kwkty
/

vxnli-v1

@@ -12,10 +12,10 @@ should probably proofread and complete it, then remove this comment. -->
 # vxnli-v1
-This model is a fine-tuned version of [microsoft/tapex-base-finetuned-wtq](https://huggingface.co/microsoft/tapex-base-finetuned-wtq) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2502
-- Exact Match: 0.5444
 ## Model description
@@ -46,18 +46,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Exact Match |
 |:-------------:|:-----:|:----:|:---------------:|:-----------:|
-| 0.8075        | 1.0   | 158  | 0.2990          | 0.2741      |
-| 0.1626        | 2.0   | 316  | 0.2192          | 0.5259      |
-| 0.085         | 3.0   | 474  | 0.2346          | 0.5333      |
-| 0.0534        | 4.0   | 632  | 0.2233          | 0.5889      |
-| 0.0371        | 5.0   | 790  | 0.2197          | 0.5630      |
-| 0.0305        | 6.0   | 948  | 0.2448          | 0.5741      |
-| 0.0245        | 7.0   | 1106 | 0.2469          | 0.6111      |
-| 0.0177        | 8.0   | 1264 | 0.2547          | 0.5926      |
-| 0.0159        | 9.0   | 1422 | 0.2496          | 0.5815      |
-| 0.0153        | 10.0  | 1580 | 0.2939          | 0.5593      |
-| 0.0117        | 11.0  | 1738 | 0.2879          | 0.5667      |
-| 0.0096        | 12.0  | 1896 | 0.2581          | 0.5815      |
 ### Framework versions

 # vxnli-v1
+This model is a fine-tuned version of [kwkty/vxnli-v0](https://huggingface.co/kwkty/vxnli-v0) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.2262
+- Exact Match: 0.6407
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Exact Match |
 |:-------------:|:-----:|:----:|:---------------:|:-----------:|
+| 0.1092        | 1.0   | 158  | 0.1820          | 0.5704      |
+| 0.0432        | 2.0   | 316  | 0.2149          | 0.5593      |
+| 0.0266        | 3.0   | 474  | 0.2721          | 0.5370      |
+| 0.0193        | 4.0   | 632  | 0.2456          | 0.5889      |
+| 0.0168        | 5.0   | 790  | 0.2382          | 0.6111      |
+| 0.0138        | 6.0   | 948  | 0.2910          | 0.5741      |
+| 0.0146        | 7.0   | 1106 | 0.2559          | 0.5889      |
+| 0.0078        | 8.0   | 1264 | 0.2927          | 0.5630      |
+| 0.0073        | 9.0   | 1422 | 0.3346          | 0.5741      |
+| 0.0063        | 10.0  | 1580 | 0.3308          | 0.5481      |
 ### Framework versions