diff --git "a/README.md" "b/README.md"
new file mode 100644--- /dev/null
+++ "b/README.md"
@@ -0,0 +1,1522 @@
+---
+language:
+- en
+tags:
+- ColBERT
+- PyLate
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:533177
+- loss:Distillation
+base_model: jhu-clsp/ettin-encoder-17m
+datasets:
+- lightonai/ms-marco-en-bge-gemma
+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 jhu-clsp/ettin-encoder-17m
+ results:
+ - task:
+ type: py-late-information-retrieval
+ name: Py Late Information Retrieval
+ dataset:
+ name: NanoClimateFEVER
+ type: NanoClimateFEVER
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.26
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.44
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.48
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.72
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.26
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.1733333333333333
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.12000000000000002
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.102
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.11999999999999998
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.23
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.25666666666666665
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.3999999999999999
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.30588764137829927
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.38180158730158725
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.23047723328383551
+ 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: 0.72
+ 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: 0.72
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.6066666666666667
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.5720000000000001
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.49
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.08124424335875133
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.1639789174109182
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.2286688535902389
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.3339202593691046
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5943825623026429
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.7955555555555555
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.47610983586026223
+ 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: 0.84
+ 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: 0.84
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.33333333333333326
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.20799999999999996
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.10599999999999998
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.7766666666666667
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.9033333333333333
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.93
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.95
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.8863719088415238
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.8903333333333333
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.8591045425163073
+ 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: 0.42
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.6
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.72
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.76
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.42
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.26666666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.22799999999999998
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.14
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.21591269841269842
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.35584920634920636
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.4972857142857143
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.5840079365079365
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.4728764591299225
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.5343571428571429
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.3849491712909313
+ 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: 0.98
+ 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: 0.54
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.3399999999999999
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.17999999999999997
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.46
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.81
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.85
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.9
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.8633780841984157
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9506666666666667
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.8068729210481762
+ 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: 0.5
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.66
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.68
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.78
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.5
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.22
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.136
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.07800000000000001
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.5
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.66
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.68
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.78
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6350694238626255
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.5893809523809523
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.6008352347387884
+ 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: 0.4
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.52
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.58
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.68
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.4
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.34
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.32
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.28
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.033468066221703355
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.07710152452729603
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.09567130100917189
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.14399069709040951
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.33024968480063904
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.47657936507936505
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.14186780303718874
+ 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: 0.54
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.76
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.8
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.84
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.54
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.25333333333333335
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.16799999999999998
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.09
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.51
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.7
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.76
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.81
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.6699201254277886
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6447222222222222
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.62006789085707
+ 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: 0.82
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.96
+ 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: 0.82
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.38666666666666655
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.244
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.12799999999999997
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.7340000000000001
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.912
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.956
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.9726666666666668
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.9086308248836141
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.9
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.8813997853997853
+ 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: 0.42
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.6
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.66
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.74
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.42
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.2866666666666667
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.22399999999999995
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.15
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.08666666666666666
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.17666666666666664
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.2286666666666666
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.30666666666666664
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.31422844901617714
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.5303571428571429
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.24770788410611272
+ 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: 0.2
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.48
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.64
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.76
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.2
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.15999999999999998
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.128
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.07600000000000001
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.2
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.48
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.64
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.76
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.45449277481893957
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.3582460317460317
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.36532756317756315
+ 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: 0.6
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.76
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.82
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.88
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.6
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.26666666666666666
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.18
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.09799999999999999
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.575
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.74
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.815
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.87
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.7356545211627262
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6952222222222221
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.691199074074074
+ 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: 0.6938775510204082
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.9183673469387755
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.9591836734693877
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 1.0
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.6938775510204082
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.6394557823129251
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.636734693877551
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.49387755102040815
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.04942817268713302
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.13043451476387394
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.2136324859904483
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.3155916739971445
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5612872974984163
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.8085519922254615
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.41548604773283626
+ name: Maxsim Map@100
+ - task:
+ type: nano-beir
+ name: Nano BEIR
+ dataset:
+ name: NanoBEIR mean
+ type: NanoBEIR_mean
+ metrics:
+ - type: MaxSim_accuracy@1
+ value: 0.5641444270015699
+ name: Maxsim Accuracy@1
+ - type: MaxSim_accuracy@3
+ value: 0.729105180533752
+ name: Maxsim Accuracy@3
+ - type: MaxSim_accuracy@5
+ value: 0.7845525902668761
+ name: Maxsim Accuracy@5
+ - type: MaxSim_accuracy@10
+ value: 0.8523076923076923
+ name: Maxsim Accuracy@10
+ - type: MaxSim_precision@1
+ value: 0.5641444270015699
+ name: Maxsim Precision@1
+ - type: MaxSim_precision@3
+ value: 0.34406070120355836
+ name: Maxsim Precision@3
+ - type: MaxSim_precision@5
+ value: 0.26959497645211933
+ name: Maxsim Precision@5
+ - type: MaxSim_precision@10
+ value: 0.18552904238618523
+ name: Maxsim Precision@10
+ - type: MaxSim_recall@1
+ value: 0.3340297318472015
+ name: Maxsim Recall@1
+ - type: MaxSim_recall@3
+ value: 0.487643397157792
+ name: Maxsim Recall@3
+ - type: MaxSim_recall@5
+ value: 0.5501224375545312
+ name: Maxsim Recall@5
+ - type: MaxSim_recall@10
+ value: 0.6251418384844561
+ name: Maxsim Recall@10
+ - type: MaxSim_ndcg@10
+ value: 0.5948022890247485
+ name: Maxsim Ndcg@10
+ - type: MaxSim_mrr@10
+ value: 0.6581364780344372
+ name: Maxsim Mrr@10
+ - type: MaxSim_map@100
+ value: 0.5170311528556101
+ name: Maxsim Map@100
+---
+
+# PyLate model based on jhu-clsp/ettin-encoder-17m
+
+This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m) on the [ms-marco-en-bge-gemma](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
+
+## Model Details
+
+### Model Description
+- **Model Type:** PyLate model
+- **Base model:** [jhu-clsp/ettin-encoder-17m](https://huggingface.co/jhu-clsp/ettin-encoder-17m)
+- **Document Length:** 300 tokens
+- **Query Length:** 32 tokens
+- **Output Dimensionality:** 128 tokens
+- **Similarity Function:** MaxSim
+- **Training Dataset:**
+ - [ms-marco-en-bge-gemma](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma)
+- **Language:** en
+
+
+### Model Sources
+
+- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
+- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
+- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
+
+### Full Model Architecture
+
+```
+ColBERT(
+ (0): Transformer({'max_seq_length': 299, 'do_lower_case': False}) with Transformer model: ModernBertModel
+ (1): Dense({'in_features': 256, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
+)
+```
+
+## Usage
+First install the PyLate library:
+
+```bash
+pip install -U pylate
+```
+
+### Retrieval
+
+PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
+
+#### Indexing documents
+
+First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
+
+```python
+from pylate import indexes, models, retrieve
+
+# Step 1: Load the ColBERT model
+model = models.ColBERT(
+ model_name_or_path=pylate_model_id,
+)
+
+# Step 2: Initialize the Voyager index
+index = indexes.Voyager(
+ index_folder="pylate-index",
+ index_name="index",
+ override=True, # This overwrites the existing index if any
+)
+
+# Step 3: Encode the documents
+documents_ids = ["1", "2", "3"]
+documents = ["document 1 text", "document 2 text", "document 3 text"]
+
+documents_embeddings = model.encode(
+ documents,
+ batch_size=32,
+ is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
+ show_progress_bar=True,
+)
+
+# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
+index.add_documents(
+ documents_ids=documents_ids,
+ documents_embeddings=documents_embeddings,
+)
+```
+
+Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
+
+```python
+# To load an index, simply instantiate it with the correct folder/name and without overriding it
+index = indexes.Voyager(
+ index_folder="pylate-index",
+ index_name="index",
+)
+```
+
+#### Retrieving top-k documents for queries
+
+Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
+To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
+
+```python
+# Step 1: Initialize the ColBERT retriever
+retriever = retrieve.ColBERT(index=index)
+
+# Step 2: Encode the queries
+queries_embeddings = model.encode(
+ ["query for document 3", "query for document 1"],
+ batch_size=32,
+ is_query=True, # # Ensure that it is set to False to indicate that these are queries
+ show_progress_bar=True,
+)
+
+# Step 3: Retrieve top-k documents
+scores = retriever.retrieve(
+ queries_embeddings=queries_embeddings,
+ k=10, # Retrieve the top 10 matches for each query
+)
+```
+
+### Reranking
+If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
+
+```python
+from pylate import rank, models
+
+queries = [
+ "query A",
+ "query B",
+]
+
+documents = [
+ ["document A", "document B"],
+ ["document 1", "document C", "document B"],
+]
+
+documents_ids = [
+ [1, 2],
+ [1, 3, 2],
+]
+
+model = models.ColBERT(
+ model_name_or_path=pylate_model_id,
+)
+
+queries_embeddings = model.encode(
+ queries,
+ is_query=True,
+)
+
+documents_embeddings = model.encode(
+ documents,
+ is_query=False,
+)
+
+reranked_documents = rank.rerank(
+ documents_ids=documents_ids,
+ queries_embeddings=queries_embeddings,
+ documents_embeddings=documents_embeddings,
+)
+```
+
+
+
+
+
+
+
+## Evaluation
+
+### Metrics
+
+#### Py Late Information Retrieval
+* Dataset: `['NanoClimateFEVER', 'NanoDBPedia', 'NanoFEVER', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNFCorpus', 'NanoNQ', 'NanoQuoraRetrieval', 'NanoSCIDOCS', 'NanoArguAna', 'NanoSciFact', '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 | 0.26 | 0.72 | 0.84 | 0.42 | 0.92 | 0.5 | 0.4 | 0.54 | 0.82 | 0.42 | 0.2 | 0.6 | 0.6939 |
+| MaxSim_accuracy@3 | 0.44 | 0.86 | 0.94 | 0.6 | 0.98 | 0.66 | 0.52 | 0.76 | 0.96 | 0.6 | 0.48 | 0.76 | 0.9184 |
+| MaxSim_accuracy@5 | 0.48 | 0.9 | 0.96 | 0.72 | 1.0 | 0.68 | 0.58 | 0.8 | 1.0 | 0.66 | 0.64 | 0.82 | 0.9592 |
+| MaxSim_accuracy@10 | 0.72 | 0.94 | 0.98 | 0.76 | 1.0 | 0.78 | 0.68 | 0.84 | 1.0 | 0.74 | 0.76 | 0.88 | 1.0 |
+| MaxSim_precision@1 | 0.26 | 0.72 | 0.84 | 0.42 | 0.92 | 0.5 | 0.4 | 0.54 | 0.82 | 0.42 | 0.2 | 0.6 | 0.6939 |
+| MaxSim_precision@3 | 0.1733 | 0.6067 | 0.3333 | 0.2667 | 0.54 | 0.22 | 0.34 | 0.2533 | 0.3867 | 0.2867 | 0.16 | 0.2667 | 0.6395 |
+| MaxSim_precision@5 | 0.12 | 0.572 | 0.208 | 0.228 | 0.34 | 0.136 | 0.32 | 0.168 | 0.244 | 0.224 | 0.128 | 0.18 | 0.6367 |
+| MaxSim_precision@10 | 0.102 | 0.49 | 0.106 | 0.14 | 0.18 | 0.078 | 0.28 | 0.09 | 0.128 | 0.15 | 0.076 | 0.098 | 0.4939 |
+| MaxSim_recall@1 | 0.12 | 0.0812 | 0.7767 | 0.2159 | 0.46 | 0.5 | 0.0335 | 0.51 | 0.734 | 0.0867 | 0.2 | 0.575 | 0.0494 |
+| MaxSim_recall@3 | 0.23 | 0.164 | 0.9033 | 0.3558 | 0.81 | 0.66 | 0.0771 | 0.7 | 0.912 | 0.1767 | 0.48 | 0.74 | 0.1304 |
+| MaxSim_recall@5 | 0.2567 | 0.2287 | 0.93 | 0.4973 | 0.85 | 0.68 | 0.0957 | 0.76 | 0.956 | 0.2287 | 0.64 | 0.815 | 0.2136 |
+| MaxSim_recall@10 | 0.4 | 0.3339 | 0.95 | 0.584 | 0.9 | 0.78 | 0.144 | 0.81 | 0.9727 | 0.3067 | 0.76 | 0.87 | 0.3156 |
+| **MaxSim_ndcg@10** | **0.3059** | **0.5944** | **0.8864** | **0.4729** | **0.8634** | **0.6351** | **0.3302** | **0.6699** | **0.9086** | **0.3142** | **0.4545** | **0.7357** | **0.5613** |
+| MaxSim_mrr@10 | 0.3818 | 0.7956 | 0.8903 | 0.5344 | 0.9507 | 0.5894 | 0.4766 | 0.6447 | 0.9 | 0.5304 | 0.3582 | 0.6952 | 0.8086 |
+| MaxSim_map@100 | 0.2305 | 0.4761 | 0.8591 | 0.3849 | 0.8069 | 0.6008 | 0.1419 | 0.6201 | 0.8814 | 0.2477 | 0.3653 | 0.6912 | 0.4155 |
+
+#### Nano BEIR
+* Dataset: `NanoBEIR_mean`
+* Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator
+
+| Metric | Value |
+|:--------------------|:-----------|
+| MaxSim_accuracy@1 | 0.5641 |
+| MaxSim_accuracy@3 | 0.7291 |
+| MaxSim_accuracy@5 | 0.7846 |
+| MaxSim_accuracy@10 | 0.8523 |
+| MaxSim_precision@1 | 0.5641 |
+| MaxSim_precision@3 | 0.3441 |
+| MaxSim_precision@5 | 0.2696 |
+| MaxSim_precision@10 | 0.1855 |
+| MaxSim_recall@1 | 0.334 |
+| MaxSim_recall@3 | 0.4876 |
+| MaxSim_recall@5 | 0.5501 |
+| MaxSim_recall@10 | 0.6251 |
+| **MaxSim_ndcg@10** | **0.5948** |
+| MaxSim_mrr@10 | 0.6581 |
+| MaxSim_map@100 | 0.517 |
+
+
+
+
+
+## Training Details
+
+### Training Dataset
+
+#### ms-marco-en-bge-gemma
+
+* Dataset: [ms-marco-en-bge-gemma](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma) at [d8bad49](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma/tree/d8bad497c8bd698c868a49721999c386d5e6ae8f)
+* Size: 533,177 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 |
+ | details | - 836: ~0.10%
- 3582: ~0.10%
- 4599: ~0.10%
- 4645: ~0.10%
- 4853: ~0.10%
- 5154: ~0.10%
- 7504: ~0.10%
- 12283: ~0.10%
- 12335: ~0.10%
- 12916: ~0.10%
- 14049: ~0.10%
- 14828: ~0.10%
- 15674: ~0.10%
- 15813: ~0.10%
- 16728: ~0.10%
- 22006: ~0.10%
- 23675: ~0.10%
- 24199: ~0.10%
- 25323: ~0.10%
- 28517: ~0.10%
- 29213: ~0.10%
- 32344: ~0.10%
- 34071: ~0.10%
- 34604: ~0.10%
- 35424: ~0.10%
- 35445: ~0.10%
- 36148: ~0.10%
- 37078: ~0.10%
- 37826: ~0.10%
- 38185: ~0.10%
- 40855: ~0.10%
- 42077: ~0.10%
- 43614: ~0.10%
- 45073: ~0.10%
- 46289: ~0.10%
- 47507: ~0.10%
- 48005: ~0.10%
- 48629: ~0.10%
- 48785: ~0.10%
- 49216: ~0.10%
- 49636: ~0.10%
- 49970: ~0.10%
- 52075: ~0.10%
- 52725: ~0.10%
- 54142: ~0.10%
- 54210: ~0.10%
- 55032: ~0.10%
- 59546: ~0.10%
- 60087: ~0.10%
- 60862: ~0.10%
- 60941: ~0.10%
- 61037: ~0.10%
- 61762: ~0.10%
- 62649: ~0.10%
- 63333: ~0.10%
- 64197: ~0.10%
- 64879: ~0.10%
- 67608: ~0.10%
- 67627: ~0.10%
- 69463: ~0.10%
- 70002: ~0.10%
- 70429: ~0.10%
- 72166: ~0.10%
- 72518: ~0.10%
- 72607: ~0.10%
- 72791: ~0.10%
- 73325: ~0.10%
- 74078: ~0.10%
- 74857: ~0.10%
- 75323: ~0.10%
- 75816: ~0.10%
- 76929: ~0.10%
- 77845: ~0.10%
- 77889: ~0.10%
- 78077: ~0.10%
- 78256: ~0.10%
- 78401: ~0.10%
- 78798: ~0.10%
- 80871: ~0.10%
- 81089: ~0.10%
- 82179: ~0.10%
- 82883: ~0.10%
- 84168: ~0.10%
- 86891: ~0.10%
- 88282: ~0.10%
- 89346: ~0.10%
- 89386: ~0.10%
- 90699: ~0.10%
- 90795: ~0.10%
- 91367: ~0.10%
- 91795: ~0.10%
- 92070: ~0.10%
- 92523: ~0.10%
- 92597: ~0.10%
- 92753: ~0.10%
- 92787: ~0.10%
- 96382: ~0.10%
- 96455: ~0.10%
- 97274: ~0.10%
- 97603: ~0.10%
- 100904: ~0.10%
- 101205: ~0.10%
- 101305: ~0.10%
- 102707: ~0.10%
- 103074: ~0.10%
- 105437: ~0.10%
- 108207: ~0.10%
- 109776: ~0.10%
- 112056: ~0.10%
- 112955: ~0.10%
- 112977: ~0.10%
- 113635: ~0.10%
- 115280: ~0.10%
- 115551: ~0.10%
- 116098: ~0.10%
- 117658: ~0.10%
- 120255: ~0.10%
- 120298: ~0.10%
- 121437: ~0.10%
- 123429: ~0.10%
- 125043: ~0.10%
- 125979: ~0.10%
- 126851: ~0.10%
- 128218: ~0.10%
- 128804: ~0.10%
- 129598: ~0.10%
- 131299: ~0.10%
- 132114: ~0.10%
- 133696: ~0.10%
- 134460: ~0.10%
- 137602: ~0.10%
- 137679: ~0.10%
- 138121: ~0.10%
- 138260: ~0.10%
- 138823: ~0.10%
- 139039: ~0.10%
- 140392: ~0.10%
- 140651: ~0.10%
- 142305: ~0.10%
- 145653: ~0.10%
- 145683: ~0.10%
- 145763: ~0.10%
- 150202: ~0.10%
- 151135: ~0.10%
- 152307: ~0.10%
- 152675: ~0.10%
- 153693: ~0.10%
- 154470: ~0.10%
- 155587: ~0.10%
- 157602: ~0.10%
- 157779: ~0.10%
- 158565: ~0.10%
- 159177: ~0.10%
- 159224: ~0.10%
- 159341: ~0.10%
- 159892: ~0.10%
- 161881: ~0.10%
- 162414: ~0.10%
- 163765: ~0.10%
- 165888: ~0.10%
- 168048: ~0.10%
- 168425: ~0.10%
- 168894: ~0.10%
- 169991: ~0.10%
- 170731: ~0.10%
- 171705: ~0.10%
- 176165: ~0.10%
- 176798: ~0.10%
- 180259: ~0.10%
- 181243: ~0.10%
- 182102: ~0.10%
- 182660: ~0.10%
- 183426: ~0.10%
- 183930: ~0.10%
- 184045: ~0.10%
- 184676: ~0.10%
- 185294: ~0.10%
- 186475: ~0.10%
- 187155: ~0.10%
- 188198: ~0.10%
- 191383: ~0.10%
- 192165: ~0.10%
- 193507: ~0.10%
- 194207: ~0.10%
- 195056: ~0.10%
- 197377: ~0.10%
- 198224: ~0.10%
- 198546: ~0.10%
- 202122: ~0.10%
- 203519: ~0.10%
- 206220: ~0.10%
- 209739: ~0.10%
- 210554: ~0.10%
- 212638: ~0.10%
- 213096: ~0.10%
- 213410: ~0.10%
- 214255: ~0.10%
- 217541: ~0.10%
- 219718: ~0.10%
- 220993: ~0.10%
- 223241: ~0.10%
- 224657: ~0.10%
- 227101: ~0.10%
- 227497: ~0.10%
- 227726: ~0.10%
- 228099: ~0.10%
- 228451: ~0.10%
- 230413: ~0.10%
- 231416: ~0.10%
- 233312: ~0.10%
- 234348: ~0.10%
- 235869: ~0.10%
- 237784: ~0.10%
- 240739: ~0.10%
- 246495: ~0.10%
- 246821: ~0.10%
- 248675: ~0.10%
- 249798: ~0.10%
- 249962: ~0.10%
- 249977: ~0.10%
- 250019: ~0.10%
- 250548: ~0.10%
- 251089: ~0.10%
- 254878: ~0.10%
- 255183: ~0.10%
- 255727: ~0.10%
- 256321: ~0.10%
- 258276: ~0.10%
- 260993: ~0.10%
- 261247: ~0.10%
- 262123: ~0.10%
- 262508: ~0.10%
- 266047: ~0.10%
- 267089: ~0.10%
- 267192: ~0.10%
- 268642: ~0.10%
- 269025: ~0.10%
- 273171: ~0.10%
- 273864: ~0.10%
- 274521: ~0.10%
- 274586: ~0.10%
- 275037: ~0.10%
- 275643: ~0.10%
- 276744: ~0.10%
- 277212: ~0.10%
- 277990: ~0.10%
- 279931: ~0.10%
- 280012: ~0.10%
- 281699: ~0.10%
- 282128: ~0.10%
- 283298: ~0.10%
- 284268: ~0.10%
- 285697: ~0.10%
- 285905: ~0.10%
- 287456: ~0.10%
- 287506: ~0.10%
- 288154: ~0.10%
- 289046: ~0.10%
- 292211: ~0.10%
- 292588: ~0.10%
- 293357: ~0.10%
- 293661: ~0.10%
- 294123: ~0.10%
- 299287: ~0.10%
- 300622: ~0.10%
- 302135: ~0.10%
- 303224: ~0.10%
- 304353: ~0.10%
- 304820: ~0.10%
- 310215: ~0.10%
- 310236: ~0.10%
- 310409: ~0.10%
- 311231: ~0.10%
- 312821: ~0.10%
- 314244: ~0.10%
- 314415: ~0.10%
- 314745: ~0.10%
- 316385: ~0.10%
- 316883: ~0.10%
- 317442: ~0.10%
- 318639: ~0.10%
- 318652: ~0.10%
- 320855: ~0.10%
- 321867: ~0.10%
- 322114: ~0.10%
- 323196: ~0.10%
- 324868: ~0.10%
- 327581: ~0.10%
- 329337: ~0.10%
- 331572: ~0.10%
- 331650: ~0.10%
- 331993: ~0.10%
- 332500: ~0.10%
- 334757: ~0.10%
- 336561: ~0.10%
- 336791: ~0.10%
- 337002: ~0.10%
- 338332: ~0.10%
- 338456: ~0.10%
- 339065: ~0.10%
- 339870: ~0.10%
- 340599: ~0.10%
- 341156: ~0.10%
- 342121: ~0.10%
- 342381: ~0.10%
- 343411: ~0.10%
- 344860: ~0.10%
- 345924: ~0.10%
- 346421: ~0.10%
- 346425: ~0.10%
- 348157: ~0.10%
- 351281: ~0.10%
- 351858: ~0.10%
- 352641: ~0.10%
- 353748: ~0.10%
- 357399: ~0.10%
- 359787: ~0.10%
- 359893: ~0.10%
- 360094: ~0.10%
- 360168: ~0.10%
- 361127: ~0.10%
- 362220: ~0.10%
- 362560: ~0.10%
- 366835: ~0.10%
- 367185: ~0.10%
- 369045: ~0.10%
- 371113: ~0.10%
- 376044: ~0.10%
- 376524: ~0.10%
- 377231: ~0.10%
- 377735: ~0.10%
- 378574: ~0.10%
- 379749: ~0.10%
- 379953: ~0.10%
- 381834: ~0.10%
- 384039: ~0.10%
- 384364: ~0.10%
- 384398: ~0.10%
- 384751: ~0.10%
- 385758: ~0.10%
- 385893: ~0.10%
- 386098: ~0.10%
- 387205: ~0.10%
- 387374: ~0.10%
- 388450: ~0.10%
- 388589: ~0.10%
- 388593: ~0.10%
- 389571: ~0.10%
- 389572: ~0.10%
- 391531: ~0.10%
- 391857: ~0.10%
- 393174: ~0.10%
- 393426: ~0.10%
- 396601: ~0.10%
- 396905: ~0.10%
- 397801: ~0.10%
- 398011: ~0.10%
- 398132: ~0.10%
- 398721: ~0.10%
- 399016: ~0.10%
- 401601: ~0.10%
- 403876: ~0.10%
- 403897: ~0.10%
- 404830: ~0.10%
- 406102: ~0.10%
- 406397: ~0.10%
- 407151: ~0.10%
- 409373: ~0.10%
- 410084: ~0.10%
- 410859: ~0.10%
- 411693: ~0.10%
- 411984: ~0.10%
- 412214: ~0.10%
- 412560: ~0.10%
- 413117: ~0.10%
- 416391: ~0.10%
- 417066: ~0.10%
- 417198: ~0.10%
- 417751: ~0.10%
- 417778: ~0.10%
- 420257: ~0.10%
- 420787: ~0.10%
- 421001: ~0.10%
- 421045: ~0.10%
- 421354: ~0.10%
- 428114: ~0.10%
- 429057: ~0.10%
- 429459: ~0.10%
- 430319: ~0.10%
- 431215: ~0.10%
- 431332: ~0.10%
- 431488: ~0.10%
- 432097: ~0.10%
- 432283: ~0.10%
- 434131: ~0.10%
- 434934: ~0.10%
- 435353: ~0.10%
- 437793: ~0.10%
- 438297: ~0.10%
- 438806: ~0.10%
- 439016: ~0.10%
- 439129: ~0.10%
- 439217: ~0.10%
- 439755: ~0.10%
- 440343: ~0.10%
- 440506: ~0.10%
- 441030: ~0.10%
- 441509: ~0.10%
- 443408: ~0.10%
- 443686: ~0.10%
- 445516: ~0.10%
- 445999: ~0.10%
- 447039: ~0.10%
- 447219: ~0.10%
- 447298: ~0.10%
- 453040: ~0.10%
- 453745: ~0.10%
- 454869: ~0.10%
- 456224: ~0.10%
- 456251: ~0.10%
- 457065: ~0.10%
- 459890: ~0.10%
- 460010: ~0.10%
- 463716: ~0.10%
- 465235: ~0.10%
- 470470: ~0.10%
- 471875: ~0.10%
- 472462: ~0.10%
- 474016: ~0.10%
- 479266: ~0.10%
- 479360: ~0.10%
- 480621: ~0.10%
- 483014: ~0.10%
- 484553: ~0.10%
- 485031: ~0.10%
- 485828: ~0.10%
- 486664: ~0.10%
- 488266: ~0.10%
- 489488: ~0.10%
- 490992: ~0.10%
- 491894: ~0.10%
- 491983: ~0.10%
- 492620: ~0.10%
- 493035: ~0.10%
- 493461: ~0.10%
- 494255: ~0.10%
- 496473: ~0.10%
- 496474: ~0.10%
- 496516: ~0.10%
- 496813: ~0.10%
- 496853: ~0.10%
- 499553: ~0.10%
- 499565: ~0.10%
- 499737: ~0.10%
- 500057: ~0.10%
- 500546: ~0.10%
- 501510: ~0.10%
- 501978: ~0.10%
- 503905: ~0.10%
- 510559: ~0.10%
- 511473: ~0.10%
- 512440: ~0.10%
- 513832: ~0.10%
- 514106: ~0.10%
- 514902: ~0.10%
- 515053: ~0.10%
- 515507: ~0.10%
- 516205: ~0.10%
- 517903: ~0.10%
- 518096: ~0.10%
- 520796: ~0.10%
- 521570: ~0.10%
- 522112: ~0.10%
- 523814: ~0.10%
- 525505: ~0.10%
- 525583: ~0.10%
- 525764: ~0.10%
- 528105: ~0.10%
- 530985: ~0.10%
- 532014: ~0.10%
- 534952: ~0.10%
- 538836: ~0.10%
- 539326: ~0.10%
- 539504: ~0.10%
- 541861: ~0.10%
- 542925: ~0.10%
- 543525: ~0.10%
- 544853: ~0.10%
- 545091: ~0.10%
- 546527: ~0.10%
- 546753: ~0.10%
- 548007: ~0.10%
- 548100: ~0.10%
- 554548: ~0.10%
- 555064: ~0.10%
- 560255: ~0.10%
- 560711: ~0.10%
- 561084: ~0.10%
- 561114: ~0.10%
- 561329: ~0.10%
- 561838: ~0.10%
- 561946: ~0.10%
- 564894: ~0.10%
- 566884: ~0.10%
- 568110: ~0.10%
- 569541: ~0.10%
- 570881: ~0.10%
- 571286: ~0.10%
- 571515: ~0.10%
- 571577: ~0.10%
- 572354: ~0.10%
- 573015: ~0.10%
- 573283: ~0.10%
- 577767: ~0.10%
- 578249: ~0.10%
- 578805: ~0.10%
- 580872: ~0.10%
- 581072: ~0.10%
- 581684: ~0.10%
- 582341: ~0.10%
- 583169: ~0.10%
- 583322: ~0.10%
- 583889: ~0.10%
- 584173: ~0.10%
- 585406: ~0.10%
- 585523: ~0.10%
- 585660: ~0.10%
- 587005: ~0.10%
- 587399: ~0.10%
- 588010: ~0.10%
- 588337: ~0.10%
- 590946: ~0.10%
- 593319: ~0.10%
- 595246: ~0.10%
- 597157: ~0.10%
- 597215: ~0.10%
- 597368: ~0.10%
- 597453: ~0.10%
- 598538: ~0.10%
- 601120: ~0.10%
- 604762: ~0.10%
- 605111: ~0.10%
- 605547: ~0.10%
- 606244: ~0.10%
- 606935: ~0.10%
- 607099: ~0.10%
- 609731: ~0.10%
- 609910: ~0.10%
- 610485: ~0.10%
- 613040: ~0.10%
- 614720: ~0.10%
- 615525: ~0.10%
- 616416: ~0.10%
- 618280: ~0.10%
- 619151: ~0.10%
- 619170: ~0.10%
- 622593: ~0.10%
- 622755: ~0.10%
- 623529: ~0.10%
- 625333: ~0.10%
- 625780: ~0.10%
- 626317: ~0.10%
- 626670: ~0.10%
- 628299: ~0.10%
- 628510: ~0.10%
- 629166: ~0.10%
- 630995: ~0.10%
- 632641: ~0.10%
- 634324: ~0.10%
- 634750: ~0.10%
- 636542: ~0.10%
- 637420: ~0.10%
- 641046: ~0.10%
- 643232: ~0.10%
- 643901: ~0.10%
- 644517: ~0.10%
- 645962: ~0.10%
- 647293: ~0.10%
- 647443: ~0.10%
- 648173: ~0.10%
- 649204: ~0.10%
- 650521: ~0.10%
- 651961: ~0.10%
- 652493: ~0.10%
- 655888: ~0.10%
- 656535: ~0.10%
- 658715: ~0.10%
- 659035: ~0.10%
- 659593: ~0.10%
- 660535: ~0.10%
- 662154: ~0.10%
- 662784: ~0.10%
- 663142: ~0.10%
- 666319: ~0.10%
- 666386: ~0.10%
- 666561: ~0.10%
- 668151: ~0.10%
- 668862: ~0.10%
- 670341: ~0.10%
- 671801: ~0.10%
- 673081: ~0.10%
- 673634: ~0.10%
- 673875: ~0.10%
- 673881: ~0.10%
- 674082: ~0.10%
- 675319: ~0.10%
- 675492: ~0.10%
- 676147: ~0.10%
- 676238: ~0.10%
- 676318: ~0.10%
- 676431: ~0.10%
- 677459: ~0.10%
- 678468: ~0.10%
- 679216: ~0.10%
- 679307: ~0.10%
- 680354: ~0.10%
- 681098: ~0.10%
- 681873: ~0.10%
- 684800: ~0.10%
- 685613: ~0.10%
- 685690: ~0.10%
- 686886: ~0.10%
- 689687: ~0.10%
- 689748: ~0.10%
- 694425: ~0.10%
- 694466: ~0.10%
- 698130: ~0.10%
- 702137: ~0.10%
- 703138: ~0.10%
- 704067: ~0.10%
- 704460: ~0.10%
- 705420: ~0.10%
- 706199: ~0.10%
- 706878: ~0.10%
- 708333: ~0.10%
- 710580: ~0.10%
- 710897: ~0.10%
- 713539: ~0.10%
- 713584: ~0.10%
- 714733: ~0.10%
- 718172: ~0.10%
- 719545: ~0.10%
- 719580: ~0.10%
- 720471: ~0.10%
- 720690: ~0.10%
- 722394: ~0.10%
- 723568: ~0.10%
- 724334: ~0.10%
- 724700: ~0.10%
- 727908: ~0.10%
- 728088: ~0.10%
- 729096: ~0.10%
- 730499: ~0.10%
- 730711: ~0.10%
- 733963: ~0.10%
- 734912: ~0.10%
- 736431: ~0.10%
- 738012: ~0.10%
- 738173: ~0.10%
- 739026: ~0.10%
- 739605: ~0.10%
- 740181: ~0.10%
- 742066: ~0.10%
- 742298: ~0.10%
- 745799: ~0.10%
- 748392: ~0.10%
- 748838: ~0.10%
- 749148: ~0.10%
- 751762: ~0.10%
- 752092: ~0.10%
- 752527: ~0.10%
- 753568: ~0.10%
- 755386: ~0.10%
- 756558: ~0.10%
- 756736: ~0.10%
- 758706: ~0.10%
- 759523: ~0.10%
- 760550: ~0.10%
- 762688: ~0.10%
- 762918: ~0.10%
- 763569: ~0.10%
- 763766: ~0.10%
- 765769: ~0.10%
- 766789: ~0.10%
- 768119: ~0.10%
- 768537: ~0.10%
- 773106: ~0.10%
- 775589: ~0.10%
- 775964: ~0.10%
- 776055: ~0.10%
- 777088: ~0.10%
- 777529: ~0.10%
- 778375: ~0.10%
- 781066: ~0.10%
- 782328: ~0.10%
- 783231: ~0.10%
- 784413: ~0.10%
- 785781: ~0.10%
- 786250: ~0.10%
- 786845: ~0.10%
- 788012: ~0.10%
- 791857: ~0.10%
- 792788: ~0.10%
- 793182: ~0.10%
- 794187: ~0.10%
- 794308: ~0.10%
- 794318: ~0.10%
- 796097: ~0.10%
- 796117: ~0.10%
- 797182: ~0.10%
- 798215: ~0.10%
- 802050: ~0.10%
- 802669: ~0.10%
- 804168: ~0.10%
- 804253: ~0.10%
- 804461: ~0.10%
- 805743: ~0.10%
- 808416: ~0.10%
- 808455: ~0.10%
- 810577: ~0.10%
- 811702: ~0.10%
- 811843: ~0.10%
- 815923: ~0.10%
- 816475: ~0.10%
- 818312: ~0.10%
- 818521: ~0.10%
- 819278: ~0.10%
- 820890: ~0.10%
- 821615: ~0.10%
- 823136: ~0.10%
- 823735: ~0.10%
- 829476: ~0.10%
- 830591: ~0.10%
- 832433: ~0.10%
- 832597: ~0.10%
- 833053: ~0.10%
- 835043: ~0.10%
- 835759: ~0.10%
- 837731: ~0.10%
- 837942: ~0.10%
- 839448: ~0.10%
- 840228: ~0.10%
- 840417: ~0.10%
- 841851: ~0.10%
- 843327: ~0.10%
- 843622: ~0.10%
- 844870: ~0.10%
- 846084: ~0.10%
- 846807: ~0.10%
- 847076: ~0.10%
- 847535: ~0.10%
- 847977: ~0.10%
- 848075: ~0.10%
- 848326: ~0.10%
- 852725: ~0.10%
- 853465: ~0.10%
- 856427: ~0.10%
- 857186: ~0.10%
- 858377: ~0.10%
- 858543: ~0.10%
- 860426: ~0.10%
- 863804: ~0.10%
- 866039: ~0.10%
- 866406: ~0.10%
- 867180: ~0.10%
- 868280: ~0.10%
- 872156: ~0.10%
- 872791: ~0.10%
- 872953: ~0.10%
- 872959: ~0.10%
- 875015: ~0.10%
- 876522: ~0.10%
- 878407: ~0.10%
- 878710: ~0.10%
- 878855: ~0.10%
- 880495: ~0.10%
- 882732: ~0.10%
- 884335: ~0.10%
- 884941: ~0.10%
- 885893: ~0.10%
- 886713: ~0.10%
- 887068: ~0.10%
- 887751: ~0.10%
- 888027: ~0.10%
- 890152: ~0.10%
- 891137: ~0.10%
- 891890: ~0.10%
- 892662: ~0.10%
- 892973: ~0.10%
- 893360: ~0.10%
- 893915: ~0.10%
- 893976: ~0.10%
- 894324: ~0.10%
- 895709: ~0.10%
- 897065: ~0.10%
- 898387: ~0.10%
- 899291: ~0.10%
- 899604: ~0.10%
- 900513: ~0.10%
- 900619: ~0.10%
- 901170: ~0.10%
- 902794: ~0.10%
- 903238: ~0.10%
- 904294: ~0.10%
- 904520: ~0.10%
- 904992: ~0.10%
- 907212: ~0.10%
- 908062: ~0.10%
- 908561: ~0.10%
- 911034: ~0.10%
- 911982: ~0.10%
- 913716: ~0.10%
- 914819: ~0.10%
- 915750: ~0.10%
- 915766: ~0.10%
- 916125: ~0.10%
- 916648: ~0.10%
- 917285: ~0.10%
- 918194: ~0.10%
- 926035: ~0.10%
- 927726: ~0.10%
- 929821: ~0.10%
- 930300: ~0.10%
- 930796: ~0.10%
- 931617: ~0.10%
- 932719: ~0.10%
- 933784: ~0.10%
- 934378: ~0.10%
- 935900: ~0.10%
- 936118: ~0.10%
- 936336: ~0.10%
- 937231: ~0.10%
- 938420: ~0.10%
- 939184: ~0.10%
- 939567: ~0.10%
- 941588: ~0.10%
- 944093: ~0.10%
- 944912: ~0.10%
- 945069: ~0.10%
- 945659: ~0.10%
- 946110: ~0.10%
- 950044: ~0.10%
- 954101: ~0.10%
- 954147: ~0.10%
- 958697: ~0.10%
- 959530: ~0.10%
- 961721: ~0.10%
- 963582: ~0.10%
- 964471: ~0.10%
- 965026: ~0.10%
- 966573: ~0.10%
- 967330: ~0.10%
- 968346: ~0.10%
- 970649: ~0.10%
- 970873: ~0.10%
- 971636: ~0.10%
- 971664: ~0.10%
- 973555: ~0.10%
- 973851: ~0.10%
- 974207: ~0.10%
- 976896: ~0.10%
- 981402: ~0.10%
- 983723: ~0.10%
- 984358: ~0.10%
- 984653: ~0.10%
- 987107: ~0.10%
- 987167: ~0.10%
- 994360: ~0.10%
- 995049: ~0.10%
- 1002688: ~0.10%
- 1004305: ~0.10%
- 1004650: ~0.10%
- 1004849: ~0.10%
- 1005118: ~0.10%
- 1005614: ~0.10%
- 1005626: ~0.10%
- 1005669: ~0.10%
- 1006835: ~0.10%
- 1011008: ~0.10%
- 1012299: ~0.10%
- 1014010: ~0.10%
- 1014030: ~0.10%
- 1016549: ~0.10%
- 1017016: ~0.10%
- 1017335: ~0.10%
- 1018386: ~0.10%
- 1020640: ~0.10%
- 1021041: ~0.10%
- 1021411: ~0.10%
- 1025341: ~0.10%
- 1025423: ~0.10%
- 1025767: ~0.10%
- 1026066: ~0.10%
- 1026434: ~0.10%
- 1027516: ~0.10%
- 1027703: ~0.10%
- 1028119: ~0.10%
- 1028642: ~0.10%
- 1031554: ~0.10%
- 1032300: ~0.10%
- 1033639: ~0.10%
- 1033660: ~0.10%
- 1034832: ~0.10%
- 1035274: ~0.10%
- 1037432: ~0.10%
- 1037536: ~0.10%
- 1037759: ~0.10%
- 1039860: ~0.10%
- 1041131: ~0.10%
- 1041892: ~0.10%
- 1043066: ~0.10%
- 1044326: ~0.10%
- 1044905: ~0.10%
- 1047848: ~0.10%
- 1048534: ~0.10%
- 1049477: ~0.10%
- 1050531: ~0.10%
- 1052073: ~0.10%
- 1052617: ~0.10%
- 1054049: ~0.10%
- 1055142: ~0.10%
- 1056933: ~0.10%
- 1057358: ~0.10%
- 1057911: ~0.10%
- 1061411: ~0.10%
- 1062328: ~0.10%
- 1062485: ~0.10%
- 1062534: ~0.10%
- 1062794: ~0.10%
- 1063269: ~0.10%
- 1063467: ~0.10%
- 1064568: ~0.10%
- 1064868: ~0.10%
- 1065481: ~0.10%
- 1065565: ~0.10%
- 1067970: ~0.10%
- 1068014: ~0.10%
- 1070203: ~0.10%
- 1070708: ~0.10%
- 1072038: ~0.10%
- 1072214: ~0.10%
- 1074885: ~0.10%
- 1075308: ~0.10%
- 1078872: ~0.10%
- 1078979: ~0.10%
- 1079266: ~0.10%
- 1079736: ~0.10%
- 1080075: ~0.10%
- 1081716: ~0.10%
- 1137391: ~0.10%
- 1138530: ~0.10%
- 1139697: ~0.10%
- 1140119: ~0.10%
- 1140869: ~0.10%
- 1141527: ~0.10%
- 1144693: ~0.10%
- 1145425: ~0.10%
- 1149162: ~0.10%
- 1149207: ~0.10%
- 1150086: ~0.10%
- 1150398: ~0.10%
- 1150731: ~0.10%
- 1151256: ~0.10%
- 1151403: ~0.10%
- 1152236: ~0.10%
- 1153693: ~0.10%
- 1155859: ~0.10%
- 1156918: ~0.10%
- 1158007: ~0.10%
- 1158559: ~0.10%
- 1158952: ~0.10%
- 1159165: ~0.10%
- 1161242: ~0.10%
- 1163227: ~0.10%
- 1166023: ~0.10%
- 1166231: ~0.10%
- 1167002: ~0.10%
- 1169844: ~0.10%
- 1170663: ~0.10%
- 1171580: ~0.10%
- 1172072: ~0.10%
- 1172083: ~0.10%
- 1173371: ~0.10%
- 1173809: ~0.10%
- 1174049: ~0.10%
- 1175044: ~0.10%
- 1175745: ~0.10%
- 1176061: ~0.10%
- 1176414: ~0.10%
- 1176993: ~0.10%
- 1177449: ~0.10%
- 1178311: ~0.10%
- 1179029: ~0.10%
- 1179069: ~0.10%
- 1180579: ~0.10%
- 1181077: ~0.10%
- 1183293: ~0.10%
- 1184313: ~0.10%
- 1185090: ~0.10%
- 1185669: ~0.10%
| | |
+* 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
+- `num_train_epochs`: 1
+- `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`: 1
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: True
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: 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 |
+|:------:|:-----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
+| 0.0030 | 100 | 0.0366 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0060 | 200 | 0.0325 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0090 | 300 | 0.0308 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0120 | 400 | 0.0277 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0150 | 500 | 0.0268 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0180 | 600 | 0.0264 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0210 | 700 | 0.0254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0240 | 800 | 0.0247 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0270 | 900 | 0.0246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0300 | 1000 | 0.0244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0330 | 1100 | 0.0242 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0360 | 1200 | 0.023 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0390 | 1300 | 0.0233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0420 | 1400 | 0.0224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0450 | 1500 | 0.0233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0480 | 1600 | 0.0221 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0510 | 1700 | 0.0222 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0540 | 1800 | 0.0216 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0570 | 1900 | 0.0215 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0600 | 2000 | 0.0211 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0630 | 2100 | 0.021 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0660 | 2200 | 0.0208 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0690 | 2300 | 0.0205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0720 | 2400 | 0.0207 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0750 | 2500 | 0.0204 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0780 | 2600 | 0.0201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0810 | 2700 | 0.0199 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0840 | 2800 | 0.0197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0870 | 2900 | 0.0201 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0900 | 3000 | 0.0187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0930 | 3100 | 0.0198 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0960 | 3200 | 0.0192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.0990 | 3300 | 0.0194 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1020 | 3400 | 0.0188 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1050 | 3500 | 0.0193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1080 | 3600 | 0.0193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1110 | 3700 | 0.0186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1140 | 3800 | 0.0187 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1170 | 3900 | 0.0186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1200 | 4000 | 0.0186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1230 | 4100 | 0.0181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1260 | 4200 | 0.0181 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1290 | 4300 | 0.0182 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1320 | 4400 | 0.0184 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1350 | 4500 | 0.0178 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1380 | 4600 | 0.017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1410 | 4700 | 0.0175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1440 | 4800 | 0.0174 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1470 | 4900 | 0.0176 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1500 | 5000 | 0.0177 | 0.2830 | 0.5426 | 0.8641 | 0.4244 | 0.8531 | 0.6244 | 0.3106 | 0.5798 | 0.9230 | 0.3109 | 0.3815 | 0.7461 | 0.5805 | 0.5711 |
+| 0.1530 | 5100 | 0.0172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1560 | 5200 | 0.017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1590 | 5300 | 0.0173 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1620 | 5400 | 0.0169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1650 | 5500 | 0.017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1680 | 5600 | 0.0168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1710 | 5700 | 0.0171 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1740 | 5800 | 0.0165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1770 | 5900 | 0.0165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1801 | 6000 | 0.0169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1831 | 6100 | 0.0167 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1861 | 6200 | 0.0169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1891 | 6300 | 0.0163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1921 | 6400 | 0.0165 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1951 | 6500 | 0.0163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.1981 | 6600 | 0.0166 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2011 | 6700 | 0.0163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2041 | 6800 | 0.0162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2071 | 6900 | 0.0163 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2101 | 7000 | 0.016 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2131 | 7100 | 0.0164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2161 | 7200 | 0.0162 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2191 | 7300 | 0.0158 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2221 | 7400 | 0.0156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2251 | 7500 | 0.0154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2281 | 7600 | 0.016 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2311 | 7700 | 0.0157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2341 | 7800 | 0.0161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2371 | 7900 | 0.0157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2401 | 8000 | 0.0156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2431 | 8100 | 0.0156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2461 | 8200 | 0.0156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2491 | 8300 | 0.0154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2521 | 8400 | 0.0153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2551 | 8500 | 0.0157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2581 | 8600 | 0.0155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2611 | 8700 | 0.0156 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2641 | 8800 | 0.0155 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2671 | 8900 | 0.0154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2701 | 9000 | 0.0152 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2731 | 9100 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2761 | 9200 | 0.0154 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2791 | 9300 | 0.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2821 | 9400 | 0.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2851 | 9500 | 0.0153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2881 | 9600 | 0.0146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2911 | 9700 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2941 | 9800 | 0.015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.2971 | 9900 | 0.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3001 | 10000 | 0.0146 | 0.2674 | 0.5466 | 0.8739 | 0.4547 | 0.8499 | 0.5933 | 0.3170 | 0.6256 | 0.9321 | 0.3137 | 0.3855 | 0.7387 | 0.5768 | 0.5750 |
+| 0.3031 | 10100 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3061 | 10200 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3091 | 10300 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3121 | 10400 | 0.0148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3151 | 10500 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3181 | 10600 | 0.0143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3211 | 10700 | 0.0144 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3241 | 10800 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3271 | 10900 | 0.0148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3301 | 11000 | 0.015 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3331 | 11100 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3361 | 11200 | 0.0148 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3391 | 11300 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3421 | 11400 | 0.014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3451 | 11500 | 0.0146 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3481 | 11600 | 0.0143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3511 | 11700 | 0.0142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3541 | 11800 | 0.0141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3571 | 11900 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3601 | 12000 | 0.0141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3631 | 12100 | 0.0142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3661 | 12200 | 0.0143 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3691 | 12300 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3721 | 12400 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3751 | 12500 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3781 | 12600 | 0.0142 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3811 | 12700 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3841 | 12800 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3871 | 12900 | 0.014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3901 | 13000 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3931 | 13100 | 0.014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3961 | 13200 | 0.0141 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.3991 | 13300 | 0.014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4021 | 13400 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4051 | 13500 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4081 | 13600 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4111 | 13700 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4141 | 13800 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4171 | 13900 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4201 | 14000 | 0.0139 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4231 | 14100 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4261 | 14200 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4291 | 14300 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4321 | 14400 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4351 | 14500 | 0.0133 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4381 | 14600 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4411 | 14700 | 0.0134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4441 | 14800 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4471 | 14900 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4501 | 15000 | 0.0131 | 0.2885 | 0.5666 | 0.8718 | 0.4695 | 0.8453 | 0.6405 | 0.3128 | 0.6500 | 0.9257 | 0.3081 | 0.3923 | 0.7361 | 0.5852 | 0.5840 |
+| 0.4531 | 15100 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4561 | 15200 | 0.0137 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4591 | 15300 | 0.0134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4621 | 15400 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4651 | 15500 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4681 | 15600 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4711 | 15700 | 0.0134 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4741 | 15800 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4771 | 15900 | 0.0136 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4801 | 16000 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4831 | 16100 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4861 | 16200 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4891 | 16300 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4921 | 16400 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4951 | 16500 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.4981 | 16600 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5011 | 16700 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5041 | 16800 | 0.0131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5071 | 16900 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5101 | 17000 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5131 | 17100 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5161 | 17200 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5191 | 17300 | 0.013 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5221 | 17400 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5251 | 17500 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5281 | 17600 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5311 | 17700 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5341 | 17800 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5372 | 17900 | 0.0128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5402 | 18000 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5432 | 18100 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5462 | 18200 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5492 | 18300 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5522 | 18400 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5552 | 18500 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5582 | 18600 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5612 | 18700 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5642 | 18800 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5672 | 18900 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5702 | 19000 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5732 | 19100 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5762 | 19200 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5792 | 19300 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5822 | 19400 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5852 | 19500 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5882 | 19600 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5912 | 19700 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5942 | 19800 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.5972 | 19900 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6002 | 20000 | 0.0124 | 0.2807 | 0.5738 | 0.8748 | 0.4586 | 0.8533 | 0.6174 | 0.3227 | 0.6215 | 0.9219 | 0.3104 | 0.4132 | 0.7348 | 0.5696 | 0.5810 |
+| 0.6032 | 20100 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6062 | 20200 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6092 | 20300 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6122 | 20400 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6152 | 20500 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6182 | 20600 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6212 | 20700 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6242 | 20800 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6272 | 20900 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6302 | 21000 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6332 | 21100 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6362 | 21200 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6392 | 21300 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6422 | 21400 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6452 | 21500 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6482 | 21600 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6512 | 21700 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6542 | 21800 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6572 | 21900 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6602 | 22000 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6632 | 22100 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6662 | 22200 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6692 | 22300 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6722 | 22400 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6752 | 22500 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6782 | 22600 | 0.0125 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6812 | 22700 | 0.0127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6842 | 22800 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6872 | 22900 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6902 | 23000 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6932 | 23100 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6962 | 23200 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.6992 | 23300 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7022 | 23400 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7052 | 23500 | 0.0124 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7082 | 23600 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7112 | 23700 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7142 | 23800 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7172 | 23900 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7202 | 24000 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7232 | 24100 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7262 | 24200 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7292 | 24300 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7322 | 24400 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7352 | 24500 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7382 | 24600 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7412 | 24700 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7442 | 24800 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7472 | 24900 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7502 | 25000 | 0.0118 | 0.2940 | 0.5881 | 0.8842 | 0.4741 | 0.8653 | 0.6288 | 0.3414 | 0.6734 | 0.9000 | 0.3151 | 0.4361 | 0.7387 | 0.5610 | 0.5923 |
+| 0.7532 | 25100 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7562 | 25200 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7592 | 25300 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7622 | 25400 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7652 | 25500 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7682 | 25600 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7712 | 25700 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7742 | 25800 | 0.0113 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7772 | 25900 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7802 | 26000 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7832 | 26100 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7862 | 26200 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7892 | 26300 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7922 | 26400 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7952 | 26500 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.7982 | 26600 | 0.0113 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8012 | 26700 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8042 | 26800 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8072 | 26900 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8102 | 27000 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8132 | 27100 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8162 | 27200 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8192 | 27300 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8222 | 27400 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8252 | 27500 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8282 | 27600 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8312 | 27700 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8342 | 27800 | 0.0112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8372 | 27900 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8402 | 28000 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8432 | 28100 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8462 | 28200 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8492 | 28300 | 0.0116 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8522 | 28400 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8552 | 28500 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8582 | 28600 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8612 | 28700 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8642 | 28800 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8672 | 28900 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8702 | 29000 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8732 | 29100 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8762 | 29200 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8792 | 29300 | 0.0113 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8822 | 29400 | 0.0114 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8852 | 29500 | 0.0119 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8882 | 29600 | 0.0118 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8912 | 29700 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8943 | 29800 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.8973 | 29900 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
+| 0.9003 | 30000 | 0.0113 | 0.3059 | 0.5944 | 0.8864 | 0.4729 | 0.8634 | 0.6351 | 0.3302 | 0.6699 | 0.9086 | 0.3142 | 0.4545 | 0.7357 | 0.5613 | 0.5948 |
+
+