| --- |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: speller-example__ |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # speller-example__ |
|
|
| This model is a fine-tuned version of [sberbank-ai/ruT5-base](https://huggingface.co/sberbank-ai/ruT5-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1437 |
| - Rouge1: 19.7034 |
| - Rouge2: 8.7571 |
| - Rougel: 19.4209 |
| - Rougelsum: 19.774 |
| - Gen Len: 41.2542 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
| | 0.3549 | 0.1 | 1500 | 0.1935 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.1356 | |
| | 0.3863 | 0.2 | 3000 | 0.1830 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.1864 | |
| | 0.3164 | 0.31 | 4500 | 0.1746 | 19.5621 | 8.4746 | 19.2797 | 19.5621 | 41.2966 | |
| | 0.367 | 0.41 | 6000 | 0.1690 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.161 | |
| | 0.3002 | 0.51 | 7500 | 0.1578 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.2458 | |
| | 0.3352 | 0.61 | 9000 | 0.1541 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.3475 | |
| | 0.2462 | 0.72 | 10500 | 0.1519 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.3475 | |
| | 0.2736 | 0.82 | 12000 | 0.1510 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.2797 | |
| | 0.2618 | 0.92 | 13500 | 0.1437 | 19.7034 | 8.7571 | 19.4209 | 19.774 | 41.2542 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.26.0 |
| - Pytorch 1.13.1+cu116 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
|
|
