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# speller-t5-900

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: 0.1758
- Rouge1: 19.3503
- Rouge2: 8.3333
- Rougel: 19.3503
- Rougelsum: 19.3503
- Gen Len: 41.4153

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.0227        | 0.03  | 500   | 0.5411          | 17.6201 | 7.1186 | 17.6554 | 17.5847   | 45.5424 |
| 0.7224        | 0.07  | 1000  | 0.4269          | 18.1497 | 7.1186 | 18.1497 | 17.9732   | 42.7797 |
| 0.7101        | 0.1   | 1500  | 0.3542          | 18.9972 | 7.9661 | 18.9972 | 18.9619   | 42.3983 |
| 0.5962        | 0.14  | 2000  | 0.3283          | 18.9972 | 7.9661 | 18.9972 | 18.9619   | 42.2542 |
| 0.535         | 0.17  | 2500  | 0.3104          | 18.9972 | 7.9661 | 18.9972 | 18.9619   | 42.2627 |
| 0.6124        | 0.2   | 3000  | 0.2843          | 18.9972 | 7.9661 | 18.9972 | 18.9619   | 42.4915 |
| 0.491         | 0.24  | 3500  | 0.2706          | 18.9972 | 7.9661 | 18.9972 | 18.9619   | 42.4322 |
| 0.5028        | 0.27  | 4000  | 0.2647          | 19.5429 | 8.5876 | 19.5429 | 19.5621   | 42.3898 |
| 0.4547        | 0.31  | 4500  | 0.2548          | 18.9972 | 7.9661 | 18.9972 | 18.9619   | 42.178  |
| 0.4335        | 0.34  | 5000  | 0.2448          | 19.5429 | 8.5876 | 19.5429 | 19.5621   | 42.178  |
| 0.4511        | 0.38  | 5500  | 0.2377          | 19.4915 | 8.5876 | 19.4915 | 19.4915   | 42.3305 |
| 0.4765        | 0.41  | 6000  | 0.2337          | 19.5429 | 8.5876 | 19.5429 | 19.5621   | 41.4237 |
| 0.4355        | 0.44  | 6500  | 0.2233          | 19.4915 | 8.5876 | 19.4915 | 19.4915   | 41.7881 |
| 0.3924        | 0.48  | 7000  | 0.2172          | 19.4915 | 8.5876 | 19.4915 | 19.4915   | 40.9492 |
| 0.3898        | 0.51  | 7500  | 0.2153          | 19.4915 | 8.5876 | 19.4915 | 19.4915   | 41.6356 |
| 0.4236        | 0.55  | 8000  | 0.2102          | 19.4915 | 8.5876 | 19.4915 | 19.4915   | 41.0254 |
| 0.3484        | 0.58  | 8500  | 0.2116          | 19.4915 | 8.5876 | 19.4915 | 19.4915   | 41.8305 |
| 0.5514        | 0.61  | 9000  | 0.2017          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.1864 |
| 0.3298        | 0.65  | 9500  | 0.1945          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.2966 |
| 0.3807        | 0.68  | 10000 | 0.1966          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.6525 |
| 0.3177        | 0.72  | 10500 | 0.1918          | 19.3503 | 8.3333 | 19.3503 | 19.3503   | 41.2627 |
| 0.3374        | 0.75  | 11000 | 0.1903          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.2373 |
| 0.3123        | 0.78  | 11500 | 0.1900          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.2203 |
| 0.3377        | 0.82  | 12000 | 0.1847          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.2712 |
| 0.3138        | 0.85  | 12500 | 0.1814          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.1864 |
| 0.335         | 0.89  | 13000 | 0.1784          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.1695 |
| 0.3142        | 0.92  | 13500 | 0.1768          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.2542 |
| 0.3245        | 0.95  | 14000 | 0.1753          | 19.6328 | 8.7571 | 19.5975 | 19.6328   | 41.2034 |
| 0.3277        | 0.99  | 14500 | 0.1758          | 19.3503 | 8.3333 | 19.3503 | 19.3503   | 41.4153 |


### 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-t5-900", "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-t5-900
==============


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: 0.1758
* Rouge1: 19.3503
* Rouge2: 8.3333
* Rougel: 19.3503
* Rougelsum: 19.3503
* Gen Len: 41.4153


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