RMWeerasinghe
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flan-t5-base-finetuned-QLoRA-v2

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RMWeerasinghe commited on Feb 26

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@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the cnn_dailymail dataset.
 It achieves the following results on the evaluation set:
-- Loss: 1.1284
-- Rouge1: 0.2459
-- Rouge2: 0.1133
-- Rougel: 0.2014
-- Rougelsum: 0.2312
 ## Model description
@@ -50,16 +50,22 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- num_epochs: 4
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
-| 3.2738        | 1.0   | 500  | 2.5624          | 0.2375 | 0.1097 | 0.1987 | 0.223     |
-| 1.8824        | 2.0   | 1000 | 1.2830          | 0.2419 | 0.11   | 0.1988 | 0.2278    |
-| 1.6192        | 3.0   | 1500 | 1.1527          | 0.2477 | 0.1149 | 0.2033 | 0.2325    |
-| 1.5256        | 4.0   | 2000 | 1.1284          | 0.2459 | 0.1133 | 0.2014 | 0.2312    |
 ### Framework versions

 This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the cnn_dailymail dataset.
 It achieves the following results on the evaluation set:
+- Loss: 1.0254
+- Rouge1: 0.244
+- Rouge2: 0.111
+- Rougel: 0.2032
+- Rougelsum: 0.2292
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- num_epochs: 10
 ### Training results
 | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
 |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
+| 3.0551        | 1.0   | 500  | 2.2941          | 0.2336 | 0.1092 | 0.1969 | 0.217     |
+| 1.6422        | 2.0   | 1000 | 1.1665          | 0.2459 | 0.1088 | 0.1991 | 0.227     |
+| 1.4067        | 3.0   | 1500 | 1.0762          | 0.2462 | 0.1089 | 0.1982 | 0.2296    |
+| 1.2856        | 4.0   | 2000 | 1.0518          | 0.2448 | 0.1112 | 0.2036 | 0.2298    |
+| 1.3478        | 5.0   | 2500 | 1.0393          | 0.2458 | 0.1125 | 0.2056 | 0.2303    |
+| 1.2114        | 6.0   | 3000 | 1.0340          | 0.2497 | 0.1145 | 0.2084 | 0.2333    |
+| 1.3311        | 7.0   | 3500 | 1.0298          | 0.2479 | 0.1143 | 0.207  | 0.233     |
+| 1.3081        | 8.0   | 4000 | 1.0270          | 0.2448 | 0.1112 | 0.2035 | 0.2301    |
+| 1.1794        | 9.0   | 4500 | 1.0258          | 0.2449 | 0.1112 | 0.2036 | 0.2301    |
+| 1.2407        | 10.0  | 5000 | 1.0254          | 0.244  | 0.111  | 0.2032 | 0.2292    |
 ### Framework versions