End of training

README.md

@@ -6,23 +6,23 @@ tags:
 metrics:
 - rouge
 model-index:
-- name: DanSumT5-large-finetuned-test_57626
+- name: DanSumT5-largeV_84227
   results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# DanSumT5-large-finetuned-test_57626
+# DanSumT5-largeV_84227
 
 This model is a fine-tuned version of [Danish-summarisation/DanSumT5-large](https://huggingface.co/Danish-summarisation/DanSumT5-large) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.5370
-- Rouge1: 31.992
-- Rouge2: 7.6605
-- Rougel: 18.1676
-- Rougelsum: 29.1825
-- Gen Len: 126.27
+- Loss: 2.2976
+- Rouge1: 32.3488
+- Rouge2: 8.638
+- Rougel: 18.8215
+- Rougelsum: 29.8654
+- Gen Len: 126.28
 
 ## Model description
 
@@ -49,14 +49,32 @@ The following hyperparameters were used during training:
 - total_train_batch_size: 4
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 2
+- num_epochs: 20
 
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
 |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
-| No log        | 1.0   | 200  | 2.5665          | 31.8442 | 7.5263 | 18.0111 | 29.1725   | 126.86  |
-| No log        | 2.0   | 400  | 2.5370          | 31.992  | 7.6605 | 18.1676 | 29.1825   | 126.27  |
+| No log        | 1.0   | 200  | 2.5620          | 31.6386 | 7.3603 | 17.9932 | 28.8935   | 126.32  |
+| No log        | 2.0   | 400  | 2.4824          | 31.8478 | 8.0477 | 18.5952 | 29.2582   | 126.77  |
+| 2.7655        | 3.0   | 600  | 2.4305          | 32.1965 | 8.4935 | 18.7317 | 29.9719   | 125.03  |
+| 2.7655        | 4.0   | 800  | 2.3945          | 31.8539 | 8.7262 | 18.5421 | 29.8472   | 125.63  |
+| 2.4368        | 5.0   | 1000 | 2.3685          | 32.0137 | 8.2933 | 18.7818 | 29.561    | 125.32  |
+| 2.4368        | 6.0   | 1200 | 2.3522          | 31.5    | 8.3477 | 18.9478 | 29.3072   | 125.11  |
+| 2.4368        | 7.0   | 1400 | 2.3364          | 31.6482 | 8.3012 | 18.9953 | 29.0985   | 123.38  |
+| 2.2645        | 8.0   | 1600 | 2.3250          | 31.9939 | 8.5944 | 18.9914 | 29.5092   | 125.18  |
+| 2.2645        | 9.0   | 1800 | 2.3212          | 31.5611 | 8.1969 | 18.7941 | 29.151    | 126.01  |
+| 2.134         | 10.0  | 2000 | 2.3117          | 32.0902 | 8.6962 | 19.0793 | 29.758    | 125.4   |
+| 2.134         | 11.0  | 2200 | 2.3064          | 31.9365 | 8.7161 | 18.9113 | 29.6812   | 125.86  |
+| 2.134         | 12.0  | 2400 | 2.3062          | 32.3185 | 9.0913 | 19.2692 | 29.9962   | 126.24  |
+| 2.0467        | 13.0  | 2600 | 2.3032          | 31.7591 | 8.4993 | 18.8326 | 29.4231   | 125.02  |
+| 2.0467        | 14.0  | 2800 | 2.3008          | 32.0532 | 8.8654 | 18.897  | 29.5819   | 126.2   |
+| 1.9931        | 15.0  | 3000 | 2.2980          | 31.8987 | 8.7669 | 19.0859 | 29.3799   | 126.0   |
+| 1.9931        | 16.0  | 3200 | 2.2982          | 32.2458 | 8.7896 | 18.6845 | 29.6991   | 126.0   |
+| 1.9931        | 17.0  | 3400 | 2.2987          | 32.0869 | 8.6678 | 18.7656 | 29.8441   | 125.66  |
+| 1.949         | 18.0  | 3600 | 2.2974          | 32.1759 | 8.6004 | 18.7892 | 29.6918   | 126.31  |
+| 1.949         | 19.0  | 3800 | 2.2970          | 32.1139 | 8.5827 | 18.7099 | 29.5327   | 126.15  |
+| 1.9257        | 20.0  | 4000 | 2.2976          | 32.3488 | 8.638  | 18.8215 | 29.8654   | 126.28  |
 
 
 ### Framework versions