update model card README.md

README.md

@@ -16,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [microsoft/codebert-base-mlm](https://huggingface.co/microsoft/codebert-base-mlm) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5977
-- Accuracy: 0.8793
-- F1: 0.8793
-- Bleu4: 0.8016
+- Loss: 0.4812
+- Accuracy: 0.8993
+- F1: 0.8993
+- Bleu4: 0.9483
 
 ## Model description
 
@@ -39,22 +39,43 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 5
+- num_epochs: 50
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Bleu4  |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
-| 0.8373        | 1.0   | 1373 | 0.6817          | 0.8587   | 0.8587 | 0.8615 |
-| 0.6984        | 2.0   | 2746 | 0.6406          | 0.8685   | 0.8685 | 0.9062 |
-| 0.6587        | 3.0   | 4119 | 0.6172          | 0.8748   | 0.8748 | 0.9067 |
-| 0.6514        | 4.0   | 5492 | 0.6017          | 0.8783   | 0.8783 | 0.9198 |
-| 0.6263        | 5.0   | 6865 | 0.5977          | 0.8793   | 0.8793 | 0.8016 |
+| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Bleu4  |
+|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|
+| 1.1319        | 1.0   | 687   | 0.6982          | 0.8562   | 0.8562 | 0.8551 |
+| 0.7784        | 2.0   | 1374  | 0.6501          | 0.8665   | 0.8665 | 0.8977 |
+| 0.6779        | 3.0   | 2061  | 0.6229          | 0.8733   | 0.8733 | 0.8535 |
+| 0.6579        | 4.0   | 2748  | 0.5978          | 0.8769   | 0.8769 | 0.9176 |
+| 0.6319        | 5.0   | 3435  | 0.5833          | 0.8808   | 0.8808 | 0.8073 |
+| 0.5988        | 6.0   | 4122  | 0.5627          | 0.8834   | 0.8834 | 0.9241 |
+| 0.5939        | 7.0   | 4809  | 0.5533          | 0.8864   | 0.8864 | 0.9212 |
+| 0.575         | 8.0   | 5496  | 0.5512          | 0.8860   | 0.8860 | 0.7943 |
+| 0.5574        | 9.0   | 6183  | 0.5412          | 0.8879   | 0.8879 | 0.9396 |
+| 0.553         | 10.0  | 6870  | 0.5276          | 0.8899   | 0.8899 | 0.8301 |
+| 0.5371        | 11.0  | 7557  | 0.5341          | 0.8893   | 0.8893 | 0.9350 |
+| 0.5302        | 12.0  | 8244  | 0.5236          | 0.8909   | 0.8909 | 0.8813 |
+| 0.5245        | 13.0  | 8931  | 0.5153          | 0.8933   | 0.8933 | 0.8817 |
+| 0.5165        | 14.0  | 9618  | 0.5138          | 0.8926   | 0.8926 | 0.9174 |
+| 0.5122        | 15.0  | 10305 | 0.5144          | 0.8930   | 0.8930 | 0.8318 |
+| 0.5007        | 16.0  | 10992 | 0.5007          | 0.8957   | 0.8957 | 0.9350 |
+| 0.4954        | 17.0  | 11679 | 0.5041          | 0.8960   | 0.8960 | 0.9355 |
+| 0.4894        | 18.0  | 12366 | 0.5000          | 0.8967   | 0.8967 | 0.7818 |
+| 0.4851        | 19.0  | 13053 | 0.4915          | 0.8982   | 0.8982 | 0.9190 |
+| 0.483         | 20.0  | 13740 | 0.4970          | 0.8962   | 0.8962 | 0.9359 |
+| 0.4792        | 21.0  | 14427 | 0.4849          | 0.8971   | 0.8971 | 0.8458 |
+| 0.4716        | 22.0  | 15114 | 0.4809          | 0.8990   | 0.8990 | 0.9367 |
+| 0.4691        | 23.0  | 15801 | 0.4732          | 0.9006   | 0.9006 | 0.9478 |
+| 0.4675        | 24.0  | 16488 | 0.4805          | 0.8989   | 0.8989 | 0.9412 |
+| 0.4618        | 25.0  | 17175 | 0.4837          | 0.8997   | 0.8997 | 0.8373 |
+| 0.4633        | 26.0  | 17862 | 0.4812          | 0.8993   | 0.8993 | 0.9483 |
 
 
 ### Framework versions