domenicrosati
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t5-small-finetuned-contradiction

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t5 text2text-generation generated_from_trainer Eval Results Inference Endpoints text-generation-inference

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domenicrosati commited on Apr 24, 2022

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@@ -20,7 +20,7 @@ model-index:
     metrics:
     - name: Rouge1
       type: rouge
-      value: 34.2745
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -28,13 +28,13 @@ should probably proofread and complete it, then remove this comment. -->
 # t5-small-finetuned-contradiction
-This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the snli dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.1770
-- Rouge1: 34.2745
-- Rouge2: 14.6382
-- Rougel: 32.5159
-- Rougelsum: 32.519
 ## Model description
@@ -59,13 +59,17 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 1
 ### Training results
-| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
-|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
-| 2.2392        | 1.0   | 2863 | 2.1770          | 34.3717 | 14.682 | 32.6218 | 32.6239   |
 ### Framework versions

     metrics:
     - name: Rouge1
       type: rouge
+      value: 34.3638
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 # t5-small-finetuned-contradiction
+This model is a fine-tuned version of [domenicrosati/t5-small-finetuned-contradiction](https://huggingface.co/domenicrosati/t5-small-finetuned-contradiction) on the snli dataset.
 It achieves the following results on the evaluation set:
+- Loss: 2.1269
+- Rouge1: 34.3638
+- Rouge2: 14.7916
+- Rougel: 32.6308
+- Rougelsum: 32.6288
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 4
+- mixed_precision_training: Native AMP
 ### Training results
+| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
+| 2.1984        | 1.0   | 2863  | 2.1556          | 34.4429 | 14.6791 | 32.5812 | 32.5896   |
+| 2.2085        | 2.0   | 5726  | 2.1390          | 34.3719 | 14.731  | 32.5979 | 32.5949   |
+| 2.188         | 3.0   | 8589  | 2.1302          | 34.4276 | 14.7191 | 32.62   | 32.6132   |
+| 2.1768        | 4.0   | 11452 | 2.1269          | 34.4408 | 14.8235 | 32.7067 | 32.7065   |
 ### Framework versions