--- 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 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 53.486914244267936 energy_consumed: 0.1376039079919011 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: 0.406 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: 0.68 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: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 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: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5700574882386609 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.49757936507936507 name: Dot Mrr@10 - type: dot_map@100 value: 0.5099077397336835 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - 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: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.62 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.2866666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.26799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.234 name: Dot Precision@10 - type: dot_recall@1 value: 0.038852553787646696 name: Dot Recall@1 - type: dot_recall@3 value: 0.060787676252818314 name: Dot Recall@3 - type: dot_recall@5 value: 0.08871070532106025 name: Dot Recall@5 - type: dot_recall@10 value: 0.1164679743390103 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.27742011622390783 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.41685714285714276 name: Dot Mrr@10 - type: dot_map@100 value: 0.1342268199818926 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - 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: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.132 name: Dot Precision@5 - type: dot_precision@10 value: 0.08 name: Dot Precision@10 - type: dot_recall@1 value: 0.41 name: Dot Recall@1 - type: dot_recall@3 value: 0.56 name: Dot Recall@3 - type: dot_recall@5 value: 0.59 name: Dot Recall@5 - type: dot_recall@10 value: 0.71 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5655257382100716 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5366666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.5200476570220556 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - 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: 0.5733333333333334 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6266666666666666 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7200000000000001 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36666666666666664 name: Dot Precision@1 - type: dot_precision@3 value: 0.23111111111111113 name: Dot Precision@3 - type: dot_precision@5 value: 0.17866666666666667 name: Dot Precision@5 - type: dot_precision@10 value: 0.13133333333333333 name: Dot Precision@10 - type: dot_recall@1 value: 0.26961751792921557 name: Dot Recall@1 - type: dot_recall@3 value: 0.40692922541760607 name: Dot Recall@3 - type: dot_recall@5 value: 0.45290356844035345 name: Dot Recall@5 - type: dot_recall@10 value: 0.5421559914463367 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4710011142242134 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.48370105820105813 name: Dot Mrr@10 - type: dot_map@100 value: 0.3880607389125439 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 256 type: NanoMSMARCO_256 metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.19333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 name: Dot Recall@1 - type: dot_recall@3 value: 0.58 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.556581518059458 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.47826984126984123 name: Dot Mrr@10 - type: dot_map@100 value: 0.49049453698389867 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - 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: 0.5 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.54 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.28800000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.258 name: Dot Precision@10 - type: dot_recall@1 value: 0.04394699993743869 name: Dot Recall@1 - type: dot_recall@3 value: 0.07346911892860693 name: Dot Recall@3 - type: dot_recall@5 value: 0.0955352050901188 name: Dot Recall@5 - type: dot_recall@10 value: 0.13423937941849148 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3138240971606582 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4724126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.1554159267082162 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - 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: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 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: 0.21333333333333335 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.59 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6092334692116076 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5592142857142858 name: Dot Mrr@10 - type: dot_map@100 value: 0.5537561375100075 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: dot_accuracy@1 value: 0.38666666666666666 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5666666666666668 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.66 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7599999999999999 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38666666666666666 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.19600000000000004 name: Dot Precision@5 - type: dot_precision@10 value: 0.14266666666666666 name: Dot Precision@10 - type: dot_recall@1 value: 0.2613156666458129 name: Dot Recall@1 - type: dot_recall@3 value: 0.41448970630953563 name: Dot Recall@3 - type: dot_recall@5 value: 0.5051784016967064 name: Dot Recall@5 - type: dot_recall@10 value: 0.5747464598061639 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.49321302814390794 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5032989417989419 name: Dot Mrr@10 - type: dot_map@100 value: 0.39988886706737414 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.34 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 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: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.176 name: Dot Precision@5 - type: dot_precision@10 value: 0.11799999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.14733333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.2723333333333333 name: Dot Recall@3 - type: dot_recall@5 value: 0.359 name: Dot Recall@5 - type: dot_recall@10 value: 0.469 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3709538178023985 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4734126984126983 name: Dot Mrr@10 - type: dot_map@100 value: 0.2810456840827194 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.78 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.88 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9 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: 0.6666666666666665 name: Dot Precision@3 - type: dot_precision@5 value: 0.5880000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.484 name: Dot Precision@10 - type: dot_recall@1 value: 0.08494800977438213 name: Dot Recall@1 - type: dot_recall@3 value: 0.17317448416542106 name: Dot Recall@3 - type: dot_recall@5 value: 0.23034114850972465 name: Dot Recall@5 - type: dot_recall@10 value: 0.3258962243107224 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6091876327956771 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8355238095238097 name: Dot Mrr@10 - type: dot_map@100 value: 0.45081375839318744 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.86 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 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: 0.86 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.19999999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.8066666666666668 name: Dot Recall@1 - type: dot_recall@3 value: 0.8666666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9266666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9266666666666667 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8841127708415583 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.894 name: Dot Mrr@10 - type: dot_map@100 value: 0.8619688731284475 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.46 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.68 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.46 name: Dot Precision@1 - type: dot_precision@3 value: 0.32666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.23199999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.14 name: Dot Precision@10 - type: dot_recall@1 value: 0.25257936507936507 name: Dot Recall@1 - type: dot_recall@3 value: 0.4653809523809523 name: Dot Recall@3 - type: dot_recall@5 value: 0.5155952380952381 name: Dot Recall@5 - type: dot_recall@10 value: 0.575563492063492 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5042980843824951 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5653333333333334 name: Dot Mrr@10 - type: dot_map@100 value: 0.4452452302579616 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - 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: 0.86 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9 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: 0.32 name: Dot Precision@5 - type: dot_precision@10 value: 0.17199999999999996 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: 0.8 name: Dot Recall@5 - type: dot_recall@10 value: 0.86 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7821924588182537 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8334920634920635 name: Dot Mrr@10 - type: dot_map@100 value: 0.7213993449971364 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.4 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: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 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: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.4 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: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5949657949660191 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5246825396825396 name: Dot Mrr@10 - type: dot_map@100 value: 0.5350828017012228 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - 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: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.35999999999999993 name: Dot Precision@3 - type: dot_precision@5 value: 0.292 name: Dot Precision@5 - type: dot_precision@10 value: 0.262 name: Dot Precision@10 - type: dot_recall@1 value: 0.03443480481548747 name: Dot Recall@1 - type: dot_recall@3 value: 0.08039614346191623 name: Dot Recall@3 - type: dot_recall@5 value: 0.09609895574877417 name: Dot Recall@5 - type: dot_recall@10 value: 0.1425768627754566 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3161920036806807 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4961031746031745 name: Dot Mrr@10 - type: dot_map@100 value: 0.1515139700880487 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.5 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: 0.8 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: 0.16399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.088 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: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6331595818344276 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5986666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.5865551394231594 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - 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: 1.0 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: 0.264 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7973333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.958 name: Dot Recall@3 - type: dot_recall@5 value: 0.986 name: Dot Recall@5 - type: dot_recall@10 value: 0.9966666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9556238046457881 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9533333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.9349527472527472 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.58 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.58 name: Dot Precision@1 - type: dot_precision@3 value: 0.3999999999999999 name: Dot Precision@3 - type: dot_precision@5 value: 0.292 name: Dot Precision@5 - type: dot_precision@10 value: 0.206 name: Dot Precision@10 - type: dot_recall@1 value: 0.12266666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.24966666666666665 name: Dot Recall@3 - type: dot_recall@5 value: 0.30166666666666664 name: Dot Recall@5 - type: dot_recall@10 value: 0.42166666666666663 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4272054291075693 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6870238095238096 name: Dot Mrr@10 - type: dot_map@100 value: 0.3390924092176022 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.78 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.26 name: Dot Precision@3 - type: dot_precision@5 value: 0.16399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.09399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.78 name: Dot Recall@3 - type: dot_recall@5 value: 0.82 name: Dot Recall@5 - type: dot_recall@10 value: 0.94 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6626337389503802 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5724920634920635 name: Dot Mrr@10 - type: dot_map@100 value: 0.5758487068487068 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.6 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.78 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6 name: Dot Precision@1 - type: dot_precision@3 value: 0.27999999999999997 name: Dot Precision@3 - type: dot_precision@5 value: 0.18 name: Dot Precision@5 - type: dot_precision@10 value: 0.09399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.575 name: Dot Recall@1 - type: dot_recall@3 value: 0.755 name: Dot Recall@3 - type: dot_recall@5 value: 0.79 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.714313571551759 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6828888888888889 name: Dot Mrr@10 - type: dot_map@100 value: 0.6825983649369914 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.5918367346938775 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: 0.5918367346938775 name: Dot Precision@1 - type: dot_precision@3 value: 0.5374149659863945 name: Dot Precision@3 - type: dot_precision@5 value: 0.4897959183673469 name: Dot Precision@5 - type: dot_precision@10 value: 0.4244897959183674 name: Dot Precision@10 - type: dot_recall@1 value: 0.042649446100483254 name: Dot Recall@1 - type: dot_recall@3 value: 0.1077957848613647 name: Dot Recall@3 - type: dot_recall@5 value: 0.1613396254665287 name: Dot Recall@5 - type: dot_recall@10 value: 0.2701410353829605 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4710841185924516 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7299562682215744 name: Dot Mrr@10 - type: dot_map@100 value: 0.34832336492939087 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.5824489795918368 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7643956043956043 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8075353218210363 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8799372056514915 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5824489795918368 name: Dot Precision@1 - type: dot_precision@3 value: 0.3613396127681842 name: Dot Precision@3 - type: dot_precision@5 value: 0.2687535321821036 name: Dot Precision@5 - type: dot_precision@10 value: 0.18480690737833594 name: Dot Precision@10 - type: dot_recall@1 value: 0.34489320198228596 name: Dot Recall@1 - type: dot_recall@3 value: 0.5152626178104862 name: Dot Recall@3 - type: dot_recall@5 value: 0.5682083308579691 name: Dot Recall@5 - type: dot_recall@10 value: 0.6421675088102025 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6096863698438044 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6805314345518426 name: Dot Mrr@10 - type: dot_map@100 value: 0.5318800304044093 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio --- # 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 with 256 maximum active dimensions 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 (trained with 256 maximum active 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-no-base-loss") # Run inference queries = [ "who is cornelius in the book of acts", ] documents = [ '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]", '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.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 4096] [3, 4096] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[133.0582, 24.5010, 26.5905]]) ``` ## 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 | 0.52 | 0.6 | | dot_accuracy@5 | 0.68 | 0.58 | 0.62 | | dot_accuracy@10 | 0.8 | 0.62 | 0.74 | | dot_precision@1 | 0.36 | 0.3 | 0.44 | | dot_precision@3 | 0.2 | 0.2867 | 0.2067 | | dot_precision@5 | 0.136 | 0.268 | 0.132 | | dot_precision@10 | 0.08 | 0.234 | 0.08 | | dot_recall@1 | 0.36 | 0.0389 | 0.41 | | dot_recall@3 | 0.6 | 0.0608 | 0.56 | | dot_recall@5 | 0.68 | 0.0887 | 0.59 | | dot_recall@10 | 0.8 | 0.1165 | 0.71 | | **dot_ndcg@10** | **0.5701** | **0.2774** | **0.5655** | | dot_mrr@10 | 0.4976 | 0.4169 | 0.5367 | | dot_map@100 | 0.5099 | 0.1342 | 0.52 | | query_active_dims | 128.0 | 128.0 | 128.0 | | query_sparsity_ratio | 0.9688 | 0.9688 | 0.9688 | | corpus_active_dims | 128.0 | 128.0 | 128.0 | | corpus_sparsity_ratio | 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 | 0.5733 | | dot_accuracy@5 | 0.6267 | | dot_accuracy@10 | 0.72 | | dot_precision@1 | 0.3667 | | dot_precision@3 | 0.2311 | | dot_precision@5 | 0.1787 | | dot_precision@10 | 0.1313 | | dot_recall@1 | 0.2696 | | dot_recall@3 | 0.4069 | | dot_recall@5 | 0.4529 | | dot_recall@10 | 0.5422 | | **dot_ndcg@10** | **0.471** | | dot_mrr@10 | 0.4837 | | dot_map@100 | 0.3881 | | query_active_dims | 128.0 | | query_sparsity_ratio | 0.9688 | | corpus_active_dims | 128.0 | | corpus_sparsity_ratio | 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 | 0.32 | 0.4 | 0.44 | | dot_accuracy@3 | 0.58 | 0.5 | 0.62 | | dot_accuracy@5 | 0.74 | 0.54 | 0.7 | | dot_accuracy@10 | 0.8 | 0.66 | 0.82 | | dot_precision@1 | 0.32 | 0.4 | 0.44 | | dot_precision@3 | 0.1933 | 0.3133 | 0.2133 | | dot_precision@5 | 0.148 | 0.288 | 0.152 | | dot_precision@10 | 0.08 | 0.258 | 0.09 | | dot_recall@1 | 0.32 | 0.0439 | 0.42 | | dot_recall@3 | 0.58 | 0.0735 | 0.59 | | dot_recall@5 | 0.74 | 0.0955 | 0.68 | | dot_recall@10 | 0.8 | 0.1342 | 0.79 | | **dot_ndcg@10** | **0.5566** | **0.3138** | **0.6092** | | dot_mrr@10 | 0.4783 | 0.4724 | 0.5592 | | dot_map@100 | 0.4905 | 0.1554 | 0.5538 | | query_active_dims | 256.0 | 256.0 | 256.0 | | query_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | | corpus_active_dims | 256.0 | 256.0 | 256.0 | | corpus_sparsity_ratio | 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 | 0.3867 | | dot_accuracy@3 | 0.5667 | | dot_accuracy@5 | 0.66 | | dot_accuracy@10 | 0.76 | | dot_precision@1 | 0.3867 | | dot_precision@3 | 0.24 | | dot_precision@5 | 0.196 | | dot_precision@10 | 0.1427 | | dot_recall@1 | 0.2613 | | dot_recall@3 | 0.4145 | | dot_recall@5 | 0.5052 | | dot_recall@10 | 0.5747 | | **dot_ndcg@10** | **0.4932** | | dot_mrr@10 | 0.5033 | | dot_map@100 | 0.3999 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 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 | 0.34 | 0.78 | 0.86 | 0.46 | 0.78 | 0.4 | 0.4 | 0.5 | 0.92 | 0.58 | 0.36 | 0.6 | 0.5918 | | dot_accuracy@3 | 0.54 | 0.88 | 0.9 | 0.68 | 0.86 | 0.6 | 0.58 | 0.68 | 1.0 | 0.8 | 0.78 | 0.78 | 0.8571 | | dot_accuracy@5 | 0.7 | 0.9 | 0.96 | 0.68 | 0.9 | 0.66 | 0.6 | 0.76 | 1.0 | 0.82 | 0.82 | 0.8 | 0.898 | | dot_accuracy@10 | 0.82 | 0.96 | 0.96 | 0.74 | 1.0 | 0.82 | 0.74 | 0.8 | 1.0 | 0.88 | 0.94 | 0.82 | 0.9592 | | dot_precision@1 | 0.34 | 0.78 | 0.86 | 0.46 | 0.78 | 0.4 | 0.4 | 0.5 | 0.92 | 0.58 | 0.36 | 0.6 | 0.5918 | | dot_precision@3 | 0.2133 | 0.6667 | 0.3133 | 0.3267 | 0.5 | 0.2 | 0.36 | 0.2333 | 0.4067 | 0.4 | 0.26 | 0.28 | 0.5374 | | dot_precision@5 | 0.176 | 0.588 | 0.2 | 0.232 | 0.32 | 0.132 | 0.292 | 0.164 | 0.264 | 0.292 | 0.164 | 0.18 | 0.4898 | | dot_precision@10 | 0.118 | 0.484 | 0.1 | 0.14 | 0.172 | 0.082 | 0.262 | 0.088 | 0.138 | 0.206 | 0.094 | 0.094 | 0.4245 | | dot_recall@1 | 0.1473 | 0.0849 | 0.8067 | 0.2526 | 0.39 | 0.4 | 0.0344 | 0.47 | 0.7973 | 0.1227 | 0.36 | 0.575 | 0.0426 | | dot_recall@3 | 0.2723 | 0.1732 | 0.8667 | 0.4654 | 0.75 | 0.6 | 0.0804 | 0.64 | 0.958 | 0.2497 | 0.78 | 0.755 | 0.1078 | | dot_recall@5 | 0.359 | 0.2303 | 0.9267 | 0.5156 | 0.8 | 0.66 | 0.0961 | 0.74 | 0.986 | 0.3017 | 0.82 | 0.79 | 0.1613 | | dot_recall@10 | 0.469 | 0.3259 | 0.9267 | 0.5756 | 0.86 | 0.82 | 0.1426 | 0.78 | 0.9967 | 0.4217 | 0.94 | 0.82 | 0.2701 | | **dot_ndcg@10** | **0.371** | **0.6092** | **0.8841** | **0.5043** | **0.7822** | **0.595** | **0.3162** | **0.6332** | **0.9556** | **0.4272** | **0.6626** | **0.7143** | **0.4711** | | dot_mrr@10 | 0.4734 | 0.8355 | 0.894 | 0.5653 | 0.8335 | 0.5247 | 0.4961 | 0.5987 | 0.9533 | 0.687 | 0.5725 | 0.6829 | 0.73 | | dot_map@100 | 0.281 | 0.4508 | 0.862 | 0.4452 | 0.7214 | 0.5351 | 0.1515 | 0.5866 | 0.935 | 0.3391 | 0.5758 | 0.6826 | 0.3483 | | query_active_dims | 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 | | query_sparsity_ratio | 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 | | corpus_active_dims | 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 | | corpus_sparsity_ratio | 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 | 0.5824 | | dot_accuracy@3 | 0.7644 | | dot_accuracy@5 | 0.8075 | | dot_accuracy@10 | 0.8799 | | dot_precision@1 | 0.5824 | | dot_precision@3 | 0.3613 | | dot_precision@5 | 0.2688 | | dot_precision@10 | 0.1848 | | dot_recall@1 | 0.3449 | | dot_recall@3 | 0.5153 | | dot_recall@5 | 0.5682 | | dot_recall@10 | 0.6422 | | **dot_ndcg@10** | **0.6097** | | dot_mrr@10 | 0.6805 | | dot_map@100 | 0.5319 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 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 - `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 - `router_mapping`: {} - `learning_rate_mapping`: {}
### 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 | - | - | 0.5667 | 0.2784 | 0.6350 | 0.4933 | 0.6324 | 0.2927 | 0.6451 | 0.5234 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0646 | 100 | 0.2571 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.2333 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.1939** | **300** | **0.2251** | **0.2188** | **0.6315** | **0.2816** | **0.5812** | **0.4981** | **0.5986** | **0.3188** | **0.6332** | **0.5169** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.2586 | 400 | 0.2203 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.2172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 600 | 0.2148 | 0.2090 | 0.6205 | 0.2824 | 0.5906 | 0.4978 | 0.5804 | 0.3145 | 0.6514 | 0.5155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4525 | 700 | 0.2131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.2114 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.2103 | 0.2044 | 0.6134 | 0.2956 | 0.5787 | 0.4959 | 0.5765 | 0.3134 | 0.6116 | 0.5005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 1000 | 0.2093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.2086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 1200 | 0.2081 | 0.2020 | 0.5954 | 0.2884 | 0.5542 | 0.4794 | 0.5806 | 0.3105 | 0.6062 | 0.4991 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8403 | 1300 | 0.2075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.2074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9696 | 1500 | 0.207 | 0.2011 | 0.5701 | 0.2774 | 0.5655 | 0.4710 | 0.5566 | 0.3138 | 0.6092 | 0.4932 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | - | - | - | - | - | - | - | - | 0.3710 | 0.6092 | 0.8841 | 0.5043 | 0.7822 | 0.5950 | 0.3162 | 0.6332 | 0.9556 | 0.4272 | 0.6626 | 0.7143 | 0.4711 | 0.6097 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.138 kWh - **Carbon Emitted**: 0.053 kg of CO2 - **Hours Used**: 0.406 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: 4.52.4 - 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} } ```