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update model card README.md

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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: 1.7476
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- - Accuracy: 0.6825
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 21
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- - eval_batch_size: 21
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  - seed: 42
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  - gradient_accumulation_steps: 3
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- - total_train_batch_size: 63
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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: 50
@@ -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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- | 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
 
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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: 1.6621
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+ - Accuracy: 0.6851
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 42
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+ - eval_batch_size: 42
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  - seed: 42
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  - gradient_accumulation_steps: 3
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+ - total_train_batch_size: 126
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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: 50
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | No log | 1.0 | 299 | 2.2298 | 0.5874 |
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+ | 2.5371 | 2.0 | 598 | 2.1358 | 0.6110 |
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+ | 2.5371 | 3.0 | 897 | 2.0865 | 0.6056 |
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+ | 2.1935 | 4.0 | 1196 | 2.0596 | 0.6179 |
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+ | 2.1935 | 5.0 | 1495 | 1.9902 | 0.6305 |
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+ | 2.0549 | 6.0 | 1794 | 1.9647 | 0.6274 |
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+ | 1.9558 | 7.0 | 2093 | 1.9462 | 0.6290 |
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+ | 1.9558 | 8.0 | 2392 | 1.9443 | 0.6261 |
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+ | 1.8732 | 9.0 | 2691 | 1.9241 | 0.6317 |
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+ | 1.8732 | 10.0 | 2990 | 1.8810 | 0.6461 |
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+ | 1.798 | 11.0 | 3289 | 1.8232 | 0.6434 |
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+ | 1.7427 | 12.0 | 3588 | 1.8621 | 0.6452 |
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+ | 1.7427 | 13.0 | 3887 | 1.7853 | 0.6596 |
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+ | 1.7124 | 14.0 | 4186 | 1.8741 | 0.6451 |
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+ | 1.7124 | 15.0 | 4485 | 1.7989 | 0.6536 |
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+ | 1.6683 | 16.0 | 4784 | 1.7783 | 0.6582 |
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+ | 1.59 | 17.0 | 5083 | 1.7738 | 0.6642 |
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+ | 1.59 | 18.0 | 5382 | 1.8241 | 0.6534 |
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+ | 1.5773 | 19.0 | 5681 | 1.8739 | 0.6547 |
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+ | 1.5773 | 20.0 | 5980 | 1.7439 | 0.6695 |
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+ | 1.532 | 21.0 | 6279 | 1.7081 | 0.6705 |
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+ | 1.4875 | 22.0 | 6578 | 1.7486 | 0.6662 |
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+ | 1.4875 | 23.0 | 6877 | 1.7568 | 0.6656 |
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+ | 1.466 | 24.0 | 7176 | 1.8062 | 0.6658 |
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+ | 1.466 | 25.0 | 7475 | 1.7666 | 0.6704 |
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+ | 1.448 | 26.0 | 7774 | 1.7219 | 0.6670 |
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+ | 1.4121 | 27.0 | 8073 | 1.6704 | 0.6745 |
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+ | 1.4121 | 28.0 | 8372 | 1.6966 | 0.6719 |
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+ | 1.3984 | 29.0 | 8671 | 1.6789 | 0.6825 |
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+ | 1.3984 | 30.0 | 8970 | 1.7001 | 0.6797 |
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+ | 1.3586 | 31.0 | 9269 | 1.7262 | 0.6712 |
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+ | 1.3433 | 32.0 | 9568 | 1.7446 | 0.6744 |
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+ | 1.3433 | 33.0 | 9867 | 1.6961 | 0.6752 |
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+ | 1.3366 | 34.0 | 10166 | 1.7180 | 0.6729 |
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+ | 1.3366 | 35.0 | 10465 | 1.6608 | 0.6773 |
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+ | 1.3227 | 36.0 | 10764 | 1.6820 | 0.6814 |
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+ | 1.3025 | 37.0 | 11063 | 1.7324 | 0.6727 |
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+ | 1.3025 | 38.0 | 11362 | 1.6705 | 0.6882 |
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+ | 1.2933 | 39.0 | 11661 | 1.6891 | 0.6742 |
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+ | 1.2933 | 40.0 | 11960 | 1.6533 | 0.6797 |
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+ | 1.2826 | 41.0 | 12259 | 1.6851 | 0.6770 |
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+ | 1.2784 | 42.0 | 12558 | 1.7140 | 0.6806 |
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+ | 1.2784 | 43.0 | 12857 | 1.6869 | 0.6769 |
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+ | 1.2703 | 44.0 | 13156 | 1.7068 | 0.6730 |
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+ | 1.2703 | 45.0 | 13455 | 1.7376 | 0.6681 |
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+ | 1.2492 | 46.0 | 13754 | 1.6944 | 0.6751 |
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+ | 1.2619 | 47.0 | 14053 | 1.8112 | 0.6644 |
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+ | 1.2619 | 48.0 | 14352 | 1.7553 | 0.6721 |
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+ | 1.2465 | 49.0 | 14651 | 1.7040 | 0.6713 |
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+ | 1.2465 | 50.0 | 14950 | 1.6621 | 0.6851 |
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  ### Framework versions