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roberta
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@@ -15,8 +15,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5096
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- - Accuracy: 0.8933
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  ## Model description
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@@ -50,56 +50,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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- | 1.1808 | 1.0 | 599 | 0.7826 | 0.8420 |
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- | 0.8381 | 2.0 | 1198 | 0.7008 | 0.8581 |
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- | 0.7733 | 3.0 | 1797 | 0.6717 | 0.8639 |
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- | 0.7416 | 4.0 | 2396 | 0.6460 | 0.8682 |
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- | 0.7143 | 5.0 | 2995 | 0.6331 | 0.8708 |
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- | 0.683 | 6.0 | 3594 | 0.6243 | 0.8723 |
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- | 0.6609 | 7.0 | 4193 | 0.6151 | 0.8744 |
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- | 0.6547 | 8.0 | 4792 | 0.5987 | 0.8765 |
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- | 0.6467 | 9.0 | 5391 | 0.5969 | 0.8776 |
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- | 0.6366 | 10.0 | 5990 | 0.5890 | 0.8786 |
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- | 0.6176 | 11.0 | 6589 | 0.5785 | 0.8801 |
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- | 0.6106 | 12.0 | 7188 | 0.5813 | 0.8803 |
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- | 0.6026 | 13.0 | 7787 | 0.5644 | 0.8834 |
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- | 0.6005 | 14.0 | 8386 | 0.5600 | 0.8841 |
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- | 0.5965 | 15.0 | 8985 | 0.5653 | 0.8832 |
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- | 0.5851 | 16.0 | 9584 | 0.5544 | 0.8850 |
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- | 0.5781 | 17.0 | 10183 | 0.5543 | 0.8849 |
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- | 0.5732 | 18.0 | 10782 | 0.5464 | 0.8862 |
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- | 0.5713 | 19.0 | 11381 | 0.5448 | 0.8860 |
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- | 0.5678 | 20.0 | 11980 | 0.5452 | 0.8869 |
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- | 0.5615 | 21.0 | 12579 | 0.5395 | 0.8883 |
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- | 0.5543 | 22.0 | 13178 | 0.5383 | 0.8881 |
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- | 0.555 | 23.0 | 13777 | 0.5456 | 0.8870 |
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- | 0.5517 | 24.0 | 14376 | 0.5314 | 0.8890 |
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- | 0.5478 | 25.0 | 14975 | 0.5355 | 0.8878 |
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- | 0.5423 | 26.0 | 15574 | 0.5316 | 0.8892 |
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- | 0.5402 | 27.0 | 16173 | 0.5261 | 0.8903 |
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- | 0.5385 | 28.0 | 16772 | 0.5343 | 0.8884 |
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- | 0.5358 | 29.0 | 17371 | 0.5288 | 0.8894 |
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- | 0.5319 | 30.0 | 17970 | 0.5200 | 0.8912 |
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- | 0.5292 | 31.0 | 18569 | 0.5142 | 0.8923 |
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- | 0.529 | 32.0 | 19168 | 0.5174 | 0.8915 |
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- | 0.5233 | 33.0 | 19767 | 0.5253 | 0.8905 |
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- | 0.5236 | 34.0 | 20366 | 0.5135 | 0.8917 |
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- | 0.5269 | 35.0 | 20965 | 0.5127 | 0.8931 |
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- | 0.5145 | 36.0 | 21564 | 0.5182 | 0.8909 |
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- | 0.5192 | 37.0 | 22163 | 0.5185 | 0.8912 |
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- | 0.5154 | 38.0 | 22762 | 0.5160 | 0.8927 |
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- | 0.5131 | 39.0 | 23361 | 0.5135 | 0.8926 |
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- | 0.513 | 40.0 | 23960 | 0.5125 | 0.8924 |
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- | 0.5106 | 41.0 | 24559 | 0.5137 | 0.8919 |
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- | 0.5079 | 42.0 | 25158 | 0.5052 | 0.8935 |
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- | 0.508 | 43.0 | 25757 | 0.5172 | 0.8926 |
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- | 0.5104 | 44.0 | 26356 | 0.5062 | 0.8933 |
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- | 0.5066 | 45.0 | 26955 | 0.5076 | 0.8933 |
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- | 0.5085 | 46.0 | 27554 | 0.5123 | 0.8922 |
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- | 0.5064 | 47.0 | 28153 | 0.5102 | 0.8937 |
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- | 0.5058 | 48.0 | 28752 | 0.5127 | 0.8929 |
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- | 0.5028 | 49.0 | 29351 | 0.5164 | 0.8930 |
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- | 0.5036 | 50.0 | 29950 | 0.5096 | 0.8933 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.7476
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+ - Accuracy: 0.6825
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 2.655 | 1.0 | 599 | 2.1802 | 0.5963 |
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+ | 2.2909 | 2.0 | 1198 | 2.0666 | 0.6172 |
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+ | 2.1673 | 3.0 | 1797 | 2.0162 | 0.6197 |
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+ | 2.0743 | 4.0 | 2396 | 1.9740 | 0.6283 |
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+ | 1.9895 | 5.0 | 2995 | 1.9439 | 0.6338 |
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+ | 1.8841 | 6.0 | 3594 | 1.9338 | 0.6291 |
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+ | 1.8547 | 7.0 | 4193 | 1.7883 | 0.6559 |
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+ | 1.7867 | 8.0 | 4792 | 1.8879 | 0.6436 |
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+ | 1.7491 | 9.0 | 5391 | 1.8640 | 0.6445 |
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+ | 1.7008 | 10.0 | 5990 | 1.7935 | 0.6591 |
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+ | 1.631 | 11.0 | 6589 | 1.7864 | 0.6556 |
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+ | 1.6094 | 12.0 | 7188 | 1.7964 | 0.6541 |
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+ | 1.5755 | 13.0 | 7787 | 1.7675 | 0.6652 |
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+ | 1.5787 | 14.0 | 8386 | 1.8498 | 0.6515 |
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+ | 1.5235 | 15.0 | 8985 | 1.7363 | 0.6674 |
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+ | 1.4996 | 16.0 | 9584 | 1.7428 | 0.6641 |
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+ | 1.4571 | 17.0 | 10183 | 1.7004 | 0.6790 |
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+ | 1.4617 | 18.0 | 10782 | 1.7714 | 0.6635 |
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+ | 1.4219 | 19.0 | 11381 | 1.8232 | 0.6563 |
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+ | 1.3959 | 20.0 | 11980 | 1.7245 | 0.6752 |
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+ | 1.3801 | 21.0 | 12579 | 1.7234 | 0.6750 |
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+ | 1.3549 | 22.0 | 13178 | 1.6884 | 0.6817 |
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+ | 1.3227 | 23.0 | 13777 | 1.7566 | 0.6687 |
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+ | 1.3455 | 24.0 | 14376 | 1.7102 | 0.6745 |
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+ | 1.3239 | 25.0 | 14975 | 1.7388 | 0.6730 |
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+ | 1.3066 | 26.0 | 15574 | 1.7391 | 0.6790 |
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+ | 1.2598 | 27.0 | 16173 | 1.6754 | 0.6869 |
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+ | 1.2552 | 28.0 | 16772 | 1.6499 | 0.6798 |
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+ | 1.2431 | 29.0 | 17371 | 1.7397 | 0.6740 |
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+ | 1.2115 | 30.0 | 17970 | 1.7096 | 0.6745 |
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+ | 1.1842 | 31.0 | 18569 | 1.7159 | 0.6751 |
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+ | 1.1799 | 32.0 | 19168 | 1.7341 | 0.6788 |
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+ | 1.1755 | 33.0 | 19767 | 1.7557 | 0.6652 |
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+ | 1.1704 | 34.0 | 20366 | 1.7147 | 0.6771 |
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+ | 1.1427 | 35.0 | 20965 | 1.7631 | 0.6670 |
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+ | 1.1464 | 36.0 | 21564 | 1.7083 | 0.6750 |
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+ | 1.1179 | 37.0 | 22163 | 1.6978 | 0.6718 |
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+ | 1.1247 | 38.0 | 22762 | 1.7205 | 0.6757 |
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+ | 1.1204 | 39.0 | 23361 | 1.7403 | 0.6663 |
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+ | 1.0939 | 40.0 | 23960 | 1.6621 | 0.6852 |
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+ | 1.0904 | 41.0 | 24559 | 1.7671 | 0.6667 |
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+ | 1.0815 | 42.0 | 25158 | 1.7304 | 0.6789 |
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+ | 1.0879 | 43.0 | 25757 | 1.7346 | 0.6858 |
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+ | 1.0718 | 44.0 | 26356 | 1.7841 | 0.6691 |
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+ | 1.0599 | 45.0 | 26955 | 1.7482 | 0.6742 |
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+ | 1.0815 | 46.0 | 27554 | 1.6738 | 0.6823 |
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+ | 1.0812 | 47.0 | 28153 | 1.7573 | 0.6799 |
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+ | 1.0529 | 48.0 | 28752 | 1.6627 | 0.6849 |
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+ | 1.0675 | 49.0 | 29351 | 1.6641 | 0.6785 |
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+ | 1.0593 | 50.0 | 29950 | 1.7476 | 0.6825 |
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  ### Framework versions