--- 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: 53.740159900184786 energy_consumed: 0.13825542420719417 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: 0.409 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: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 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: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 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: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.603846580732656 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.529079365079365 name: Dot Mrr@10 - type: dot_map@100 value: 0.535577429489216 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: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.34 name: Dot Precision@3 - type: dot_precision@5 value: 0.336 name: Dot Precision@5 - type: dot_precision@10 value: 0.28600000000000003 name: Dot Precision@10 - type: dot_recall@1 value: 0.02662938222230507 name: Dot Recall@1 - type: dot_recall@3 value: 0.08583886950771044 name: Dot Recall@3 - type: dot_recall@5 value: 0.10539572959638349 name: Dot Recall@5 - type: dot_recall@10 value: 0.1390606096616216 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.33155673498755867 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4815555555555555 name: Dot Mrr@10 - type: dot_map@100 value: 0.14591039936040862 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: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 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: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.43 name: Dot Recall@1 - type: dot_recall@3 value: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.73 name: Dot Recall@5 - type: dot_recall@10 value: 0.76 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6020077639360719 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5624999999999999 name: Dot Mrr@10 - type: dot_map@100 value: 0.5519887965031844 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: 0.4066666666666667 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.5933333333333334 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7066666666666667 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7733333333333334 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4066666666666667 name: Dot Precision@1 - type: dot_precision@3 value: 0.25333333333333335 name: Dot Precision@3 - type: dot_precision@5 value: 0.21333333333333335 name: Dot Precision@5 - type: dot_precision@10 value: 0.15133333333333332 name: Dot Precision@10 - type: dot_recall@1 value: 0.27887646074076833 name: Dot Recall@1 - type: dot_recall@3 value: 0.4352796231692368 name: Dot Recall@3 - type: dot_recall@5 value: 0.5184652431987945 name: Dot Recall@5 - type: dot_recall@10 value: 0.5796868698872072 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5124703598854289 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5243783068783068 name: Dot Mrr@10 - type: dot_map@100 value: 0.411158875117603 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: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.22 name: Dot Precision@3 - type: dot_precision@5 value: 0.156 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.44 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.78 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6402220356297674 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.576079365079365 name: Dot Mrr@10 - type: dot_map@100 value: 0.5819739218018417 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: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7 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: 0.344 name: Dot Precision@5 - type: dot_precision@10 value: 0.29200000000000004 name: Dot Precision@10 - type: dot_recall@1 value: 0.018848269093365854 name: Dot Recall@1 - type: dot_recall@3 value: 0.07354907247001424 name: Dot Recall@3 - type: dot_recall@5 value: 0.09781289475269293 name: Dot Recall@5 - type: dot_recall@10 value: 0.1418672876485781 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.33652365839683074 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4957698412698413 name: Dot Mrr@10 - type: dot_map@100 value: 0.14165509490208594 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: 0.56 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 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: 0.56 name: Dot Precision@1 - type: dot_precision@3 value: 0.23333333333333336 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.09399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.54 name: Dot Recall@1 - type: dot_recall@3 value: 0.65 name: Dot Recall@3 - type: dot_recall@5 value: 0.73 name: Dot Recall@5 - type: dot_recall@10 value: 0.83 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6813657040884066 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.647301587301587 name: Dot Mrr@10 - type: dot_map@100 value: 0.6310147772294485 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: 0.47333333333333333 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6333333333333334 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7133333333333333 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7999999999999999 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.47333333333333333 name: Dot Precision@1 - type: dot_precision@3 value: 0.27111111111111114 name: Dot Precision@3 - type: dot_precision@5 value: 0.22 name: Dot Precision@5 - type: dot_precision@10 value: 0.15666666666666665 name: Dot Precision@10 - type: dot_recall@1 value: 0.33294942303112196 name: Dot Recall@1 - type: dot_recall@3 value: 0.46118302415667145 name: Dot Recall@3 - type: dot_recall@5 value: 0.5359376315842309 name: Dot Recall@5 - type: dot_recall@10 value: 0.6039557625495261 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5527037993716682 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5730502645502644 name: Dot Mrr@10 - type: dot_map@100 value: 0.4515479313111254 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: 0.2 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.56 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.2 name: Dot Precision@1 - type: dot_precision@3 value: 0.19333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.132 name: Dot Precision@5 - type: dot_precision@10 value: 0.088 name: Dot Precision@10 - type: dot_recall@1 value: 0.07833333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.24499999999999997 name: Dot Recall@3 - type: dot_recall@5 value: 0.28333333333333327 name: Dot Recall@5 - type: dot_recall@10 value: 0.3473333333333333 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.27333419680435084 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.3666031746031747 name: Dot Mrr@10 - type: dot_map@100 value: 0.21266834216817831 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: 0.74 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.86 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.92 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.74 name: Dot Precision@1 - type: dot_precision@3 value: 0.5866666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.556 name: Dot Precision@5 - type: dot_precision@10 value: 0.484 name: Dot Precision@10 - type: dot_recall@1 value: 0.08366724054361292 name: Dot Recall@1 - type: dot_recall@3 value: 0.16227352802558825 name: Dot Recall@3 - type: dot_recall@5 value: 0.2213882427797012 name: Dot Recall@5 - type: dot_recall@10 value: 0.3353731792736538 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5972307350486245 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8152222222222223 name: Dot Mrr@10 - type: dot_map@100 value: 0.45303559906331897 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: 0.98 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.98 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.86 name: Dot Precision@1 - type: dot_precision@3 value: 0.34666666666666657 name: Dot Precision@3 - type: dot_precision@5 value: 0.20799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.10399999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.8066666666666668 name: Dot Recall@1 - type: dot_recall@3 value: 0.9433333333333332 name: Dot Recall@3 - type: dot_recall@5 value: 0.9433333333333332 name: Dot Recall@5 - type: dot_recall@10 value: 0.9433333333333332 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9054259418093692 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9133333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.8844551282051283 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: 0.5 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.22399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.2725793650793651 name: Dot Recall@1 - type: dot_recall@3 value: 0.4129047619047619 name: Dot Recall@3 - type: dot_recall@5 value: 0.4605714285714286 name: Dot Recall@5 - type: dot_recall@10 value: 0.5500873015873016 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.49585690755175454 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5641666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.4425504355719097 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: 0.84 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.92 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: 0.4733333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.316 name: Dot Precision@5 - type: dot_precision@10 value: 0.17399999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.71 name: Dot Recall@3 - type: dot_recall@5 value: 0.79 name: Dot Recall@5 - type: dot_recall@10 value: 0.87 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.802663278529999 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8856666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.7334779802028212 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: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.22 name: Dot Precision@3 - type: dot_precision@5 value: 0.156 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.78 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6354592257726257 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5694126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.5752130160409359 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: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7 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: 0.34 name: Dot Precision@5 - type: dot_precision@10 value: 0.29 name: Dot Precision@10 - type: dot_recall@1 value: 0.018848269093365854 name: Dot Recall@1 - type: dot_recall@3 value: 0.07354907247001424 name: Dot Recall@3 - type: dot_recall@5 value: 0.0962744332142314 name: Dot Recall@5 - type: dot_recall@10 value: 0.14178823626517886 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3352519406973144 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.49602380952380964 name: Dot Mrr@10 - type: dot_map@100 value: 0.14142955254174144 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: 0.56 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 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: 0.56 name: Dot Precision@1 - type: dot_precision@3 value: 0.23333333333333336 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.09399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.54 name: Dot Recall@1 - type: dot_recall@3 value: 0.65 name: Dot Recall@3 - type: dot_recall@5 value: 0.73 name: Dot Recall@5 - type: dot_recall@10 value: 0.83 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6813657040884066 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.647301587301587 name: Dot Mrr@10 - type: dot_map@100 value: 0.6311451301239768 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: 0.86 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.98 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.98 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.86 name: Dot Precision@1 - type: dot_precision@3 value: 0.4 name: Dot Precision@3 - type: dot_precision@5 value: 0.26799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7373333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.9353333333333333 name: Dot Recall@3 - type: dot_recall@5 value: 0.9733333333333334 name: Dot Recall@5 - type: dot_recall@10 value: 0.9966666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9283913808760963 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9166666666666665 name: Dot Mrr@10 - type: dot_map@100 value: 0.8996944444444444 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: 0.54 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.76 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.86 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.54 name: Dot Precision@1 - type: dot_precision@3 value: 0.37999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.30400000000000005 name: Dot Precision@5 - type: dot_precision@10 value: 0.204 name: Dot Precision@10 - type: dot_recall@1 value: 0.11466666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.23766666666666666 name: Dot Recall@3 - type: dot_recall@5 value: 0.31466666666666665 name: Dot Recall@5 - type: dot_recall@10 value: 0.4196666666666665 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.42030245497944485 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6498333333333332 name: Dot Mrr@10 - type: dot_map@100 value: 0.3374015286377059 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: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.76 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: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.25333333333333335 name: Dot Precision@3 - type: dot_precision@5 value: 0.17999999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.09599999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.28 name: Dot Recall@1 - type: dot_recall@3 value: 0.76 name: Dot Recall@3 - type: dot_recall@5 value: 0.9 name: Dot Recall@5 - type: dot_recall@10 value: 0.96 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.651941051318052 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5498571428571428 name: Dot Mrr@10 - type: dot_map@100 value: 0.5515326278659611 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: 0.76 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6 name: Dot Precision@1 - type: dot_precision@3 value: 0.2733333333333334 name: Dot Precision@3 - type: dot_precision@5 value: 0.17599999999999993 name: Dot Precision@5 - type: dot_precision@10 value: 0.1 name: Dot Precision@10 - type: dot_recall@1 value: 0.565 name: Dot Recall@1 - type: dot_recall@3 value: 0.74 name: Dot Recall@3 - type: dot_recall@5 value: 0.76 name: Dot Recall@5 - type: dot_recall@10 value: 0.88 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7313116540920006 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6887698412698413 name: Dot Mrr@10 - type: dot_map@100 value: 0.6840924219150025 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: 0.6326530612244898 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8571428571428571 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8775510204081632 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9795918367346939 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6326530612244898 name: Dot Precision@1 - type: dot_precision@3 value: 0.5986394557823129 name: Dot Precision@3 - type: dot_precision@5 value: 0.5265306122448979 name: Dot Precision@5 - type: dot_precision@10 value: 0.4326530612244897 name: Dot Precision@10 - type: dot_recall@1 value: 0.0443108966783425 name: Dot Recall@1 - type: dot_recall@3 value: 0.12651297913694023 name: Dot Recall@3 - type: dot_recall@5 value: 0.1807810185085916 name: Dot Recall@5 - type: dot_recall@10 value: 0.2908183366162545 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4946170299181126 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7585276967930031 name: Dot Mrr@10 - type: dot_map@100 value: 0.3733282842478698 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: 0.5732810047095762 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7628571428571429 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8105808477237049 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8707378335949765 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5732810047095762 name: Dot Precision@1 - type: dot_precision@3 value: 0.356305599162742 name: Dot Precision@3 - type: dot_precision@5 value: 0.27281004709576134 name: Dot Precision@5 - type: dot_precision@10 value: 0.1866656200941915 name: Dot Precision@10 - type: dot_recall@1 value: 0.3370312131842067 name: Dot Recall@1 - type: dot_recall@3 value: 0.512044128836203 name: Dot Recall@3 - type: dot_recall@5 value: 0.5718216761338938 name: Dot Recall@5 - type: dot_recall@10 value: 0.6465436195186451 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6117808847297039 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6785680645884727 name: Dot Mrr@10 - type: dot_map@100 value: 0.5323095762329995 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-reconstruction") # 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([[55.6462, 14.4637, 16.8866]]) ``` ## 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 | 0.38 | 0.4 | 0.44 | | dot_accuracy@3 | 0.62 | 0.52 | 0.64 | | dot_accuracy@5 | 0.72 | 0.62 | 0.78 | | dot_accuracy@10 | 0.84 | 0.68 | 0.8 | | dot_precision@1 | 0.38 | 0.4 | 0.44 | | dot_precision@3 | 0.2067 | 0.34 | 0.2133 | | dot_precision@5 | 0.144 | 0.336 | 0.16 | | dot_precision@10 | 0.084 | 0.286 | 0.084 | | dot_recall@1 | 0.38 | 0.0266 | 0.43 | | dot_recall@3 | 0.62 | 0.0858 | 0.6 | | dot_recall@5 | 0.72 | 0.1054 | 0.73 | | dot_recall@10 | 0.84 | 0.1391 | 0.76 | | **dot_ndcg@10** | **0.6038** | **0.3316** | **0.602** | | dot_mrr@10 | 0.5291 | 0.4816 | 0.5625 | | dot_map@100 | 0.5356 | 0.1459 | 0.552 | | 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 | 0.4067 | | dot_accuracy@3 | 0.5933 | | dot_accuracy@5 | 0.7067 | | dot_accuracy@10 | 0.7733 | | dot_precision@1 | 0.4067 | | dot_precision@3 | 0.2533 | | dot_precision@5 | 0.2133 | | dot_precision@10 | 0.1513 | | dot_recall@1 | 0.2789 | | dot_recall@3 | 0.4353 | | dot_recall@5 | 0.5185 | | dot_recall@10 | 0.5797 | | **dot_ndcg@10** | **0.5125** | | dot_mrr@10 | 0.5244 | | dot_map@100 | 0.4112 | | 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 | 0.44 | 0.42 | 0.56 | | dot_accuracy@3 | 0.66 | 0.54 | 0.7 | | dot_accuracy@5 | 0.78 | 0.58 | 0.78 | | dot_accuracy@10 | 0.84 | 0.7 | 0.86 | | dot_precision@1 | 0.44 | 0.42 | 0.56 | | dot_precision@3 | 0.22 | 0.36 | 0.2333 | | dot_precision@5 | 0.156 | 0.344 | 0.16 | | dot_precision@10 | 0.084 | 0.292 | 0.094 | | dot_recall@1 | 0.44 | 0.0188 | 0.54 | | dot_recall@3 | 0.66 | 0.0735 | 0.65 | | dot_recall@5 | 0.78 | 0.0978 | 0.73 | | dot_recall@10 | 0.84 | 0.1419 | 0.83 | | **dot_ndcg@10** | **0.6402** | **0.3365** | **0.6814** | | dot_mrr@10 | 0.5761 | 0.4958 | 0.6473 | | dot_map@100 | 0.582 | 0.1417 | 0.631 | | 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 | 0.4733 | | dot_accuracy@3 | 0.6333 | | dot_accuracy@5 | 0.7133 | | dot_accuracy@10 | 0.8 | | dot_precision@1 | 0.4733 | | dot_precision@3 | 0.2711 | | dot_precision@5 | 0.22 | | dot_precision@10 | 0.1567 | | dot_recall@1 | 0.3329 | | dot_recall@3 | 0.4612 | | dot_recall@5 | 0.5359 | | dot_recall@10 | 0.604 | | **dot_ndcg@10** | **0.5527** | | dot_mrr@10 | 0.5731 | | dot_map@100 | 0.4515 | | 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 | 0.2 | 0.74 | 0.86 | 0.5 | 0.84 | 0.42 | 0.42 | 0.56 | 0.86 | 0.54 | 0.28 | 0.6 | 0.6327 | | dot_accuracy@3 | 0.52 | 0.86 | 0.98 | 0.62 | 0.92 | 0.66 | 0.54 | 0.7 | 0.98 | 0.76 | 0.76 | 0.76 | 0.8571 | | dot_accuracy@5 | 0.56 | 0.92 | 0.98 | 0.64 | 0.96 | 0.78 | 0.58 | 0.78 | 0.98 | 0.82 | 0.9 | 0.76 | 0.8776 | | dot_accuracy@10 | 0.68 | 0.94 | 0.98 | 0.68 | 0.96 | 0.84 | 0.7 | 0.86 | 1.0 | 0.86 | 0.96 | 0.88 | 0.9796 | | dot_precision@1 | 0.2 | 0.74 | 0.86 | 0.5 | 0.84 | 0.42 | 0.42 | 0.56 | 0.86 | 0.54 | 0.28 | 0.6 | 0.6327 | | dot_precision@3 | 0.1933 | 0.5867 | 0.3467 | 0.3133 | 0.4733 | 0.22 | 0.36 | 0.2333 | 0.4 | 0.38 | 0.2533 | 0.2733 | 0.5986 | | dot_precision@5 | 0.132 | 0.556 | 0.208 | 0.224 | 0.316 | 0.156 | 0.34 | 0.16 | 0.268 | 0.304 | 0.18 | 0.176 | 0.5265 | | dot_precision@10 | 0.088 | 0.484 | 0.104 | 0.138 | 0.174 | 0.084 | 0.29 | 0.094 | 0.138 | 0.204 | 0.096 | 0.1 | 0.4327 | | dot_recall@1 | 0.0783 | 0.0837 | 0.8067 | 0.2726 | 0.42 | 0.42 | 0.0188 | 0.54 | 0.7373 | 0.1147 | 0.28 | 0.565 | 0.0443 | | dot_recall@3 | 0.245 | 0.1623 | 0.9433 | 0.4129 | 0.71 | 0.66 | 0.0735 | 0.65 | 0.9353 | 0.2377 | 0.76 | 0.74 | 0.1265 | | dot_recall@5 | 0.2833 | 0.2214 | 0.9433 | 0.4606 | 0.79 | 0.78 | 0.0963 | 0.73 | 0.9733 | 0.3147 | 0.9 | 0.76 | 0.1808 | | dot_recall@10 | 0.3473 | 0.3354 | 0.9433 | 0.5501 | 0.87 | 0.84 | 0.1418 | 0.83 | 0.9967 | 0.4197 | 0.96 | 0.88 | 0.2908 | | **dot_ndcg@10** | **0.2733** | **0.5972** | **0.9054** | **0.4959** | **0.8027** | **0.6355** | **0.3353** | **0.6814** | **0.9284** | **0.4203** | **0.6519** | **0.7313** | **0.4946** | | dot_mrr@10 | 0.3666 | 0.8152 | 0.9133 | 0.5642 | 0.8857 | 0.5694 | 0.496 | 0.6473 | 0.9167 | 0.6498 | 0.5499 | 0.6888 | 0.7585 | | dot_map@100 | 0.2127 | 0.453 | 0.8845 | 0.4426 | 0.7335 | 0.5752 | 0.1414 | 0.6311 | 0.8997 | 0.3374 | 0.5515 | 0.6841 | 0.3733 | | 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 | 0.5733 | | dot_accuracy@3 | 0.7629 | | dot_accuracy@5 | 0.8106 | | dot_accuracy@10 | 0.8707 | | dot_precision@1 | 0.5733 | | dot_precision@3 | 0.3563 | | dot_precision@5 | 0.2728 | | dot_precision@10 | 0.1867 | | dot_recall@1 | 0.337 | | dot_recall@3 | 0.512 | | dot_recall@5 | 0.5718 | | dot_recall@10 | 0.6465 | | **dot_ndcg@10** | **0.6118** | | dot_mrr@10 | 0.6786 | | dot_map@100 | 0.5323 | | 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 | - | - | 0.6253 | 0.3224 | 0.5893 | 0.5123 | 0.6112 | 0.3278 | 0.6352 | 0.5248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0646 | 100 | 0.0542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.0566 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1939 | 300 | 0.0455 | 0.0390 | 0.5697 | 0.3083 | 0.6074 | 0.4952 | 0.5709 | 0.3402 | 0.6637 | 0.5249 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2586 | 400 | 0.0445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.0463 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 600 | 0.056 | 0.0454 | 0.5981 | 0.3334 | 0.6076 | 0.5130 | 0.6217 | 0.3417 | 0.6337 | 0.5324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4525 | 700 | 0.0505 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.0549 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.0614 | 0.0350 | 0.6058 | 0.3401 | 0.6084 | 0.5181 | 0.6293 | 0.3178 | 0.6585 | 0.5352 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 1000 | 0.0519 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 1200 | 0.045 | 0.0384 | 0.6045 | 0.3348 | 0.6124 | 0.5172 | 0.6227 | 0.3333 | 0.6829 | 0.5463 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8403 | 1300 | 0.0536 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.0389 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.9696** | **1500** | **0.0413** | **0.0401** | **0.6038** | **0.3316** | **0.602** | **0.5125** | **0.6402** | **0.3365** | **0.6814** | **0.5527** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | -1 | -1 | - | - | - | - | - | - | - | - | - | - | 0.2733 | 0.5972 | 0.9054 | 0.4959 | 0.8027 | 0.6355 | 0.3353 | 0.6814 | 0.9284 | 0.4203 | 0.6519 | 0.7313 | 0.4946 | 0.6118 | * 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**: 0.054 kg of CO2 - **Hours Used**: 0.409 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} } ```