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+---
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:640000
+- loss:Distillation
+base_model: Alibaba-NLP/gte-modernbert-base
+pipeline_tag: sentence-similarity
+library_name: PyLate
+metrics:
+- MaxSim_accuracy@1
+- MaxSim_accuracy@3
+- MaxSim_accuracy@5
+- MaxSim_accuracy@10
+- MaxSim_precision@1
+- MaxSim_precision@3
+- MaxSim_precision@5
+- MaxSim_precision@10
+- MaxSim_recall@1
+- MaxSim_recall@3
+- MaxSim_recall@5
+- MaxSim_recall@10
+- MaxSim_ndcg@10
+- MaxSim_mrr@10
+- MaxSim_map@100
+model-index:
+- name: PyLate model based on Alibaba-NLP/gte-modernbert-base
+ results:
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoClimateFEVER
+ type: NanoClimateFEVER
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.36
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.62
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.78
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.86
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.36
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.2333333333333333
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.20799999999999996
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.12799999999999997
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.18333333333333332
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.289
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.41566666666666663
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.49566666666666664
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.41477895139843374
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.526579365079365
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.33473812643311207
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoDBPedia
+ type: NanoDBPedia
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.88
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.94
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.96
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.98
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.88
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.7133333333333334
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.6560000000000001
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.572
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.11798996781634019
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.23074158968531658
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.2961618059276896
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.4145532152487909
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.7295518860528665
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9168571428571428
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.5883869727264871
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoFEVER
+ type: NanoFEVER
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.92
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.98
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.98
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 1.0
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.92
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.35999999999999993
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.21599999999999994
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.10999999999999999
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.8566666666666667
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.96
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.96
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.98
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.9451911044041129
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9522222222222223
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.9270501207729468
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoFiQA2018
+ type: NanoFiQA2018
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.56
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.66
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.74
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.8
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.56
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.32666666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.25599999999999995
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.15199999999999997
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.30924603174603177
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.47840476190476194
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.5751746031746031
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.6411984126984127
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5669909336903424
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6359444444444444
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.5031998196513616
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoHotpotQA
+ type: NanoHotpotQA
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.92
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 1.0
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 1.0
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 1.0
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.92
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.58
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.35999999999999993
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.18599999999999994
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.46
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.87
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.9
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.93
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.9011747095216048
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.96
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.8591508921772081
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoMSMARCO
+ type: NanoMSMARCO
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.54
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.68
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.74
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.92
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.54
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.22666666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.14800000000000002
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.092
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.54
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.68
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.74
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.92
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.7088869908160952
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6446507936507936
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.6496349206349206
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoNFCorpus
+ type: NanoNFCorpus
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.56
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.68
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.74
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.76
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.56
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.43333333333333335
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.39199999999999996
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.304
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.06640185752724687
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.10198877096622012
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.12839743828750172
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.15658989769166
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.3957047406068243
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.627
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.1917924344366858
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoNQ
+ type: NanoNQ
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.64
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.82
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.86
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.9
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.64
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.2866666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.17999999999999997
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.1
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.61
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.78
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.82
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.88
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.7645227466201794
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.7390000000000001
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.7239323294755705
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoQuoraRetrieval
+ type: NanoQuoraRetrieval
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.96
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 1.0
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 1.0
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 1.0
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.96
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.4
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.25599999999999995
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.13399999999999998
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.8473333333333334
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.9453333333333334
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.9693333333333334
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.9893333333333334
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.9691448095973965
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9766666666666667
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.9551871794871795
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoSCIDOCS
+ type: NanoSCIDOCS
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.48
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.74
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.78
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.84
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.48
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.3999999999999999
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.292
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.19399999999999995
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.10066666666666667
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.24666666666666665
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.29966666666666664
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.39666666666666667
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.3986767701602276
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6137222222222222
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.3163385555719993
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoArguAna
+ type: NanoArguAna
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.3
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.62
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.7
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.82
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.3
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.20666666666666667
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.14
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.08199999999999999
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.3
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.62
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.7
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.82
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5609089627577635
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.4774603174603175
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.4824361431413148
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoSciFact
+ type: NanoSciFact
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.74
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.86
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.9
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.94
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.74
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.3
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.19599999999999995
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.10399999999999998
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.715
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.83
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.885
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.93
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.8371556505161787
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.8116666666666668
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.8048798701298702
+ name: Maxsim Map@100
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoTouche2020
+ type: NanoTouche2020
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.7755102040816326
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.9387755102040817
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.9795918367346939
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.9795918367346939
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.7755102040816326
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.6598639455782314
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.6571428571428573
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.5183673469387755
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.05176652252904378
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.13618168510556633
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.2193408037582337
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.33397423594107617
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5926586898856947
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.8629251700680272
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.42574993112112997
+ name: Maxsim Map@100
+ - task:
+ type: nano-beir
+ name: Nano BEIR
+ dataset:
+ name: NanoBEIR mean
+ type: NanoBEIR_mean
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.6642700156985872
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.8106750392464678
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.8584301412872841
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.9076609105180532
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.6642700156985872
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.39434850863422294
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.3043956043956044
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.20587441130298273
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.3968003368937432
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.5514089852047589
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.6083647167549766
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.6836909560189698
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6757959189252093
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.7495919239490669
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.5971136381353681
+ name: Maxsim Map@100
+---
+
+# PyLate model based on Alibaba-NLP/gte-modernbert-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base) on the train dataset. It maps sentences & paragraphs to a 128-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base)
+- **Maximum Sequence Length:** tokens
+- **Output Dimensionality:** 128 dimensions
+- **Similarity Function:** MaxSim
+- **Training Dataset:**
+ - train
+
+
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+ColBERT(
+ (0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: ModernBertModel
+ (1): Dense({'in_features': 768, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
+```
+
+## 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 SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sentence_transformers_model_id")
+# Run inference
+sentences = [
+ 'The weather is lovely today.',
+ "It's so sunny outside!",
+ 'He drove to the stadium.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 128]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Py Late Information Retrieval
+
+* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
+* Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator
+
+| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
+|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
+| MaxSim_accuracy@1 | 0.36 | 0.88 | 0.92 | 0.56 | 0.92 | 0.54 | 0.56 | 0.64 | 0.96 | 0.48 | 0.3 | 0.74 | 0.7755 |
+| MaxSim_accuracy@3 | 0.62 | 0.94 | 0.98 | 0.66 | 1.0 | 0.68 | 0.68 | 0.82 | 1.0 | 0.74 | 0.62 | 0.86 | 0.9388 |
+| MaxSim_accuracy@5 | 0.78 | 0.96 | 0.98 | 0.74 | 1.0 | 0.74 | 0.74 | 0.86 | 1.0 | 0.78 | 0.7 | 0.9 | 0.9796 |
+| MaxSim_accuracy@10 | 0.86 | 0.98 | 1.0 | 0.8 | 1.0 | 0.92 | 0.76 | 0.9 | 1.0 | 0.84 | 0.82 | 0.94 | 0.9796 |
+| MaxSim_precision@1 | 0.36 | 0.88 | 0.92 | 0.56 | 0.92 | 0.54 | 0.56 | 0.64 | 0.96 | 0.48 | 0.3 | 0.74 | 0.7755 |
+| MaxSim_precision@3 | 0.2333 | 0.7133 | 0.36 | 0.3267 | 0.58 | 0.2267 | 0.4333 | 0.2867 | 0.4 | 0.4 | 0.2067 | 0.3 | 0.6599 |
+| MaxSim_precision@5 | 0.208 | 0.656 | 0.216 | 0.256 | 0.36 | 0.148 | 0.392 | 0.18 | 0.256 | 0.292 | 0.14 | 0.196 | 0.6571 |
+| MaxSim_precision@10 | 0.128 | 0.572 | 0.11 | 0.152 | 0.186 | 0.092 | 0.304 | 0.1 | 0.134 | 0.194 | 0.082 | 0.104 | 0.5184 |
+| MaxSim_recall@1 | 0.1833 | 0.118 | 0.8567 | 0.3092 | 0.46 | 0.54 | 0.0664 | 0.61 | 0.8473 | 0.1007 | 0.3 | 0.715 | 0.0518 |
+| MaxSim_recall@3 | 0.289 | 0.2307 | 0.96 | 0.4784 | 0.87 | 0.68 | 0.102 | 0.78 | 0.9453 | 0.2467 | 0.62 | 0.83 | 0.1362 |
+| MaxSim_recall@5 | 0.4157 | 0.2962 | 0.96 | 0.5752 | 0.9 | 0.74 | 0.1284 | 0.82 | 0.9693 | 0.2997 | 0.7 | 0.885 | 0.2193 |
+| MaxSim_recall@10 | 0.4957 | 0.4146 | 0.98 | 0.6412 | 0.93 | 0.92 | 0.1566 | 0.88 | 0.9893 | 0.3967 | 0.82 | 0.93 | 0.334 |
+| **MaxSim_ndcg@10** | **0.4148** | **0.7296** | **0.9452** | **0.567** | **0.9012** | **0.7089** | **0.3957** | **0.7645** | **0.9691** | **0.3987** | **0.5609** | **0.8372** | **0.5927** |
+| MaxSim_mrr@10 | 0.5266 | 0.9169 | 0.9522 | 0.6359 | 0.96 | 0.6447 | 0.627 | 0.739 | 0.9767 | 0.6137 | 0.4775 | 0.8117 | 0.8629 |
+| MaxSim_map@100 | 0.3347 | 0.5884 | 0.9271 | 0.5032 | 0.8592 | 0.6496 | 0.1918 | 0.7239 | 0.9552 | 0.3163 | 0.4824 | 0.8049 | 0.4257 |
+
+#### Nano BEIR
+
+* Dataset: `NanoBEIR_mean`
+* Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator
+
+| Metric | Value |
+|:--------------------|:-----------|
+| MaxSim_accuracy@1 | 0.6643 |
+| MaxSim_accuracy@3 | 0.8107 |
+| MaxSim_accuracy@5 | 0.8584 |
+| MaxSim_accuracy@10 | 0.9077 |
+| MaxSim_precision@1 | 0.6643 |
+| MaxSim_precision@3 | 0.3943 |
+| MaxSim_precision@5 | 0.3044 |
+| MaxSim_precision@10 | 0.2059 |
+| MaxSim_recall@1 | 0.3968 |
+| MaxSim_recall@3 | 0.5514 |
+| MaxSim_recall@5 | 0.6084 |
+| MaxSim_recall@10 | 0.6837 |
+| **MaxSim_ndcg@10** | **0.6758** |
+| MaxSim_mrr@10 | 0.7496 |
+| MaxSim_map@100 | 0.5971 |
+
+
+
+
+
+## Training Details
+
+### Training Dataset
+
+#### train
+
+* Dataset: train
+* Size: 640,000 training samples
+* Columns: query_id, document_ids, and scores
+* Approximate statistics based on the first 1000 samples:
+ | | query_id | document_ids | scores |
+ |:--------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
+ | type | int | list | list |
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| | |
+* Samples:
+ | query_id | document_ids | scores |
+ |:--------------------|:----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------|
+ | 685613 | [7546874, 1176459, 197677, 2306318, 8541504, ...] | [0.9999999992804947, 0.24845418756716053, 0.7594154013647826, 0.26644182105618575, 0.390668914839766, ...] |
+ | 237784 | [6366584, 4034101, 2325374, 6914618, 6042146, ...] | [0.9999999991784339, 0.42233632827946693, 0.5956354295491569, 0.12644415907455164, 0.6636713730105909, ...] |
+ | 904294 | [448408, 8743975, 49600, 7339401, 2714261, ...] | [0.9999999991841937, 0.877629062381539, 0.8330146583389045, 0.3116634796692611, 0.4633524534142185, ...] |
+* Loss: pylate.losses.distillation.Distillation
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 16
+- `learning_rate`: 3e-05
+- `bf16`: 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`: 8
+- `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`: 3e-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`: 3
+- `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`: 6
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: True
+- `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`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`:
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: batch_sampler
+- `multi_dataset_batch_sampler`: proportional
+
+Click to expand
+
+| Epoch | Step | Training Loss | NanoClimateFEVER_MaxSim_ndcg@10 | NanoDBPedia_MaxSim_ndcg@10 | NanoFEVER_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNFCorpus_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoQuoraRetrieval_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoArguAna_MaxSim_ndcg@10 | NanoSciFact_MaxSim_ndcg@10 | NanoTouche2020_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 |
+|:------:|:-----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
+| 0.004 | 20 | 0.0493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.008 | 40 | 0.0434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.012 | 60 | 0.0324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.016 | 80 | 0.0238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.02 | 100 | 0.0202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.024 | 120 | 0.0186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.028 | 140 | 0.0172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.032 | 160 | 0.0164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.036 | 180 | 0.0157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.04 | 200 | 0.0153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.044 | 220 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.048 | 240 | 0.014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.052 | 260 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.056 | 280 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.06 | 300 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.064 | 320 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.068 | 340 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.072 | 360 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.076 | 380 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.08 | 400 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.084 | 420 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.088 | 440 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.092 | 460 | 0.0113 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.096 | 480 | 0.0112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1 | 500 | 0.0111 | 0.3085 | 0.6309 | 0.9206 | 0.5303 | 0.8618 | 0.6893 | 0.3703 | 0.7163 | 0.9548 | 0.3885 | 0.4682 | 0.7930 | 0.5982 | 0.6331 |
+| 0.104 | 520 | 0.0109 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.108 | 540 | 0.0109 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.112 | 560 | 0.0109 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.116 | 580 | 0.0105 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.12 | 600 | 0.0102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.124 | 620 | 0.0104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.128 | 640 | 0.0103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.132 | 660 | 0.01 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.136 | 680 | 0.0101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.14 | 700 | 0.0098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.144 | 720 | 0.0097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.148 | 740 | 0.0097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.152 | 760 | 0.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.156 | 780 | 0.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.16 | 800 | 0.0094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.164 | 820 | 0.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.168 | 840 | 0.0095 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.172 | 860 | 0.0093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.176 | 880 | 0.0092 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.18 | 900 | 0.0093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.184 | 920 | 0.009 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.188 | 940 | 0.009 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.192 | 960 | 0.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.196 | 980 | 0.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2 | 1000 | 0.0089 | 0.3148 | 0.6586 | 0.9335 | 0.5374 | 0.8810 | 0.6805 | 0.3746 | 0.7368 | 0.9486 | 0.3955 | 0.4824 | 0.8219 | 0.6089 | 0.6442 |
+| 0.204 | 1020 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.208 | 1040 | 0.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.212 | 1060 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.216 | 1080 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.22 | 1100 | 0.0087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.224 | 1120 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.228 | 1140 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.232 | 1160 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.236 | 1180 | 0.0084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.24 | 1200 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.244 | 1220 | 0.0085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.248 | 1240 | 0.0084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.252 | 1260 | 0.0084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.256 | 1280 | 0.0081 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.26 | 1300 | 0.0083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.264 | 1320 | 0.0084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.268 | 1340 | 0.0082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.272 | 1360 | 0.0082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.276 | 1380 | 0.008 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.28 | 1400 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.284 | 1420 | 0.0079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.288 | 1440 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.292 | 1460 | 0.0081 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.296 | 1480 | 0.0081 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3 | 1500 | 0.0079 | 0.3510 | 0.6590 | 0.9285 | 0.5463 | 0.8893 | 0.6853 | 0.3800 | 0.7370 | 0.9513 | 0.3980 | 0.5268 | 0.8268 | 0.6130 | 0.6533 |
+| 0.304 | 1520 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.308 | 1540 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.312 | 1560 | 0.0077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.316 | 1580 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.32 | 1600 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.324 | 1620 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.328 | 1640 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.332 | 1660 | 0.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.336 | 1680 | 0.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.34 | 1700 | 0.0077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.344 | 1720 | 0.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.348 | 1740 | 0.0074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.352 | 1760 | 0.0074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.356 | 1780 | 0.0075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.36 | 1800 | 0.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.364 | 1820 | 0.0075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.368 | 1840 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.372 | 1860 | 0.0075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.376 | 1880 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.38 | 1900 | 0.0074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.384 | 1920 | 0.0072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.388 | 1940 | 0.0072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.392 | 1960 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.396 | 1980 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4 | 2000 | 0.0071 | 0.3551 | 0.6807 | 0.9311 | 0.5340 | 0.8951 | 0.7019 | 0.3767 | 0.7460 | 0.9559 | 0.3912 | 0.5121 | 0.8245 | 0.6058 | 0.6546 |
+| 0.404 | 2020 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.408 | 2040 | 0.0072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.412 | 2060 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.416 | 2080 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.42 | 2100 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.424 | 2120 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.428 | 2140 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.432 | 2160 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.436 | 2180 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.44 | 2200 | 0.007 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.444 | 2220 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.448 | 2240 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.452 | 2260 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.456 | 2280 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.46 | 2300 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.464 | 2320 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.468 | 2340 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.472 | 2360 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.476 | 2380 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.48 | 2400 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.484 | 2420 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.488 | 2440 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.492 | 2460 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.496 | 2480 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5 | 2500 | 0.0068 | 0.3647 | 0.6883 | 0.9435 | 0.5624 | 0.8946 | 0.7065 | 0.3815 | 0.7709 | 0.9658 | 0.3993 | 0.5631 | 0.8371 | 0.6076 | 0.6681 |
+| 0.504 | 2520 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.508 | 2540 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.512 | 2560 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.516 | 2580 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.52 | 2600 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.524 | 2620 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.528 | 2640 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.532 | 2660 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.536 | 2680 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.54 | 2700 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.544 | 2720 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.548 | 2740 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.552 | 2760 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.556 | 2780 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.56 | 2800 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.564 | 2820 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.568 | 2840 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.572 | 2860 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.576 | 2880 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.58 | 2900 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.584 | 2920 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.588 | 2940 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.592 | 2960 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.596 | 2980 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6 | 3000 | 0.0064 | 0.3585 | 0.7081 | 0.9409 | 0.5474 | 0.8915 | 0.7037 | 0.3796 | 0.7763 | 0.9540 | 0.4038 | 0.5628 | 0.8424 | 0.6042 | 0.6672 |
+| 0.604 | 3020 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.608 | 3040 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.612 | 3060 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.616 | 3080 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.62 | 3100 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.624 | 3120 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.628 | 3140 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.632 | 3160 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.636 | 3180 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.64 | 3200 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.644 | 3220 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.648 | 3240 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.652 | 3260 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.656 | 3280 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.66 | 3300 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.664 | 3320 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.668 | 3340 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.672 | 3360 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.676 | 3380 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.68 | 3400 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.684 | 3420 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.688 | 3440 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.692 | 3460 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.696 | 3480 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7 | 3500 | 0.0061 | 0.3783 | 0.7080 | 0.9441 | 0.5603 | 0.8902 | 0.7022 | 0.3824 | 0.7780 | 0.9612 | 0.3995 | 0.5414 | 0.8450 | 0.6049 | 0.6689 |
+| 0.704 | 3520 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.708 | 3540 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
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+| 2.892 | 14460 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.896 | 14480 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.9 | 14500 | 0.004 | 0.4177 | 0.7296 | 0.9452 | 0.5663 | 0.9012 | 0.7095 | 0.3917 | 0.7645 | 0.9708 | 0.3985 | 0.5609 | 0.8369 | 0.5952 | 0.6760 |
+| 2.904 | 14520 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.908 | 14540 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.912 | 14560 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.916 | 14580 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.92 | 14600 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.924 | 14620 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.928 | 14640 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.932 | 14660 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.936 | 14680 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.94 | 14700 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.944 | 14720 | 0.0038 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.948 | 14740 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.952 | 14760 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.956 | 14780 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.96 | 14800 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.964 | 14820 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.968 | 14840 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.972 | 14860 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.976 | 14880 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.98 | 14900 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.984 | 14920 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.988 | 14940 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.992 | 14960 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 2.996 | 14980 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 3.0 | 15000 | 0.0039 | 0.4148 | 0.7296 | 0.9452 | 0.5670 | 0.9012 | 0.7089 | 0.3957 | 0.7645 | 0.9691 | 0.3987 | 0.5609 | 0.8372 | 0.5927 | 0.6758 |
+
+