bert-base-greek-uncased-v5-finetuned-polylex-mg
This model is a fine-tuned version of nlpaueb/bert-base-greek-uncased-v1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.3369
Model description
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).
Results, intended uses & limitations
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.
Greek-MWE-Bert was trained in a masked language model setting with full-expression-subdataset. The model perplexity was measured tฮฟ 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.
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.
Training and evaluation data
Sample of PolylexMG full expression subdataset
| text | label |
|---|---|
| ฮฑฮดฮตฮนฮฌฮถฯ ฯฮท ฮณฯฮฝฮนฮฌ ฯฮต | 1 |
| ฮฑฮดฮตฮนฮฌฮถฯ ฯฮนฯฯฯฮปฮน ฯฮฌฮฝฯ ฯฮต | 1 |
| ฮฑฮปฮปฮฌฮถฯ ฯฮฟฮฝ ฮฑฮดฯฮพฮฑฯฯฮฟ ฯฮต | 1 |
| ฮฑฮปฮปฮฌฮถฯ ฯฮทฮฝ ฯฮฏฯฯฮท ฯฮต | 1 |
| ฮดฮตฮฝ ฮฑฮปฮปฮฌฮถฯ ฮฟฯฯฮต ฮบฯฮผฮฑ ฯฮต | 1 |
| ฮฑฮปฮปฮฌฮถฯ ฮปฯฮณฮนฮฑ ฮผฮต | 1 |
| ฮฑฮปฮปฮฌฮถฯ ฮบฮฟฯ ฮฒฮญฮฝฯฮตฯ ฮผฮต | 1 |
| ฮฑฮปฮปฮฌฮถฯ ฯฮฑ ฮผฯ ฮฑฮปฮฌ ฯฮต | 1 |
| ฮฑฮปฮปฮฌฮถฯ ฯฮฑ ฯฯฯฮฑ ฯฮต | 1 |
| ฮฑฮปฮปฮฌฮถฯ ฯฮฑ ฯฮตฯฯฮญฮปฮฑฮนฮฑ ฯฮต | 1 |
| ฮฑฮปฮปฮฌฮถฯ ฯฮฑ ฯฮญฯฮฑฮปฮฑ ฯฮต | 1 |
Sample of PolylexMG classification subdataset
| text | label |
|---|---|
| ฮฮญฯฮฑ ฯฮต ฮปฮฏฮณฮฑ ฮปฮตฯฯฮฌ ฮฌฮฝฮฑฯฮฑฮฝ ฯฮฑ ฮฑฮฏฮผฮฑฯฮฑ ฮบฮฑฮน ฮฟ ฮดฮนฮฑฯฮปฮทฮบฯฮนฯฮผฯฯ ฮฌฯฯฮนฯฮต ฮฝฮฑ ฮณฮฏฮฝฮตฯฮฑฮน ฯฮปฮฟ ฮบฮฑฮน ฯฮนฮฟ ฮญฮฝฯฮฟฮฝฮฟฯ | 1 |
| ฮ ฯฯฯฯฮท ฮญฮบฯฮปฮทฮพฮท ฮฎฯฮธฮต ฮฑฮผฮญฯฯฯ ฮผฯฮปฮนฯ ฮฌฮฝฮฑฯฮฑฮฝ ฯฮฑ ฯฮญฯฯฮตฯฮฑ ฮบฯฮบฮบฮนฮฝฮฑ ฯฮฑฮฝฮฌฯฮนฮฑ ฮบฮฑฮน ฯฮฟ ฮญฮฝฮฑ ฯฯฮฌฯฮนฮฝฮฟ | 0 |
| ฮฮนฮฑฯฮฏ ฯฮฑ ฮบฮฌฮฝฮตฯฮต ฮฑฯ ฯฮฌ, ฮณฮนฮฑ ฮฝฮฑ ฮณฮตฮปฮฌฮฝฮต ฮฟฮน ฮฌฮปฮปฮฟฮน ฮผฮฑฮถฮฏ ฮผฮฑฯ; | 0 |
| ฮฮฌฮธฮต ฯฮฟฯฮฌ ฯฮฟฯ ฮญฮผฯฮฑฮนฮฝฮต ฮบฮฑฮปฮฌฮธฮน, ฮญฮฒฮณฮฑฮถฮฑฮฝ ฯฮนฯ ฮฏฮดฮนฮตฯ ฮฑฮบฯฮนฮฒฯฯ ฮนฮฑฯฮญฯ ฮณฮนฮฑ ฮฝฮฑ ฯฮฌฮตฮน ฮณฮฟฯฯฮน ฮบฮฑฮน ฮฝฮฑ ฮผฮทฮฝ ฮบฯฯฮตฮน ฮท ฮผฮฑฮณฮนฮฟฮฝฮญฮถฮฑ | 1 |
| ฮ ฮฝฮญฮฑ ฯฯ ฯฮบฮฑฮณฮนฮฌ ฮพฮตฮบฮนฮฝฮฌ ฮฑฯฯ ฯฮทฮฝ ฯฮฏฯฯ ฯฮปฮตฯ ฯฮฌ ฯฮฟฯ ฮ ฮตฮฝฯฮตฮปฮนฮบฮฟฯ ฮฯฮฟฯ ฯ, ฯฮต ฯฮทฮผฮตฮฏฮฟ ฯฮฟฯ ฮดฮตฮฝ ฮตฮฏฯฮต ฮบฮฑฮตฮฏ | 0 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 512
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 500
Training results (Summary)
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.2105 | 1.0 | 13 | 4.4870 |
| 4.4319 | 2.0 | 26 | 3.8456 |
| 4.0318 | 3.0 | 39 | 3.4164 |
| 3.7558 | 4.0 | 52 | 3.2849 |
| 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 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
Training results (Full)
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.2105 | 1.0 | 13 | 4.4870 |
| 4.4319 | 2.0 | 26 | 3.8456 |
| 4.0318 | 3.0 | 39 | 3.4164 |
| 3.7558 | 4.0 | 52 | 3.2849 |
| 3.5626 | 5.0 | 65 | 3.3146 |
| 3.4355 | 6.0 | 78 | 3.1532 |
| 3.3299 | 7.0 | 91 | 3.0451 |
| 3.2313 | 8.0 | 104 | 2.9359 |
| 3.1758 | 9.0 | 117 | 2.8543 |
| 3.0762 | 10.0 | 130 | 2.8034 |
| 3.0318 | 11.0 | 143 | 2.7975 |
| 2.9481 | 12.0 | 156 | 2.6439 |
| 2.8848 | 13.0 | 169 | 2.6623 |
| 2.9002 | 14.0 | 182 | 2.6425 |
| 2.8435 | 15.0 | 195 | 2.6639 |
| 2.8451 | 16.0 | 208 | 2.6203 |
| 2.7987 | 17.0 | 221 | 2.5597 |
| 2.7522 | 18.0 | 234 | 2.5719 |
| 2.7194 | 19.0 | 247 | 2.6220 |
| 2.6923 | 20.0 | 260 | 2.5566 |
| 2.678 | 21.0 | 273 | 2.4172 |
| 2.6612 | 22.0 | 286 | 2.5726 |
| 2.6272 | 23.0 | 299 | 2.4478 |
| 2.6052 | 24.0 | 312 | 2.4366 |
| 2.5694 | 25.0 | 325 | 2.3694 |
| 2.593 | 26.0 | 338 | 2.4324 |
| 2.548 | 27.0 | 351 | 2.4070 |
| 2.4954 | 28.0 | 364 | 2.3651 |
| 2.5097 | 29.0 | 377 | 2.3268 |
| 2.5041 | 30.0 | 390 | 2.4208 |
| 2.4919 | 31.0 | 403 | 2.4321 |
| 2.461 | 32.0 | 416 | 2.3477 |
| 2.4698 | 33.0 | 429 | 2.4017 |
| 2.4557 | 34.0 | 442 | 2.3050 |
| 2.4464 | 35.0 | 455 | 2.3282 |
| 2.4215 | 36.0 | 468 | 2.3339 |
| 2.4037 | 37.0 | 481 | 2.2429 |
| 2.386 | 38.0 | 494 | 2.3452 |
| 2.3961 | 39.0 | 507 | 2.3312 |
| 2.3985 | 40.0 | 520 | 2.2921 |
| 2.3302 | 41.0 | 533 | 2.2711 |
| 2.3128 | 42.0 | 546 | 2.2344 |
| 2.3158 | 43.0 | 559 | 2.1982 |
| 2.2927 | 44.0 | 572 | 2.1473 |
| 2.3122 | 45.0 | 585 | 2.2317 |
| 2.2885 | 46.0 | 598 | 2.2060 |
| 2.2592 | 47.0 | 611 | 2.1943 |
| 2.2492 | 48.0 | 624 | 2.2361 |
| 2.2495 | 49.0 | 637 | 2.2059 |
| 2.2402 | 50.0 | 650 | 2.1461 |
| 2.241 | 51.0 | 663 | 2.2181 |
| 2.211 | 52.0 | 676 | 2.0885 |
| 2.2165 | 53.0 | 689 | 2.1567 |
| 2.2063 | 54.0 | 702 | 2.2112 |
| 2.1715 | 55.0 | 715 | 2.2934 |
| 2.1601 | 56.0 | 728 | 2.0745 |
| 2.1796 | 57.0 | 741 | 2.1070 |
| 2.152 | 58.0 | 754 | 2.0930 |
| 2.1562 | 59.0 | 767 | 2.1106 |
| 2.125 | 60.0 | 780 | 2.1529 |
| 2.1318 | 61.0 | 793 | 2.0296 |
| 2.1194 | 62.0 | 806 | 2.0323 |
| 2.1396 | 63.0 | 819 | 1.9835 |
| 2.1108 | 64.0 | 832 | 2.0066 |
| 2.0874 | 65.0 | 845 | 1.9062 |
| 2.0754 | 66.0 | 858 | 2.1728 |
| 2.0928 | 67.0 | 871 | 2.0197 |
| 2.0835 | 68.0 | 884 | 2.0767 |
| 2.0684 | 69.0 | 897 | 2.1482 |
| 2.0505 | 70.0 | 910 | 2.0667 |
| 2.0564 | 71.0 | 923 | 2.1489 |
| 2.0478 | 72.0 | 936 | 2.0015 |
| 2.0478 | 73.0 | 949 | 1.9215 |
| 2.0316 | 74.0 | 962 | 2.0238 |
| 2.0171 | 75.0 | 975 | 2.0014 |
| 2.0248 | 76.0 | 988 | 2.0775 |
| 2.0066 | 77.0 | 1001 | 2.0390 |
| 2.0018 | 78.0 | 1014 | 2.0043 |
| 1.9925 | 79.0 | 1027 | 2.0138 |
| 1.9614 | 80.0 | 1040 | 1.9499 |
| 1.9877 | 81.0 | 1053 | 1.9642 |
| 1.9499 | 82.0 | 1066 | 1.9676 |
| 1.932 | 83.0 | 1079 | 1.9332 |
| 1.9353 | 84.0 | 1092 | 1.8787 |
| 1.9672 | 85.0 | 1105 | 1.9720 |
| 1.9313 | 86.0 | 1118 | 1.9343 |
| 1.9292 | 87.0 | 1131 | 1.8964 |
| 1.9277 | 88.0 | 1144 | 1.9619 |
| 1.9158 | 89.0 | 1157 | 1.9608 |
| 1.921 | 90.0 | 1170 | 1.9171 |
| 1.9191 | 91.0 | 1183 | 1.8871 |
| 1.8935 | 92.0 | 1196 | 1.8857 |
| 1.8818 | 93.0 | 1209 | 1.8909 |
| 1.8782 | 94.0 | 1222 | 1.8951 |
| 1.9028 | 95.0 | 1235 | 1.9164 |
| 1.8907 | 96.0 | 1248 | 1.9650 |
| 1.8626 | 97.0 | 1261 | 1.8906 |
| 1.8413 | 98.0 | 1274 | 1.8957 |
| 1.854 | 99.0 | 1287 | 1.9644 |
| 1.8608 | 100.0 | 1300 | 1.8329 |
| 1.8623 | 101.0 | 1313 | 1.8693 |
| 1.7798 | 102.0 | 1326 | 1.8913 |
| 1.846 | 103.0 | 1339 | 1.7854 |
| 1.7972 | 104.0 | 1352 | 1.8611 |
| 1.8443 | 105.0 | 1365 | 1.8482 |
| 1.791 | 106.0 | 1378 | 1.7168 |
| 1.7879 | 107.0 | 1391 | 1.8093 |
| 1.7886 | 108.0 | 1404 | 1.8924 |
| 1.8192 | 109.0 | 1417 | 1.7715 |
| 1.7919 | 110.0 | 1430 | 1.7415 |
| 1.7581 | 111.0 | 1443 | 1.7956 |
| 1.7873 | 112.0 | 1456 | 1.7213 |
| 1.7873 | 113.0 | 1469 | 1.7340 |
| 1.7764 | 114.0 | 1482 | 1.8535 |
| 1.7612 | 115.0 | 1495 | 1.8554 |
| 1.7737 | 116.0 | 1508 | 1.8126 |
| 1.7416 | 117.0 | 1521 | 1.8327 |
| 1.7648 | 118.0 | 1534 | 1.6832 |
| 1.7262 | 119.0 | 1547 | 1.6972 |
| 1.7334 | 120.0 | 1560 | 1.7930 |
| 1.7172 | 121.0 | 1573 | 1.6962 |
| 1.7282 | 122.0 | 1586 | 1.8800 |
| 1.7038 | 123.0 | 1599 | 1.7828 |
| 1.6935 | 124.0 | 1612 | 1.7646 |
| 1.758 | 125.0 | 1625 | 1.8069 |
| 1.7018 | 126.0 | 1638 | 1.6958 |
| 1.6886 | 127.0 | 1651 | 1.6692 |
| 1.7004 | 128.0 | 1664 | 1.7256 |
| 1.6947 | 129.0 | 1677 | 1.7587 |
| 1.6897 | 130.0 | 1690 | 1.7484 |
| 1.7037 | 131.0 | 1703 | 1.8455 |
| 1.6981 | 132.0 | 1716 | 1.7588 |
| 1.6828 | 133.0 | 1729 | 1.7421 |
| 1.6596 | 134.0 | 1742 | 1.6933 |
| 1.6782 | 135.0 | 1755 | 1.7040 |
| 1.6595 | 136.0 | 1768 | 1.6705 |
| 1.6567 | 137.0 | 1781 | 1.7744 |
| 1.6588 | 138.0 | 1794 | 1.6545 |
| 1.6225 | 139.0 | 1807 | 1.7576 |
| 1.6394 | 140.0 | 1820 | 1.7256 |
| 1.6515 | 141.0 | 1833 | 1.6668 |
| 1.6331 | 142.0 | 1846 | 1.7884 |
| 1.6367 | 143.0 | 1859 | 1.7093 |
| 1.6335 | 144.0 | 1872 | 1.7098 |
| 1.6501 | 145.0 | 1885 | 1.6671 |
| 1.6192 | 146.0 | 1898 | 1.7073 |
| 1.6198 | 147.0 | 1911 | 1.6653 |
| 1.6182 | 148.0 | 1924 | 1.6723 |
| 1.6172 | 149.0 | 1937 | 1.7293 |
| 1.6129 | 150.0 | 1950 | 1.6545 |
| 1.6054 | 151.0 | 1963 | 1.6850 |
| 1.5967 | 152.0 | 1976 | 1.7064 |
| 1.6028 | 153.0 | 1989 | 1.5292 |
| 1.6156 | 154.0 | 2002 | 1.6477 |
| 1.5965 | 155.0 | 2015 | 1.6110 |
| 1.5695 | 156.0 | 2028 | 1.7071 |
| 1.5586 | 157.0 | 2041 | 1.6504 |
| 1.561 | 158.0 | 2054 | 1.6147 |
| 1.5643 | 159.0 | 2067 | 1.6941 |
| 1.5797 | 160.0 | 2080 | 1.7398 |
| 1.5609 | 161.0 | 2093 | 1.5761 |
| 1.5465 | 162.0 | 2106 | 1.6003 |
| 1.5467 | 163.0 | 2119 | 1.5839 |
| 1.5935 | 164.0 | 2132 | 1.6530 |
| 1.5439 | 165.0 | 2145 | 1.6743 |
| 1.559 | 166.0 | 2158 | 1.5143 |
| 1.5648 | 167.0 | 2171 | 1.6390 |
| 1.552 | 168.0 | 2184 | 1.5389 |
| 1.5164 | 169.0 | 2197 | 1.5879 |
| 1.5342 | 170.0 | 2210 | 1.6785 |
| 1.5319 | 171.0 | 2223 | 1.6341 |
| 1.5477 | 172.0 | 2236 | 1.7071 |
| 1.5364 | 173.0 | 2249 | 1.6268 |
| 1.5366 | 174.0 | 2262 | 1.7247 |
| 1.5445 | 175.0 | 2275 | 1.6668 |
| 1.4916 | 176.0 | 2288 | 1.5756 |
| 1.509 | 177.0 | 2301 | 1.5412 |
| 1.5316 | 178.0 | 2314 | 1.6270 |
| 1.5156 | 179.0 | 2327 | 1.6423 |
| 1.4918 | 180.0 | 2340 | 1.6112 |
| 1.4997 | 181.0 | 2353 | 1.5775 |
| 1.5187 | 182.0 | 2366 | 1.6248 |
| 1.5254 | 183.0 | 2379 | 1.5884 |
| 1.4732 | 184.0 | 2392 | 1.5787 |
| 1.4844 | 185.0 | 2405 | 1.5358 |
| 1.4882 | 186.0 | 2418 | 1.5144 |
| 1.478 | 187.0 | 2431 | 1.5223 |
| 1.5101 | 188.0 | 2444 | 1.5787 |
| 1.4688 | 189.0 | 2457 | 1.5479 |
| 1.4815 | 190.0 | 2470 | 1.5141 |
| 1.4925 | 191.0 | 2483 | 1.5939 |
| 1.467 | 192.0 | 2496 | 1.5471 |
| 1.4718 | 193.0 | 2509 | 1.6845 |
| 1.4699 | 194.0 | 2522 | 1.5943 |
| 1.4562 | 195.0 | 2535 | 1.4745 |
| 1.4451 | 196.0 | 2548 | 1.5922 |
| 1.4451 | 197.0 | 2561 | 1.5856 |
| 1.4624 | 198.0 | 2574 | 1.5519 |
| 1.444 | 199.0 | 2587 | 1.6538 |
| 1.4498 | 200.0 | 2600 | 1.5037 |
| 1.4285 | 201.0 | 2613 | 1.5539 |
| 1.4439 | 202.0 | 2626 | 1.5387 |
| 1.4177 | 203.0 | 2639 | 1.5756 |
| 1.436 | 204.0 | 2652 | 1.6136 |
| 1.4184 | 205.0 | 2665 | 1.5014 |
| 1.43 | 206.0 | 2678 | 1.4983 |
| 1.4347 | 207.0 | 2691 | 1.5896 |
| 1.39 | 208.0 | 2704 | 1.5506 |
| 1.4198 | 209.0 | 2717 | 1.5142 |
| 1.4101 | 210.0 | 2730 | 1.4930 |
| 1.4219 | 211.0 | 2743 | 1.4814 |
| 1.4039 | 212.0 | 2756 | 1.3750 |
| 1.4479 | 213.0 | 2769 | 1.5330 |
| 1.4354 | 214.0 | 2782 | 1.5179 |
| 1.4163 | 215.0 | 2795 | 1.5970 |
| 1.4459 | 216.0 | 2808 | 1.4755 |
| 1.3714 | 217.0 | 2821 | 1.4230 |
| 1.3957 | 218.0 | 2834 | 1.5087 |
| 1.396 | 219.0 | 2847 | 1.5570 |
| 1.3866 | 220.0 | 2860 | 1.4955 |
| 1.4122 | 221.0 | 2873 | 1.4272 |
| 1.371 | 222.0 | 2886 | 1.5209 |
| 1.3907 | 223.0 | 2899 | 1.4725 |
| 1.3856 | 224.0 | 2912 | 1.5021 |
| 1.4053 | 225.0 | 2925 | 1.4880 |
| 1.4074 | 226.0 | 2938 | 1.4988 |
| 1.3827 | 227.0 | 2951 | 1.5527 |
| 1.4045 | 228.0 | 2964 | 1.5350 |
| 1.3626 | 229.0 | 2977 | 1.5093 |
| 1.3795 | 230.0 | 2990 | 1.4497 |
| 1.3973 | 231.0 | 3003 | 1.5106 |
| 1.3703 | 232.0 | 3016 | 1.4619 |
| 1.3942 | 233.0 | 3029 | 1.4553 |
| 1.3447 | 234.0 | 3042 | 1.5061 |
| 1.3438 | 235.0 | 3055 | 1.5167 |
| 1.3496 | 236.0 | 3068 | 1.4060 |
| 1.3614 | 237.0 | 3081 | 1.4211 |
| 1.3618 | 238.0 | 3094 | 1.4624 |
| 1.359 | 239.0 | 3107 | 1.4450 |
| 1.3657 | 240.0 | 3120 | 1.4795 |
| 1.3599 | 241.0 | 3133 | 1.4887 |
| 1.3532 | 242.0 | 3146 | 1.4606 |
| 1.3528 | 243.0 | 3159 | 1.4225 |
| 1.3445 | 244.0 | 3172 | 1.3912 |
| 1.3344 | 245.0 | 3185 | 1.4055 |
| 1.3358 | 246.0 | 3198 | 1.5152 |
| 1.3591 | 247.0 | 3211 | 1.4825 |
| 1.3162 | 248.0 | 3224 | 1.4721 |
| 1.3197 | 249.0 | 3237 | 1.4375 |
| 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 |
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Model tree for polylexmg/bert-base-greek-uncased-v6-finetuned-polylex-mg
Base model
nlpaueb/bert-base-greek-uncased-v1