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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: 0.4970
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- - Accuracy: 0.8980
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 3.3128682238257206e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
@@ -47,16 +47,16 @@ 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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- | 0.7405 | 1.0 | 2440 | 0.6288 | 0.8734 |
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- | 0.6564 | 2.0 | 4880 | 0.5823 | 0.8806 |
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- | 0.617 | 3.0 | 7320 | 0.5683 | 0.8840 |
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- | 0.5824 | 4.0 | 9760 | 0.5393 | 0.8886 |
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- | 0.5455 | 5.0 | 12200 | 0.5233 | 0.8923 |
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- | 0.5346 | 6.0 | 14640 | 0.5160 | 0.8936 |
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- | 0.5315 | 7.0 | 17080 | 0.5139 | 0.8942 |
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- | 0.5072 | 8.0 | 19520 | 0.5033 | 0.8960 |
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- | 0.5056 | 9.0 | 21960 | 0.5007 | 0.8965 |
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- | 0.5046 | 10.0 | 24400 | 0.4970 | 0.8980 |
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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: 0.5131
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+ - Accuracy: 0.8949
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 2.222657529023189e-05
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  - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.7592 | 1.0 | 2440 | 0.6467 | 0.8680 |
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+ | 0.673 | 2.0 | 4880 | 0.5955 | 0.8781 |
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+ | 0.6342 | 3.0 | 7320 | 0.5801 | 0.8816 |
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+ | 0.6022 | 4.0 | 9760 | 0.5492 | 0.8869 |
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+ | 0.5656 | 5.0 | 12200 | 0.5343 | 0.8905 |
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+ | 0.5567 | 6.0 | 14640 | 0.5273 | 0.8914 |
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+ | 0.5571 | 7.0 | 17080 | 0.5284 | 0.8914 |
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+ | 0.5329 | 8.0 | 19520 | 0.5174 | 0.8932 |
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+ | 0.535 | 9.0 | 21960 | 0.5168 | 0.8933 |
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+ | 0.5341 | 10.0 | 24400 | 0.5131 | 0.8949 |
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  ### Framework versions