--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - splade - generated_from_trainer - dataset_size:90000 - loss:SpladeLoss - loss:SparseMarginMSELoss - loss:FlopsLoss base_model: Luyu/co-condenser-marco widget: - text: how old do you have to be to have lasik - text: when is house of cards on netflix - text: Answer by lauryn (194). The length of time it takes a women to get her period after giving birth varies from women to women. For many women it can take about 2 to 3 months before your period returns to normal. If you are nursing than this time frame will last even longer. - text: what are cys residues - text: "You heard about fastest cars, bikes and plans but today we have world fastest\ \ bird collection. In our collection we have top 10 fastest birds of the world.\ \ Birdâ\x80\x99s flight speed is fundamentally changeable; a hunting bird speed\ \ will increase while diving-to-catch prey as compared to its gliding speeds.\ \ Here we have the top 10 fastest birds with their flight speed. 10. Teal 109\ \ km/h (68mph) This bird can fly 109 km/ h (68mph); they are 53 to 59cm long.\ \ This bird always lives in group. 09." datasets: - sentence-transformers/msmarco 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: 34.21475343773813 energy_consumed: 0.0926891546467269 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: AMD EPYC 7R13 Processor ram_total_size: 248.0 hours_used: 0.305 hardware_used: 1 x NVIDIA H100 80GB HBM3 model-index: - name: splade-co-condenser-marco trained on MS MARCO hard negatives with distillation results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.4 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6076647728795561 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5352777777777777 name: Dot Mrr@10 - type: dot_map@100 value: 0.5419469179877314 name: Dot Map@100 - type: query_active_dims value: 54.119998931884766 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982268527969371 name: Query Sparsity Ratio - type: corpus_active_dims value: 187.67538452148438 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.993851143944647 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.4 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.4 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6076647728795561 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5352777777777777 name: Dot Mrr@10 - type: dot_map@100 value: 0.5419469179877314 name: Dot Map@100 - type: query_active_dims value: 54.119998931884766 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982268527969371 name: Query Sparsity Ratio - type: corpus_active_dims value: 187.67538452148438 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.993851143944647 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 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: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.34 name: Dot Precision@3 - type: dot_precision@5 value: 0.316 name: Dot Precision@5 - type: dot_precision@10 value: 0.27 name: Dot Precision@10 - type: dot_recall@1 value: 0.06311467051346893 name: Dot Recall@1 - type: dot_recall@3 value: 0.09895898433766803 name: Dot Recall@3 - type: dot_recall@5 value: 0.1169352131561954 name: Dot Recall@5 - type: dot_recall@10 value: 0.14677603057730104 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.34523070842752446 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5258333333333334 name: Dot Mrr@10 - type: dot_map@100 value: 0.16994217536385264 name: Dot Map@100 - type: query_active_dims value: 51.70000076293945 name: Query Active Dims - type: query_sparsity_ratio value: 0.9983061398085663 name: Query Sparsity Ratio - type: corpus_active_dims value: 336.32476806640625 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9889809066225539 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.34 name: Dot Precision@3 - type: dot_precision@5 value: 0.316 name: Dot Precision@5 - type: dot_precision@10 value: 0.27 name: Dot Precision@10 - type: dot_recall@1 value: 0.06311467051346893 name: Dot Recall@1 - type: dot_recall@3 value: 0.09895898433766803 name: Dot Recall@3 - type: dot_recall@5 value: 0.1169352131561954 name: Dot Recall@5 - type: dot_recall@10 value: 0.14677603057730104 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.34523070842752446 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5258333333333334 name: Dot Mrr@10 - type: dot_map@100 value: 0.16994217536385264 name: Dot Map@100 - type: query_active_dims value: 51.70000076293945 name: Query Active Dims - type: query_sparsity_ratio value: 0.9983061398085663 name: Query Sparsity Ratio - type: corpus_active_dims value: 336.32476806640625 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9889809066225539 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: 0.74 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: 0.52 name: Dot Precision@1 - type: dot_precision@3 value: 0.2533333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.48 name: Dot Recall@1 - type: dot_recall@3 value: 0.69 name: Dot Recall@3 - type: dot_recall@5 value: 0.73 name: Dot Recall@5 - type: dot_recall@10 value: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6594960548473345 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6369365079365078 name: Dot Mrr@10 - type: dot_map@100 value: 0.6105143613696246 name: Dot Map@100 - type: query_active_dims value: 53.34000015258789 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982524080940768 name: Query Sparsity Ratio - type: corpus_active_dims value: 223.5908660888672 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9926744359449294 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.52 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.74 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: 0.52 name: Dot Precision@1 - type: dot_precision@3 value: 0.2533333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.48 name: Dot Recall@1 - type: dot_recall@3 value: 0.69 name: Dot Recall@3 - type: dot_recall@5 value: 0.73 name: Dot Recall@5 - type: dot_recall@10 value: 0.8 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6594960548473345 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6369365079365078 name: Dot Mrr@10 - type: dot_map@100 value: 0.6105143613696246 name: Dot Map@100 - type: query_active_dims value: 53.34000015258789 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982524080940768 name: Query Sparsity Ratio - type: corpus_active_dims value: 223.5908660888672 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9926744359449294 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: 0.45333333333333337 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6533333333333333 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7000000000000001 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7866666666666666 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.45333333333333337 name: Dot Precision@1 - type: dot_precision@3 value: 0.26666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.204 name: Dot Precision@5 - type: dot_precision@10 value: 0.148 name: Dot Precision@10 - type: dot_recall@1 value: 0.314371556837823 name: Dot Recall@1 - type: dot_recall@3 value: 0.4696529947792227 name: Dot Recall@3 - type: dot_recall@5 value: 0.5089784043853984 name: Dot Recall@5 - type: dot_recall@10 value: 0.5955920101924337 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5374638453848051 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.566015873015873 name: Dot Mrr@10 - type: dot_map@100 value: 0.4408011515737362 name: Dot Map@100 - type: query_active_dims value: 53.0533332824707 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982618002331933 name: Query Sparsity Ratio - type: corpus_active_dims value: 235.2385860639544 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9922928187515905 name: Corpus Sparsity Ratio - type: dot_accuracy@1 value: 0.5580533751962323 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7137205651491366 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7722448979591837 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8291679748822605 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5580533751962323 name: Dot Precision@1 - type: dot_precision@3 value: 0.3332705389848246 name: Dot Precision@3 - type: dot_precision@5 value: 0.26179591836734695 name: Dot Precision@5 - type: dot_precision@10 value: 0.179171114599686 name: Dot Precision@10 - type: dot_recall@1 value: 0.32499349487208484 name: Dot Recall@1 - type: dot_recall@3 value: 0.4721752731683537 name: Dot Recall@3 - type: dot_recall@5 value: 0.5337131771857326 name: Dot Recall@5 - type: dot_recall@10 value: 0.6042058945750339 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.578707182604652 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6493701377987092 name: Dot Mrr@10 - type: dot_map@100 value: 0.5041070229886567 name: Dot Map@100 - type: query_active_dims value: 86.67950763908115 name: Query Active Dims - type: query_sparsity_ratio value: 0.997160097384212 name: Query Sparsity Ratio - type: corpus_active_dims value: 230.5675761418069 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.992445856230201 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.32 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.54 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.62 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.32 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.14 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.165 name: Dot Recall@1 - type: dot_recall@3 value: 0.26 name: Dot Recall@3 - type: dot_recall@5 value: 0.28733333333333333 name: Dot Recall@5 - type: dot_recall@10 value: 0.32233333333333336 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.30365156381250225 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4207222222222222 name: Dot Mrr@10 - type: dot_map@100 value: 0.25580876542561 name: Dot Map@100 - type: query_active_dims value: 135.3000030517578 name: Query Active Dims - type: query_sparsity_ratio value: 0.99556713180487 name: Query Sparsity Ratio - type: corpus_active_dims value: 270.1291198730469 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9911496913743186 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: 0.9 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: 0.6133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.588 name: Dot Precision@5 - type: dot_precision@10 value: 0.508 name: Dot Precision@10 - type: dot_recall@1 value: 0.07635143960629845 name: Dot Recall@1 - type: dot_recall@3 value: 0.1800129405239251 name: Dot Recall@3 - type: dot_recall@5 value: 0.23739681193828663 name: Dot Recall@5 - type: dot_recall@10 value: 0.33976750488378327 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.622759301760137 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8137142857142856 name: Dot Mrr@10 - type: dot_map@100 value: 0.4830025510651395 name: Dot Map@100 - type: query_active_dims value: 52.2599983215332 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982877924670227 name: Query Sparsity Ratio - type: corpus_active_dims value: 219.79901123046875 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9927986694439921 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.8 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.92 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.94 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.8 name: Dot Precision@1 - type: dot_precision@3 value: 0.31999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.204 name: Dot Precision@5 - type: dot_precision@10 value: 0.10599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7566666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.8866666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.92 name: Dot Recall@5 - type: dot_recall@10 value: 0.95 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.871923100931238 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8608333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.8427126216077829 name: Dot Map@100 - type: query_active_dims value: 79.13999938964844 name: Query Active Dims - type: query_sparsity_ratio value: 0.9974071161984913 name: Query Sparsity Ratio - type: corpus_active_dims value: 287.1961669921875 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9905905193961015 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.58 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.11 name: Dot Precision@10 - type: dot_recall@1 value: 0.23607936507936508 name: Dot Recall@1 - type: dot_recall@3 value: 0.31813492063492066 name: Dot Recall@3 - type: dot_recall@5 value: 0.3794920634920635 name: Dot Recall@5 - type: dot_recall@10 value: 0.4829047619047619 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.41245963928815416 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4934444444444444 name: Dot Mrr@10 - type: dot_map@100 value: 0.35636809652397866 name: Dot Map@100 - type: query_active_dims value: 54.040000915527344 name: Query Active Dims - type: query_sparsity_ratio value: 0.9982294737921654 name: Query Sparsity Ratio - type: corpus_active_dims value: 213.87989807128906 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.992992598844398 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.88 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: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.88 name: Dot Precision@1 - type: dot_precision@3 value: 0.5133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.3399999999999999 name: Dot Precision@5 - type: dot_precision@10 value: 0.17199999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.44 name: Dot Recall@1 - type: dot_recall@3 value: 0.77 name: Dot Recall@3 - type: dot_recall@5 value: 0.85 name: Dot Recall@5 - type: dot_recall@10 value: 0.86 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8259863564109206 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9116666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.772433308579342 name: Dot Map@100 - type: query_active_dims value: 68.36000061035156 name: Query Active Dims - type: query_sparsity_ratio value: 0.9977603040229883 name: Query Sparsity Ratio - type: corpus_active_dims value: 223.86521911621094 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9926654472473556 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: 1.0 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 1.0 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9 name: Dot Precision@1 - type: dot_precision@3 value: 0.38666666666666655 name: Dot Precision@3 - type: dot_precision@5 value: 0.24799999999999997 name: Dot Precision@5 - type: dot_precision@10 value: 0.12999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.8073333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.938 name: Dot Recall@3 - type: dot_recall@5 value: 0.9653333333333333 name: Dot Recall@5 - type: dot_recall@10 value: 0.98 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9411045044022702 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9466666666666665 name: Dot Mrr@10 - type: dot_map@100 value: 0.9183274196019293 name: Dot Map@100 - type: query_active_dims value: 57.5 name: Query Active Dims - type: query_sparsity_ratio value: 0.9981161129676954 name: Query Sparsity Ratio - type: corpus_active_dims value: 58.39020919799805 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9980869468187538 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.28 name: Dot Precision@3 - type: dot_precision@5 value: 0.25199999999999995 name: Dot Precision@5 - type: dot_precision@10 value: 0.154 name: Dot Precision@10 - type: dot_recall@1 value: 0.08766666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.17266666666666666 name: Dot Recall@3 - type: dot_recall@5 value: 0.25766666666666665 name: Dot Recall@5 - type: dot_recall@10 value: 0.31566666666666665 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3183178982652113 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5296904761904762 name: Dot Mrr@10 - type: dot_map@100 value: 0.24557421391176226 name: Dot Map@100 - type: query_active_dims value: 73.30000305175781 name: Query Active Dims - type: query_sparsity_ratio value: 0.9975984534744854 name: Query Sparsity Ratio - type: corpus_active_dims value: 293.607177734375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9903804738308638 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.14 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.42 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: 0.14 name: Dot Precision@1 - type: dot_precision@3 value: 0.13999999999999999 name: Dot Precision@3 - type: dot_precision@5 value: 0.11600000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.14 name: Dot Recall@1 - type: dot_recall@3 value: 0.42 name: Dot Recall@3 - type: dot_recall@5 value: 0.58 name: Dot Recall@5 - type: dot_recall@10 value: 0.7 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.40946212538272647 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.317547619047619 name: Dot Mrr@10 - type: dot_map@100 value: 0.3292918677514585 name: Dot Map@100 - type: query_active_dims value: 281.1600036621094 name: Query Active Dims - type: query_sparsity_ratio value: 0.990788283740839 name: Query Sparsity Ratio - type: corpus_active_dims value: 268.114990234375 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.991215680812713 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.54 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.54 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 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: 0.52 name: Dot Recall@1 - type: dot_recall@3 value: 0.65 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.81 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.668993132237426 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.623968253968254 name: Dot Mrr@10 - type: dot_map@100 value: 0.6278823742890459 name: Dot Map@100 - type: query_active_dims value: 109.4000015258789 name: Query Active Dims - type: query_sparsity_ratio value: 0.9964157001007182 name: Query Sparsity Ratio - type: corpus_active_dims value: 348.5179748535156 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9885814175069289 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.7346938775510204 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9183673469387755 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9591836734693877 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9591836734693877 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.7346938775510204 name: Dot Precision@1 - type: dot_precision@3 value: 0.6258503401360545 name: Dot Precision@3 - type: dot_precision@5 value: 0.5673469387755103 name: Dot Precision@5 - type: dot_precision@10 value: 0.4612244897959184 name: Dot Precision@10 - type: dot_recall@1 value: 0.052703291471304 name: Dot Recall@1 - type: dot_recall@3 value: 0.1338383723587515 name: Dot Recall@3 - type: dot_recall@5 value: 0.19411388149464573 name: Dot Recall@5 - type: dot_recall@10 value: 0.30722833210959427 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5361442152154757 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8255102040816327 name: Dot Mrr@10 - type: dot_map@100 value: 0.3995866253752792 name: Dot Map@100 - type: query_active_dims value: 56.61224365234375 name: Query Active Dims - type: query_sparsity_ratio value: 0.9981451987532814 name: Query Sparsity Ratio - type: corpus_active_dims value: 224.8710174560547 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9926324940221462 name: Corpus Sparsity Ratio --- # splade-co-condenser-marco trained on MS MARCO hard negatives with distillation This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) on the [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** SPLADE Sparse Encoder - **Base model:** [Luyu/co-condenser-marco](https://huggingface.co/Luyu/co-condenser-marco) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 30522 dimensions - **Similarity Function:** Dot Product - **Training Dataset:** - [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) - **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): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522}) ) ``` ## 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("arthurbresnu/co-condenser-marco-msmarco-hard-negatives") # Run inference queries = [ "fastest super cars in the world", ] documents = [ 'The McLaren F1 is amongst the fastest cars in the McLaren series and also the fastest car in the world. The McLaren F1 can clock a maximum speed of 240 miles per hour, or an equivalent of 386 km per hour.', 'You heard about fastest cars, bikes and plans but today we have world fastest bird collection. In our collection we have top 10 fastest birds of the world. Birdâ\x80\x99s flight speed is fundamentally changeable; a hunting bird speed will increase while diving-to-catch prey as compared to its gliding speeds. Here we have the top 10 fastest birds with their flight speed. 10. Teal 109 km/h (68mph) This bird can fly 109 km/ h (68mph); they are 53 to 59cm long. This bird always lives in group. 09.', 'Where is Langley, BC? Location of Langley on a map. Langley is a city found in British Columbia, Canada. It is located 49.08 latitude and -122.59 longitude and it is situated at elevation 78 meters above sea level. Langley has a population of 93,726 making it the 13th biggest city in British Columbia.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 30522] [3, 30522] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[35.7080, 24.5349, 3.8619]]) ``` ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [SparseInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:----------------------|:------------|:-------------|:-----------|:-----------------|:------------|:-----------|:-------------|:-------------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.4 | 0.44 | 0.52 | 0.32 | 0.74 | 0.8 | 0.42 | 0.88 | 0.9 | 0.42 | 0.14 | 0.54 | 0.7347 | | dot_accuracy@3 | 0.62 | 0.6 | 0.74 | 0.52 | 0.86 | 0.92 | 0.52 | 0.94 | 1.0 | 0.56 | 0.42 | 0.66 | 0.9184 | | dot_accuracy@5 | 0.68 | 0.64 | 0.78 | 0.54 | 0.9 | 0.94 | 0.58 | 0.96 | 1.0 | 0.74 | 0.58 | 0.74 | 0.9592 | | dot_accuracy@10 | 0.84 | 0.68 | 0.84 | 0.62 | 0.94 | 0.96 | 0.68 | 0.96 | 1.0 | 0.78 | 0.7 | 0.82 | 0.9592 | | dot_precision@1 | 0.4 | 0.44 | 0.52 | 0.32 | 0.74 | 0.8 | 0.42 | 0.88 | 0.9 | 0.42 | 0.14 | 0.54 | 0.7347 | | dot_precision@3 | 0.2067 | 0.34 | 0.2533 | 0.2 | 0.6133 | 0.32 | 0.2133 | 0.5133 | 0.3867 | 0.28 | 0.14 | 0.24 | 0.6259 | | dot_precision@5 | 0.136 | 0.316 | 0.16 | 0.14 | 0.588 | 0.204 | 0.168 | 0.34 | 0.248 | 0.252 | 0.116 | 0.168 | 0.5673 | | dot_precision@10 | 0.084 | 0.27 | 0.09 | 0.082 | 0.508 | 0.106 | 0.11 | 0.172 | 0.13 | 0.154 | 0.07 | 0.092 | 0.4612 | | dot_recall@1 | 0.4 | 0.0631 | 0.48 | 0.165 | 0.0764 | 0.7567 | 0.2361 | 0.44 | 0.8073 | 0.0877 | 0.14 | 0.52 | 0.0527 | | dot_recall@3 | 0.62 | 0.099 | 0.69 | 0.26 | 0.18 | 0.8867 | 0.3181 | 0.77 | 0.938 | 0.1727 | 0.42 | 0.65 | 0.1338 | | dot_recall@5 | 0.68 | 0.1169 | 0.73 | 0.2873 | 0.2374 | 0.92 | 0.3795 | 0.85 | 0.9653 | 0.2577 | 0.58 | 0.74 | 0.1941 | | dot_recall@10 | 0.84 | 0.1468 | 0.8 | 0.3223 | 0.3398 | 0.95 | 0.4829 | 0.86 | 0.98 | 0.3157 | 0.7 | 0.81 | 0.3072 | | **dot_ndcg@10** | **0.6077** | **0.3452** | **0.6595** | **0.3037** | **0.6228** | **0.8719** | **0.4125** | **0.826** | **0.9411** | **0.3183** | **0.4095** | **0.669** | **0.5361** | | dot_mrr@10 | 0.5353 | 0.5258 | 0.6369 | 0.4207 | 0.8137 | 0.8608 | 0.4934 | 0.9117 | 0.9467 | 0.5297 | 0.3175 | 0.624 | 0.8255 | | dot_map@100 | 0.5419 | 0.1699 | 0.6105 | 0.2558 | 0.483 | 0.8427 | 0.3564 | 0.7724 | 0.9183 | 0.2456 | 0.3293 | 0.6279 | 0.3996 | | query_active_dims | 54.12 | 51.7 | 53.34 | 135.3 | 52.26 | 79.14 | 54.04 | 68.36 | 57.5 | 73.3 | 281.16 | 109.4 | 56.6122 | | query_sparsity_ratio | 0.9982 | 0.9983 | 0.9983 | 0.9956 | 0.9983 | 0.9974 | 0.9982 | 0.9978 | 0.9981 | 0.9976 | 0.9908 | 0.9964 | 0.9981 | | corpus_active_dims | 187.6754 | 336.3248 | 223.5909 | 270.1291 | 219.799 | 287.1962 | 213.8799 | 223.8652 | 58.3902 | 293.6072 | 268.115 | 348.518 | 224.871 | | corpus_sparsity_ratio | 0.9939 | 0.989 | 0.9927 | 0.9911 | 0.9928 | 0.9906 | 0.993 | 0.9927 | 0.9981 | 0.9904 | 0.9912 | 0.9886 | 0.9926 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [SparseNanoBEIREvaluator](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ] } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.4533 | | dot_accuracy@3 | 0.6533 | | dot_accuracy@5 | 0.7 | | dot_accuracy@10 | 0.7867 | | dot_precision@1 | 0.4533 | | dot_precision@3 | 0.2667 | | dot_precision@5 | 0.204 | | dot_precision@10 | 0.148 | | dot_recall@1 | 0.3144 | | dot_recall@3 | 0.4697 | | dot_recall@5 | 0.509 | | dot_recall@10 | 0.5956 | | **dot_ndcg@10** | **0.5375** | | dot_mrr@10 | 0.566 | | dot_map@100 | 0.4408 | | query_active_dims | 53.0533 | | query_sparsity_ratio | 0.9983 | | corpus_active_dims | 235.2386 | | corpus_sparsity_ratio | 0.9923 | #### 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 | 0.5581 | | dot_accuracy@3 | 0.7137 | | dot_accuracy@5 | 0.7722 | | dot_accuracy@10 | 0.8292 | | dot_precision@1 | 0.5581 | | dot_precision@3 | 0.3333 | | dot_precision@5 | 0.2618 | | dot_precision@10 | 0.1792 | | dot_recall@1 | 0.325 | | dot_recall@3 | 0.4722 | | dot_recall@5 | 0.5337 | | dot_recall@10 | 0.6042 | | **dot_ndcg@10** | **0.5787** | | dot_mrr@10 | 0.6494 | | dot_map@100 | 0.5041 | | query_active_dims | 86.6795 | | query_sparsity_ratio | 0.9972 | | corpus_active_dims | 230.5676 | | corpus_sparsity_ratio | 0.9924 | ## Training Details ### Training Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 90,000 training samples * Columns: score, query, positive, and negative * Approximate statistics based on the first 1000 samples: | | score | query | positive | negative | |:--------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | float | string | string | string | | details | | | | | * Samples: | score | query | positive | negative | |:--------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 2.1688317457834883 | what is ast test used for | The AST test is commonly used to check for liver diseases. It is usually measured together with alanine aminotransferase (ALT). The AST to ALT ratio can help your doctor diagnose liver disease. Symptoms of liver disease that may cause your doctor to order an AST test include: 1 fatigue. 2 weakness.3 loss of appetite.t is usually measured together with alanine aminotransferase (ALT). The AST to ALT ratio can help your doctor diagnose liver disease. Symptoms of liver disease that may cause your doctor to order an AST test include: 1 fatigue. 2 weakness. 3 loss of appetite. | An aspartate aminotransferase (AST) test measures the amount of this enzyme in the blood. AST is normally found in red blood cells, liver, heart, muscle tissue, pancreas, and kidneys. AST formerly was called serum glutamic oxaloacetic transaminase (SGOT).he amount of AST in the blood is directly related to the extent of the tissue damage. After severe damage, AST levels rise in 6 to 10 hours and remain high for about 4 days. The AST test may be done at the same time as a test for alanine aminotransferase, or ALT. | | 12.405409197012585 | what does the suspensory ligament do when the cillary muscles contract | Suspensory Ligaments of the Ciliary Body: The suspensory ligaments of the ciliary body are ligaments that attach the ciliary body to the lens of the eye. Suspensory ligaments enable the ciliary body to change the shape of the lens as needed to focus light reflected from objects at different distances from the eye. | Ossification of the posterior longitudinal ligament of the spine: Introduction. Ossification of the posterior longitudinal ligament of the spine: Abnormal calcification of a spinal ligament. The progressive calcification can starts within months of birth and affects the ability to move arms and legs.ssification of the posterior longitudinal ligament of the spine: Introduction. Ossification of the posterior longitudinal ligament of the spine: Abnormal calcification of a spinal ligament. The progressive calcification can starts within months of birth and affects the ability to move arms and legs. | | 19.407212177912392 | how many kids does trump have | Donald Trump has 5 children: Donald Jr., Eric, and Ivanka- mother Ivana Trump Tiffany -mother Marla Maples Barron-mother Malania Trump Donald Trump Jr. has 2 children: … Kai Madison Trump and Donald Trump III. | Copyright © 2018, Trump Make America Great Again Committee. Paid for by Trump Make America Great Again Committee, a joint fundraising committee authorized by and composed of Donald J. Trump for President, Inc. and the Republican National Committee. x Close | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMarginMSELoss", "lambda_corpus": 0.08, "lambda_query": 0.1 } ``` ### Evaluation Dataset #### msmarco * Dataset: [msmarco](https://huggingface.co/datasets/sentence-transformers/msmarco) at [9e329ed](https://huggingface.co/datasets/sentence-transformers/msmarco/tree/9e329ed2e649c9d37b0d91dd6b764ff6fe671d83) * Size: 10,000 evaluation samples * Columns: score, query, positive, and negative * Approximate statistics based on the first 1000 samples: | | score | query | positive | negative | |:--------|:--------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | float | string | string | string | | details | | | | | * Samples: | score | query | positive | negative | |:--------------------------------|:-------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 11.227776050567627 | tabernacle definition | Wiktionary(0.00 / 0 votes)Rate this definition: tabernacle(Noun) any temporary dwelling, a hut, tent, booth. tabernacle(Noun) (Old Testament) The portable tent used before the construction of the temple, where the shekinah (presence of God) was believed to dwell. 1611 ... So Moses finished the work. Then a cloud covered the tent of the congregation, and the glory of the LORD filled the tabernacle. | Both the Annunciation tabernacle in Santa Croce and the Cantoria (the singer's pulpit) in the Duomo (now in the Museo dell'Opera del Duomo) show a vastly increased repertory of forms derived from ancient art, the harvest of Donatello's long stay in Rome (1430-33). | | 12.354041655858357 | what scientist discovered radiation | Becquerel used an apparatus similar to that displayed below to show that the radiation he discovered could not be x-rays. X-rays are neutral and cannot be bent in a magnetic field. The new radiation was bent by the magnetic field so that the radiation must be charged and different than x-rays. | 5a-Hydroxy Laxogenin. 5a-Hydroxy Laxogenin was discovered by a American scientist in 1996. It was shown to possess an anabolic/androgenic ratio similar to one of the most efficient anabolic substances, in particular Anavar but without the side effects of liver toxicity or testing positive for steroidal therapy. | | 11.721514344215393 | are horses primates | Primates still do, but many, if not most, mammals do not. Horses, deer, cows and many other mammals have a reduced number of digits on their forelimbs and hindlimbs. Primates also retain other generalized skeletal features like the clavicle or collar bone. | The only primates that live in Canada are humans. The species originated in east Africa and is unrelated to South American primates. Humans first arrived in large numbers to Canada around 15,000 years ago from North Asia, and surged in migration starting 400 years ago from around the world, especially from Europe. | * Loss: [SpladeLoss](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters: ```json { "loss": "SparseMarginMSELoss", "lambda_corpus": 0.08, "lambda_query": 0.1 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 1 - `warmup_ratio`: 0.1 - `bf16`: True - `load_best_model_at_end`: True #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `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`: 2e-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`: 0.1 - `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} - `tp_size`: 0 - `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`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | |:----------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:| | 0.0178 | 100 | 664548.88 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0356 | 200 | 1912.7461 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0533 | 300 | 89.4823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0711 | 400 | 57.4213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0889 | 500 | 43.5322 | 37.8169 | 0.5271 | 0.2411 | 0.5761 | 0.4481 | - | - | - | - | - | - | - | - | - | - | | 0.1067 | 600 | 38.8042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1244 | 700 | 34.1112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1422 | 800 | 30.3487 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.16 | 900 | 30.4368 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1778 | 1000 | 30.9444 | 27.4550 | 0.5513 | 0.3375 | 0.6122 | 0.5003 | - | - | - | - | - | - | - | - | - | - | | 0.1956 | 1100 | 27.7082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2133 | 1200 | 28.6251 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2311 | 1300 | 27.6298 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2489 | 1400 | 24.1523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2667 | 1500 | 25.3053 | 23.4952 | 0.5898 | 0.3416 | 0.6296 | 0.5203 | - | - | - | - | - | - | - | - | - | - | | 0.2844 | 1600 | 24.8645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3022 | 1700 | 25.9037 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.32 | 1800 | 25.255 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3378 | 1900 | 24.4475 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3556 | 2000 | 22.8183 | 26.7798 | 0.5579 | 0.3407 | 0.6160 | 0.5049 | - | - | - | - | - | - | - | - | - | - | | 0.3733 | 2100 | 22.0948 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3911 | 2200 | 22.9483 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4089 | 2300 | 20.8408 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4267 | 2400 | 19.5543 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4444 | 2500 | 20.9379 | 18.6976 | 0.6327 | 0.3216 | 0.6255 | 0.5266 | - | - | - | - | - | - | - | - | - | - | | 0.4622 | 2600 | 20.2078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.48 | 2700 | 20.6449 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4978 | 2800 | 19.1764 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5156 | 2900 | 19.4603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5333 | 3000 | 20.3068 | 18.4043 | 0.6081 | 0.3220 | 0.6515 | 0.5272 | - | - | - | - | - | - | - | - | - | - | | 0.5511 | 3100 | 19.1402 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5689 | 3200 | 18.0542 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5867 | 3300 | 17.9658 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6044 | 3400 | 18.4345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6222 | 3500 | 19.4609 | 17.0769 | 0.6155 | 0.3219 | 0.6545 | 0.5306 | - | - | - | - | - | - | - | - | - | - | | 0.64 | 3600 | 17.4228 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6578 | 3700 | 17.8939 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6756 | 3800 | 16.2358 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6933 | 3900 | 16.6908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 4000 | 15.9995 | 17.7298 | 0.6022 | 0.3555 | 0.6525 | 0.5367 | - | - | - | - | - | - | - | - | - | - | | 0.7289 | 4100 | 16.3495 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7467 | 4200 | 15.559 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7644 | 4300 | 17.4544 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7822 | 4400 | 15.8666 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8 | 4500 | 16.3616 | 18.8307 | 0.6036 | 0.3472 | 0.6112 | 0.5207 | - | - | - | - | - | - | - | - | - | - | | 0.8178 | 4600 | 15.276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8356 | 4700 | 15.2697 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8533 | 4800 | 16.6727 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8711 | 4900 | 15.2223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8889 | 5000 | 15.7583 | 16.2949 | 0.6177 | 0.3438 | 0.6505 | 0.5373 | - | - | - | - | - | - | - | - | - | - | | 0.9067 | 5100 | 15.3164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9244 | 5200 | 14.9429 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9422 | 5300 | 15.5992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.96 | 5400 | 14.8593 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.9778** | **5500** | **14.7565** | **16.423** | **0.6077** | **0.3452** | **0.6595** | **0.5375** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.9956 | 5600 | 14.5115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | 0.6077 | 0.3452 | 0.6595 | 0.5787 | 0.3037 | 0.6228 | 0.8719 | 0.4125 | 0.8260 | 0.9411 | 0.3183 | 0.4095 | 0.6690 | 0.5361 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.093 kWh - **Carbon Emitted**: 0.034 kg of CO2 - **Hours Used**: 0.305 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA H100 80GB HBM3 - **CPU Model**: AMD EPYC 7R13 Processor - **RAM Size**: 248.00 GB ### Framework Versions - Python: 3.13.3 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.51.3 - PyTorch: 2.7.1+cu126 - Accelerate: 0.26.0 - 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", } ``` #### SpladeLoss ```bibtex @misc{formal2022distillationhardnegativesampling, title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective}, author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant}, year={2022}, eprint={2205.04733}, archivePrefix={arXiv}, primaryClass={cs.IR}, url={https://arxiv.org/abs/2205.04733}, } ``` #### SparseMarginMSELoss ```bibtex @misc{hofstätter2021improving, title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation}, author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury}, year={2021}, eprint={2010.02666}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` #### FlopsLoss ```bibtex @article{paria2020minimizing, title={Minimizing flops to learn efficient sparse representations}, author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s}, journal={arXiv preprint arXiv:2004.05665}, year={2020} } ```