NasimB
/

guten

@@ -16,7 +16,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
 It achieves the following results on the evaluation set:
-- Loss: 4.1135
 ## Model description
@@ -49,26 +49,26 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
-| 6.3466        | 0.29  | 500   | 5.3356          |
-| 5.0281        | 0.59  | 1000  | 4.9230          |
-| 4.7091        | 0.88  | 1500  | 4.6942          |
-| 4.4483        | 1.17  | 2000  | 4.5549          |
-| 4.2978        | 1.47  | 2500  | 4.4434          |
-| 4.2034        | 1.76  | 3000  | 4.3369          |
-| 4.0744        | 2.05  | 3500  | 4.2701          |
-| 3.9019        | 2.34  | 4000  | 4.2181          |
-| 3.8733        | 2.64  | 4500  | 4.1615          |
-| 3.8337        | 2.93  | 5000  | 4.1144          |
-| 3.6343        | 3.22  | 5500  | 4.1048          |
-| 3.5899        | 3.52  | 6000  | 4.0780          |
-| 3.5756        | 3.81  | 6500  | 4.0444          |
-| 3.4659        | 4.1   | 7000  | 4.0415          |
-| 3.3232        | 4.4   | 7500  | 4.0359          |
-| 3.3119        | 4.69  | 8000  | 4.0229          |
-| 3.3091        | 4.98  | 8500  | 4.0102          |
-| 3.1468        | 5.28  | 9000  | 4.0226          |
-| 3.1365        | 5.57  | 9500  | 4.0216          |
-| 3.1398        | 5.86  | 10000 | 4.0204          |
 ### Framework versions

 This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
 It achieves the following results on the evaluation set:
+- Loss: 4.1054
 ## Model description
 | Training Loss | Epoch | Step  | Validation Loss |
 |:-------------:|:-----:|:-----:|:---------------:|
+| 6.3418        | 0.29  | 500   | 5.3350          |
+| 5.0312        | 0.58  | 1000  | 4.9322          |
+| 4.6974        | 0.87  | 1500  | 4.6839          |
+| 4.4435        | 1.17  | 2000  | 4.5502          |
+| 4.2915        | 1.46  | 2500  | 4.4247          |
+| 4.1863        | 1.75  | 3000  | 4.3206          |
+| 4.0756        | 2.04  | 3500  | 4.2468          |
+| 3.89          | 2.33  | 4000  | 4.2082          |
+| 3.8602        | 2.62  | 4500  | 4.1531          |
+| 3.8239        | 2.91  | 5000  | 4.1034          |
+| 3.6381        | 3.21  | 5500  | 4.1005          |
+| 3.5871        | 3.5   | 6000  | 4.0692          |
+| 3.559         | 3.79  | 6500  | 4.0366          |
+| 3.481         | 4.08  | 7000  | 4.0358          |
+| 3.3172        | 4.37  | 7500  | 4.0295          |
+| 3.3056        | 4.66  | 8000  | 4.0174          |
+| 3.295         | 4.95  | 8500  | 4.0045          |
+| 3.1594        | 5.24  | 9000  | 4.0171          |
+| 3.1303        | 5.54  | 9500  | 4.0157          |
+| 3.1295        | 5.83  | 10000 | 4.0147          |
 ### Framework versions