gayanin
/

bart-mlm-pubmed

@@ -15,9 +15,9 @@ 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 an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.7223
-- Rouge2 Precision: 0.6577
-- Rouge2 Recall: 0.5167
-- Rouge2 Fmeasure: 0.5667
 ## Model description
@@ -49,16 +49,16 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
 |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
-| 1.0322        | 1.0   | 663  | 0.7891          | 0.6398           | 0.4995        | 0.5499          |
-| 0.8545        | 2.0   | 1326 | 0.7433          | 0.6464           | 0.5061        | 0.5559          |
-| 0.758         | 3.0   | 1989 | 0.7299          | 0.6479           | 0.5041        | 0.5553          |
-| 0.6431        | 4.0   | 2652 | 0.7185          | 0.656            | 0.5106        | 0.562           |
-| 0.6058        | 5.0   | 3315 | 0.7126          | 0.6542           | 0.5153        | 0.5648          |
-| 0.5726        | 6.0   | 3978 | 0.7117          | 0.6572           | 0.5176        | 0.5674          |
-| 0.5168        | 7.0   | 4641 | 0.7150          | 0.659            | 0.5161        | 0.5668          |
-| 0.5011        | 8.0   | 5304 | 0.7220          | 0.6574           | 0.517         | 0.5669          |
-| 0.4803        | 9.0   | 5967 | 0.7208          | 0.6579           | 0.5167        | 0.5668          |
-| 0.4577        | 10.0  | 6630 | 0.7223          | 0.6577           | 0.5167        | 0.5667          |
 ### Framework versions

 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.7223
+- Rouge2 Precision: 0.6572
+- Rouge2 Recall: 0.5164
+- Rouge2 Fmeasure: 0.5662
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
 |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
+| 1.0322        | 1.0   | 663  | 0.7891          | 0.639            | 0.4989        | 0.5491          |
+| 0.8545        | 2.0   | 1326 | 0.7433          | 0.6461           | 0.5057        | 0.5556          |
+| 0.758         | 3.0   | 1989 | 0.7299          | 0.647            | 0.5033        | 0.5547          |
+| 0.6431        | 4.0   | 2652 | 0.7185          | 0.6556           | 0.5101        | 0.5616          |
+| 0.6058        | 5.0   | 3315 | 0.7126          | 0.6537           | 0.5144        | 0.5638          |
+| 0.5726        | 6.0   | 3978 | 0.7117          | 0.6567           | 0.5169        | 0.5666          |
+| 0.5168        | 7.0   | 4641 | 0.7150          | 0.6585           | 0.5154        | 0.566           |
+| 0.5011        | 8.0   | 5304 | 0.7220          | 0.6568           | 0.5164        | 0.5664          |
+| 0.4803        | 9.0   | 5967 | 0.7208          | 0.6573           | 0.5161        | 0.5662          |
+| 0.4577        | 10.0  | 6630 | 0.7223          | 0.6572           | 0.5164        | 0.5662          |
 ### Framework versions