---
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:640000
- loss:Distillation
base_model: Alibaba-NLP/gte-modernbert-base
pipeline_tag: sentence-similarity
license: apache-2.0
library_name: PyLate
metrics:
- MaxSim_accuracy@1
- MaxSim_accuracy@3
- MaxSim_accuracy@5
- MaxSim_accuracy@10
- MaxSim_precision@1
- MaxSim_precision@3
- MaxSim_precision@5
- MaxSim_precision@10
- MaxSim_recall@1
- MaxSim_recall@3
- MaxSim_recall@5
- MaxSim_recall@10
- MaxSim_ndcg@10
- MaxSim_mrr@10
- MaxSim_map@100
model-index:
- name: PyLate model based on Alibaba-NLP/gte-modernbert-base
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.36
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.62
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.78
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.86
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.36
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.20799999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.12799999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.18333333333333332
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.289
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.41566666666666663
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.49566666666666664
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.41477895139843374
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.526579365079365
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.33473812643311207
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: MaxSim_accuracy@1
value: 0.88
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.94
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.96
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.98
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.88
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.7133333333333334
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6560000000000001
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.572
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.11798996781634019
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.23074158968531658
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2961618059276896
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.4145532152487909
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7295518860528665
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9168571428571428
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5883869727264871
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: MaxSim_accuracy@1
value: 0.92
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.98
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.98
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.92
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.35999999999999993
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.21599999999999994
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10999999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.8566666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.96
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.96
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.98
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9451911044041129
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9522222222222223
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9270501207729468
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: MaxSim_accuracy@1
value: 0.56
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.66
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.74
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.56
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.32666666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.25599999999999995
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.15199999999999997
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.30924603174603177
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.47840476190476194
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.5751746031746031
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6411984126984127
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5669909336903424
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6359444444444444
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5031998196513616
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: MaxSim_accuracy@1
value: 0.92
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 1.0
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1.0
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.92
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.58
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.35999999999999993
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18599999999999994
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.46
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.87
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.93
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9011747095216048
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.96
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8591508921772081
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: MaxSim_accuracy@1
value: 0.54
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.74
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.92
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.54
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.22666666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.14800000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.092
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.54
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.68
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.74
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.92
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7088869908160952
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6446507936507936
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6496349206349206
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: MaxSim_accuracy@1
value: 0.56
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.74
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.76
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.56
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.43333333333333335
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.39199999999999996
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.304
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.06640185752724687
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.10198877096622012
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.12839743828750172
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.15658989769166
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3957047406068243
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.627
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.1917924344366858
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: MaxSim_accuracy@1
value: 0.64
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.82
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.86
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.64
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2866666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.17999999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.1
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.61
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.78
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.82
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.88
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.7645227466201794
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7390000000000001
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7239323294755705
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: MaxSim_accuracy@1
value: 0.96
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 1.0
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 1.0
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 1.0
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.96
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.4
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.25599999999999995
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.13399999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.8473333333333334
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.9453333333333334
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.9693333333333334
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.9893333333333334
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.9691448095973965
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.9766666666666667
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.9551871794871795
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: MaxSim_accuracy@1
value: 0.48
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.74
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.78
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.84
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.48
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3999999999999999
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.292
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.19399999999999995
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.10066666666666667
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.24666666666666665
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.29966666666666664
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.39666666666666667
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3986767701602276
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6137222222222222
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3163385555719993
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: MaxSim_accuracy@1
value: 0.3
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.62
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.7
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.82
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.3
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.20666666666666667
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.14
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08199999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.3
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.62
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.7
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.82
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5609089627577635
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4774603174603175
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4824361431413148
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: MaxSim_accuracy@1
value: 0.74
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.86
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.94
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.74
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.19599999999999995
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.10399999999999998
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.715
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.83
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.885
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.93
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8371556505161787
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8116666666666668
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.8048798701298702
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: MaxSim_accuracy@1
value: 0.7755102040816326
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9387755102040817
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.9795918367346939
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9795918367346939
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.7755102040816326
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6598639455782314
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.6571428571428573
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.5183673469387755
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.05176652252904378
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.13618168510556633
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2193408037582337
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.33397423594107617
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5926586898856947
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8629251700680272
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.42574993112112997
name: Maxsim Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.6642700156985872
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.8106750392464678
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.8584301412872841
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.9076609105180532
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.6642700156985872
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.39434850863422294
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.3043956043956044
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.20587441130298273
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.3968003368937432
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.5514089852047589
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.6083647167549766
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.6836909560189698
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6757959189252093
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.7495919239490669
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5971136381353681
name: Maxsim Map@100
---
# PyLate model based on Alibaba-NLP/gte-modernbert-base
This is a [PyLate](https://github.com/lightonai/pylate) model trained 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:** [Alibaba-NLP/gte-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-modernbert-base)
- **Document Length:** 300 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 dimensions
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [ms-marco-en-bge-gemma](https://huggingface.co/datasets/lightonai/ms-marco-en-bge-gemma)
- **Language:** English
- **License:** Apache 2.0
### Document length
GTE-ModernColBERT has been trained with knowledge distillation on MS MARCO with a document length of 300 tokens, explaining its default value for documents length.
However, as illustrated in the ModernBERT paper, ColBERT models can generalize to documents lengths way beyond their training length and GTE-ModernColBERT actually yields results way above SOTA in long-context embedding benchmarks, see [LongEmbed results](#longembed-benchmark).
Simply change adapt the document length parameter to your needs when loading the model:
```python
model = models.ColBERT(
model_name_or_path=lightonai/GTE-ModernColBERT-v1,
document_length=8192,
)
```
ModernBERT itself has only been trained on 8K context length, but it seems that GTE-ModernColBERT can generalize to even bigger context sizes, though it is not guaranteed so please perform your own benches!
### 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': 768, '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
#### BEIR Benchmark
GTE-ModernColBERT is the first model to outpeform ColBERT-small on the BEIR benchmark.
As reproduction in the IR domain is challenging, we worked closely with Benjamin Clavié, the author of ColBERT-small to reproduce the evaluation setup of this model. Despite all these efforts and reducing to the maximum the difference in scores in most of the datasets, some are still a bit different.
For this reason, we also report the results of ColBERT-small in the same setup we used to evaluate GTE-ModernColBERT for completness and fair comparison.
| Model | Average | FiQA2018 | NFCorpus | TREC-COVID | Touche2020 | ArguAna | QuoraRetrieval | SCIDOCS | SciFact | NQ | ClimateFEVER | HotpotQA | DBPedia | CQADupstack | FEVER | MSMARCO |
|--------------------------|-------------|----------|----------|------------|------------|---------|----------------|---------|---------|-------|--------------|----------|---------|-------------|-------|---------|
| GTE-ModernColBERT | **54.89** | **48.51** | 37.93 | 83.59 | **31.23** | 48.51 | 86.61 | **19.06** | **76.34** | 61.8 | 30.62 | **77.32** | **48.03** | **41** | 87.44 | **45.32** |
| ColBERT-small (reported) | 53.79 | 41.15 | 37.3 | **84.59** | 25.69 | **50.09** | 87.72 | 18.42 | 74.77 | 59.1 | **33.07** | 76.11 | 45.58 | 38.75 | **90.96** | 43.5 |
| JinaColBERT-v2 | | 40.8 | 34.6 | 83.4 | 27.4 | 36.6 | **88.7** | 18.6 | 67.8 | **64** | 23.9 | 76.6 | 47.1 | | 80.5 | |
| ColBERT-small (rerunned) | 53.35 | 41.01 | 36.86 | 83.14 | 24.95 | 46.76 | 87.89 | 18.72 | 74.02 | 59.42 | 32.83 | 76.88 | 46.36 | 39.36 | 88.66 | 43.44 |
#### LongEmbed Benchmark
GTE-ModernColBERT has been trained with knowledge distillation on MS MARCO with a document length of 300 tokens, explaining its default value for documents length.
However, as illustrated in the ModernBERT paper, ColBERT models can generalize to documents lengths way beyond their training length and GTE-ModernColBERT actually yields results way above SOTA (almost 10 points above previous SOTA) in long-context embedding benchmark:
| Model | Mean | LEMBNarrativeQARetrieval | LEMBNeedleRetrieval | LEMBPasskeyRetrieval | LEMBQMSumRetrieval | LEMBSummScreenFDRetrieval | LEMBWikimQARetrieval |
|-----------------------------------------------|-----------|-------------------------|---------------------|----------------------|---------------------|---------------------------|----------------------|
| GTE-ModernColBERT (with 32k document length) | **88.39** | **78.82** | **92.5** | 92 | **72.17** | 94.98 | **99.87** |
| voyage-multilingual-2 | 79.17| 64.694 | 75.25 | **97** | 51.495 | **99.105** | 87.489 |
| inf-retriever-v1 | 73.19 | 60.702 | 61.5 | 78.75 | 55.072 | 97.387 | 85.751 |
| snowflake-arctic-embed-l-v2,0 | 63.73 | 43.632 | 50.25 | 77.25 | 40.04 | 96.383 | 74.843 |
| gte-multilingual-base | 62.12| 52.358 | 42.25 | 55.5 | 43.033 | 95.499 | 84.078 |
| jasper_en_vision_language_v1 | 60.93 | 37.928 | 55 | 62.25 | 41.186 | 97.206 | 72.025 |
| bge-m3 | 58.73 | 45.761 | 40.25 | 59 | 35.543 | 94.089 | 77.726 |
| jina-embeddings-v3 | 55.66| 34.297 | 64 | 38 | 39.337 | 92.334 | 66.018 |
| e5-base-4k | 54.51| 30.03 | 37.75 | 65.25 | 31.268 | 93.868 | 68.875 |
| gte-Qwen2-7B-instruct | 47.24| 45.46 | 31 | 38.5 | 31.272 | 76.08 | 61.151 |
ModernBERT itself has only been trained on 8K context length, but it seems that GTE-ModernColBERT can generalize to even bigger context sizes, though it is not guaranteed so please perform your own benches!
#### PyLate Information Retrieval
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| MaxSim_accuracy@1 | 0.36 | 0.88 | 0.92 | 0.56 | 0.92 | 0.54 | 0.56 | 0.64 | 0.96 | 0.48 | 0.3 | 0.74 | 0.7755 |
| MaxSim_accuracy@3 | 0.62 | 0.94 | 0.98 | 0.66 | 1.0 | 0.68 | 0.68 | 0.82 | 1.0 | 0.74 | 0.62 | 0.86 | 0.9388 |
| MaxSim_accuracy@5 | 0.78 | 0.96 | 0.98 | 0.74 | 1.0 | 0.74 | 0.74 | 0.86 | 1.0 | 0.78 | 0.7 | 0.9 | 0.9796 |
| MaxSim_accuracy@10 | 0.86 | 0.98 | 1.0 | 0.8 | 1.0 | 0.92 | 0.76 | 0.9 | 1.0 | 0.84 | 0.82 | 0.94 | 0.9796 |
| MaxSim_precision@1 | 0.36 | 0.88 | 0.92 | 0.56 | 0.92 | 0.54 | 0.56 | 0.64 | 0.96 | 0.48 | 0.3 | 0.74 | 0.7755 |
| MaxSim_precision@3 | 0.2333 | 0.7133 | 0.36 | 0.3267 | 0.58 | 0.2267 | 0.4333 | 0.2867 | 0.4 | 0.4 | 0.2067 | 0.3 | 0.6599 |
| MaxSim_precision@5 | 0.208 | 0.656 | 0.216 | 0.256 | 0.36 | 0.148 | 0.392 | 0.18 | 0.256 | 0.292 | 0.14 | 0.196 | 0.6571 |
| MaxSim_precision@10 | 0.128 | 0.572 | 0.11 | 0.152 | 0.186 | 0.092 | 0.304 | 0.1 | 0.134 | 0.194 | 0.082 | 0.104 | 0.5184 |
| MaxSim_recall@1 | 0.1833 | 0.118 | 0.8567 | 0.3092 | 0.46 | 0.54 | 0.0664 | 0.61 | 0.8473 | 0.1007 | 0.3 | 0.715 | 0.0518 |
| MaxSim_recall@3 | 0.289 | 0.2307 | 0.96 | 0.4784 | 0.87 | 0.68 | 0.102 | 0.78 | 0.9453 | 0.2467 | 0.62 | 0.83 | 0.1362 |
| MaxSim_recall@5 | 0.4157 | 0.2962 | 0.96 | 0.5752 | 0.9 | 0.74 | 0.1284 | 0.82 | 0.9693 | 0.2997 | 0.7 | 0.885 | 0.2193 |
| MaxSim_recall@10 | 0.4957 | 0.4146 | 0.98 | 0.6412 | 0.93 | 0.92 | 0.1566 | 0.88 | 0.9893 | 0.3967 | 0.82 | 0.93 | 0.334 |
| **MaxSim_ndcg@10** | **0.4148** | **0.7296** | **0.9452** | **0.567** | **0.9012** | **0.7089** | **0.3957** | **0.7645** | **0.9691** | **0.3987** | **0.5609** | **0.8372** | **0.5927** |
| MaxSim_mrr@10 | 0.5266 | 0.9169 | 0.9522 | 0.6359 | 0.96 | 0.6447 | 0.627 | 0.739 | 0.9767 | 0.6137 | 0.4775 | 0.8117 | 0.8629 |
| MaxSim_map@100 | 0.3347 | 0.5884 | 0.9271 | 0.5032 | 0.8592 | 0.6496 | 0.1918 | 0.7239 | 0.9552 | 0.3163 | 0.4824 | 0.8049 | 0.4257 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with pylate.evaluation.nano_beir_evaluator.NanoBEIREvaluator
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.6643 |
| MaxSim_accuracy@3 | 0.8107 |
| MaxSim_accuracy@5 | 0.8584 |
| MaxSim_accuracy@10 | 0.9077 |
| MaxSim_precision@1 | 0.6643 |
| MaxSim_precision@3 | 0.3943 |
| MaxSim_precision@5 | 0.3044 |
| MaxSim_precision@10 | 0.2059 |
| MaxSim_recall@1 | 0.3968 |
| MaxSim_recall@3 | 0.5514 |
| MaxSim_recall@5 | 0.6084 |
| MaxSim_recall@10 | 0.6837 |
| **MaxSim_ndcg@10** | **0.6758** |
| MaxSim_mrr@10 | 0.7496 |
| MaxSim_map@100 | 0.5971 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `learning_rate`: 3e-05
- `bf16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 6
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | NanoClimateFEVER_MaxSim_ndcg@10 | NanoDBPedia_MaxSim_ndcg@10 | NanoFEVER_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNFCorpus_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoQuoraRetrieval_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoArguAna_MaxSim_ndcg@10 | NanoSciFact_MaxSim_ndcg@10 | NanoTouche2020_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 |
|:------:|:-----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.004 | 20 | 0.0493 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.008 | 40 | 0.0434 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.012 | 60 | 0.0324 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.016 | 80 | 0.0238 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.02 | 100 | 0.0202 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.024 | 120 | 0.0186 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.028 | 140 | 0.0172 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.032 | 160 | 0.0164 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.036 | 180 | 0.0157 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.04 | 200 | 0.0153 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.044 | 220 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.048 | 240 | 0.014 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.052 | 260 | 0.0138 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.056 | 280 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.06 | 300 | 0.0132 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.064 | 320 | 0.0129 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.068 | 340 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.072 | 360 | 0.0123 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.076 | 380 | 0.0122 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.08 | 400 | 0.012 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.084 | 420 | 0.0121 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.088 | 440 | 0.0115 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.092 | 460 | 0.0113 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.096 | 480 | 0.0112 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1 | 500 | 0.0111 | 0.3085 | 0.6309 | 0.9206 | 0.5303 | 0.8618 | 0.6893 | 0.3703 | 0.7163 | 0.9548 | 0.3885 | 0.4682 | 0.7930 | 0.5982 | 0.6331 |
| 0.104 | 520 | 0.0109 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.108 | 540 | 0.0109 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.112 | 560 | 0.0109 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.116 | 580 | 0.0105 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.12 | 600 | 0.0102 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.124 | 620 | 0.0104 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.128 | 640 | 0.0103 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.132 | 660 | 0.01 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.136 | 680 | 0.0101 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.14 | 700 | 0.0098 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.144 | 720 | 0.0097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.148 | 740 | 0.0097 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.152 | 760 | 0.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.156 | 780 | 0.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.16 | 800 | 0.0094 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.164 | 820 | 0.0096 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.168 | 840 | 0.0095 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.172 | 860 | 0.0093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.176 | 880 | 0.0092 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.18 | 900 | 0.0093 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.184 | 920 | 0.009 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.188 | 940 | 0.009 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.192 | 960 | 0.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.196 | 980 | 0.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2 | 1000 | 0.0089 | 0.3148 | 0.6586 | 0.9335 | 0.5374 | 0.8810 | 0.6805 | 0.3746 | 0.7368 | 0.9486 | 0.3955 | 0.4824 | 0.8219 | 0.6089 | 0.6442 |
| 0.204 | 1020 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.208 | 1040 | 0.0089 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.212 | 1060 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.216 | 1080 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.22 | 1100 | 0.0087 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.224 | 1120 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.228 | 1140 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.232 | 1160 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.236 | 1180 | 0.0084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.24 | 1200 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.244 | 1220 | 0.0085 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.248 | 1240 | 0.0084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.252 | 1260 | 0.0084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.256 | 1280 | 0.0081 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.26 | 1300 | 0.0083 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.264 | 1320 | 0.0084 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.268 | 1340 | 0.0082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.272 | 1360 | 0.0082 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.276 | 1380 | 0.008 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.28 | 1400 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.284 | 1420 | 0.0079 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.288 | 1440 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.292 | 1460 | 0.0081 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.296 | 1480 | 0.0081 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3 | 1500 | 0.0079 | 0.3510 | 0.6590 | 0.9285 | 0.5463 | 0.8893 | 0.6853 | 0.3800 | 0.7370 | 0.9513 | 0.3980 | 0.5268 | 0.8268 | 0.6130 | 0.6533 |
| 0.304 | 1520 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.308 | 1540 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.312 | 1560 | 0.0077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.316 | 1580 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.32 | 1600 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.324 | 1620 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.328 | 1640 | 0.0078 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.332 | 1660 | 0.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.336 | 1680 | 0.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.34 | 1700 | 0.0077 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.344 | 1720 | 0.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.348 | 1740 | 0.0074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.352 | 1760 | 0.0074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.356 | 1780 | 0.0075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.36 | 1800 | 0.0076 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.364 | 1820 | 0.0075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.368 | 1840 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.372 | 1860 | 0.0075 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.376 | 1880 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.38 | 1900 | 0.0074 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.384 | 1920 | 0.0072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.388 | 1940 | 0.0072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.392 | 1960 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.396 | 1980 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4 | 2000 | 0.0071 | 0.3551 | 0.6807 | 0.9311 | 0.5340 | 0.8951 | 0.7019 | 0.3767 | 0.7460 | 0.9559 | 0.3912 | 0.5121 | 0.8245 | 0.6058 | 0.6546 |
| 0.404 | 2020 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.408 | 2040 | 0.0072 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.412 | 2060 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.416 | 2080 | 0.0073 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.42 | 2100 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.424 | 2120 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.428 | 2140 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.432 | 2160 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.436 | 2180 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.44 | 2200 | 0.007 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.444 | 2220 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.448 | 2240 | 0.0071 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.452 | 2260 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.456 | 2280 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.46 | 2300 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.464 | 2320 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.468 | 2340 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.472 | 2360 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.476 | 2380 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.48 | 2400 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.484 | 2420 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.488 | 2440 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.492 | 2460 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.496 | 2480 | 0.0069 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5 | 2500 | 0.0068 | 0.3647 | 0.6883 | 0.9435 | 0.5624 | 0.8946 | 0.7065 | 0.3815 | 0.7709 | 0.9658 | 0.3993 | 0.5631 | 0.8371 | 0.6076 | 0.6681 |
| 0.504 | 2520 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.508 | 2540 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.512 | 2560 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.516 | 2580 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.52 | 2600 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.524 | 2620 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.528 | 2640 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.532 | 2660 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.536 | 2680 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.54 | 2700 | 0.0068 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.544 | 2720 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.548 | 2740 | 0.0067 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.552 | 2760 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.556 | 2780 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.56 | 2800 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.564 | 2820 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.568 | 2840 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.572 | 2860 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.576 | 2880 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.58 | 2900 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.584 | 2920 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.588 | 2940 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.592 | 2960 | 0.0066 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.596 | 2980 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6 | 3000 | 0.0064 | 0.3585 | 0.7081 | 0.9409 | 0.5474 | 0.8915 | 0.7037 | 0.3796 | 0.7763 | 0.9540 | 0.4038 | 0.5628 | 0.8424 | 0.6042 | 0.6672 |
| 0.604 | 3020 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.608 | 3040 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.612 | 3060 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.616 | 3080 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.62 | 3100 | 0.0065 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.624 | 3120 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.628 | 3140 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.632 | 3160 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.636 | 3180 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.64 | 3200 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.644 | 3220 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.648 | 3240 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.652 | 3260 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.656 | 3280 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.66 | 3300 | 0.0064 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.664 | 3320 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.668 | 3340 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.672 | 3360 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.676 | 3380 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.68 | 3400 | 0.0063 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.684 | 3420 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.688 | 3440 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.692 | 3460 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.696 | 3480 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7 | 3500 | 0.0061 | 0.3783 | 0.7080 | 0.9441 | 0.5603 | 0.8902 | 0.7022 | 0.3824 | 0.7780 | 0.9612 | 0.3995 | 0.5414 | 0.8450 | 0.6049 | 0.6689 |
| 0.704 | 3520 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.708 | 3540 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.712 | 3560 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.716 | 3580 | 0.0062 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.72 | 3600 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.724 | 3620 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.728 | 3640 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.732 | 3660 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.736 | 3680 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.74 | 3700 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.744 | 3720 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.748 | 3740 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.752 | 3760 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.756 | 3780 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.76 | 3800 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.764 | 3820 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.768 | 3840 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.772 | 3860 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.776 | 3880 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.78 | 3900 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.784 | 3920 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.788 | 3940 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.792 | 3960 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.796 | 3980 | 0.0061 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8 | 4000 | 0.0059 | 0.3820 | 0.7028 | 0.9441 | 0.5722 | 0.8890 | 0.7135 | 0.3825 | 0.7790 | 0.9659 | 0.4012 | 0.5425 | 0.8446 | 0.6085 | 0.6714 |
| 0.804 | 4020 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.808 | 4040 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.812 | 4060 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.816 | 4080 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.82 | 4100 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.824 | 4120 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.828 | 4140 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.832 | 4160 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.836 | 4180 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.84 | 4200 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.844 | 4220 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.848 | 4240 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.852 | 4260 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.856 | 4280 | 0.0057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.86 | 4300 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.864 | 4320 | 0.006 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.868 | 4340 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.872 | 4360 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.876 | 4380 | 0.0057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.88 | 4400 | 0.0059 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.884 | 4420 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.888 | 4440 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.892 | 4460 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.896 | 4480 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9 | 4500 | 0.0059 | 0.3703 | 0.7111 | 0.9441 | 0.5555 | 0.8886 | 0.7251 | 0.3934 | 0.7632 | 0.9671 | 0.4052 | 0.5390 | 0.8442 | 0.6068 | 0.6703 |
| 0.904 | 4520 | 0.0057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.908 | 4540 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.912 | 4560 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.916 | 4580 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.92 | 4600 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.924 | 4620 | 0.0057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.928 | 4640 | 0.0057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.932 | 4660 | 0.0057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.936 | 4680 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.94 | 4700 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.944 | 4720 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.948 | 4740 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.952 | 4760 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.956 | 4780 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.96 | 4800 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.964 | 4820 | 0.0057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.968 | 4840 | 0.0058 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.972 | 4860 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.976 | 4880 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.98 | 4900 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.984 | 4920 | 0.0057 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.988 | 4940 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.992 | 4960 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.996 | 4980 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0 | 5000 | 0.0056 | 0.3760 | 0.7131 | 0.9441 | 0.5522 | 0.8882 | 0.7157 | 0.3980 | 0.7739 | 0.9755 | 0.3987 | 0.5492 | 0.8501 | 0.5990 | 0.6718 |
| 1.004 | 5020 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.008 | 5040 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.012 | 5060 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.016 | 5080 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.02 | 5100 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.024 | 5120 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.028 | 5140 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.032 | 5160 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.036 | 5180 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.04 | 5200 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.044 | 5220 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.048 | 5240 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.052 | 5260 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.056 | 5280 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.06 | 5300 | 0.0056 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.064 | 5320 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.068 | 5340 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.072 | 5360 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.076 | 5380 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.08 | 5400 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.084 | 5420 | 0.0055 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.088 | 5440 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.092 | 5460 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.096 | 5480 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1 | 5500 | 0.0054 | 0.3777 | 0.7109 | 0.9367 | 0.5705 | 0.8919 | 0.7136 | 0.3956 | 0.7750 | 0.9590 | 0.3947 | 0.5336 | 0.8368 | 0.6016 | 0.6690 |
| 1.104 | 5520 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.108 | 5540 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.112 | 5560 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.116 | 5580 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.12 | 5600 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.124 | 5620 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1280 | 5640 | 0.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1320 | 5660 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1360 | 5680 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1400 | 5700 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.144 | 5720 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.148 | 5740 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.152 | 5760 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.156 | 5780 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.16 | 5800 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.164 | 5820 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.168 | 5840 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.172 | 5860 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.176 | 5880 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.18 | 5900 | 0.0053 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.184 | 5920 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.188 | 5940 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.192 | 5960 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.196 | 5980 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2 | 6000 | 0.0051 | 0.3998 | 0.7171 | 0.9446 | 0.5699 | 0.8899 | 0.7194 | 0.4022 | 0.7631 | 0.9674 | 0.3960 | 0.5395 | 0.8389 | 0.6025 | 0.6731 |
| 1.204 | 6020 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.208 | 6040 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.212 | 6060 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.216 | 6080 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.22 | 6100 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.224 | 6120 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.228 | 6140 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.232 | 6160 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.236 | 6180 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.24 | 6200 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.244 | 6220 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.248 | 6240 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.252 | 6260 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.256 | 6280 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.26 | 6300 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.264 | 6320 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.268 | 6340 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.272 | 6360 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.276 | 6380 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.28 | 6400 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.284 | 6420 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.288 | 6440 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.292 | 6460 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.296 | 6480 | 0.0052 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3 | 6500 | 0.005 | 0.4047 | 0.7137 | 0.9443 | 0.5690 | 0.8998 | 0.7120 | 0.3963 | 0.7689 | 0.9829 | 0.3956 | 0.5504 | 0.8363 | 0.5999 | 0.6749 |
| 1.304 | 6520 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.308 | 6540 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.312 | 6560 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.316 | 6580 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.32 | 6600 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.324 | 6620 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.328 | 6640 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.332 | 6660 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.336 | 6680 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.34 | 6700 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3440 | 6720 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3480 | 6740 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3520 | 6760 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3560 | 6780 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3600 | 6800 | 0.0051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3640 | 6820 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3680 | 6840 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3720 | 6860 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.376 | 6880 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.38 | 6900 | 0.005 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.384 | 6920 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.388 | 6940 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.392 | 6960 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.396 | 6980 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4 | 7000 | 0.0049 | 0.4084 | 0.7156 | 0.9441 | 0.5700 | 0.8978 | 0.7134 | 0.4024 | 0.7557 | 0.9758 | 0.3997 | 0.5521 | 0.8366 | 0.5919 | 0.6741 |
| 1.404 | 7020 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.408 | 7040 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.412 | 7060 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.416 | 7080 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.42 | 7100 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.424 | 7120 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.428 | 7140 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.432 | 7160 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.436 | 7180 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.44 | 7200 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.444 | 7220 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.448 | 7240 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.452 | 7260 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.456 | 7280 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.46 | 7300 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.464 | 7320 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.468 | 7340 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.472 | 7360 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.476 | 7380 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.48 | 7400 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.484 | 7420 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.488 | 7440 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.492 | 7460 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.496 | 7480 | 0.0049 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5 | 7500 | 0.0047 | 0.4125 | 0.7186 | 0.9443 | 0.5723 | 0.8974 | 0.6941 | 0.3962 | 0.7676 | 0.9677 | 0.3990 | 0.5455 | 0.8433 | 0.5945 | 0.6733 |
| 1.504 | 7520 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.508 | 7540 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.512 | 7560 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.516 | 7580 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.52 | 7600 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.524 | 7620 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.528 | 7640 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.532 | 7660 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.536 | 7680 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.54 | 7700 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.544 | 7720 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.548 | 7740 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.552 | 7760 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.556 | 7780 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.56 | 7800 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.564 | 7820 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.568 | 7840 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.572 | 7860 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.576 | 7880 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.58 | 7900 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.584 | 7920 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.588 | 7940 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.592 | 7960 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.596 | 7980 | 0.0048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6 | 8000 | 0.0046 | 0.4036 | 0.7208 | 0.9441 | 0.5737 | 0.8961 | 0.7160 | 0.3942 | 0.7609 | 0.9715 | 0.3936 | 0.5534 | 0.8419 | 0.6009 | 0.6747 |
| 1.604 | 8020 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.608 | 8040 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.612 | 8060 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.616 | 8080 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.62 | 8100 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.624 | 8120 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6280 | 8140 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6320 | 8160 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6360 | 8180 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6400 | 8200 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6440 | 8220 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6480 | 8240 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6520 | 8260 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6560 | 8280 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6600 | 8300 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6640 | 8320 | 0.0047 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6680 | 8340 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6720 | 8360 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6760 | 8380 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6800 | 8400 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6840 | 8420 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.688 | 8440 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.692 | 8460 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.696 | 8480 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7 | 8500 | 0.0046 | 0.4047 | 0.7210 | 0.9443 | 0.5729 | 0.8953 | 0.7038 | 0.3987 | 0.7716 | 0.9685 | 0.3912 | 0.5620 | 0.8444 | 0.6059 | 0.6757 |
| 1.704 | 8520 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.708 | 8540 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.712 | 8560 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.716 | 8580 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.72 | 8600 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.724 | 8620 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.728 | 8640 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.732 | 8660 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.736 | 8680 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.74 | 8700 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.744 | 8720 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.748 | 8740 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.752 | 8760 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.756 | 8780 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.76 | 8800 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.764 | 8820 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.768 | 8840 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.772 | 8860 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.776 | 8880 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.78 | 8900 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.784 | 8920 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.788 | 8940 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.792 | 8960 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.796 | 8980 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8 | 9000 | 0.0045 | 0.4126 | 0.7250 | 0.9443 | 0.5742 | 0.8927 | 0.7178 | 0.3952 | 0.7688 | 0.9681 | 0.4031 | 0.5558 | 0.8451 | 0.6057 | 0.6776 |
| 1.804 | 9020 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.808 | 9040 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.812 | 9060 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8160 | 9080 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8200 | 9100 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8240 | 9120 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8280 | 9140 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8320 | 9160 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8360 | 9180 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8400 | 9200 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8440 | 9220 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8480 | 9240 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8520 | 9260 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8560 | 9280 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8600 | 9300 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8640 | 9320 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8680 | 9340 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8720 | 9360 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.876 | 9380 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.88 | 9400 | 0.0046 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.884 | 9420 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.888 | 9440 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.892 | 9460 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.896 | 9480 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9 | 9500 | 0.0045 | 0.4140 | 0.7249 | 0.9452 | 0.5728 | 0.8944 | 0.7147 | 0.3917 | 0.7648 | 0.9679 | 0.4018 | 0.5640 | 0.8311 | 0.6013 | 0.6760 |
| 1.904 | 9520 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.908 | 9540 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.912 | 9560 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.916 | 9580 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.92 | 9600 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.924 | 9620 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.928 | 9640 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.932 | 9660 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.936 | 9680 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.94 | 9700 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.944 | 9720 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.948 | 9740 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.952 | 9760 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.956 | 9780 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.96 | 9800 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.964 | 9820 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.968 | 9840 | 0.0045 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.972 | 9860 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.976 | 9880 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.98 | 9900 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.984 | 9920 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.988 | 9940 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.992 | 9960 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.996 | 9980 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0 | 10000 | 0.0044 | 0.4098 | 0.7192 | 0.9443 | 0.5594 | 0.8970 | 0.7056 | 0.3964 | 0.7729 | 0.9709 | 0.4013 | 0.5623 | 0.8414 | 0.5960 | 0.6751 |
| 2.004 | 10020 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.008 | 10040 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.012 | 10060 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.016 | 10080 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.02 | 10100 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.024 | 10120 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.028 | 10140 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.032 | 10160 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.036 | 10180 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.04 | 10200 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.044 | 10220 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.048 | 10240 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.052 | 10260 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.056 | 10280 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.06 | 10300 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.064 | 10320 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.068 | 10340 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.072 | 10360 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.076 | 10380 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.08 | 10400 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.084 | 10420 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.088 | 10440 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.092 | 10460 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.096 | 10480 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1 | 10500 | 0.0043 | 0.4131 | 0.7248 | 0.9443 | 0.5673 | 0.9022 | 0.7101 | 0.3982 | 0.7736 | 0.9652 | 0.4000 | 0.5649 | 0.8322 | 0.6020 | 0.6768 |
| 2.104 | 10520 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.108 | 10540 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.112 | 10560 | 0.0044 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.116 | 10580 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.12 | 10600 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.124 | 10620 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.128 | 10640 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.132 | 10660 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.136 | 10680 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.14 | 10700 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.144 | 10720 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.148 | 10740 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.152 | 10760 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.156 | 10780 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.16 | 10800 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.164 | 10820 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.168 | 10840 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.172 | 10860 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.176 | 10880 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.18 | 10900 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.184 | 10920 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.188 | 10940 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.192 | 10960 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.196 | 10980 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2 | 11000 | 0.0042 | 0.4191 | 0.7187 | 0.9443 | 0.5652 | 0.9001 | 0.7071 | 0.4007 | 0.7631 | 0.9605 | 0.3964 | 0.5631 | 0.8363 | 0.5962 | 0.6747 |
| 2.204 | 11020 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.208 | 11040 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.212 | 11060 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.216 | 11080 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.22 | 11100 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.224 | 11120 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.228 | 11140 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.232 | 11160 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2360 | 11180 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.24 | 11200 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2440 | 11220 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.248 | 11240 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.252 | 11260 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2560 | 11280 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.26 | 11300 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2640 | 11320 | 0.0043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.268 | 11340 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2720 | 11360 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.276 | 11380 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2800 | 11400 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.284 | 11420 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.288 | 11440 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.292 | 11460 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.296 | 11480 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3 | 11500 | 0.0041 | 0.4129 | 0.7232 | 0.9443 | 0.5662 | 0.9020 | 0.7100 | 0.3936 | 0.7655 | 0.9750 | 0.3956 | 0.5633 | 0.8368 | 0.5952 | 0.6757 |
| 2.304 | 11520 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.308 | 11540 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.312 | 11560 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.316 | 11580 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.32 | 11600 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.324 | 11620 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.328 | 11640 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.332 | 11660 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.336 | 11680 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.34 | 11700 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.344 | 11720 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.348 | 11740 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.352 | 11760 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.356 | 11780 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.36 | 11800 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.364 | 11820 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.368 | 11840 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.372 | 11860 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.376 | 11880 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.38 | 11900 | 0.0042 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.384 | 11920 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.388 | 11940 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.392 | 11960 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.396 | 11980 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4 | 12000 | 0.0041 | 0.4152 | 0.7204 | 0.9443 | 0.5601 | 0.8967 | 0.7104 | 0.3978 | 0.7688 | 0.9751 | 0.3918 | 0.5609 | 0.8368 | 0.5988 | 0.6752 |
| 2.404 | 12020 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.408 | 12040 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.412 | 12060 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.416 | 12080 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.42 | 12100 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.424 | 12120 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.428 | 12140 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.432 | 12160 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.436 | 12180 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.44 | 12200 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.444 | 12220 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.448 | 12240 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.452 | 12260 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.456 | 12280 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.46 | 12300 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.464 | 12320 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.468 | 12340 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.472 | 12360 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.476 | 12380 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.48 | 12400 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.484 | 12420 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.488 | 12440 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.492 | 12460 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.496 | 12480 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5 | 12500 | 0.004 | 0.4194 | 0.7222 | 0.9443 | 0.5677 | 0.9031 | 0.7103 | 0.3955 | 0.7726 | 0.9708 | 0.3966 | 0.5573 | 0.8380 | 0.5966 | 0.6765 |
| 2.504 | 12520 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.508 | 12540 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.512 | 12560 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.516 | 12580 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.52 | 12600 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.524 | 12620 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.528 | 12640 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.532 | 12660 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.536 | 12680 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.54 | 12700 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.544 | 12720 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.548 | 12740 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.552 | 12760 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.556 | 12780 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.56 | 12800 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.564 | 12820 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.568 | 12840 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.572 | 12860 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.576 | 12880 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.58 | 12900 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.584 | 12920 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.588 | 12940 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.592 | 12960 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.596 | 12980 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6 | 13000 | 0.004 | 0.4201 | 0.7257 | 0.9443 | 0.5676 | 0.9012 | 0.7103 | 0.3984 | 0.7577 | 0.9705 | 0.4011 | 0.5609 | 0.8366 | 0.5990 | 0.6764 |
| 2.604 | 13020 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.608 | 13040 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.612 | 13060 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.616 | 13080 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.62 | 13100 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.624 | 13120 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.628 | 13140 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.632 | 13160 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.636 | 13180 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.64 | 13200 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.644 | 13220 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.648 | 13240 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.652 | 13260 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.656 | 13280 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.66 | 13300 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.664 | 13320 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.668 | 13340 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.672 | 13360 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.676 | 13380 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.68 | 13400 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.684 | 13420 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6880 | 13440 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.692 | 13460 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6960 | 13480 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7 | 13500 | 0.004 | 0.4158 | 0.7244 | 0.9452 | 0.5662 | 0.9012 | 0.7042 | 0.3966 | 0.7709 | 0.9705 | 0.3919 | 0.5640 | 0.8370 | 0.5941 | 0.6755 |
| 2.7040 | 13520 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.708 | 13540 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7120 | 13560 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.716 | 13580 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7200 | 13600 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.724 | 13620 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7280 | 13640 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.732 | 13660 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7360 | 13680 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.74 | 13700 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7440 | 13720 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.748 | 13740 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.752 | 13760 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7560 | 13780 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.76 | 13800 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7640 | 13820 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.768 | 13840 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7720 | 13860 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.776 | 13880 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7800 | 13900 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.784 | 13920 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7880 | 13940 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.792 | 13960 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7960 | 13980 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8 | 14000 | 0.0039 | 0.4230 | 0.7262 | 0.9443 | 0.5669 | 0.9028 | 0.7100 | 0.3930 | 0.7645 | 0.9750 | 0.3998 | 0.5635 | 0.8366 | 0.5975 | 0.6772 |
| 2.8040 | 14020 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.808 | 14040 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8120 | 14060 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.816 | 14080 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.82 | 14100 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.824 | 14120 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.828 | 14140 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.832 | 14160 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.836 | 14180 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.84 | 14200 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.844 | 14220 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.848 | 14240 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.852 | 14260 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.856 | 14280 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.86 | 14300 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.864 | 14320 | 0.0041 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.868 | 14340 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.872 | 14360 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.876 | 14380 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.88 | 14400 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.884 | 14420 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.888 | 14440 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.892 | 14460 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.896 | 14480 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9 | 14500 | 0.004 | 0.4177 | 0.7296 | 0.9452 | 0.5663 | 0.9012 | 0.7095 | 0.3917 | 0.7645 | 0.9708 | 0.3985 | 0.5609 | 0.8369 | 0.5952 | 0.6760 |
| 2.904 | 14520 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.908 | 14540 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.912 | 14560 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.916 | 14580 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.92 | 14600 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.924 | 14620 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.928 | 14640 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.932 | 14660 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.936 | 14680 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.94 | 14700 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.944 | 14720 | 0.0038 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.948 | 14740 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.952 | 14760 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.956 | 14780 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.96 | 14800 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.964 | 14820 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.968 | 14840 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.972 | 14860 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.976 | 14880 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.98 | 14900 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.984 | 14920 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.988 | 14940 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.992 | 14960 | 0.0039 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.996 | 14980 | 0.004 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 3.0 | 15000 | 0.0039 | 0.4148 | 0.7296 | 0.9452 | 0.5670 | 0.9012 | 0.7089 | 0.3957 | 0.7645 | 0.9691 | 0.3987 | 0.5609 | 0.8372 | 0.5927 | 0.6758 |