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speller-t5-9
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: 0.1614
- Rouge1: 14.9554
- Rouge2: 8.3333
- Rougel: 14.9554
- Rougelsum: 14.9554
- Gen Len: 42.8661
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 |
---|---|---|---|---|---|---|---|---|
1.0873 | 0.04 | 500 | 0.5259 | 13.7946 | 7.1429 | 13.8393 | 13.8393 | 40.7946 |
0.6932 | 0.07 | 1000 | 0.3914 | 14.0625 | 8.3333 | 14.0625 | 14.0625 | 43.5357 |
0.5471 | 0.11 | 1500 | 0.3349 | 13.9633 | 7.9507 | 13.8641 | 13.9633 | 45.0089 |
0.5566 | 0.14 | 2000 | 0.2954 | 14.0625 | 8.3333 | 14.0625 | 14.0625 | 43.1429 |
0.4985 | 0.18 | 2500 | 0.2802 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 44.125 |
0.5175 | 0.22 | 3000 | 0.2631 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 44.4286 |
0.4377 | 0.25 | 3500 | 0.2431 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.5893 |
0.4356 | 0.29 | 4000 | 0.2315 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.9286 |
0.4052 | 0.32 | 4500 | 0.2258 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 43.2232 |
0.3888 | 0.36 | 5000 | 0.2179 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6607 |
0.3731 | 0.39 | 5500 | 0.2063 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.9196 |
0.436 | 0.43 | 6000 | 0.2075 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7589 |
0.42 | 0.47 | 6500 | 0.1993 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.5446 |
0.378 | 0.5 | 7000 | 0.2036 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 43.0179 |
0.3431 | 0.54 | 7500 | 0.1914 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6875 |
0.3574 | 0.57 | 8000 | 0.1852 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7321 |
0.302 | 0.61 | 8500 | 0.1900 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7946 |
0.3081 | 0.65 | 9000 | 0.1807 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7054 |
0.3266 | 0.68 | 9500 | 0.1755 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.5714 |
0.3834 | 0.72 | 10000 | 0.1726 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.8482 |
0.2802 | 0.75 | 10500 | 0.1736 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.8036 |
0.3013 | 0.79 | 11000 | 0.1675 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7054 |
0.3404 | 0.83 | 11500 | 0.1630 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6786 |
0.2945 | 0.86 | 12000 | 0.1627 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6607 |
0.2819 | 0.9 | 12500 | 0.1633 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.7321 |
0.3028 | 0.93 | 13000 | 0.1597 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.6429 |
0.3138 | 0.97 | 13500 | 0.1614 | 14.9554 | 8.3333 | 14.9554 | 14.9554 | 42.8661 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2