--- 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: - text: 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] - text: 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] - text: who was the first president of indian science congress meeting held in kolkata in 1914 - text: 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. - text: '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: 78.63547133575128 energy_consumed: 0.20230271862699775 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: 0.571 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 type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.34 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.44 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.34 name: Dot Precision@1 - type: dot_precision@3 value: 0.14666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.12400000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.34 name: Dot Recall@1 - type: dot_recall@3 value: 0.44 name: Dot Recall@3 - type: dot_recall@5 value: 0.62 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.535047397862425 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4492380952380952 name: Dot Mrr@10 - type: dot_map@100 value: 0.4565956812862131 name: Dot Map@100 - type: row_non_zero_mean_query value: 32.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9921875 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 32.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9921875 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 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: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 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: 0.4 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 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: 0.6142058022889539 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5477142857142856 name: Dot Mrr@10 - type: dot_map@100 value: 0.5535645073071618 name: Dot Map@100 - type: row_non_zero_mean_query value: 64.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.984375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 64.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.984375 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.36 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: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.08 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.72 name: Dot Recall@3 - type: dot_recall@5 value: 0.8 name: Dot Recall@5 - type: dot_recall@10 value: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6119801006837546 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5479999999999999 name: Dot Mrr@10 - type: dot_map@100 value: 0.5570329635790349 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 - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.68 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.22666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.68 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6202495574521795 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5495 name: Dot Mrr@10 - type: dot_map@100 value: 0.5567587644744507 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 - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.68 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: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.22666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.4 name: Dot Recall@1 - type: dot_recall@3 value: 0.68 name: Dot Recall@3 - type: dot_recall@5 value: 0.76 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6233479483972318 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5590238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.5667471833817065 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: 0.22 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.3 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.36 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.52 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.22 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666663 name: Dot Precision@3 - type: dot_precision@5 value: 0.156 name: Dot Precision@5 - type: dot_precision@10 value: 0.15 name: Dot Precision@10 - type: dot_recall@1 value: 0.005369382143489658 name: Dot Recall@1 - type: dot_recall@3 value: 0.016195110222025074 name: Dot Recall@3 - type: dot_recall@5 value: 0.049293570620457035 name: Dot Recall@5 - type: dot_recall@10 value: 0.0806937671045514 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.17174320910928226 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.2927619047619048 name: Dot Mrr@10 - type: dot_map@100 value: 0.05298975181660711 name: Dot Map@100 - type: row_non_zero_mean_query value: 32.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9921875 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 32.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9921875 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.26 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.4 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.46 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.56 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.26 name: Dot Precision@1 - type: dot_precision@3 value: 0.2333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.21599999999999994 name: Dot Precision@5 - type: dot_precision@10 value: 0.18 name: Dot Precision@10 - type: dot_recall@1 value: 0.010097102114744272 name: Dot Recall@1 - type: dot_recall@3 value: 0.04537644219647232 name: Dot Recall@3 - type: dot_recall@5 value: 0.06148760758910991 name: Dot Recall@5 - type: dot_recall@10 value: 0.09415095559842784 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2096821639525137 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.34343650793650793 name: Dot Mrr@10 - type: dot_map@100 value: 0.08064284502822883 name: Dot Map@100 - type: row_non_zero_mean_query value: 64.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.984375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 64.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.984375 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.34 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.48 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.34 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.27599999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.23 name: Dot Precision@10 - type: dot_recall@1 value: 0.03101859044799731 name: Dot Recall@1 - type: dot_recall@3 value: 0.06237480359765744 name: Dot Recall@3 - type: dot_recall@5 value: 0.07386821785513752 name: Dot Recall@5 - type: dot_recall@10 value: 0.10186854211536649 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.27455891665154974 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.42166666666666663 name: Dot Mrr@10 - type: dot_map@100 value: 0.11672912090576673 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 - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.46 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.3 name: Dot Precision@3 - type: dot_precision@5 value: 0.324 name: Dot Precision@5 - type: dot_precision@10 value: 0.28600000000000003 name: Dot Precision@10 - type: dot_recall@1 value: 0.010179819259573217 name: Dot Recall@1 - type: dot_recall@3 value: 0.04444946823515787 name: Dot Recall@3 - type: dot_recall@5 value: 0.07791010802255334 name: Dot Recall@5 - type: dot_recall@10 value: 0.13377621691836752 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3108609159740967 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.43744444444444447 name: Dot Mrr@10 - type: dot_map@100 value: 0.12265426034977883 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 - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.35999999999999993 name: Dot Precision@3 - type: dot_precision@5 value: 0.32 name: Dot Precision@5 - type: dot_precision@10 value: 0.27 name: Dot Precision@10 - type: dot_recall@1 value: 0.04635628984780851 name: Dot Recall@1 - type: dot_recall@3 value: 0.07762856181796872 name: Dot Recall@3 - type: dot_recall@5 value: 0.09496420727524445 name: Dot Recall@5 - type: dot_recall@10 value: 0.12650888877020955 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3261739681282223 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5003888888888889 name: Dot Mrr@10 - type: dot_map@100 value: 0.15272488982108906 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: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.16666666666666669 name: Dot Precision@3 - type: dot_precision@5 value: 0.11599999999999999 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.28 name: Dot Recall@1 - type: dot_recall@3 value: 0.47 name: Dot Recall@3 - type: dot_recall@5 value: 0.55 name: Dot Recall@5 - type: dot_recall@10 value: 0.64 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.45947191204401955 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.40702380952380957 name: Dot Mrr@10 - type: dot_map@100 value: 0.40647141879184173 name: Dot Map@100 - type: row_non_zero_mean_query value: 32.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.9921875 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 32.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.9921875 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.14 name: Dot Precision@5 - type: dot_precision@10 value: 0.07800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 name: Dot Recall@1 - type: dot_recall@3 value: 0.59 name: Dot Recall@3 - type: dot_recall@5 value: 0.65 name: Dot Recall@5 - type: dot_recall@10 value: 0.72 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5338423179297352 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.47974603174603175 name: Dot Mrr@10 - type: dot_map@100 value: 0.4773890418843979 name: Dot Map@100 - type: row_non_zero_mean_query value: 64.0 name: Row Non Zero Mean Query - type: row_sparsity_mean_query value: 0.984375 name: Row Sparsity Mean Query - type: row_non_zero_mean_corpus value: 64.0 name: Row Non Zero Mean Corpus - type: row_sparsity_mean_corpus value: 0.984375 name: Row Sparsity Mean Corpus - type: dot_accuracy@1 value: 0.5 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5 name: Dot Precision@1 - type: dot_precision@3 value: 0.23333333333333336 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: 0.49 name: Dot Recall@1 - type: dot_recall@3 value: 0.65 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6242982941698777 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5989682539682539 name: Dot Mrr@10 - type: dot_map@100 value: 0.5901794633844323 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 - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.68 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.86 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.22666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.47 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.69 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6403993438837419 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5924126984126983 name: Dot Mrr@10 - type: dot_map@100 value: 0.5839678374146947 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 - type: dot_accuracy@1 value: 0.48 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: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.24666666666666665 name: Dot Precision@3 - type: dot_precision@5 value: 0.15600000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.47 name: Dot Recall@1 - type: dot_recall@3 value: 0.68 name: Dot Recall@3 - type: dot_recall@5 value: 0.71 name: Dot Recall@5 - type: dot_recall@10 value: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6448325805638914 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6067142857142857 name: Dot Mrr@10 - type: dot_map@100 value: 0.5961039318128456 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: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.41333333333333333 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.52 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6733333333333333 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.16 name: Dot Precision@3 - type: dot_precision@5 value: 0.132 name: Dot Precision@5 - type: dot_precision@10 value: 0.10066666666666667 name: Dot Precision@10 - type: dot_recall@1 value: 0.20845646071449656 name: Dot Recall@1 - type: dot_recall@3 value: 0.3087317034073417 name: Dot Recall@3 - type: dot_recall@5 value: 0.406431190206819 name: Dot Recall@5 - type: dot_recall@10 value: 0.5135645890348505 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.38875417300524223 name: Dot Ndcg@10 - 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type: dot_recall@5 value: 0.5724982683825325 name: Dot Recall@5 - type: dot_recall@10 value: 0.6452176942587184 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6079916454695821 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6703401734320101 name: Dot Mrr@10 - type: dot_map@100 value: 0.5307417107665151 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: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.48 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.56 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.64 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.18 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.086 name: Dot Precision@10 - type: dot_recall@1 value: 0.115 name: Dot Recall@1 - type: dot_recall@3 value: 0.21166666666666664 name: Dot Recall@3 - type: dot_recall@5 value: 0.2756666666666666 name: Dot Recall@5 - type: dot_recall@10 value: 0.33399999999999996 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2808719551174852 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.39607936507936503 name: Dot Mrr@10 - type: dot_map@100 value: 0.22053769794247585 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: 0.74 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.92 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.74 name: Dot Precision@1 - type: dot_precision@3 value: 0.64 name: Dot Precision@3 - type: dot_precision@5 value: 0.5920000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.468 name: Dot Precision@10 - type: dot_recall@1 value: 0.08983751675202471 name: Dot Recall@1 - type: dot_recall@3 value: 0.1711487813957697 name: Dot Recall@3 - type: dot_recall@5 value: 0.23824154407745554 name: Dot Recall@5 - type: dot_recall@10 value: 0.3593446163014364 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6048782764547271 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8311904761904763 name: Dot Mrr@10 - type: dot_map@100 value: 0.44329574170124053 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: 0.84 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.96 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.84 name: Dot Precision@1 - type: dot_precision@3 value: 0.32666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.19999999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.102 name: Dot Precision@10 - type: dot_recall@1 value: 0.7866666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.9166666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9233333333333333 name: Dot Recall@5 - type: dot_recall@10 value: 0.9333333333333332 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8812058128870981 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.89 name: Dot Mrr@10 - type: dot_map@100 value: 0.8538462377203007 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: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.29333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.22399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.13599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.20724603174603173 name: Dot Recall@1 - type: dot_recall@3 value: 0.4124603174603174 name: Dot Recall@3 - type: dot_recall@5 value: 0.5158968253968254 name: Dot Recall@5 - type: dot_recall@10 value: 0.6268412698412699 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4880473026320133 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5356349206349206 name: Dot Mrr@10 - type: dot_map@100 value: 0.4061457504951077 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: 0.94 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: 0.78 name: Dot Precision@1 - type: dot_precision@3 value: 0.5266666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.33599999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.17999999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.39 name: Dot Recall@1 - type: dot_recall@3 value: 0.79 name: Dot Recall@3 - type: dot_recall@5 value: 0.84 name: Dot Recall@5 - type: dot_recall@10 value: 0.9 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8241120096573138 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8728571428571428 name: Dot Mrr@10 - type: dot_map@100 value: 0.7643662862369045 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: 0.92 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.98 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 1.0 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.92 name: Dot Precision@1 - type: dot_precision@3 value: 0.40666666666666657 name: Dot Precision@3 - type: dot_precision@5 value: 0.25999999999999995 name: Dot Precision@5 - type: dot_precision@10 value: 0.13599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.8073333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.9420000000000001 name: Dot Recall@3 - type: dot_recall@5 value: 0.976 name: Dot Recall@5 - type: dot_recall@10 value: 0.9933333333333334 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9567316042376142 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.955 name: Dot Mrr@10 - type: dot_map@100 value: 0.9393269841269841 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: 0.46 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.86 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.46 name: Dot Precision@1 - type: dot_precision@3 value: 0.34 name: Dot Precision@3 - type: dot_precision@5 value: 0.28 name: Dot Precision@5 - type: dot_precision@10 value: 0.198 name: Dot Precision@10 - type: dot_recall@1 value: 0.09766666666666665 name: Dot Recall@1 - type: dot_recall@3 value: 0.21366666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.28966666666666663 name: Dot Recall@5 - type: dot_recall@10 value: 0.4056666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3897243669463839 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5808015873015874 name: Dot Mrr@10 - type: dot_map@100 value: 0.3103398502941357 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: 0.82 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.88 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.2733333333333334 name: Dot Precision@3 - type: dot_precision@5 value: 0.176 name: Dot Precision@5 - type: dot_precision@10 value: 0.09599999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 name: Dot Recall@1 - type: dot_recall@3 value: 0.82 name: Dot Recall@3 - type: dot_recall@5 value: 0.88 name: Dot Recall@5 - type: dot_recall@10 value: 0.96 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.661824665356718 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.563047619047619 name: Dot Mrr@10 - type: dot_map@100 value: 0.5655109621561234 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: 0.7 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.86 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.7 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: 0.09599999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.665 name: Dot Recall@1 - type: dot_recall@3 value: 0.715 name: Dot Recall@3 - type: dot_recall@5 value: 0.765 name: Dot Recall@5 - type: dot_recall@10 value: 0.85 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7555617268006612 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7335238095238098 name: Dot Mrr@10 - type: dot_map@100 value: 0.7269493414387032 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: 0.5306122448979592 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8571428571428571 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8979591836734694 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9591836734693877 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5306122448979592 name: Dot Precision@1 - type: dot_precision@3 value: 0.5510204081632653 name: Dot Precision@3 - type: dot_precision@5 value: 0.4979591836734694 name: Dot Precision@5 - type: dot_precision@10 value: 0.4163265306122449 name: Dot Precision@10 - type: dot_recall@1 value: 0.039127695785450424 name: Dot Recall@1 - type: dot_recall@3 value: 0.1155438931843869 name: Dot Recall@3 - type: dot_recall@5 value: 0.17370824555673137 name: Dot Recall@5 - type: dot_recall@10 value: 0.2788019171170908 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.46657917392520565 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6901603498542274 name: Dot Mrr@10 - type: dot_map@100 value: 0.35374738283707957 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`, `NanoNFCorpus`, `NanoNQ`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:-------------------------|:------------|:-------------|:-----------|:-----------------|:------------|:-----------|:-------------|:-------------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.4 | 0.42 | 0.48 | 0.28 | 0.74 | 0.84 | 0.4 | 0.78 | 0.92 | 0.46 | 0.32 | 0.7 | 0.5306 | | dot_accuracy@3 | 0.68 | 0.56 | 0.72 | 0.48 | 0.9 | 0.96 | 0.64 | 0.94 | 0.98 | 0.66 | 0.82 | 0.72 | 0.8571 | | dot_accuracy@5 | 0.76 | 0.6 | 0.76 | 0.56 | 0.92 | 0.96 | 0.7 | 0.98 | 1.0 | 0.74 | 0.88 | 0.78 | 0.898 | | dot_accuracy@10 | 0.82 | 0.68 | 0.84 | 0.64 | 0.98 | 0.96 | 0.78 | 1.0 | 1.0 | 0.86 | 0.96 | 0.86 | 0.9592 | | dot_precision@1 | 0.4 | 0.42 | 0.48 | 0.28 | 0.74 | 0.84 | 0.4 | 0.78 | 0.92 | 0.46 | 0.32 | 0.7 | 0.5306 | | dot_precision@3 | 0.2267 | 0.36 | 0.2467 | 0.18 | 0.64 | 0.3267 | 0.2933 | 0.5267 | 0.4067 | 0.34 | 0.2733 | 0.2667 | 0.551 | | dot_precision@5 | 0.152 | 0.32 | 0.156 | 0.136 | 0.592 | 0.2 | 0.224 | 0.336 | 0.26 | 0.28 | 0.176 | 0.172 | 0.498 | | dot_precision@10 | 0.082 | 0.27 | 0.09 | 0.086 | 0.468 | 0.102 | 0.136 | 0.18 | 0.136 | 0.198 | 0.096 | 0.096 | 0.4163 | | dot_recall@1 | 0.4 | 0.0464 | 0.47 | 0.115 | 0.0898 | 0.7867 | 0.2072 | 0.39 | 0.8073 | 0.0977 | 0.32 | 0.665 | 0.0391 | | dot_recall@3 | 0.68 | 0.0776 | 0.68 | 0.2117 | 0.1711 | 0.9167 | 0.4125 | 0.79 | 0.942 | 0.2137 | 0.82 | 0.715 | 0.1155 | | dot_recall@5 | 0.76 | 0.095 | 0.71 | 0.2757 | 0.2382 | 0.9233 | 0.5159 | 0.84 | 0.976 | 0.2897 | 0.88 | 0.765 | 0.1737 | | dot_recall@10 | 0.82 | 0.1265 | 0.8 | 0.334 | 0.3593 | 0.9333 | 0.6268 | 0.9 | 0.9933 | 0.4057 | 0.96 | 0.85 | 0.2788 | | **dot_ndcg@10** | **0.6233** | **0.3262** | **0.6448** | **0.2809** | **0.6049** | **0.8812** | **0.488** | **0.8241** | **0.9567** | **0.3897** | **0.6618** | **0.7556** | **0.4666** | | dot_mrr@10 | 0.559 | 0.5004 | 0.6067 | 0.3961 | 0.8312 | 0.89 | 0.5356 | 0.8729 | 0.955 | 0.5808 | 0.563 | 0.7335 | 0.6902 | | dot_map@100 | 0.5667 | 0.1527 | 0.5961 | 0.2205 | 0.4433 | 0.8538 | 0.4061 | 0.7644 | 0.9393 | 0.3103 | 0.5655 | 0.7269 | 0.3537 | | 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": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:-------------------------|:-----------| | dot_accuracy@1 | 0.28 | | dot_accuracy@3 | 0.4133 | | dot_accuracy@5 | 0.52 | | dot_accuracy@10 | 0.6733 | | dot_precision@1 | 0.28 | | dot_precision@3 | 0.16 | | dot_precision@5 | 0.132 | | dot_precision@10 | 0.1007 | | dot_recall@1 | 0.2085 | | dot_recall@3 | 0.3087 | | dot_recall@5 | 0.4064 | | dot_recall@10 | 0.5136 | | **dot_ndcg@10** | **0.3888** | | dot_mrr@10 | 0.383 | | dot_map@100 | 0.3054 | | row_non_zero_mean_query | 32.0 | | row_sparsity_mean_query | 0.9922 | | row_non_zero_mean_corpus | 32.0 | | row_sparsity_mean_corpus | 0.9922 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:-------------------------|:-----------| | dot_accuracy@1 | 0.3267 | | dot_accuracy@3 | 0.5533 | | dot_accuracy@5 | 0.6333 | | dot_accuracy@10 | 0.7133 | | dot_precision@1 | 0.3267 | | dot_precision@3 | 0.2178 | | dot_precision@5 | 0.168 | | dot_precision@10 | 0.1133 | | dot_recall@1 | 0.2434 | | dot_recall@3 | 0.4251 | | dot_recall@5 | 0.4838 | | dot_recall@10 | 0.5447 | | **dot_ndcg@10** | **0.4526** | | dot_mrr@10 | 0.457 | | dot_map@100 | 0.3705 | | row_non_zero_mean_query | 64.0 | | row_sparsity_mean_query | 0.9844 | | row_non_zero_mean_corpus | 64.0 | | row_sparsity_mean_corpus | 0.9844 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:-------------------------|:-----------| | dot_accuracy@1 | 0.4 | | dot_accuracy@3 | 0.6333 | | dot_accuracy@5 | 0.68 | | dot_accuracy@10 | 0.72 | | dot_precision@1 | 0.4 | | dot_precision@3 | 0.2622 | | dot_precision@5 | 0.1947 | | dot_precision@10 | 0.1307 | | dot_recall@1 | 0.2937 | | dot_recall@3 | 0.4775 | | dot_recall@5 | 0.518 | | dot_recall@10 | 0.5473 | | **dot_ndcg@10** | **0.5036** | | dot_mrr@10 | 0.5229 | | dot_map@100 | 0.4213 | | 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 Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:-------------------------|:-----------| | dot_accuracy@1 | 0.3867 | | dot_accuracy@3 | 0.6067 | | dot_accuracy@5 | 0.7 | | dot_accuracy@10 | 0.8133 | | dot_precision@1 | 0.3867 | | dot_precision@3 | 0.2511 | | dot_precision@5 | 0.2067 | | dot_precision@10 | 0.154 | | dot_recall@1 | 0.2867 | | dot_recall@3 | 0.4548 | | dot_recall@5 | 0.5026 | | dot_recall@10 | 0.5979 | | **dot_ndcg@10** | **0.5238** | | dot_mrr@10 | 0.5265 | | dot_map@100 | 0.4211 | | 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 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 | 0.5593 | | dot_accuracy@3 | 0.7629 | | dot_accuracy@5 | 0.8106 | | dot_accuracy@10 | 0.8722 | | dot_precision@1 | 0.5593 | | dot_precision@3 | 0.3567 | | dot_precision@5 | 0.2694 | | dot_precision@10 | 0.1813 | | dot_recall@1 | 0.3411 | | dot_recall@3 | 0.5189 | | dot_recall@5 | 0.5725 | | dot_recall@10 | 0.6452 | | **dot_ndcg@10** | **0.608** | | dot_mrr@10 | 0.6703 | | dot_map@100 | 0.5307 | | 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_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | |:----------:|:-------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:| | 0.0646 | 100 | 0.3429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.3521 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.1939** | **300** | **0.3399** | **0.3572** | **0.6207** | **0.3281** | **0.6434** | **0.5308** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.2586 | 400 | 0.3458 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.3383 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 600 | 0.3613 | 0.3705 | 0.5998 | 0.3108 | 0.6044 | 0.5050 | - | - | - | - | - | - | - | - | - | - | | 0.4525 | 700 | 0.3323 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.3336 | 0.3499 | 0.5970 | 0.3092 | 0.6616 | 0.5226 | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 1000 | 0.3161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.3329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 1200 | 0.3615 | 0.3609 | 0.6036 | 0.3108 | 0.6372 | 0.5172 | - | - | - | - | - | - | - | - | - | - | | 0.8403 | 1300 | 0.337 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.3265 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9696 | 1500 | 0.3246 | 0.3527 | 0.6202 | 0.3109 | 0.6404 | 0.5238 | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | 0.6233 | 0.3262 | 0.6448 | 0.6080 | 0.2809 | 0.6049 | 0.8812 | 0.4880 | 0.8241 | 0.9567 | 0.3897 | 0.6618 | 0.7556 | 0.4666 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.202 kWh - **Carbon Emitted**: 0.079 kg of CO2 - **Hours Used**: 0.571 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} } ```