---
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