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mt5-small-finetuned-xsum
This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.5196
- Rouge1: 0.3378
- Rouge2: 0.275
- Rougel: 0.3372
- Rougelsum: 0.3367
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 21 | 11.8500 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 42 | 11.1279 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 63 | 10.0382 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 4.0 | 84 | 9.1579 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 5.0 | 105 | 8.6827 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 6.0 | 126 | 7.3651 | 0.0028 | 0.0016 | 0.0028 | 0.0028 |
| No log | 7.0 | 147 | 6.4400 | 0.019 | 0.0129 | 0.0191 | 0.0197 |
| No log | 8.0 | 168 | 5.2631 | 0.0272 | 0.0229 | 0.0288 | 0.0288 |
| No log | 9.0 | 189 | 4.5832 | 0.1095 | 0.0688 | 0.1053 | 0.1051 |
| No log | 10.0 | 210 | 4.2350 | 0.1263 | 0.0824 | 0.1216 | 0.1235 |
| No log | 11.0 | 231 | 3.9249 | 0.1541 | 0.1051 | 0.1513 | 0.1532 |
| No log | 12.0 | 252 | 3.5469 | 0.1701 | 0.1156 | 0.1665 | 0.1683 |
| No log | 13.0 | 273 | 3.3689 | 0.2672 | 0.2095 | 0.2667 | 0.2659 |
| No log | 14.0 | 294 | 3.1733 | 0.3102 | 0.2483 | 0.3103 | 0.3104 |
| No log | 15.0 | 315 | 3.0810 | 0.3073 | 0.2457 | 0.3074 | 0.3071 |
| No log | 16.0 | 336 | 3.0005 | 0.3071 | 0.2451 | 0.3075 | 0.3069 |
| No log | 17.0 | 357 | 2.9663 | 0.3015 | 0.2364 | 0.3022 | 0.3018 |
| No log | 18.0 | 378 | 2.8718 | 0.3195 | 0.2583 | 0.3197 | 0.3187 |
| No log | 19.0 | 399 | 2.8061 | 0.3159 | 0.2554 | 0.316 | 0.3143 |
| No log | 20.0 | 420 | 2.7009 | 0.3351 | 0.273 | 0.3338 | 0.3341 |
| No log | 21.0 | 441 | 2.6307 | 0.3384 | 0.2763 | 0.3382 | 0.3381 |
| No log | 22.0 | 462 | 2.6006 | 0.3364 | 0.2743 | 0.3362 | 0.3357 |
| No log | 23.0 | 483 | 2.5819 | 0.3334 | 0.2712 | 0.3331 | 0.3333 |
| 13.1102 | 24.0 | 504 | 2.5606 | 0.3309 | 0.269 | 0.3302 | 0.3305 |
| 13.1102 | 25.0 | 525 | 2.5458 | 0.338 | 0.2744 | 0.3369 | 0.3373 |
| 13.1102 | 26.0 | 546 | 2.5366 | 0.3361 | 0.2715 | 0.3352 | 0.3352 |
| 13.1102 | 27.0 | 567 | 2.5301 | 0.3413 | 0.2787 | 0.3408 | 0.3406 |
| 13.1102 | 28.0 | 588 | 2.5236 | 0.341 | 0.2783 | 0.3402 | 0.3401 |
| 13.1102 | 29.0 | 609 | 2.5206 | 0.3405 | 0.2779 | 0.3399 | 0.3397 |
| 13.1102 | 30.0 | 630 | 2.5196 | 0.3378 | 0.275 | 0.3372 | 0.3367 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.13.2
