--- 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: 66.56126466621346 energy_consumed: 0.17123983068318005 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: 0.564 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 8 type: NanoMSMARCO_8 metrics: - type: dot_accuracy@1 value: 0.12 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.24 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.28 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.3 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.12 name: Dot Precision@1 - type: dot_precision@3 value: 0.07999999999999999 name: Dot Precision@3 - type: dot_precision@5 value: 0.056000000000000015 name: Dot Precision@5 - type: dot_precision@10 value: 0.030000000000000006 name: Dot Precision@10 - type: dot_recall@1 value: 0.12 name: Dot Recall@1 - type: dot_recall@3 value: 0.24 name: Dot Recall@3 - type: dot_recall@5 value: 0.28 name: Dot Recall@5 - type: dot_recall@10 value: 0.3 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.21196909248837792 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.18355555555555556 name: Dot Mrr@10 - type: dot_map@100 value: 0.19168473018432397 name: Dot Map@100 - type: query_active_dims value: 8.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.998046875 name: Query Sparsity Ratio - type: corpus_active_dims value: 8.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.998046875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 8 type: NanoBEIR_mean_8 metrics: - type: dot_accuracy@1 value: 0.12 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.24 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.28 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.3 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.12 name: Dot Precision@1 - type: dot_precision@3 value: 0.07999999999999999 name: Dot Precision@3 - type: dot_precision@5 value: 0.056000000000000015 name: Dot Precision@5 - type: dot_precision@10 value: 0.030000000000000006 name: Dot Precision@10 - type: dot_recall@1 value: 0.12 name: Dot Recall@1 - type: dot_recall@3 value: 0.24 name: Dot Recall@3 - type: dot_recall@5 value: 0.28 name: Dot Recall@5 - type: dot_recall@10 value: 0.3 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.21196909248837792 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.18355555555555556 name: Dot Mrr@10 - type: dot_map@100 value: 0.19168473018432397 name: Dot Map@100 - type: query_active_dims value: 8.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.998046875 name: Query Sparsity Ratio - type: corpus_active_dims value: 8.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.998046875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 16 type: NanoMSMARCO_16 metrics: - type: dot_accuracy@1 value: 0.22 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.34 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.4 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.44 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.22 name: Dot Precision@1 - type: dot_precision@3 value: 0.11333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.08000000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.044000000000000004 name: Dot Precision@10 - type: dot_recall@1 value: 0.22 name: Dot Recall@1 - type: dot_recall@3 value: 0.34 name: Dot Recall@3 - type: dot_recall@5 value: 0.4 name: Dot Recall@5 - type: dot_recall@10 value: 0.44 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3259646473373541 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.28955555555555557 name: Dot Mrr@10 - type: dot_map@100 value: 0.306813602994791 name: Dot Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 16 type: NanoBEIR_mean_16 metrics: - type: dot_accuracy@1 value: 0.22 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.34 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.4 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.44 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.22 name: Dot Precision@1 - type: dot_precision@3 value: 0.11333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.08000000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.044000000000000004 name: Dot Precision@10 - type: dot_recall@1 value: 0.22 name: Dot Recall@1 - type: dot_recall@3 value: 0.34 name: Dot Recall@3 - type: dot_recall@5 value: 0.4 name: Dot Recall@5 - type: dot_recall@10 value: 0.44 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3259646473373541 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.28955555555555557 name: Dot Mrr@10 - type: dot_map@100 value: 0.306813602994791 name: Dot Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 32 type: NanoMSMARCO_32 metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.36 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.4 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.11999999999999998 name: Dot Precision@3 - type: dot_precision@5 value: 0.08 name: Dot Precision@5 - type: dot_precision@10 value: 0.06 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.36 name: Dot Recall@3 - type: dot_recall@5 value: 0.4 name: Dot Recall@5 - type: dot_recall@10 value: 0.6 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4175000854041106 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.36360317460317454 name: Dot Mrr@10 - type: dot_map@100 value: 0.37705054554799494 name: Dot Map@100 - type: query_active_dims value: 32.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9921875 name: Query Sparsity Ratio - type: corpus_active_dims value: 32.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9921875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 32 type: NanoBEIR_mean_32 metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.36 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.4 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.6 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.11999999999999998 name: Dot Precision@3 - type: dot_precision@5 value: 0.08 name: Dot Precision@5 - type: dot_precision@10 value: 0.06 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.36 name: Dot Recall@3 - type: dot_recall@5 value: 0.4 name: Dot Recall@5 - type: dot_recall@10 value: 0.6 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4175000854041106 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.36360317460317454 name: Dot Mrr@10 - type: dot_map@100 value: 0.37705054554799494 name: Dot Map@100 - type: query_active_dims value: 32.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9921875 name: Query Sparsity Ratio - type: corpus_active_dims value: 32.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9921875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 64 type: NanoMSMARCO_64 metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.48 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.56 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.64 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.15999999999999998 name: Dot Precision@3 - type: dot_precision@5 value: 0.11200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.06400000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 name: Dot Recall@1 - type: dot_recall@3 value: 0.48 name: Dot Recall@3 - type: dot_recall@5 value: 0.56 name: Dot Recall@5 - type: dot_recall@10 value: 0.64 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4747516265872855 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4225 name: Dot Mrr@10 - type: dot_map@100 value: 0.43804482701175623 name: Dot Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 64 type: NanoBEIR_mean_64 metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.48 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.56 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.64 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.15999999999999998 name: Dot Precision@3 - type: dot_precision@5 value: 0.11200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.06400000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.32 name: Dot Recall@1 - type: dot_recall@3 value: 0.48 name: Dot Recall@3 - type: dot_recall@5 value: 0.56 name: Dot Recall@5 - type: dot_recall@10 value: 0.64 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4747516265872855 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4225 name: Dot Mrr@10 - type: dot_map@100 value: 0.43804482701175623 name: Dot Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 128 type: NanoMSMARCO_128 metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.18 name: Dot Precision@3 - type: dot_precision@5 value: 0.128 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.54 name: Dot Recall@3 - type: dot_recall@5 value: 0.64 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5165502329637498 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4448571428571429 name: Dot Mrr@10 - type: dot_map@100 value: 0.4609321037436295 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: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.18 name: Dot Precision@3 - type: dot_precision@5 value: 0.128 name: Dot Precision@5 - type: dot_precision@10 value: 0.07400000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.54 name: Dot Recall@3 - type: dot_recall@5 value: 0.64 name: Dot Recall@5 - type: dot_recall@10 value: 0.74 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5165502329637498 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4448571428571429 name: Dot Mrr@10 - type: dot_map@100 value: 0.4609321037436295 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: 0.34 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.34 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.34 name: Dot Recall@1 - type: dot_recall@3 value: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5842381969358662 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5026904761904762 name: Dot Mrr@10 - type: dot_map@100 value: 0.5098488479343186 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: 0.34 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.34 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.34 name: Dot Recall@1 - type: dot_recall@3 value: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5842381969358662 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5026904761904762 name: Dot Mrr@10 - type: dot_map@100 value: 0.5098488479343186 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: 0.26 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 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: 0.26 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.156 name: Dot Precision@5 - type: dot_precision@10 value: 0.102 name: Dot Precision@10 - type: dot_recall@1 value: 0.12333333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.29333333333333333 name: Dot Recall@3 - type: dot_recall@5 value: 0.34666666666666673 name: Dot Recall@5 - type: dot_recall@10 value: 0.41566666666666663 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.33074042963512007 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.41507936507936505 name: Dot Mrr@10 - type: dot_map@100 value: 0.2605037455645458 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: 0.92 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 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: 0.68 name: Dot Precision@3 - type: dot_precision@5 value: 0.6 name: Dot Precision@5 - type: dot_precision@10 value: 0.49 name: Dot Precision@10 - type: dot_recall@1 value: 0.08787178599815837 name: Dot Recall@1 - type: dot_recall@3 value: 0.20076849643437242 name: Dot Recall@3 - type: dot_recall@5 value: 0.2551529754028007 name: Dot Recall@5 - type: dot_recall@10 value: 0.35977856932473445 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.631230472759085 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8546666666666668 name: Dot Mrr@10 - type: dot_map@100 value: 0.4715050434861439 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: 0.82 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.94 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.82 name: Dot Precision@1 - type: dot_precision@3 value: 0.32666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.19999999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.10399999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7666666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.9066666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9266666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9433333333333332 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8786397520542688 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8795555555555555 name: Dot Mrr@10 - type: dot_map@100 value: 0.8474023961509473 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: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.76 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.46 name: Dot Precision@1 - type: dot_precision@3 value: 0.3 name: Dot Precision@3 - type: dot_precision@5 value: 0.22799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.13999999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.22924603174603175 name: Dot Recall@1 - type: dot_recall@3 value: 0.4312936507936508 name: Dot Recall@3 - type: dot_recall@5 value: 0.5035396825396825 name: Dot Recall@5 - type: dot_recall@10 value: 0.6116190476190476 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.505122448452203 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5688888888888889 name: Dot Mrr@10 - type: dot_map@100 value: 0.4305964674526582 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: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.78 name: Dot Precision@1 - type: dot_precision@3 value: 0.48666666666666664 name: Dot Precision@3 - type: dot_precision@5 value: 0.32799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.16999999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.39 name: Dot Recall@1 - type: dot_recall@3 value: 0.73 name: Dot Recall@3 - type: dot_recall@5 value: 0.82 name: Dot Recall@5 - type: dot_recall@10 value: 0.85 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7891312606021372 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8563333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.7308084845910934 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: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.078 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.76 name: Dot Recall@5 - type: dot_recall@10 value: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5906197363202759 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.528 name: Dot Mrr@10 - type: dot_map@100 value: 0.5404706257099874 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: 0.58 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: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.3533333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32 name: Dot Precision@5 - type: dot_precision@10 value: 0.26799999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.044434174313891364 name: Dot Recall@1 - type: dot_recall@3 value: 0.06886292486806139 name: Dot Recall@3 - type: dot_recall@5 value: 0.10018663091887436 name: Dot Recall@5 - type: dot_recall@10 value: 0.135993408976131 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3272577842417522 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5120238095238094 name: Dot Mrr@10 - type: dot_map@100 value: 0.1540609053707419 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: 0.52 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.68 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.52 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: 0.5 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: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6523707439369819 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6238571428571428 name: Dot Mrr@10 - type: dot_map@100 value: 0.6127092058948297 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: 0.9 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.94 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: 0.9 name: Dot Precision@1 - type: dot_precision@3 value: 0.4 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: 0.7773333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.912 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: 0.9408238851178163 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.935 name: Dot Mrr@10 - type: dot_map@100 value: 0.9156785714285713 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: 0.56 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.56 name: Dot Precision@1 - type: dot_precision@3 value: 0.3666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.3 name: Dot Precision@5 - type: dot_precision@10 value: 0.21 name: Dot Precision@10 - type: dot_recall@1 value: 0.11866666666666668 name: Dot Recall@1 - type: dot_recall@3 value: 0.2296666666666666 name: Dot Recall@3 - type: dot_recall@5 value: 0.30966666666666665 name: Dot Recall@5 - type: dot_recall@10 value: 0.43066666666666664 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4238434123293462 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6637142857142857 name: Dot Mrr@10 - type: dot_map@100 value: 0.33702650955588553 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: 0.82 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.84 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.2733333333333334 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.28 name: Dot Recall@1 - type: dot_recall@3 value: 0.82 name: Dot Recall@3 - type: dot_recall@5 value: 0.84 name: Dot Recall@5 - type: dot_recall@10 value: 0.92 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6320575399829071 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5360714285714285 name: Dot Mrr@10 - type: dot_map@100 value: 0.5398250835421888 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: 0.7 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.86 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.7 name: Dot Precision@1 - type: dot_precision@3 value: 0.24666666666666665 name: Dot Precision@3 - type: dot_precision@5 value: 0.176 name: Dot Precision@5 - type: dot_precision@10 value: 0.09599999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.665 name: Dot Recall@1 - type: dot_recall@3 value: 0.68 name: Dot Recall@3 - type: dot_recall@5 value: 0.785 name: Dot Recall@5 - type: dot_recall@10 value: 0.85 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7512560957647406 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7302222222222224 name: Dot Mrr@10 - type: dot_map@100 value: 0.7208552252945762 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: 0.8979591836734694 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9591836734693877 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6326530612244898 name: Dot Precision@1 - type: dot_precision@3 value: 0.5918367346938774 name: Dot Precision@3 - type: dot_precision@5 value: 0.5510204081632653 name: Dot Precision@5 - type: dot_precision@10 value: 0.4489795918367347 name: Dot Precision@10 - type: dot_recall@1 value: 0.04395130839858616 name: Dot Recall@1 - type: dot_recall@3 value: 0.12411835933794488 name: Dot Recall@3 - type: dot_recall@5 value: 0.18456901766491046 name: Dot Recall@5 - type: dot_recall@10 value: 0.30287435988004324 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5113851766135886 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7748542274052478 name: Dot Mrr@10 - type: dot_map@100 value: 0.375999626455593 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: 0.5763579277864993 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7629199372056513 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8276295133437992 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5763579277864993 name: Dot Precision@1 - type: dot_precision@3 value: 0.35988487702773414 name: Dot Precision@3 - type: dot_precision@5 value: 0.2774631083202512 name: Dot Precision@5 - type: dot_precision@10 value: 0.18653689167974882 name: Dot Precision@10 - type: dot_recall@1 value: 0.3389617923428206 name: Dot Recall@1 - type: dot_recall@3 value: 0.514362315238515 name: Dot Recall@3 - type: dot_recall@5 value: 0.5805729466558668 name: Dot Recall@5 - type: dot_recall@10 value: 0.6443537476256377 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6126522106007865 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6829436096783036 name: Dot Mrr@10 - type: dot_map@100 value: 0.5336493761921356 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-2") # 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([[57.9578, 15.8308, 16.0606]]) ``` ## Evaluation ### Metrics #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_8` * 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": 8 } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.12 | | dot_accuracy@3 | 0.24 | | dot_accuracy@5 | 0.28 | | dot_accuracy@10 | 0.3 | | dot_precision@1 | 0.12 | | dot_precision@3 | 0.08 | | dot_precision@5 | 0.056 | | dot_precision@10 | 0.03 | | dot_recall@1 | 0.12 | | dot_recall@3 | 0.24 | | dot_recall@5 | 0.28 | | dot_recall@10 | 0.3 | | **dot_ndcg@10** | **0.212** | | dot_mrr@10 | 0.1836 | | dot_map@100 | 0.1917 | | query_active_dims | 8.0 | | query_sparsity_ratio | 0.998 | | corpus_active_dims | 8.0 | | corpus_sparsity_ratio | 0.998 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_8` * 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" ], "max_active_dims": 8 } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.12 | | dot_accuracy@3 | 0.24 | | dot_accuracy@5 | 0.28 | | dot_accuracy@10 | 0.3 | | dot_precision@1 | 0.12 | | dot_precision@3 | 0.08 | | dot_precision@5 | 0.056 | | dot_precision@10 | 0.03 | | dot_recall@1 | 0.12 | | dot_recall@3 | 0.24 | | dot_recall@5 | 0.28 | | dot_recall@10 | 0.3 | | **dot_ndcg@10** | **0.212** | | dot_mrr@10 | 0.1836 | | dot_map@100 | 0.1917 | | query_active_dims | 8.0 | | query_sparsity_ratio | 0.998 | | corpus_active_dims | 8.0 | | corpus_sparsity_ratio | 0.998 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_16` * 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": 16 } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.22 | | dot_accuracy@3 | 0.34 | | dot_accuracy@5 | 0.4 | | dot_accuracy@10 | 0.44 | | dot_precision@1 | 0.22 | | dot_precision@3 | 0.1133 | | dot_precision@5 | 0.08 | | dot_precision@10 | 0.044 | | dot_recall@1 | 0.22 | | dot_recall@3 | 0.34 | | dot_recall@5 | 0.4 | | dot_recall@10 | 0.44 | | **dot_ndcg@10** | **0.326** | | dot_mrr@10 | 0.2896 | | dot_map@100 | 0.3068 | | query_active_dims | 16.0 | | query_sparsity_ratio | 0.9961 | | corpus_active_dims | 16.0 | | corpus_sparsity_ratio | 0.9961 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_16` * 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" ], "max_active_dims": 16 } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.22 | | dot_accuracy@3 | 0.34 | | dot_accuracy@5 | 0.4 | | dot_accuracy@10 | 0.44 | | dot_precision@1 | 0.22 | | dot_precision@3 | 0.1133 | | dot_precision@5 | 0.08 | | dot_precision@10 | 0.044 | | dot_recall@1 | 0.22 | | dot_recall@3 | 0.34 | | dot_recall@5 | 0.4 | | dot_recall@10 | 0.44 | | **dot_ndcg@10** | **0.326** | | dot_mrr@10 | 0.2896 | | dot_map@100 | 0.3068 | | query_active_dims | 16.0 | | query_sparsity_ratio | 0.9961 | | corpus_active_dims | 16.0 | | corpus_sparsity_ratio | 0.9961 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_32` * 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": 32 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.3 | | dot_accuracy@3 | 0.36 | | dot_accuracy@5 | 0.4 | | dot_accuracy@10 | 0.6 | | dot_precision@1 | 0.3 | | dot_precision@3 | 0.12 | | dot_precision@5 | 0.08 | | dot_precision@10 | 0.06 | | dot_recall@1 | 0.3 | | dot_recall@3 | 0.36 | | dot_recall@5 | 0.4 | | dot_recall@10 | 0.6 | | **dot_ndcg@10** | **0.4175** | | dot_mrr@10 | 0.3636 | | dot_map@100 | 0.3771 | | query_active_dims | 32.0 | | query_sparsity_ratio | 0.9922 | | corpus_active_dims | 32.0 | | corpus_sparsity_ratio | 0.9922 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_32` * 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" ], "max_active_dims": 32 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.3 | | dot_accuracy@3 | 0.36 | | dot_accuracy@5 | 0.4 | | dot_accuracy@10 | 0.6 | | dot_precision@1 | 0.3 | | dot_precision@3 | 0.12 | | dot_precision@5 | 0.08 | | dot_precision@10 | 0.06 | | dot_recall@1 | 0.3 | | dot_recall@3 | 0.36 | | dot_recall@5 | 0.4 | | dot_recall@10 | 0.6 | | **dot_ndcg@10** | **0.4175** | | dot_mrr@10 | 0.3636 | | dot_map@100 | 0.3771 | | query_active_dims | 32.0 | | query_sparsity_ratio | 0.9922 | | corpus_active_dims | 32.0 | | corpus_sparsity_ratio | 0.9922 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_64` * 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": 64 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.32 | | dot_accuracy@3 | 0.48 | | dot_accuracy@5 | 0.56 | | dot_accuracy@10 | 0.64 | | dot_precision@1 | 0.32 | | dot_precision@3 | 0.16 | | dot_precision@5 | 0.112 | | dot_precision@10 | 0.064 | | dot_recall@1 | 0.32 | | dot_recall@3 | 0.48 | | dot_recall@5 | 0.56 | | dot_recall@10 | 0.64 | | **dot_ndcg@10** | **0.4748** | | dot_mrr@10 | 0.4225 | | dot_map@100 | 0.438 | | query_active_dims | 64.0 | | query_sparsity_ratio | 0.9844 | | corpus_active_dims | 64.0 | | corpus_sparsity_ratio | 0.9844 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_64` * 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" ], "max_active_dims": 64 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.32 | | dot_accuracy@3 | 0.48 | | dot_accuracy@5 | 0.56 | | dot_accuracy@10 | 0.64 | | dot_precision@1 | 0.32 | | dot_precision@3 | 0.16 | | dot_precision@5 | 0.112 | | dot_precision@10 | 0.064 | | dot_recall@1 | 0.32 | | dot_recall@3 | 0.48 | | dot_recall@5 | 0.56 | | dot_recall@10 | 0.64 | | **dot_ndcg@10** | **0.4748** | | dot_mrr@10 | 0.4225 | | dot_map@100 | 0.438 | | query_active_dims | 64.0 | | query_sparsity_ratio | 0.9844 | | corpus_active_dims | 64.0 | | corpus_sparsity_ratio | 0.9844 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_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 | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.3 | | dot_accuracy@3 | 0.54 | | dot_accuracy@5 | 0.64 | | dot_accuracy@10 | 0.74 | | dot_precision@1 | 0.3 | | dot_precision@3 | 0.18 | | dot_precision@5 | 0.128 | | dot_precision@10 | 0.074 | | dot_recall@1 | 0.3 | | dot_recall@3 | 0.54 | | dot_recall@5 | 0.64 | | dot_recall@10 | 0.74 | | **dot_ndcg@10** | **0.5166** | | dot_mrr@10 | 0.4449 | | dot_map@100 | 0.4609 | | query_active_dims | 128.0 | | query_sparsity_ratio | 0.9688 | | corpus_active_dims | 128.0 | | corpus_sparsity_ratio | 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" ], "max_active_dims": 128 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.3 | | dot_accuracy@3 | 0.54 | | dot_accuracy@5 | 0.64 | | dot_accuracy@10 | 0.74 | | dot_precision@1 | 0.3 | | dot_precision@3 | 0.18 | | dot_precision@5 | 0.128 | | dot_precision@10 | 0.074 | | dot_recall@1 | 0.3 | | dot_recall@3 | 0.54 | | dot_recall@5 | 0.64 | | dot_recall@10 | 0.74 | | **dot_ndcg@10** | **0.5166** | | dot_mrr@10 | 0.4449 | | dot_map@100 | 0.4609 | | query_active_dims | 128.0 | | query_sparsity_ratio | 0.9688 | | corpus_active_dims | 128.0 | | corpus_sparsity_ratio | 0.9688 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_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 | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.34 | | dot_accuracy@3 | 0.6 | | dot_accuracy@5 | 0.74 | | dot_accuracy@10 | 0.84 | | dot_precision@1 | 0.34 | | dot_precision@3 | 0.2 | | dot_precision@5 | 0.148 | | dot_precision@10 | 0.084 | | dot_recall@1 | 0.34 | | dot_recall@3 | 0.6 | | dot_recall@5 | 0.74 | | dot_recall@10 | 0.84 | | **dot_ndcg@10** | **0.5842** | | dot_mrr@10 | 0.5027 | | dot_map@100 | 0.5098 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 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" ], "max_active_dims": 256 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.34 | | dot_accuracy@3 | 0.6 | | dot_accuracy@5 | 0.74 | | dot_accuracy@10 | 0.84 | | dot_precision@1 | 0.34 | | dot_precision@3 | 0.2 | | dot_precision@5 | 0.148 | | dot_precision@10 | 0.084 | | dot_recall@1 | 0.34 | | dot_recall@3 | 0.6 | | dot_recall@5 | 0.74 | | dot_recall@10 | 0.84 | | **dot_ndcg@10** | **0.5842** | | dot_mrr@10 | 0.5027 | | dot_map@100 | 0.5098 | | 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 | 0.26 | 0.78 | 0.82 | 0.46 | 0.78 | 0.38 | 0.42 | 0.52 | 0.9 | 0.56 | 0.28 | 0.7 | 0.6327 | | dot_accuracy@3 | 0.56 | 0.92 | 0.94 | 0.64 | 0.9 | 0.64 | 0.58 | 0.68 | 0.94 | 0.7 | 0.82 | 0.7 | 0.898 | | dot_accuracy@5 | 0.62 | 0.96 | 0.96 | 0.7 | 0.96 | 0.76 | 0.62 | 0.78 | 1.0 | 0.8 | 0.84 | 0.8 | 0.9592 | | dot_accuracy@10 | 0.74 | 1.0 | 0.98 | 0.76 | 0.98 | 0.78 | 0.68 | 0.82 | 1.0 | 0.92 | 0.92 | 0.86 | 1.0 | | dot_precision@1 | 0.26 | 0.78 | 0.82 | 0.46 | 0.78 | 0.38 | 0.42 | 0.52 | 0.9 | 0.56 | 0.28 | 0.7 | 0.6327 | | dot_precision@3 | 0.2067 | 0.68 | 0.3267 | 0.3 | 0.4867 | 0.2133 | 0.3533 | 0.2333 | 0.4 | 0.3667 | 0.2733 | 0.2467 | 0.5918 | | dot_precision@5 | 0.156 | 0.6 | 0.2 | 0.228 | 0.328 | 0.152 | 0.32 | 0.164 | 0.264 | 0.3 | 0.168 | 0.176 | 0.551 | | dot_precision@10 | 0.102 | 0.49 | 0.104 | 0.14 | 0.17 | 0.078 | 0.268 | 0.088 | 0.138 | 0.21 | 0.092 | 0.096 | 0.449 | | dot_recall@1 | 0.1233 | 0.0879 | 0.7667 | 0.2292 | 0.39 | 0.38 | 0.0444 | 0.5 | 0.7773 | 0.1187 | 0.28 | 0.665 | 0.044 | | dot_recall@3 | 0.2933 | 0.2008 | 0.9067 | 0.4313 | 0.73 | 0.64 | 0.0689 | 0.65 | 0.912 | 0.2297 | 0.82 | 0.68 | 0.1241 | | dot_recall@5 | 0.3467 | 0.2552 | 0.9267 | 0.5035 | 0.82 | 0.76 | 0.1002 | 0.73 | 0.986 | 0.3097 | 0.84 | 0.785 | 0.1846 | | dot_recall@10 | 0.4157 | 0.3598 | 0.9433 | 0.6116 | 0.85 | 0.78 | 0.136 | 0.78 | 0.9967 | 0.4307 | 0.92 | 0.85 | 0.3029 | | **dot_ndcg@10** | **0.3307** | **0.6312** | **0.8786** | **0.5051** | **0.7891** | **0.5906** | **0.3273** | **0.6524** | **0.9408** | **0.4238** | **0.6321** | **0.7513** | **0.5114** | | dot_mrr@10 | 0.4151 | 0.8547 | 0.8796 | 0.5689 | 0.8563 | 0.528 | 0.512 | 0.6239 | 0.935 | 0.6637 | 0.5361 | 0.7302 | 0.7749 | | dot_map@100 | 0.2605 | 0.4715 | 0.8474 | 0.4306 | 0.7308 | 0.5405 | 0.1541 | 0.6127 | 0.9157 | 0.337 | 0.5398 | 0.7209 | 0.376 | | 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 | 0.5764 | | dot_accuracy@3 | 0.7629 | | dot_accuracy@5 | 0.8276 | | dot_accuracy@10 | 0.88 | | dot_precision@1 | 0.5764 | | dot_precision@3 | 0.3599 | | dot_precision@5 | 0.2775 | | dot_precision@10 | 0.1865 | | dot_recall@1 | 0.339 | | dot_recall@3 | 0.5144 | | dot_recall@5 | 0.5806 | | dot_recall@10 | 0.6444 | | **dot_ndcg@10** | **0.6127** | | dot_mrr@10 | 0.6829 | | dot_map@100 | 0.5336 | | 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": 3.0, "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": 3.0, "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_8_dot_ndcg@10 | NanoBEIR_mean_8_dot_ndcg@10 | NanoMSMARCO_16_dot_ndcg@10 | NanoBEIR_mean_16_dot_ndcg@10 | NanoMSMARCO_32_dot_ndcg@10 | NanoBEIR_mean_32_dot_ndcg@10 | NanoMSMARCO_64_dot_ndcg@10 | NanoBEIR_mean_64_dot_ndcg@10 | NanoMSMARCO_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_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 | - | - | 0.2445 | 0.2445 | 0.3517 | 0.3517 | 0.5001 | 0.5001 | 0.5672 | 0.5672 | 0.6083 | 0.6083 | 0.6025 | 0.6025 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0646 | 100 | 0.1844 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.1765 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.1939** | **300** | **0.1581** | **0.1742** | **0.2187** | **0.2187** | **0.3538** | **0.3538** | **0.4677** | **0.4677** | **0.5313** | **0.5313** | **0.5713** | **0.5713** | **0.5932** | **0.5932** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.2586 | 400 | 0.134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.179 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 600 | 0.1414 | 0.2028 | 0.2075 | 0.2075 | 0.3395 | 0.3395 | 0.4250 | 0.4250 | 0.4930 | 0.4930 | 0.5670 | 0.5670 | 0.5534 | 0.5534 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4525 | 700 | 0.162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.1632 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.1684 | 0.1907 | 0.1784 | 0.1784 | 0.3429 | 0.3429 | 0.4207 | 0.4207 | 0.4764 | 0.4764 | 0.5705 | 0.5705 | 0.5861 | 0.5861 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 1000 | 0.1577 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.1249 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 1200 | 0.1494 | 0.1506 | 0.1993 | 0.1993 | 0.3459 | 0.3459 | 0.4185 | 0.4185 | 0.4925 | 0.4925 | 0.5248 | 0.5248 | 0.5880 | 0.5880 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8403 | 1300 | 0.1457 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.1208 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9696 | 1500 | 0.1346 | 0.1349 | 0.2120 | 0.2120 | 0.3260 | 0.3260 | 0.4175 | 0.4175 | 0.4748 | 0.4748 | 0.5166 | 0.5166 | 0.5842 | 0.5842 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.3307 | 0.6312 | 0.8786 | 0.5051 | 0.7891 | 0.5906 | 0.3273 | 0.6524 | 0.9408 | 0.4238 | 0.6321 | 0.7513 | 0.5114 | 0.6127 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.171 kWh - **Carbon Emitted**: 0.067 kg of CO2 - **Hours Used**: 0.563 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} } ```