t5-small-mse-summarization
This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1108
- Rouge1: 43.1145
- Rouge2: 23.2262
- Rougel: 37.218
- Rougelsum: 41.0897
- Bleurt: -0.8051
- Gen Len: 18.549
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleurt | Gen Len |
|---|---|---|---|---|---|---|---|---|---|
| 1.5207 | 1.0 | 267 | 1.2922 | 38.8738 | 19.1958 | 32.8458 | 36.9993 | -0.9061 | 18.668 |
| 1.363 | 2.0 | 534 | 1.2340 | 39.8466 | 20.0452 | 33.9101 | 37.7708 | -0.8925 | 18.657 |
| 1.3062 | 3.0 | 801 | 1.2057 | 40.5536 | 20.8249 | 34.5221 | 38.4648 | -0.8625 | 18.602 |
| 1.272 | 4.0 | 1068 | 1.1782 | 41.0078 | 21.2186 | 35.0101 | 38.9186 | -0.8595 | 18.602 |
| 1.2312 | 5.0 | 1335 | 1.1688 | 41.521 | 21.7934 | 35.704 | 39.4718 | -0.842 | 18.486 |
| 1.2052 | 6.0 | 1602 | 1.1557 | 42.1037 | 22.4291 | 36.3554 | 40.1124 | -0.8432 | 18.533 |
| 1.1842 | 7.0 | 1869 | 1.1440 | 42.4438 | 22.6456 | 36.5729 | 40.3134 | -0.8288 | 18.553 |
| 1.1643 | 8.0 | 2136 | 1.1408 | 42.245 | 22.4859 | 36.3637 | 40.2193 | -0.8284 | 18.622 |
| 1.1495 | 9.0 | 2403 | 1.1320 | 42.5362 | 22.5034 | 36.5092 | 40.4552 | -0.8211 | 18.57 |
| 1.1368 | 10.0 | 2670 | 1.1301 | 42.5159 | 22.462 | 36.4646 | 40.3968 | -0.819 | 18.538 |
| 1.1203 | 11.0 | 2937 | 1.1243 | 42.2803 | 22.5963 | 36.3454 | 40.2987 | -0.8242 | 18.522 |
| 1.1116 | 12.0 | 3204 | 1.1197 | 42.8078 | 22.8409 | 36.7344 | 40.8186 | -0.821 | 18.565 |
| 1.099 | 13.0 | 3471 | 1.1193 | 42.7423 | 22.9397 | 36.7894 | 40.7298 | -0.8125 | 18.552 |
| 1.0976 | 14.0 | 3738 | 1.1176 | 42.9002 | 23.2394 | 37.0215 | 40.9211 | -0.8156 | 18.568 |
| 1.0816 | 15.0 | 4005 | 1.1133 | 43.0007 | 23.3093 | 37.2037 | 40.9719 | -0.8059 | 18.519 |
| 1.084 | 16.0 | 4272 | 1.1146 | 42.9053 | 23.2391 | 37.0542 | 40.8826 | -0.8104 | 18.533 |
| 1.0755 | 17.0 | 4539 | 1.1124 | 43.0429 | 23.2773 | 37.1389 | 41.0755 | -0.8086 | 18.544 |
| 1.0748 | 18.0 | 4806 | 1.1121 | 43.2243 | 23.4179 | 37.2039 | 41.143 | -0.8048 | 18.548 |
| 1.072 | 19.0 | 5073 | 1.1106 | 43.1776 | 23.3061 | 37.3105 | 41.1392 | -0.8039 | 18.549 |
| 1.0671 | 20.0 | 5340 | 1.1108 | 43.1145 | 23.2262 | 37.218 | 41.0897 | -0.8051 | 18.549 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
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