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# 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

{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "speller-example__", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

speller-example\_\_
===================


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: 0.1437
* 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



### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### 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 ### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### 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### Framework versions


* Transformers 4.26.0
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2