update model card README.md

README.md

@@ -16,9 +16,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli](https://huggingface.co/ynie/electra-large-discriminator-snli_mnli_fever_anli_R1_R2_R3-nli) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4166
 - Accuracy: 0.798
 - F1: 0.7968
+- Loss: 0.4166
 
 ## Model description
 
@@ -49,28 +49,28 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
-| 0.4175        | 1.0   | 210  | 0.4020          | 0.7317   | 0.7305 |
-| 0.3061        | 2.0   | 420  | 0.3520          | 0.768    | 0.7675 |
-| 0.2588        | 3.0   | 630  | 0.3253          | 0.79     | 0.7888 |
-| 0.234         | 4.0   | 840  | 0.3373          | 0.788    | 0.7877 |
-| 0.2116        | 5.0   | 1050 | 0.3247          | 0.804    | 0.8033 |
-| 0.1974        | 6.0   | 1260 | 0.3400          | 0.793    | 0.7928 |
-| 0.1807        | 7.0   | 1470 | 0.3511          | 0.7973   | 0.7969 |
-| 0.1715        | 8.0   | 1680 | 0.3496          | 0.7993   | 0.7989 |
-| 0.1577        | 9.0   | 1890 | 0.3507          | 0.8043   | 0.8032 |
-| 0.1469        | 10.0  | 2100 | 0.3604          | 0.798    | 0.7970 |
-| 0.1394        | 11.0  | 2310 | 0.3734          | 0.7967   | 0.7957 |
-| 0.1322        | 12.0  | 2520 | 0.3929          | 0.7913   | 0.7906 |
-| 0.1231        | 13.0  | 2730 | 0.3954          | 0.795    | 0.7941 |
-| 0.1189        | 14.0  | 2940 | 0.3994          | 0.7977   | 0.7963 |
-| 0.1143        | 15.0  | 3150 | 0.3995          | 0.7993   | 0.7980 |
-| 0.1083        | 16.0  | 3360 | 0.4125          | 0.7927   | 0.7918 |
-| 0.1079        | 17.0  | 3570 | 0.4036          | 0.7993   | 0.7979 |
-| 0.1055        | 18.0  | 3780 | 0.4121          | 0.7967   | 0.7956 |
-| 0.1006        | 19.0  | 3990 | 0.4152          | 0.7973   | 0.7961 |
-| 0.101         | 20.0  | 4200 | 0.4166          | 0.798    | 0.7968 |
+| Training Loss | Epoch | Step | Accuracy | F1     | Validation Loss |
+|:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|
+| 0.4175        | 1.0   | 210  | 0.7317   | 0.7305 | 0.4020          |
+| 0.3061        | 2.0   | 420  | 0.768    | 0.7675 | 0.3520          |
+| 0.2588        | 3.0   | 630  | 0.79     | 0.7888 | 0.3253          |
+| 0.234         | 4.0   | 840  | 0.788    | 0.7877 | 0.3373          |
+| 0.2116        | 5.0   | 1050 | 0.804    | 0.8033 | 0.3247          |
+| 0.1974        | 6.0   | 1260 | 0.793    | 0.7928 | 0.3400          |
+| 0.1807        | 7.0   | 1470 | 0.7973   | 0.7969 | 0.3511          |
+| 0.1715        | 8.0   | 1680 | 0.7993   | 0.7989 | 0.3496          |
+| 0.1577        | 9.0   | 1890 | 0.8043   | 0.8032 | 0.3507          |
+| 0.1469        | 10.0  | 2100 | 0.798    | 0.7970 | 0.3604          |
+| 0.1394        | 11.0  | 2310 | 0.7967   | 0.7957 | 0.3734          |
+| 0.1322        | 12.0  | 2520 | 0.7913   | 0.7906 | 0.3929          |
+| 0.1231        | 13.0  | 2730 | 0.795    | 0.7941 | 0.3954          |
+| 0.1189        | 14.0  | 2940 | 0.7977   | 0.7963 | 0.3994          |
+| 0.1143        | 15.0  | 3150 | 0.7993   | 0.7980 | 0.3995          |
+| 0.1083        | 16.0  | 3360 | 0.7927   | 0.7918 | 0.4125          |
+| 0.1079        | 17.0  | 3570 | 0.7993   | 0.7979 | 0.4036          |
+| 0.1055        | 18.0  | 3780 | 0.7967   | 0.7956 | 0.4121          |
+| 0.1006        | 19.0  | 3990 | 0.7973   | 0.7961 | 0.4152          |
+| 0.101         | 20.0  | 4200 | 0.798    | 0.7968 | 0.4166          |
 
 
 ### Framework versions