mamiksik
/

CommitPredictor

@@ -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: 0.4812
-- Accuracy: 0.8993
-- F1: 0.8993
-- Bleu4: 0.9483
 ## Model description
@@ -50,32 +50,32 @@ The following hyperparameters were used during training:
 | 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

 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: 0.4811
+- Accuracy: 0.8991
+- F1: 0.8991
+- Bleu4: 0.9479
 ## Model description
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Bleu4  |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|
+| 1.143         | 1.0   | 687   | 0.6993          | 0.8563   | 0.8563 | 0.8531 |
+| 0.7772        | 2.0   | 1374  | 0.6482          | 0.8677   | 0.8677 | 0.9036 |
+| 0.6738        | 3.0   | 2061  | 0.6211          | 0.8734   | 0.8734 | 0.8189 |
+| 0.6544        | 4.0   | 2748  | 0.5942          | 0.8782   | 0.8782 | 0.9196 |
+| 0.6295        | 5.0   | 3435  | 0.5805          | 0.8815   | 0.8815 | 0.8079 |
+| 0.5966        | 6.0   | 4122  | 0.5609          | 0.8838   | 0.8838 | 0.8186 |
+| 0.5916        | 7.0   | 4809  | 0.5514          | 0.8870   | 0.8870 | 0.9103 |
+| 0.5732        | 8.0   | 5496  | 0.5492          | 0.8861   | 0.8861 | 0.8067 |
+| 0.5559        | 9.0   | 6183  | 0.5389          | 0.8881   | 0.8881 | 0.9353 |
+| 0.5511        | 10.0  | 6870  | 0.5257          | 0.8901   | 0.8901 | 0.9297 |
+| 0.5345        | 11.0  | 7557  | 0.5319          | 0.8905   | 0.8905 | 0.9363 |
+| 0.5287        | 12.0  | 8244  | 0.5220          | 0.8911   | 0.8911 | 0.8816 |
+| 0.5226        | 13.0  | 8931  | 0.5139          | 0.8938   | 0.8938 | 0.9438 |
+| 0.5147        | 14.0  | 9618  | 0.5124          | 0.8929   | 0.8929 | 0.9145 |
+| 0.511         | 15.0  | 10305 | 0.5131          | 0.8932   | 0.8932 | 0.8570 |
+| 0.4996        | 16.0  | 10992 | 0.4997          | 0.8964   | 0.8964 | 0.9287 |
+| 0.4949        | 17.0  | 11679 | 0.5033          | 0.8958   | 0.8958 | 0.9460 |
+| 0.4882        | 18.0  | 12366 | 0.5003          | 0.8971   | 0.8971 | 0.7739 |
+| 0.4837        | 19.0  | 13053 | 0.4914          | 0.8979   | 0.8979 | 0.9014 |
+| 0.4822        | 20.0  | 13740 | 0.4962          | 0.8963   | 0.8963 | 0.9330 |
+| 0.4778        | 21.0  | 14427 | 0.4844          | 0.8971   | 0.8971 | 0.8454 |
+| 0.4704        | 22.0  | 15114 | 0.4809          | 0.8988   | 0.8988 | 0.9274 |
+| 0.4676        | 23.0  | 15801 | 0.4735          | 0.9009   | 0.9009 | 0.9445 |
+| 0.4663        | 24.0  | 16488 | 0.4792          | 0.8990   | 0.8990 | 0.9001 |
+| 0.4605        | 25.0  | 17175 | 0.4826          | 0.8995   | 0.8995 | 0.8313 |
+| 0.4621        | 26.0  | 17862 | 0.4811          | 0.8991   | 0.8991 | 0.9479 |
 ### Framework versions