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speller-t5-big-3
This model is a fine-tuned version of sberbank-ai/ruT5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1829
- Rouge1: 27.4616
- Rouge2: 11.1083
- Rougel: 27.5146
- Rougelsum: 27.3079
- Gen Len: 39.1171
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
---|---|---|---|---|---|---|---|---|
1.0936 | 0.04 | 500 | 0.5587 | 23.1392 | 7.0032 | 23.1709 | 23.1908 | 41.1081 |
0.8042 | 0.07 | 1000 | 0.4168 | 25.1867 | 8.9696 | 25.2993 | 25.1779 | 43.6486 |
0.634 | 0.11 | 1500 | 0.3611 | 26.0366 | 8.521 | 26.1568 | 25.9359 | 40.2613 |
0.5041 | 0.14 | 2000 | 0.3255 | 26.1019 | 8.7002 | 26.2473 | 25.983 | 40.7928 |
0.5279 | 0.18 | 2500 | 0.3041 | 26.1352 | 8.6265 | 26.2606 | 25.9482 | 39.6216 |
0.4838 | 0.22 | 3000 | 0.2784 | 26.6137 | 9.8094 | 26.8372 | 26.5692 | 39.3694 |
0.4512 | 0.25 | 3500 | 0.2700 | 25.6152 | 9.5832 | 25.7503 | 25.6898 | 38.7387 |
0.4412 | 0.29 | 4000 | 0.2612 | 25.6113 | 9.6697 | 25.7482 | 25.6838 | 39.1171 |
0.405 | 0.33 | 4500 | 0.2426 | 26.5151 | 9.6882 | 26.7719 | 26.4825 | 39.1892 |
0.3987 | 0.36 | 5000 | 0.2390 | 26.479 | 9.6144 | 26.6499 | 26.3759 | 39.0991 |
0.407 | 0.4 | 5500 | 0.2325 | 26.4499 | 9.6544 | 26.6649 | 26.3821 | 39.3784 |
0.406 | 0.43 | 6000 | 0.2266 | 26.6224 | 9.875 | 26.8468 | 26.6058 | 38.6486 |
0.3827 | 0.47 | 6500 | 0.2213 | 26.8997 | 10.0139 | 27.1249 | 26.8252 | 39.1712 |
0.334 | 0.51 | 7000 | 0.2247 | 26.7779 | 9.9399 | 26.9951 | 26.6453 | 39.7207 |
0.3463 | 0.54 | 7500 | 0.2145 | 26.879 | 9.9911 | 27.0863 | 26.7372 | 39.2432 |
0.3439 | 0.58 | 8000 | 0.2102 | 26.8839 | 10.0139 | 27.0715 | 26.7186 | 39.3694 |
0.3644 | 0.61 | 8500 | 0.2050 | 26.9076 | 10.0704 | 27.1328 | 26.8411 | 39.2252 |
0.3161 | 0.65 | 9000 | 0.2008 | 26.9219 | 10.1927 | 27.1542 | 26.8697 | 38.7928 |
0.3273 | 0.69 | 9500 | 0.2018 | 26.8221 | 9.9879 | 27.0473 | 26.7137 | 39.1892 |
0.3423 | 0.72 | 10000 | 0.1992 | 26.8572 | 10.0937 | 27.0701 | 26.7469 | 39.2342 |
0.3129 | 0.76 | 10500 | 0.1964 | 26.9076 | 10.0704 | 27.1328 | 26.8411 | 39.1712 |
0.2841 | 0.79 | 11000 | 0.1937 | 27.4202 | 10.9493 | 27.5146 | 27.2724 | 39.1261 |
0.2865 | 0.83 | 11500 | 0.1901 | 27.4559 | 11.0314 | 27.5146 | 27.3022 | 39.2072 |
0.2747 | 0.87 | 12000 | 0.1862 | 27.4127 | 10.9878 | 27.5146 | 27.2611 | 38.9459 |
0.2766 | 0.9 | 12500 | 0.1905 | 27.4616 | 11.1083 | 27.5146 | 27.3079 | 39.0991 |
0.3 | 0.94 | 13000 | 0.1866 | 27.4616 | 11.1083 | 27.5146 | 27.3079 | 39.0541 |
0.2729 | 0.98 | 13500 | 0.1829 | 27.4616 | 11.1083 | 27.5146 | 27.3079 | 39.1171 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2