precision@3 | 0.348 | | dot_precision@5 | 0.2697 | | dot_precision@10 | 0.1833 | | dot_recall@1 | 0.3319 | | dot_recall@3 | 0.5068 | | dot_recall@5 | 0.5645 | | dot_recall@10 | 0.6384 | | **dot_ndcg@10** | **0.6006** | | dot_mrr@10 | 0.6584 | | dot_map@100 | 0.5229 | | row_non_zero_mean_query | 256.0 | | row_sparsity_mean_query | 0.9375 | | row_non_zero_mean_corpus | 256.0 | | row_sparsity_mean_corpus | 0.9375 | ## 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/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### 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/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 4e-05 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_128_dot_ndcg@10 | NanoNFCorpus_128_dot_ndcg@10 | NanoNQ_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_256_dot_ndcg@10 | NanoNFCorpus_256_dot_ndcg@10 | NanoNQ_256_dot_ndcg@10 | NanoBEIR_mean_256_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | |:----------:|:-------:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:------------------------:|:------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|:-------------------------:| | -1 | -1 | - | - | 0.5920 | 0.2869 | 0.6003 | 0.4930 | 0.5785 | 0.3370 | 0.6392 | 0.5183 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0646 | 100 | 0.3598 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.3648 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1939 | 300 | 0.3272 | 0.3362 | 0.5728 | 0.2771 | 0.5552 | 0.4684 | 0.5932 | 0.3225 | 0.6162 | 0.5107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2586 | 400 | 0.3534 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.3423 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.3878** | **600** | **0.3601** | **0.3204** | **0.5672** | **0.2679** | **0.5813** | **0.4721** | **0.611** | **0.3195** | **0.6453** | **0.5253** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.4525 | 700 | 0.3279 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.3235 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.3359 | 0.3098 | 0.5840 | 0.2496 | 0.5808 | 0.4715 | 0.6014 | 0.3208 | 0.6265 | 0.5162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 1000 | 0.3215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 1200 | 0.3394 | 0.3065 | 0.5838 | 0.2449 | 0.5739 | 0.4676 | 0.6022 | 0.3227 | 0.6069 | 0.5106 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8403 | 1300 | 0.331 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.3188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9696 | 1500 | 0.3225 | 0.3034 | 0.5701 | 0.2706 | 0.5760 | 0.4722 | 0.6020 | 0.3186 | 0.6113 | 0.5107 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | - | - | - | - | - | - | - | - | 0.3182 | 0.6035 | 0.8475 | 0.4422 | 0.8076 | 0.6106 | 0.3188 | 0.6459 | 0.9508 | 0.4004 | 0.6565 | 0.7238 | 0.4824 | 0.6006 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.136 kWh - **Carbon Emitted**: 0.053 kg of CO2 - **Hours Used**: 0.398 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.49.0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CSRLoss ```bibtex @misc{wen2025matryoshkarevisitingsparsecoding, title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation}, author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You}, year={2025}, eprint={2503.01776}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.01776}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```