mt5-simplification-spanish-clara-med
This model is a fine-tuned version of oskrmiguel/mt5-simplification-spanish on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9610
- Rouge1: 33.7922
- Rouge2: 19.5758
- Rougel: 31.3737
- Rougelsum: 31.3428
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: 5.6e-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 | 190 | 2.6876 | 32.236 | 18.2352 | 29.7852 | 29.7539 |
No log | 2.0 | 380 | 2.4617 | 32.8521 | 18.9712 | 30.4958 | 30.4635 |
3.3018 | 3.0 | 570 | 2.3487 | 33.2554 | 19.3441 | 30.9036 | 30.8525 |
3.3018 | 4.0 | 760 | 2.2711 | 33.0105 | 19.01 | 30.6851 | 30.5767 |
2.7431 | 5.0 | 950 | 2.2254 | 33.1301 | 18.9618 | 30.6744 | 30.6284 |
2.7431 | 6.0 | 1140 | 2.1847 | 33.3701 | 19.1884 | 30.9138 | 30.8611 |
2.7431 | 7.0 | 1330 | 2.1443 | 33.3158 | 19.101 | 30.8317 | 30.7747 |
2.5154 | 8.0 | 1520 | 2.1072 | 33.1638 | 19.0139 | 30.7295 | 30.7162 |
2.5154 | 9.0 | 1710 | 2.0989 | 33.4925 | 19.2107 | 31.0253 | 30.9908 |
2.3763 | 10.0 | 1900 | 2.0709 | 33.3007 | 18.9519 | 30.847 | 30.8018 |
2.3763 | 11.0 | 2090 | 2.0631 | 33.4689 | 19.1995 | 31.0712 | 31.0327 |
2.3763 | 12.0 | 2280 | 2.0418 | 33.2536 | 19.027 | 30.898 | 30.8695 |
2.2811 | 13.0 | 2470 | 2.0345 | 33.5097 | 19.2219 | 31.1057 | 31.0683 |
2.2811 | 14.0 | 2660 | 2.0185 | 33.3544 | 19.1241 | 30.913 | 30.8873 |
2.2173 | 15.0 | 2850 | 2.0138 | 33.3856 | 19.2065 | 31.0173 | 30.9447 |
2.2173 | 16.0 | 3040 | 2.0019 | 33.4035 | 19.1803 | 31.0154 | 30.981 |
2.2173 | 17.0 | 3230 | 1.9977 | 33.4059 | 19.3078 | 31.1196 | 31.0692 |
2.1612 | 18.0 | 3420 | 1.9883 | 33.5097 | 19.3637 | 31.0966 | 31.0554 |
2.1612 | 19.0 | 3610 | 1.9828 | 33.4965 | 19.2754 | 31.1267 | 31.1021 |
2.1115 | 20.0 | 3800 | 1.9834 | 33.7514 | 19.5325 | 31.2833 | 31.2418 |
2.1115 | 21.0 | 3990 | 1.9754 | 33.6193 | 19.429 | 31.2721 | 31.2267 |
2.1115 | 22.0 | 4180 | 1.9716 | 33.5212 | 19.3637 | 31.1326 | 31.1162 |
2.0824 | 23.0 | 4370 | 1.9667 | 33.5156 | 19.3223 | 31.1023 | 31.0709 |
2.0824 | 24.0 | 4560 | 1.9735 | 33.6089 | 19.3842 | 31.1539 | 31.1419 |
2.0657 | 25.0 | 4750 | 1.9674 | 33.6317 | 19.4044 | 31.2361 | 31.2222 |
2.0657 | 26.0 | 4940 | 1.9617 | 33.745 | 19.5099 | 31.3061 | 31.2643 |
2.0657 | 27.0 | 5130 | 1.9613 | 33.7798 | 19.5496 | 31.3761 | 31.3356 |
2.0511 | 28.0 | 5320 | 1.9635 | 33.8568 | 19.594 | 31.4454 | 31.4141 |
2.0511 | 29.0 | 5510 | 1.9609 | 33.805 | 19.5962 | 31.393 | 31.3493 |
2.0377 | 30.0 | 5700 | 1.9610 | 33.7922 | 19.5758 | 31.3737 | 31.3428 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0
- Datasets 2.8.0
- Tokenizers 0.12.1
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