speller-t5-8
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: 0.1626
- Rouge1: 14.5345
- Rouge2: 9.6847
- Rougel: 14.4144
- Rougelsum: 14.3544
- Gen Len: 37.7748
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 |
|---|---|---|---|---|---|---|---|---|
| 0.9929 | 0.04 | 500 | 0.5644 | 12.8915 | 6.982 | 12.7499 | 12.6126 | 39.3604 |
| 1.2778 | 0.07 | 1000 | 0.4272 | 13.8323 | 8.1081 | 13.7538 | 13.6059 | 38.8288 |
| 0.6384 | 0.11 | 1500 | 0.3654 | 14.1341 | 8.5586 | 14.034 | 13.9139 | 38.4865 |
| 0.5993 | 0.15 | 2000 | 0.3318 | 14.009 | 8.3655 | 13.9339 | 13.7688 | 38.8018 |
| 0.5327 | 0.18 | 2500 | 0.2969 | 14.2342 | 8.7838 | 14.1141 | 13.9339 | 37.7928 |
| 0.4831 | 0.22 | 3000 | 0.2761 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 38.9369 |
| 0.4582 | 0.25 | 3500 | 0.2562 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 39.2162 |
| 0.3983 | 0.29 | 4000 | 0.2449 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 38.9459 |
| 0.4459 | 0.33 | 4500 | 0.2422 | 14.4717 | 9.5045 | 14.4144 | 14.2206 | 38.8378 |
| 0.4073 | 0.36 | 5000 | 0.2375 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 38.3964 |
| 0.4047 | 0.4 | 5500 | 0.2263 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 39.2703 |
| 0.3423 | 0.44 | 6000 | 0.2208 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 38.5405 |
| 0.3348 | 0.47 | 6500 | 0.2109 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 37.7568 |
| 0.3421 | 0.51 | 7000 | 0.2053 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 37.7117 |
| 0.3319 | 0.54 | 7500 | 0.2025 | 14.2342 | 8.7838 | 14.1141 | 14.0541 | 37.5586 |
| 0.3239 | 0.58 | 8000 | 0.1991 | 14.2342 | 8.7838 | 14.1141 | 14.0541 | 38.0541 |
| 0.2963 | 0.62 | 8500 | 0.1959 | 14.2342 | 8.7838 | 14.1141 | 14.0541 | 38.0 |
| 0.3117 | 0.65 | 9000 | 0.1899 | 14.2342 | 8.7838 | 14.1141 | 14.0541 | 37.7117 |
| 0.2737 | 0.69 | 9500 | 0.1898 | 14.2342 | 8.7838 | 14.1141 | 14.0541 | 37.5135 |
| 0.3425 | 0.73 | 10000 | 0.1830 | 14.2342 | 8.7838 | 14.1141 | 14.0541 | 37.6306 |
| 0.2986 | 0.76 | 10500 | 0.1831 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 37.7658 |
| 0.3312 | 0.8 | 11000 | 0.1734 | 14.2342 | 8.7838 | 14.1141 | 14.0541 | 37.973 |
| 0.3461 | 0.83 | 11500 | 0.1753 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 37.6847 |
| 0.2786 | 0.87 | 12000 | 0.1740 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 37.7748 |
| 0.2911 | 0.91 | 12500 | 0.1672 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 37.7387 |
| 0.2618 | 0.94 | 13000 | 0.1691 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 37.5135 |
| 0.2844 | 0.98 | 13500 | 0.1626 | 14.5345 | 9.6847 | 14.4144 | 14.3544 | 37.7748 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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