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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: 1.6621
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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
@@ -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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- | 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
 
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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: 1.9935
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+ - Accuracy: 0.6325
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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
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | No log | 1.0 | 448 | 2.4744 | 0.5376 |
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+ | 2.9007 | 2.0 | 896 | 2.4149 | 0.5473 |
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+ | 2.5284 | 3.0 | 1344 | 2.3077 | 0.5639 |
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+ | 2.3292 | 4.0 | 1792 | 2.2617 | 0.5640 |
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+ | 2.2692 | 5.0 | 2240 | 2.2155 | 0.5719 |
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+ | 2.1766 | 6.0 | 2688 | 2.1555 | 0.5792 |
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+ | 2.0842 | 7.0 | 3136 | 2.0758 | 0.6030 |
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+ | 2.0268 | 8.0 | 3584 | 2.1446 | 0.5942 |
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+ | 1.9416 | 9.0 | 4032 | 2.1110 | 0.5840 |
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+ | 1.9416 | 10.0 | 4480 | 2.1379 | 0.5888 |
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+ | 1.8969 | 11.0 | 4928 | 2.0461 | 0.6082 |
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+ | 1.8247 | 12.0 | 5376 | 2.0585 | 0.6007 |
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+ | 1.8038 | 13.0 | 5824 | 2.0541 | 0.6022 |
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+ | 1.7601 | 14.0 | 6272 | 2.0832 | 0.6043 |
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+ | 1.7086 | 15.0 | 6720 | 2.0224 | 0.6096 |
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+ | 1.7087 | 16.0 | 7168 | 2.0853 | 0.6057 |
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+ | 1.653 | 17.0 | 7616 | 2.0259 | 0.6124 |
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+ | 1.5953 | 18.0 | 8064 | 1.9913 | 0.6207 |
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+ | 1.6074 | 19.0 | 8512 | 1.9798 | 0.6157 |
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+ | 1.6074 | 20.0 | 8960 | 2.0234 | 0.6033 |
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+ | 1.5749 | 21.0 | 9408 | 1.9686 | 0.6197 |
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+ | 1.535 | 22.0 | 9856 | 2.0068 | 0.6163 |
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+ | 1.4942 | 23.0 | 10304 | 1.9486 | 0.6310 |
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+ | 1.4765 | 24.0 | 10752 | 1.9502 | 0.6304 |
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+ | 1.4558 | 25.0 | 11200 | 1.9509 | 0.6328 |
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+ | 1.4617 | 26.0 | 11648 | 1.9903 | 0.6196 |
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+ | 1.4224 | 27.0 | 12096 | 1.9849 | 0.6321 |
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+ | 1.4019 | 28.0 | 12544 | 1.9781 | 0.6193 |
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+ | 1.4019 | 29.0 | 12992 | 2.0661 | 0.6145 |
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+ | 1.3624 | 30.0 | 13440 | 1.9948 | 0.6191 |
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+ | 1.3517 | 31.0 | 13888 | 1.9117 | 0.6392 |
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+ | 1.3613 | 32.0 | 14336 | 2.0300 | 0.6176 |
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+ | 1.3428 | 33.0 | 14784 | 2.0005 | 0.6226 |
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+ | 1.3257 | 34.0 | 15232 | 2.0079 | 0.6149 |
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+ | 1.3127 | 35.0 | 15680 | 2.0231 | 0.6213 |
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+ | 1.289 | 36.0 | 16128 | 1.9961 | 0.6296 |
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+ | 1.2689 | 37.0 | 16576 | 1.9930 | 0.6221 |
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+ | 1.2651 | 38.0 | 17024 | 1.9675 | 0.6314 |
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+ | 1.2651 | 39.0 | 17472 | 1.9835 | 0.6220 |
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+ | 1.2638 | 40.0 | 17920 | nan | 0.6275 |
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+ | 1.235 | 41.0 | 18368 | 2.0100 | 0.6299 |
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+ | 1.2239 | 42.0 | 18816 | 2.0384 | 0.6152 |
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+ | 1.2147 | 43.0 | 19264 | 2.0421 | 0.6209 |
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+ | 1.1961 | 44.0 | 19712 | 2.0041 | 0.6212 |
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+ | 1.1988 | 45.0 | 20160 | 1.9905 | 0.6230 |
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+ | 1.2007 | 46.0 | 20608 | 2.0222 | 0.6275 |
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+ | 1.2029 | 47.0 | 21056 | 1.9856 | 0.6361 |
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+ | 1.1779 | 48.0 | 21504 | 2.0348 | 0.6184 |
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+ | 1.1779 | 49.0 | 21952 | 1.9196 | 0.6324 |
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+ | 1.1973 | 50.0 | 22400 | 1.9935 | 0.6325 |
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  ### Framework versions