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--- |
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base_model: nlpaueb/bert-base-greek-uncased-v1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-base-greek-uncased-v5-finetuned-polylex-mg |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-greek-uncased-v5-finetuned-polylex-mg |
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This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3369 |
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## Model description |
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In this work we appropriately adapt a corpus of multiword expressions in Modern Greek, namely PolylexMG, characterised by the features detailed above formulating its spectrum of idiosyncrasy to finetune Greek BERT transformer model for masked language modelling classification and tasks. The GREEK-BERT model is pre-trained on free text corpora extracted from (a) the Greek part of Wikipedia, (b) the Greek part of the European Parliament Proceedings Parallel Corpus (Europarl), and (c) the Greek part of OSCAR (Koutsikakis et al, 2020:113), this monolingual model is based on the architecture of BERT-BASE-UNCASED. Specifically, Greek BERT has been finetuned with expressions derived from each syntactic category as they are described in PolylexMG (Fotopoulou et al., 2023) that includes 6,000 Greek lexical entries dataset entailing frozen idioms which are semantically fixed with no paradigmatic variation (Lamiroy, 2003) and light verb constructions in which the semantics are traced in the predicative noun and not the verb (Anastassiadis-Symeonidis et al., 2020). |
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## Results, intended uses & limitations |
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This subsection presents the experimental evaluation results for the MWE-fine-tuned Greek BERT model with respect to classification use case. The derived setup assumes that raw text in modern Greek that may contain multiple sentences is processed by the language model and reports class with regards to whether the text segment contains multiword stereotypical expressions or not. We compared the fine-tuned BERT model with a baseline logistic regression model. The latter is using as input the same word embeddings as the MWE-fine-tuned BERT model. |
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Greek-MWE-Bert was trained in a masked language model setting with full-expression-subdataset. The model perplexity was measured tο 303.21 before finetuning and 3.81 after finetuning demonstrating that the model has gained domain knowledge on multiword expressions. Qualitative outcomes presented in the following tables demonstrating the model performance in the case of 16 verbal constructs. The qualitative evaluation demonstrates that the fine-tuned model in all cases generates stereotypical multiword expressions while the original Greek BERT yields incomplete free-text related parts of sentences. |
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The finetuned model was further finetuned using for classification-oriented architecture with the classification-task-subdataset. The Bert classifier demonstrates an accuracy equivalent to 80% with a higher precision for free text reaching 80% and lower precision of 79% for MWE. In comparison the baseline classifier yields 70% for the free text and 67% for the MWE. We can observe that the two models have only 10% difference in accuracy despite the simplicity of baseline classifier. We can explain the small difference in the trained GreekBERT tokenizer that is used by both our model and the simplistic logistic regression model. However, the MWE-finetuned-Greek-BERT model can better capture sentences that contain MWEs due to the inherent benefits that the architecture offers. |
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## Training and evaluation data |
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### Sample of PolylexMG full expression subdataset |
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|text |label| |
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|-----------------------|-----| |
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|αδειάζω τη γωνιά σε |1 | |
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|αδειάζω πιστόλι πάνω σε|1 | |
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|αλλάζω τον αδόξαστο σε |1 | |
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|αλλάζω την πίστη σε |1 | |
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|δεν αλλάζω ούτε κόμα σε|1 | |
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|αλλάζω λόγια με |1 | |
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|αλλάζω κουβέντες με |1 | |
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|αλλάζω τα μυαλά σε |1 | |
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|αλλάζω τα φώτα σε |1 | |
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|αλλάζω τα πετρέλαια σε |1 | |
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|αλλάζω τα πέταλα σε |1 | |
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### Sample of PolylexMG classification subdataset |
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|text |label| |
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|-----------------------|-----| |
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|Μέσα σε λίγα λεπτά άναψαν τα αίματα και ο διαπληκτισμός άρχισε να γίνεται όλο και πιο έντονος|1 | |
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|Η πρώτη έκπληξη ήρθε αμέσως μόλις άναψαν τα τέσσερα κόκκινα φανάρια και το ένα πράσινο|0 | |
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|Γιατί τα κάνετε αυτά, για να γελάνε οι άλλοι μαζί μας;|0 | |
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|Κάθε φορά που έμπαινε καλάθι, έβγαζαν τις ίδιες ακριβώς ιαχές για να πάει γούρι και να μην κόψει η μαγιονέζα|1 | |
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|Η νέα πυρκαγιά ξεκινά από την πίσω πλευρά του Πεντελικού Όρους, σε σημείο που δεν είχε καεί|0 | |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-06 |
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- train_batch_size: 512 |
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- eval_batch_size: 512 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 500 |
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### Training results (Summary) |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 5.2105 | 1.0 | 13 | 4.4870 | |
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| 4.4319 | 2.0 | 26 | 3.8456 | |
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| 4.0318 | 3.0 | 39 | 3.4164 | |
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| 3.7558 | 4.0 | 52 | 3.2849 | |
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| 1.1307 | 497.0 | 6461 | 1.3311 | |
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| 1.1163 | 498.0 | 6474 | 1.3016 | |
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| 1.099 | 499.0 | 6487 | 1.3532 | |
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| 1.1246 | 500.0 | 6500 | 1.2222 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.3 |
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- Tokenizers 0.13.3 |
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### Training results (Full) |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 5.2105 | 1.0 | 13 | 4.4870 | |
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| 4.4319 | 2.0 | 26 | 3.8456 | |
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| 4.0318 | 3.0 | 39 | 3.4164 | |
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| 3.7558 | 4.0 | 52 | 3.2849 | |
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| 3.5626 | 5.0 | 65 | 3.3146 | |
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| 3.4355 | 6.0 | 78 | 3.1532 | |
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| 3.3299 | 7.0 | 91 | 3.0451 | |
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| 3.2313 | 8.0 | 104 | 2.9359 | |
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| 3.1758 | 9.0 | 117 | 2.8543 | |
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| 3.0762 | 10.0 | 130 | 2.8034 | |
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| 3.0318 | 11.0 | 143 | 2.7975 | |
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| 2.9481 | 12.0 | 156 | 2.6439 | |
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| 2.8848 | 13.0 | 169 | 2.6623 | |
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| 2.9002 | 14.0 | 182 | 2.6425 | |
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| 2.8435 | 15.0 | 195 | 2.6639 | |
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| 2.8451 | 16.0 | 208 | 2.6203 | |
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| 2.7987 | 17.0 | 221 | 2.5597 | |
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| 2.7522 | 18.0 | 234 | 2.5719 | |
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| 2.7194 | 19.0 | 247 | 2.6220 | |
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| 2.6923 | 20.0 | 260 | 2.5566 | |
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| 2.678 | 21.0 | 273 | 2.4172 | |
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| 2.6612 | 22.0 | 286 | 2.5726 | |
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| 2.6272 | 23.0 | 299 | 2.4478 | |
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| 2.6052 | 24.0 | 312 | 2.4366 | |
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| 2.5694 | 25.0 | 325 | 2.3694 | |
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| 2.593 | 26.0 | 338 | 2.4324 | |
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| 2.548 | 27.0 | 351 | 2.4070 | |
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| 2.4954 | 28.0 | 364 | 2.3651 | |
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| 2.5097 | 29.0 | 377 | 2.3268 | |
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| 2.5041 | 30.0 | 390 | 2.4208 | |
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| 2.4919 | 31.0 | 403 | 2.4321 | |
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| 2.461 | 32.0 | 416 | 2.3477 | |
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| 2.4698 | 33.0 | 429 | 2.4017 | |
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| 2.4557 | 34.0 | 442 | 2.3050 | |
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| 2.4464 | 35.0 | 455 | 2.3282 | |
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| 2.4215 | 36.0 | 468 | 2.3339 | |
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| 2.4037 | 37.0 | 481 | 2.2429 | |
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| 2.386 | 38.0 | 494 | 2.3452 | |
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| 2.3961 | 39.0 | 507 | 2.3312 | |
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| 2.3985 | 40.0 | 520 | 2.2921 | |
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| 2.3302 | 41.0 | 533 | 2.2711 | |
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| 2.3128 | 42.0 | 546 | 2.2344 | |
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| 2.3158 | 43.0 | 559 | 2.1982 | |
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| 2.2927 | 44.0 | 572 | 2.1473 | |
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| 2.3122 | 45.0 | 585 | 2.2317 | |
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| 2.2885 | 46.0 | 598 | 2.2060 | |
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| 2.2592 | 47.0 | 611 | 2.1943 | |
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| 2.2492 | 48.0 | 624 | 2.2361 | |
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| 2.2495 | 49.0 | 637 | 2.2059 | |
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| 2.2402 | 50.0 | 650 | 2.1461 | |
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| 2.241 | 51.0 | 663 | 2.2181 | |
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| 2.211 | 52.0 | 676 | 2.0885 | |
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| 2.2165 | 53.0 | 689 | 2.1567 | |
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| 2.2063 | 54.0 | 702 | 2.2112 | |
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| 2.1715 | 55.0 | 715 | 2.2934 | |
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| 2.1601 | 56.0 | 728 | 2.0745 | |
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| 2.1796 | 57.0 | 741 | 2.1070 | |
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| 2.152 | 58.0 | 754 | 2.0930 | |
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| 2.1562 | 59.0 | 767 | 2.1106 | |
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| 2.125 | 60.0 | 780 | 2.1529 | |
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| 2.1318 | 61.0 | 793 | 2.0296 | |
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| 2.1194 | 62.0 | 806 | 2.0323 | |
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| 2.1396 | 63.0 | 819 | 1.9835 | |
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| 2.1108 | 64.0 | 832 | 2.0066 | |
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| 2.0874 | 65.0 | 845 | 1.9062 | |
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| 2.0754 | 66.0 | 858 | 2.1728 | |
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| 2.0928 | 67.0 | 871 | 2.0197 | |
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| 2.0835 | 68.0 | 884 | 2.0767 | |
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| 2.0684 | 69.0 | 897 | 2.1482 | |
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| 2.0505 | 70.0 | 910 | 2.0667 | |
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| 2.0564 | 71.0 | 923 | 2.1489 | |
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| 2.0478 | 72.0 | 936 | 2.0015 | |
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| 2.0478 | 73.0 | 949 | 1.9215 | |
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| 2.0316 | 74.0 | 962 | 2.0238 | |
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| 2.0171 | 75.0 | 975 | 2.0014 | |
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| 2.0248 | 76.0 | 988 | 2.0775 | |
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| 2.0066 | 77.0 | 1001 | 2.0390 | |
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| 2.0018 | 78.0 | 1014 | 2.0043 | |
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| 1.9925 | 79.0 | 1027 | 2.0138 | |
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| 1.9614 | 80.0 | 1040 | 1.9499 | |
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| 1.9877 | 81.0 | 1053 | 1.9642 | |
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| 1.9499 | 82.0 | 1066 | 1.9676 | |
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| 1.932 | 83.0 | 1079 | 1.9332 | |
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| 1.9353 | 84.0 | 1092 | 1.8787 | |
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| 1.9672 | 85.0 | 1105 | 1.9720 | |
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| 1.9313 | 86.0 | 1118 | 1.9343 | |
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| 1.9292 | 87.0 | 1131 | 1.8964 | |
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| 1.9277 | 88.0 | 1144 | 1.9619 | |
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| 1.9158 | 89.0 | 1157 | 1.9608 | |
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| 1.921 | 90.0 | 1170 | 1.9171 | |
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| 1.9191 | 91.0 | 1183 | 1.8871 | |
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| 1.8935 | 92.0 | 1196 | 1.8857 | |
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| 1.8818 | 93.0 | 1209 | 1.8909 | |
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| 1.8782 | 94.0 | 1222 | 1.8951 | |
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| 1.9028 | 95.0 | 1235 | 1.9164 | |
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| 1.8907 | 96.0 | 1248 | 1.9650 | |
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| 1.8626 | 97.0 | 1261 | 1.8906 | |
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| 1.8413 | 98.0 | 1274 | 1.8957 | |
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| 1.854 | 99.0 | 1287 | 1.9644 | |
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| 1.8608 | 100.0 | 1300 | 1.8329 | |
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| 1.8623 | 101.0 | 1313 | 1.8693 | |
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| 1.7798 | 102.0 | 1326 | 1.8913 | |
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| 1.846 | 103.0 | 1339 | 1.7854 | |
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| 1.7972 | 104.0 | 1352 | 1.8611 | |
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| 1.8443 | 105.0 | 1365 | 1.8482 | |
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| 1.791 | 106.0 | 1378 | 1.7168 | |
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| 1.7879 | 107.0 | 1391 | 1.8093 | |
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| 1.7886 | 108.0 | 1404 | 1.8924 | |
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| 1.8192 | 109.0 | 1417 | 1.7715 | |
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| 1.7919 | 110.0 | 1430 | 1.7415 | |
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| 1.7581 | 111.0 | 1443 | 1.7956 | |
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| 1.7873 | 112.0 | 1456 | 1.7213 | |
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| 1.7873 | 113.0 | 1469 | 1.7340 | |
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| 1.7764 | 114.0 | 1482 | 1.8535 | |
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| 1.7612 | 115.0 | 1495 | 1.8554 | |
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| 1.7737 | 116.0 | 1508 | 1.8126 | |
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| 1.7416 | 117.0 | 1521 | 1.8327 | |
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| 1.7648 | 118.0 | 1534 | 1.6832 | |
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| 1.7262 | 119.0 | 1547 | 1.6972 | |
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| 1.7334 | 120.0 | 1560 | 1.7930 | |
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| 1.7172 | 121.0 | 1573 | 1.6962 | |
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| 1.7282 | 122.0 | 1586 | 1.8800 | |
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| 1.7038 | 123.0 | 1599 | 1.7828 | |
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| 1.6935 | 124.0 | 1612 | 1.7646 | |
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| 1.758 | 125.0 | 1625 | 1.8069 | |
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| 1.7018 | 126.0 | 1638 | 1.6958 | |
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| 1.6886 | 127.0 | 1651 | 1.6692 | |
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| 1.7004 | 128.0 | 1664 | 1.7256 | |
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| 1.6947 | 129.0 | 1677 | 1.7587 | |
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| 1.6897 | 130.0 | 1690 | 1.7484 | |
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| 1.7037 | 131.0 | 1703 | 1.8455 | |
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| 1.6981 | 132.0 | 1716 | 1.7588 | |
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| 1.6828 | 133.0 | 1729 | 1.7421 | |
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| 1.6596 | 134.0 | 1742 | 1.6933 | |
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| 1.6782 | 135.0 | 1755 | 1.7040 | |
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| 1.6595 | 136.0 | 1768 | 1.6705 | |
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| 1.6567 | 137.0 | 1781 | 1.7744 | |
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| 1.6588 | 138.0 | 1794 | 1.6545 | |
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| 1.6225 | 139.0 | 1807 | 1.7576 | |
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| 1.6394 | 140.0 | 1820 | 1.7256 | |
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| 1.6515 | 141.0 | 1833 | 1.6668 | |
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| 1.6331 | 142.0 | 1846 | 1.7884 | |
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| 1.6367 | 143.0 | 1859 | 1.7093 | |
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| 1.6335 | 144.0 | 1872 | 1.7098 | |
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| 1.6501 | 145.0 | 1885 | 1.6671 | |
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| 1.6192 | 146.0 | 1898 | 1.7073 | |
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| 1.6198 | 147.0 | 1911 | 1.6653 | |
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| 1.6182 | 148.0 | 1924 | 1.6723 | |
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| 1.6172 | 149.0 | 1937 | 1.7293 | |
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| 1.6129 | 150.0 | 1950 | 1.6545 | |
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| 1.6054 | 151.0 | 1963 | 1.6850 | |
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| 1.5967 | 152.0 | 1976 | 1.7064 | |
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| 1.6028 | 153.0 | 1989 | 1.5292 | |
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| 1.6156 | 154.0 | 2002 | 1.6477 | |
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| 1.5965 | 155.0 | 2015 | 1.6110 | |
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| 1.5695 | 156.0 | 2028 | 1.7071 | |
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| 1.5586 | 157.0 | 2041 | 1.6504 | |
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| 1.561 | 158.0 | 2054 | 1.6147 | |
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| 1.5643 | 159.0 | 2067 | 1.6941 | |
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| 1.5797 | 160.0 | 2080 | 1.7398 | |
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| 1.5609 | 161.0 | 2093 | 1.5761 | |
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| 1.5465 | 162.0 | 2106 | 1.6003 | |
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| 1.5467 | 163.0 | 2119 | 1.5839 | |
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| 1.5935 | 164.0 | 2132 | 1.6530 | |
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| 1.5439 | 165.0 | 2145 | 1.6743 | |
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| 1.559 | 166.0 | 2158 | 1.5143 | |
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| 1.5648 | 167.0 | 2171 | 1.6390 | |
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| 1.552 | 168.0 | 2184 | 1.5389 | |
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| 1.5164 | 169.0 | 2197 | 1.5879 | |
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| 1.5342 | 170.0 | 2210 | 1.6785 | |
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| 1.5319 | 171.0 | 2223 | 1.6341 | |
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| 1.5477 | 172.0 | 2236 | 1.7071 | |
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| 1.5364 | 173.0 | 2249 | 1.6268 | |
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| 1.5366 | 174.0 | 2262 | 1.7247 | |
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| 1.5445 | 175.0 | 2275 | 1.6668 | |
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| 1.4916 | 176.0 | 2288 | 1.5756 | |
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| 1.509 | 177.0 | 2301 | 1.5412 | |
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| 1.5316 | 178.0 | 2314 | 1.6270 | |
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| 1.5156 | 179.0 | 2327 | 1.6423 | |
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| 1.4918 | 180.0 | 2340 | 1.6112 | |
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| 1.4997 | 181.0 | 2353 | 1.5775 | |
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| 1.5187 | 182.0 | 2366 | 1.6248 | |
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| 1.5254 | 183.0 | 2379 | 1.5884 | |
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| 1.4732 | 184.0 | 2392 | 1.5787 | |
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| 1.4844 | 185.0 | 2405 | 1.5358 | |
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| 1.4882 | 186.0 | 2418 | 1.5144 | |
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| 1.478 | 187.0 | 2431 | 1.5223 | |
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| 1.5101 | 188.0 | 2444 | 1.5787 | |
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| 1.4688 | 189.0 | 2457 | 1.5479 | |
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| 1.4815 | 190.0 | 2470 | 1.5141 | |
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| 1.4925 | 191.0 | 2483 | 1.5939 | |
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| 1.467 | 192.0 | 2496 | 1.5471 | |
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| 1.4718 | 193.0 | 2509 | 1.6845 | |
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| 1.4699 | 194.0 | 2522 | 1.5943 | |
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| 1.4562 | 195.0 | 2535 | 1.4745 | |
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| 1.4451 | 196.0 | 2548 | 1.5922 | |
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| 1.4451 | 197.0 | 2561 | 1.5856 | |
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| 1.4624 | 198.0 | 2574 | 1.5519 | |
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| 1.444 | 199.0 | 2587 | 1.6538 | |
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| 1.4498 | 200.0 | 2600 | 1.5037 | |
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| 1.4285 | 201.0 | 2613 | 1.5539 | |
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| 1.4439 | 202.0 | 2626 | 1.5387 | |
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| 1.4177 | 203.0 | 2639 | 1.5756 | |
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| 1.436 | 204.0 | 2652 | 1.6136 | |
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| 1.4184 | 205.0 | 2665 | 1.5014 | |
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| 1.43 | 206.0 | 2678 | 1.4983 | |
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| 1.4347 | 207.0 | 2691 | 1.5896 | |
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| 1.39 | 208.0 | 2704 | 1.5506 | |
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| 1.4198 | 209.0 | 2717 | 1.5142 | |
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| 1.4101 | 210.0 | 2730 | 1.4930 | |
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| 1.4219 | 211.0 | 2743 | 1.4814 | |
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| 1.4039 | 212.0 | 2756 | 1.3750 | |
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| 1.4479 | 213.0 | 2769 | 1.5330 | |
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| 1.4354 | 214.0 | 2782 | 1.5179 | |
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| 1.4163 | 215.0 | 2795 | 1.5970 | |
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| 1.4459 | 216.0 | 2808 | 1.4755 | |
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| 1.3714 | 217.0 | 2821 | 1.4230 | |
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| 1.3957 | 218.0 | 2834 | 1.5087 | |
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| 1.396 | 219.0 | 2847 | 1.5570 | |
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| 1.3866 | 220.0 | 2860 | 1.4955 | |
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| 1.4122 | 221.0 | 2873 | 1.4272 | |
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| 1.371 | 222.0 | 2886 | 1.5209 | |
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| 1.3907 | 223.0 | 2899 | 1.4725 | |
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| 1.3856 | 224.0 | 2912 | 1.5021 | |
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| 1.4053 | 225.0 | 2925 | 1.4880 | |
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| 1.4074 | 226.0 | 2938 | 1.4988 | |
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| 1.3827 | 227.0 | 2951 | 1.5527 | |
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| 1.4045 | 228.0 | 2964 | 1.5350 | |
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| 1.3626 | 229.0 | 2977 | 1.5093 | |
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| 1.3795 | 230.0 | 2990 | 1.4497 | |
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| 1.3973 | 231.0 | 3003 | 1.5106 | |
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| 1.3703 | 232.0 | 3016 | 1.4619 | |
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| 1.3942 | 233.0 | 3029 | 1.4553 | |
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| 1.3447 | 234.0 | 3042 | 1.5061 | |
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| 1.3438 | 235.0 | 3055 | 1.5167 | |
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| 1.3496 | 236.0 | 3068 | 1.4060 | |
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| 1.3614 | 237.0 | 3081 | 1.4211 | |
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| 1.3618 | 238.0 | 3094 | 1.4624 | |
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| 1.359 | 239.0 | 3107 | 1.4450 | |
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| 1.3657 | 240.0 | 3120 | 1.4795 | |
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| 1.3599 | 241.0 | 3133 | 1.4887 | |
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| 1.3532 | 242.0 | 3146 | 1.4606 | |
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| 1.3528 | 243.0 | 3159 | 1.4225 | |
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| 1.3445 | 244.0 | 3172 | 1.3912 | |
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| 1.3344 | 245.0 | 3185 | 1.4055 | |
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| 1.3358 | 246.0 | 3198 | 1.5152 | |
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| 1.3591 | 247.0 | 3211 | 1.4825 | |
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| 1.3162 | 248.0 | 3224 | 1.4721 | |
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| 1.3197 | 249.0 | 3237 | 1.4375 | |
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| 1.3358 | 250.0 | 3250 | 1.4644 | |
|
| 1.3374 | 251.0 | 3263 | 1.4449 | |
|
| 1.3548 | 252.0 | 3276 | 1.4405 | |
|
| 1.3266 | 253.0 | 3289 | 1.5357 | |
|
| 1.3172 | 254.0 | 3302 | 1.3515 | |
|
| 1.3089 | 255.0 | 3315 | 1.4408 | |
|
| 1.3209 | 256.0 | 3328 | 1.3895 | |
|
| 1.3047 | 257.0 | 3341 | 1.4508 | |
|
| 1.2877 | 258.0 | 3354 | 1.3954 | |
|
| 1.3409 | 259.0 | 3367 | 1.4417 | |
|
| 1.31 | 260.0 | 3380 | 1.5124 | |
|
| 1.3229 | 261.0 | 3393 | 1.4047 | |
|
| 1.3275 | 262.0 | 3406 | 1.3780 | |
|
| 1.295 | 263.0 | 3419 | 1.4209 | |
|
| 1.3279 | 264.0 | 3432 | 1.3867 | |
|
| 1.291 | 265.0 | 3445 | 1.4694 | |
|
| 1.2839 | 266.0 | 3458 | 1.5100 | |
|
| 1.3064 | 267.0 | 3471 | 1.3646 | |
|
| 1.3086 | 268.0 | 3484 | 1.4390 | |
|
| 1.3381 | 269.0 | 3497 | 1.4367 | |
|
| 1.3333 | 270.0 | 3510 | 1.4078 | |
|
| 1.2775 | 271.0 | 3523 | 1.5213 | |
|
| 1.2989 | 272.0 | 3536 | 1.4341 | |
|
| 1.2759 | 273.0 | 3549 | 1.5165 | |
|
| 1.2796 | 274.0 | 3562 | 1.4705 | |
|
| 1.3037 | 275.0 | 3575 | 1.3945 | |
|
| 1.3132 | 276.0 | 3588 | 1.4560 | |
|
| 1.2816 | 277.0 | 3601 | 1.4123 | |
|
| 1.2934 | 278.0 | 3614 | 1.3742 | |
|
| 1.2873 | 279.0 | 3627 | 1.3824 | |
|
| 1.2842 | 280.0 | 3640 | 1.3269 | |
|
| 1.2617 | 281.0 | 3653 | 1.4345 | |
|
| 1.2661 | 282.0 | 3666 | 1.4682 | |
|
| 1.3096 | 283.0 | 3679 | 1.3989 | |
|
| 1.2724 | 284.0 | 3692 | 1.3142 | |
|
| 1.2529 | 285.0 | 3705 | 1.2795 | |
|
| 1.2611 | 286.0 | 3718 | 1.3844 | |
|
| 1.2578 | 287.0 | 3731 | 1.3536 | |
|
| 1.2854 | 288.0 | 3744 | 1.3770 | |
|
| 1.2811 | 289.0 | 3757 | 1.3892 | |
|
| 1.2189 | 290.0 | 3770 | 1.3767 | |
|
| 1.283 | 291.0 | 3783 | 1.4034 | |
|
| 1.2684 | 292.0 | 3796 | 1.3867 | |
|
| 1.241 | 293.0 | 3809 | 1.3572 | |
|
| 1.2503 | 294.0 | 3822 | 1.3583 | |
|
| 1.2605 | 295.0 | 3835 | 1.4600 | |
|
| 1.2697 | 296.0 | 3848 | 1.2754 | |
|
| 1.2469 | 297.0 | 3861 | 1.4295 | |
|
| 1.2451 | 298.0 | 3874 | 1.4645 | |
|
| 1.2765 | 299.0 | 3887 | 1.3605 | |
|
| 1.2482 | 300.0 | 3900 | 1.4915 | |
|
| 1.2564 | 301.0 | 3913 | 1.3490 | |
|
| 1.233 | 302.0 | 3926 | 1.3273 | |
|
| 1.2313 | 303.0 | 3939 | 1.3861 | |
|
| 1.2491 | 304.0 | 3952 | 1.4016 | |
|
| 1.2607 | 305.0 | 3965 | 1.3714 | |
|
| 1.2548 | 306.0 | 3978 | 1.3572 | |
|
| 1.2536 | 307.0 | 3991 | 1.3630 | |
|
| 1.24 | 308.0 | 4004 | 1.3070 | |
|
| 1.2352 | 309.0 | 4017 | 1.4311 | |
|
| 1.2643 | 310.0 | 4030 | 1.2794 | |
|
| 1.2281 | 311.0 | 4043 | 1.3855 | |
|
| 1.2428 | 312.0 | 4056 | 1.3784 | |
|
| 1.2196 | 313.0 | 4069 | 1.3430 | |
|
| 1.2116 | 314.0 | 4082 | 1.4230 | |
|
| 1.2261 | 315.0 | 4095 | 1.4760 | |
|
| 1.25 | 316.0 | 4108 | 1.3658 | |
|
| 1.2281 | 317.0 | 4121 | 1.3563 | |
|
| 1.2308 | 318.0 | 4134 | 1.3107 | |
|
| 1.2247 | 319.0 | 4147 | 1.3554 | |
|
| 1.2354 | 320.0 | 4160 | 1.3956 | |
|
| 1.2168 | 321.0 | 4173 | 1.2753 | |
|
| 1.2078 | 322.0 | 4186 | 1.3253 | |
|
| 1.2481 | 323.0 | 4199 | 1.3025 | |
|
| 1.2331 | 324.0 | 4212 | 1.3707 | |
|
| 1.1974 | 325.0 | 4225 | 1.2874 | |
|
| 1.212 | 326.0 | 4238 | 1.3210 | |
|
| 1.225 | 327.0 | 4251 | 1.4129 | |
|
| 1.2161 | 328.0 | 4264 | 1.3364 | |
|
| 1.2304 | 329.0 | 4277 | 1.3822 | |
|
| 1.1903 | 330.0 | 4290 | 1.4887 | |
|
| 1.2208 | 331.0 | 4303 | 1.2687 | |
|
| 1.229 | 332.0 | 4316 | 1.3730 | |
|
| 1.205 | 333.0 | 4329 | 1.3521 | |
|
| 1.2023 | 334.0 | 4342 | 1.3770 | |
|
| 1.2151 | 335.0 | 4355 | 1.3095 | |
|
| 1.2255 | 336.0 | 4368 | 1.3003 | |
|
| 1.2205 | 337.0 | 4381 | 1.2123 | |
|
| 1.203 | 338.0 | 4394 | 1.2995 | |
|
| 1.2013 | 339.0 | 4407 | 1.2838 | |
|
| 1.1997 | 340.0 | 4420 | 1.3023 | |
|
| 1.2033 | 341.0 | 4433 | 1.3111 | |
|
| 1.1934 | 342.0 | 4446 | 1.4057 | |
|
| 1.1832 | 343.0 | 4459 | 1.3468 | |
|
| 1.2405 | 344.0 | 4472 | 1.3362 | |
|
| 1.1803 | 345.0 | 4485 | 1.4813 | |
|
| 1.2154 | 346.0 | 4498 | 1.3207 | |
|
| 1.2314 | 347.0 | 4511 | 1.3236 | |
|
| 1.1927 | 348.0 | 4524 | 1.3428 | |
|
| 1.2194 | 349.0 | 4537 | 1.3533 | |
|
| 1.1995 | 350.0 | 4550 | 1.3465 | |
|
| 1.177 | 351.0 | 4563 | 1.3484 | |
|
| 1.1993 | 352.0 | 4576 | 1.2859 | |
|
| 1.1687 | 353.0 | 4589 | 1.2699 | |
|
| 1.2045 | 354.0 | 4602 | 1.3686 | |
|
| 1.2084 | 355.0 | 4615 | 1.3515 | |
|
| 1.1837 | 356.0 | 4628 | 1.2735 | |
|
| 1.1937 | 357.0 | 4641 | 1.2835 | |
|
| 1.2004 | 358.0 | 4654 | 1.2793 | |
|
| 1.1838 | 359.0 | 4667 | 1.2798 | |
|
| 1.2026 | 360.0 | 4680 | 1.3856 | |
|
| 1.1669 | 361.0 | 4693 | 1.3719 | |
|
| 1.1716 | 362.0 | 4706 | 1.2613 | |
|
| 1.1906 | 363.0 | 4719 | 1.2719 | |
|
| 1.1914 | 364.0 | 4732 | 1.3864 | |
|
| 1.1874 | 365.0 | 4745 | 1.3255 | |
|
| 1.1848 | 366.0 | 4758 | 1.2984 | |
|
| 1.1778 | 367.0 | 4771 | 1.3461 | |
|
| 1.1964 | 368.0 | 4784 | 1.3320 | |
|
| 1.16 | 369.0 | 4797 | 1.2962 | |
|
| 1.1873 | 370.0 | 4810 | 1.3035 | |
|
| 1.1632 | 371.0 | 4823 | 1.3465 | |
|
| 1.1807 | 372.0 | 4836 | 1.3453 | |
|
| 1.1331 | 373.0 | 4849 | 1.3527 | |
|
| 1.1694 | 374.0 | 4862 | 1.2928 | |
|
| 1.1615 | 375.0 | 4875 | 1.3519 | |
|
| 1.1944 | 376.0 | 4888 | 1.4072 | |
|
| 1.163 | 377.0 | 4901 | 1.3156 | |
|
| 1.1719 | 378.0 | 4914 | 1.3074 | |
|
| 1.1721 | 379.0 | 4927 | 1.3121 | |
|
| 1.1618 | 380.0 | 4940 | 1.3039 | |
|
| 1.1852 | 381.0 | 4953 | 1.3562 | |
|
| 1.1838 | 382.0 | 4966 | 1.3383 | |
|
| 1.1616 | 383.0 | 4979 | 1.2922 | |
|
| 1.1401 | 384.0 | 4992 | 1.2676 | |
|
| 1.165 | 385.0 | 5005 | 1.2625 | |
|
| 1.1564 | 386.0 | 5018 | 1.1716 | |
|
| 1.1662 | 387.0 | 5031 | 1.2738 | |
|
| 1.1761 | 388.0 | 5044 | 1.4011 | |
|
| 1.1587 | 389.0 | 5057 | 1.3821 | |
|
| 1.1517 | 390.0 | 5070 | 1.2879 | |
|
| 1.1699 | 391.0 | 5083 | 1.2898 | |
|
| 1.149 | 392.0 | 5096 | 1.2710 | |
|
| 1.1541 | 393.0 | 5109 | 1.2612 | |
|
| 1.1597 | 394.0 | 5122 | 1.2993 | |
|
| 1.1449 | 395.0 | 5135 | 1.2522 | |
|
| 1.1332 | 396.0 | 5148 | 1.3367 | |
|
| 1.1537 | 397.0 | 5161 | 1.3018 | |
|
| 1.1789 | 398.0 | 5174 | 1.3705 | |
|
| 1.169 | 399.0 | 5187 | 1.3128 | |
|
| 1.1685 | 400.0 | 5200 | 1.3068 | |
|
| 1.137 | 401.0 | 5213 | 1.2384 | |
|
| 1.177 | 402.0 | 5226 | 1.2547 | |
|
| 1.1592 | 403.0 | 5239 | 1.3295 | |
|
| 1.1477 | 404.0 | 5252 | 1.3415 | |
|
| 1.1465 | 405.0 | 5265 | 1.2466 | |
|
| 1.1743 | 406.0 | 5278 | 1.3045 | |
|
| 1.1386 | 407.0 | 5291 | 1.3124 | |
|
| 1.1379 | 408.0 | 5304 | 1.2826 | |
|
| 1.1828 | 409.0 | 5317 | 1.2788 | |
|
| 1.1353 | 410.0 | 5330 | 1.3787 | |
|
| 1.1536 | 411.0 | 5343 | 1.2968 | |
|
| 1.1495 | 412.0 | 5356 | 1.2920 | |
|
| 1.1424 | 413.0 | 5369 | 1.3238 | |
|
| 1.158 | 414.0 | 5382 | 1.3301 | |
|
| 1.1715 | 415.0 | 5395 | 1.2298 | |
|
| 1.1559 | 416.0 | 5408 | 1.2769 | |
|
| 1.1399 | 417.0 | 5421 | 1.3263 | |
|
| 1.186 | 418.0 | 5434 | 1.2924 | |
|
| 1.1653 | 419.0 | 5447 | 1.3279 | |
|
| 1.14 | 420.0 | 5460 | 1.2892 | |
|
| 1.1463 | 421.0 | 5473 | 1.3875 | |
|
| 1.1406 | 422.0 | 5486 | 1.3136 | |
|
| 1.1705 | 423.0 | 5499 | 1.2579 | |
|
| 1.1065 | 424.0 | 5512 | 1.2955 | |
|
| 1.145 | 425.0 | 5525 | 1.2970 | |
|
| 1.1538 | 426.0 | 5538 | 1.3030 | |
|
| 1.1674 | 427.0 | 5551 | 1.3060 | |
|
| 1.1283 | 428.0 | 5564 | 1.2325 | |
|
| 1.1683 | 429.0 | 5577 | 1.3085 | |
|
| 1.1598 | 430.0 | 5590 | 1.2469 | |
|
| 1.1429 | 431.0 | 5603 | 1.2523 | |
|
| 1.1552 | 432.0 | 5616 | 1.3124 | |
|
| 1.1722 | 433.0 | 5629 | 1.2955 | |
|
| 1.1329 | 434.0 | 5642 | 1.3249 | |
|
| 1.1486 | 435.0 | 5655 | 1.3245 | |
|
| 1.124 | 436.0 | 5668 | 1.4052 | |
|
| 1.1092 | 437.0 | 5681 | 1.2399 | |
|
| 1.135 | 438.0 | 5694 | 1.2788 | |
|
| 1.1637 | 439.0 | 5707 | 1.2844 | |
|
| 1.1712 | 440.0 | 5720 | 1.2531 | |
|
| 1.1401 | 441.0 | 5733 | 1.2790 | |
|
| 1.1195 | 442.0 | 5746 | 1.2876 | |
|
| 1.1524 | 443.0 | 5759 | 1.2565 | |
|
| 1.1292 | 444.0 | 5772 | 1.1492 | |
|
| 1.1342 | 445.0 | 5785 | 1.3050 | |
|
| 1.1628 | 446.0 | 5798 | 1.2911 | |
|
| 1.1286 | 447.0 | 5811 | 1.3624 | |
|
| 1.1193 | 448.0 | 5824 | 1.2382 | |
|
| 1.1521 | 449.0 | 5837 | 1.2717 | |
|
| 1.1128 | 450.0 | 5850 | 1.2865 | |
|
| 1.1321 | 451.0 | 5863 | 1.2785 | |
|
| 1.1707 | 452.0 | 5876 | 1.3514 | |
|
| 1.1431 | 453.0 | 5889 | 1.3321 | |
|
| 1.1413 | 454.0 | 5902 | 1.2886 | |
|
| 1.0983 | 455.0 | 5915 | 1.3165 | |
|
| 1.1202 | 456.0 | 5928 | 1.2375 | |
|
| 1.1259 | 457.0 | 5941 | 1.2166 | |
|
| 1.1353 | 458.0 | 5954 | 1.3579 | |
|
| 1.1272 | 459.0 | 5967 | 1.2890 | |
|
| 1.1411 | 460.0 | 5980 | 1.2397 | |
|
| 1.115 | 461.0 | 5993 | 1.2803 | |
|
| 1.14 | 462.0 | 6006 | 1.2439 | |
|
| 1.11 | 463.0 | 6019 | 1.1894 | |
|
| 1.1539 | 464.0 | 6032 | 1.2979 | |
|
| 1.1052 | 465.0 | 6045 | 1.2281 | |
|
| 1.1092 | 466.0 | 6058 | 1.2853 | |
|
| 1.1229 | 467.0 | 6071 | 1.2988 | |
|
| 1.1209 | 468.0 | 6084 | 1.3058 | |
|
| 1.1147 | 469.0 | 6097 | 1.2705 | |
|
| 1.1228 | 470.0 | 6110 | 1.2435 | |
|
| 1.1124 | 471.0 | 6123 | 1.2188 | |
|
| 1.0922 | 472.0 | 6136 | 1.2892 | |
|
| 1.1228 | 473.0 | 6149 | 1.2250 | |
|
| 1.1341 | 474.0 | 6162 | 1.2373 | |
|
| 1.1295 | 475.0 | 6175 | 1.2126 | |
|
| 1.1105 | 476.0 | 6188 | 1.3032 | |
|
| 1.1223 | 477.0 | 6201 | 1.2190 | |
|
| 1.1487 | 478.0 | 6214 | 1.2728 | |
|
| 1.1288 | 479.0 | 6227 | 1.3258 | |
|
| 1.1398 | 480.0 | 6240 | 1.2114 | |
|
| 1.1127 | 481.0 | 6253 | 1.2695 | |
|
| 1.135 | 482.0 | 6266 | 1.3376 | |
|
| 1.106 | 483.0 | 6279 | 1.2860 | |
|
| 1.0978 | 484.0 | 6292 | 1.3001 | |
|
| 1.1254 | 485.0 | 6305 | 1.3180 | |
|
| 1.1117 | 486.0 | 6318 | 1.3036 | |
|
| 1.1249 | 487.0 | 6331 | 1.2380 | |
|
| 1.1111 | 488.0 | 6344 | 1.3112 | |
|
| 1.119 | 489.0 | 6357 | 1.2587 | |
|
| 1.1203 | 490.0 | 6370 | 1.2867 | |
|
| 1.1195 | 491.0 | 6383 | 1.3153 | |
|
| 1.1304 | 492.0 | 6396 | 1.2762 | |
|
| 1.1268 | 493.0 | 6409 | 1.2757 | |
|
| 1.1478 | 494.0 | 6422 | 1.2493 | |
|
| 1.1527 | 495.0 | 6435 | 1.2793 | |
|
| 1.1252 | 496.0 | 6448 | 1.2435 | |
|
| 1.1307 | 497.0 | 6461 | 1.3311 | |
|
| 1.1163 | 498.0 | 6474 | 1.3016 | |
|
| 1.099 | 499.0 | 6487 | 1.3532 | |
|
| 1.1246 | 500.0 | 6500 | 1.2222 | |
|
|
|
|
|
|
