e22vvb
/

EN_mt5-base_15_spider

@@ -14,10 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4677
-- Rouge2 Precision: 0.5384
-- Rouge2 Recall: 0.3712
-- Rouge2 Fmeasure: 0.413
 ## Model description
@@ -37,7 +37,7 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 16
 - eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
@@ -46,23 +46,23 @@ The following hyperparameters were used during training:
 ### Training results
-| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
-|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
-| No log        | 1.0   | 438  | 1.7721          | 0.0              | 0.0           | 0.0             |
-| 10.9203       | 2.0   | 876  | 1.5463          | 0.0048           | 0.0008        | 0.0015          |
-| 1.7683        | 3.0   | 1314 | 1.3623          | 0.0164           | 0.0008        | 0.0014          |
-| 1.6091        | 4.0   | 1752 | 1.0649          | 0.0036           | 0.0012        | 0.0014          |
-| 1.2253        | 5.0   | 2190 | 0.3614          | 0.2985           | 0.1906        | 0.2088          |
-| 0.3973        | 6.0   | 2628 | 0.3133          | 0.4588           | 0.2914        | 0.3317          |
-| 0.2032        | 7.0   | 3066 | 0.3007          | 0.5065           | 0.3375        | 0.3799          |
-| 0.1597        | 8.0   | 3504 | 0.3109          | 0.5212           | 0.3579        | 0.3988          |
-| 0.1597        | 9.0   | 3942 | 0.3431          | 0.5194           | 0.3529        | 0.3931          |
-| 0.1385        | 10.0  | 4380 | 0.4681          | 0.5315           | 0.3636        | 0.4051          |
-| 0.1285        | 11.0  | 4818 | 0.5505          | 0.5331           | 0.3666        | 0.4074          |
-| 0.1228        | 12.0  | 5256 | 0.5351          | 0.5331           | 0.3645        | 0.4063          |
-| 0.1092        | 13.0  | 5694 | 0.4808          | 0.5355           | 0.3697        | 0.4102          |
-| 0.1078        | 14.0  | 6132 | 0.4519          | 0.5407           | 0.3727        | 0.4144          |
-| 0.1034        | 15.0  | 6570 | 0.4677          | 0.5384           | 0.3712        | 0.413           |
 ### Framework versions

 This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.3557
+- Rouge2 Precision: 0.6104
+- Rouge2 Recall: 0.4164
+- Rouge2 Fmeasure: 0.4669
 ## Model description
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
+- train_batch_size: 12
 - eval_batch_size: 16
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 ### Training results
+| Training Loss | Epoch | Step  | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
+|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
+| 9.1945        | 1.0   | 722   | 1.9988          | 0.2524           | 0.1722        | 0.1809          |
+| 2.7281        | 2.0   | 1444  | 0.3721          | 0.4727           | 0.3003        | 0.3376          |
+| 0.2067        | 3.0   | 2166  | 0.3160          | 0.5435           | 0.3541        | 0.4033          |
+| 0.1692        | 4.0   | 2888  | 0.3153          | 0.5628           | 0.3863        | 0.4314          |
+| 0.1252        | 5.0   | 3610  | 0.3225          | 0.5764           | 0.3907        | 0.4381          |
+| 0.1117        | 6.0   | 4332  | 0.3165          | 0.5707           | 0.3891        | 0.435           |
+| 0.0973        | 7.0   | 5054  | 0.3269          | 0.5693           | 0.3868        | 0.4335          |
+| 0.0897        | 8.0   | 5776  | 0.3365          | 0.585            | 0.4009        | 0.4478          |
+| 0.082         | 9.0   | 6498  | 0.3407          | 0.6035           | 0.4129        | 0.4619          |
+| 0.0721        | 10.0  | 7220  | 0.3453          | 0.5963           | 0.4089        | 0.4568          |
+| 0.0703        | 11.0  | 7942  | 0.3434          | 0.6032           | 0.4137        | 0.4629          |
+| 0.0638        | 12.0  | 8664  | 0.3505          | 0.6077           | 0.4178        | 0.4667          |
+| 0.0615        | 13.0  | 9386  | 0.3509          | 0.611            | 0.4169        | 0.467           |
+| 0.0599        | 14.0  | 10108 | 0.3554          | 0.608            | 0.4153        | 0.4651          |
+| 0.058         | 15.0  | 10830 | 0.3557          | 0.6104           | 0.4164        | 0.4669          |
 ### Framework versions