tchoudh8
/

bart-base-finetuned-w-data-augm-4e-5

@@ -19,15 +19,15 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3985
-- Sacrebleu: 89.8136
-- Rouge1: 95.6369
-- Rouge2: 91.8617
-- Rougel: 94.6909
-- Rougelsum: 94.6811
-- Bertscore Precision: 0.9424
-- Bertscore Recall: 0.9374
-- Bertscore F1: 0.9399
 ## Model description
@@ -59,11 +59,11 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|
-| 0.1107        | 1.0   | 761  | 0.2850          | 90.5237   | 96.15   | 92.6707 | 95.2684 | 95.2821   | 0.9487              | 0.9425           | 0.9456       |
-| 0.0435        | 2.0   | 1522 | 0.2695          | 91.4933   | 96.4613 | 93.4149 | 95.6712 | 95.6642   | 0.9515              | 0.9522           | 0.9518       |
-| 0.0421        | 3.0   | 2283 | 0.2579          | 91.4926   | 96.4713 | 93.2669 | 95.7036 | 95.7071   | 0.9522              | 0.9505           | 0.9513       |
-| 0.0233        | 4.0   | 3044 | 0.2717          | 91.8243   | 96.6369 | 93.443  | 95.8509 | 95.8593   | 0.9537              | 0.9521           | 0.9529       |
-| 0.0327        | 5.0   | 3805 | 0.2804          | 92.095    | 96.6849 | 93.7485 | 95.9279 | 95.9247   | 0.9551              | 0.9526           | 0.9538       |
 ### Framework versions

 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.3874
+- Sacrebleu: 89.8161
+- Rouge1: 95.6774
+- Rouge2: 91.8937
+- Rougel: 94.6649
+- Rougelsum: 94.6595
+- Bertscore Precision: 0.9414
+- Bertscore Recall: 0.9376
+- Bertscore F1: 0.9395
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Sacrebleu | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 |
 |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|
+| 0.1504        | 1.0   | 761  | 0.2797          | 90.9313   | 96.2421 | 92.8783 | 95.4262 | 95.4043   | 0.9496              | 0.9444           | 0.9469       |
+| 0.0348        | 2.0   | 1522 | 0.2473          | 91.7583   | 96.3865 | 93.2655 | 95.6899 | 95.6811   | 0.9532              | 0.9504           | 0.9517       |
+| 0.0587        | 3.0   | 2283 | 0.2413          | 91.828    | 96.4392 | 93.4124 | 95.7079 | 95.6976   | 0.9517              | 0.9508           | 0.9512       |
+| 0.0269        | 4.0   | 3044 | 0.2588          | 91.9835   | 96.578  | 93.6221 | 95.8992 | 95.8798   | 0.9524              | 0.9527           | 0.9525       |
+| 0.0439        | 5.0   | 3805 | 0.2678          | 92.1033   | 96.6815 | 93.6391 | 95.9677 | 95.9469   | 0.9544              | 0.9536           | 0.954        |
 ### Framework versions