End of training

README.md

@@ -22,7 +22,7 @@ model-index:
     metrics:
     - name: Rouge1
       type: rouge
-      value: 42.6528
+      value: 42.6
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -32,12 +32,12 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the samsum dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.6754
-- Rouge1: 42.6528
-- Rouge2: 18.3481
-- Rougel: 35.2388
-- Rougelsum: 38.9352
-- Gen Len: 16.8474
+- Loss: 1.6729
+- Rouge1: 42.6
+- Rouge2: 18.7153
+- Rougel: 35.4138
+- Rougelsum: 38.8543
+- Gen Len: 16.9170
 
 ## Model description
 
@@ -57,8 +57,8 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 5e-05
-- train_batch_size: 52
-- eval_batch_size: 52
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
@@ -69,11 +69,15 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
 |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
-| 1.8824        | 0.35  | 100  | 1.7015          | 42.4522 | 18.3268 | 35.0509 | 38.8425   | 16.6532 |
-| 1.8578        | 0.7   | 200  | 1.6878          | 41.9777 | 18.2479 | 34.9247 | 38.5155   | 16.7216 |
-| 1.835         | 1.06  | 300  | 1.6823          | 42.727  | 18.6215 | 35.3788 | 39.0045   | 16.9048 |
-| 1.8144        | 1.41  | 400  | 1.6786          | 42.6033 | 18.4035 | 35.2884 | 38.9211   | 16.6618 |
-| 1.8094        | 1.76  | 500  | 1.6754          | 42.6528 | 18.3481 | 35.2388 | 38.9352   | 16.8474 |
+| 1.8863        | 0.22  | 100  | 1.7049          | 42.0859 | 18.0002 | 34.7349 | 38.3446   | 16.5788 |
+| 1.8463        | 0.43  | 200  | 1.6947          | 42.4056 | 18.3005 | 34.9821 | 38.8013   | 17.3614 |
+| 1.8548        | 0.65  | 300  | 1.6792          | 42.585  | 18.5643 | 35.2235 | 38.8298   | 17.1514 |
+| 1.8358        | 0.87  | 400  | 1.6772          | 42.1544 | 18.2303 | 34.8971 | 38.3609   | 16.5873 |
+| 1.8129        | 1.08  | 500  | 1.6729          | 42.6    | 18.7153 | 35.4138 | 38.8543   | 16.9170 |
+| 1.8068        | 1.3   | 600  | 1.6709          | 42.5217 | 18.3285 | 35.1455 | 38.5954   | 16.9451 |
+| 1.7973        | 1.52  | 700  | 1.6687          | 42.8667 | 18.624  | 35.3429 | 38.9322   | 16.7546 |
+| 1.7979        | 1.74  | 800  | 1.6668          | 42.919  | 18.7388 | 35.4528 | 39.0561   | 16.8791 |
+| 1.7899        | 1.95  | 900  | 1.6670          | 43.0931 | 18.741  | 35.5047 | 39.2321   | 16.9109 |
 
 
 ### Framework versions

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