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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 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
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