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# speller-t5-90

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: 0.1486
- Rouge1: 19.3503
- Rouge2: 8.3898
- Rougel: 19.4209
- Rougelsum: 19.4915
- Gen Len: 41.3136

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 0.3435        | 0.03  | 500   | 0.2100          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.4492 |
| 0.3245        | 0.07  | 1000  | 0.2102          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.1949 |
| 0.3777        | 0.1   | 1500  | 0.2010          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.0    |
| 0.3643        | 0.14  | 2000  | 0.1980          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.0593 |
| 0.3212        | 0.17  | 2500  | 0.1986          | 19.209  | 8.2062 | 19.2797 | 19.2797   | 41.1525 |
| 0.4181        | 0.2   | 3000  | 0.1896          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 42.2373 |
| 0.3175        | 0.24  | 3500  | 0.1879          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.4576 |
| 0.3399        | 0.27  | 4000  | 0.1838          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.1102 |
| 0.314         | 0.31  | 4500  | 0.1837          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.0339 |
| 0.3063        | 0.34  | 5000  | 0.1796          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 40.9407 |
| 0.3434        | 0.38  | 5500  | 0.1769          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 40.8814 |
| 0.376         | 0.41  | 6000  | 0.1790          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.0593 |
| 0.3355        | 0.44  | 6500  | 0.1735          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.4153 |
| 0.3181        | 0.48  | 7000  | 0.1665          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.0508 |
| 0.3017        | 0.51  | 7500  | 0.1701          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.2881 |
| 0.2953        | 0.55  | 8000  | 0.1664          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.2458 |
| 0.2711        | 0.58  | 8500  | 0.1664          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.4068 |
| 0.3661        | 0.61  | 9000  | 0.1626          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.2797 |
| 0.273         | 0.65  | 9500  | 0.1585          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.3051 |
| 0.3346        | 0.68  | 10000 | 0.1627          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.2797 |
| 0.2529        | 0.72  | 10500 | 0.1590          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.2627 |
| 0.2926        | 0.75  | 11000 | 0.1601          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.2712 |
| 0.2677        | 0.78  | 11500 | 0.1551          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.2797 |
| 0.2746        | 0.82  | 12000 | 0.1570          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.1186 |
| 0.2494        | 0.85  | 12500 | 0.1513          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.2373 |
| 0.2834        | 0.89  | 13000 | 0.1506          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.2458 |
| 0.2646        | 0.92  | 13500 | 0.1512          | 19.5975 | 8.7571 | 19.7034 | 19.774    | 41.3729 |
| 0.2782        | 0.95  | 14000 | 0.1528          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.3644 |
| 0.2954        | 0.99  | 14500 | 0.1486          | 19.3503 | 8.3898 | 19.4209 | 19.4915   | 41.3136 |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.7.1+cu110
- Datasets 2.9.0
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "speller-t5-90", "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-t5-90
=============


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: 0.1486
* Rouge1: 19.3503
* Rouge2: 8.3898
* Rougel: 19.4209
* Rougelsum: 19.4915
* Gen Len: 41.3136


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 1.7.1+cu110
* 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 1.7.1+cu110
* 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 1.7.1+cu110
* 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 1.7.1+cu110
* Datasets 2.9.0
* Tokenizers 0.13.2