update model card README.md

README.md

@@ -1,6 +1,8 @@
 ---
 tags:
 - generated_from_trainer
+metrics:
+- rouge
 model-index:
 - name: bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum-finetuned_med_sum
   results: []
@@ -12,6 +14,13 @@ should probably proofread and complete it, then remove this comment. -->
 # bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum-finetuned_med_sum
 
 This model is a fine-tuned version of [Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum](https://huggingface.co/Chemsseddine/bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.0684
+- Rouge1: 34.1248
+- Rouge2: 17.7006
+- Rougel: 33.4661
+- Rougelsum: 33.4419
+- Gen Len: 22.6429
 
 ## Model description
 
@@ -31,16 +40,28 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
+- train_batch_size: 1
+- eval_batch_size: 1
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 1
+- num_epochs: 10
 - mixed_precision_training: Native AMP
 
 ### Training results
 
+| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
+|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
+| 2.9107        | 1.0   | 1000  | 2.0877          | 30.4547 | 14.4024 | 30.3642 | 30.3788   | 21.9714 |
+| 1.8782        | 2.0   | 2000  | 1.8151          | 32.6607 | 16.8089 | 32.3844 | 32.4762   | 21.7714 |
+| 1.291         | 3.0   | 3000  | 1.7523          | 33.6391 | 16.7866 | 32.4256 | 32.3306   | 22.7429 |
+| 0.819         | 4.0   | 4000  | 1.7650          | 35.0633 | 19.1222 | 34.4902 | 34.6796   | 22.4714 |
+| 0.4857        | 5.0   | 5000  | 1.8129          | 33.8763 | 16.9303 | 32.8845 | 32.9225   | 22.3857 |
+| 0.3232        | 6.0   | 6000  | 1.9339          | 33.9272 | 17.1784 | 32.9301 | 33.0253   | 22.4286 |
+| 0.2022        | 7.0   | 7000  | 1.9634          | 33.9869 | 16.4238 | 33.7336 | 33.65     | 22.6429 |
+| 0.1452        | 8.0   | 8000  | 2.0090          | 33.8892 | 18.2723 | 33.7514 | 33.6531   | 22.5714 |
+| 0.0845        | 9.0   | 9000  | 2.0337          | 33.9649 | 17.1339 | 33.5061 | 33.4157   | 22.7857 |
+| 0.0531        | 10.0  | 10000 | 2.0684          | 34.1248 | 17.7006 | 33.4661 | 33.4419   | 22.6429 |
 
 
 ### Framework versions