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# speller-t5-big-3

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: 0.1829
- Rouge1: 27.4616
- Rouge2: 11.1083
- Rougel: 27.5146
- Rougelsum: 27.3079
- Gen Len: 39.1171

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0936        | 0.04  | 500   | 0.5587          | 23.1392 | 7.0032  | 23.1709 | 23.1908   | 41.1081 |
| 0.8042        | 0.07  | 1000  | 0.4168          | 25.1867 | 8.9696  | 25.2993 | 25.1779   | 43.6486 |
| 0.634         | 0.11  | 1500  | 0.3611          | 26.0366 | 8.521   | 26.1568 | 25.9359   | 40.2613 |
| 0.5041        | 0.14  | 2000  | 0.3255          | 26.1019 | 8.7002  | 26.2473 | 25.983    | 40.7928 |
| 0.5279        | 0.18  | 2500  | 0.3041          | 26.1352 | 8.6265  | 26.2606 | 25.9482   | 39.6216 |
| 0.4838        | 0.22  | 3000  | 0.2784          | 26.6137 | 9.8094  | 26.8372 | 26.5692   | 39.3694 |
| 0.4512        | 0.25  | 3500  | 0.2700          | 25.6152 | 9.5832  | 25.7503 | 25.6898   | 38.7387 |
| 0.4412        | 0.29  | 4000  | 0.2612          | 25.6113 | 9.6697  | 25.7482 | 25.6838   | 39.1171 |
| 0.405         | 0.33  | 4500  | 0.2426          | 26.5151 | 9.6882  | 26.7719 | 26.4825   | 39.1892 |
| 0.3987        | 0.36  | 5000  | 0.2390          | 26.479  | 9.6144  | 26.6499 | 26.3759   | 39.0991 |
| 0.407         | 0.4   | 5500  | 0.2325          | 26.4499 | 9.6544  | 26.6649 | 26.3821   | 39.3784 |
| 0.406         | 0.43  | 6000  | 0.2266          | 26.6224 | 9.875   | 26.8468 | 26.6058   | 38.6486 |
| 0.3827        | 0.47  | 6500  | 0.2213          | 26.8997 | 10.0139 | 27.1249 | 26.8252   | 39.1712 |
| 0.334         | 0.51  | 7000  | 0.2247          | 26.7779 | 9.9399  | 26.9951 | 26.6453   | 39.7207 |
| 0.3463        | 0.54  | 7500  | 0.2145          | 26.879  | 9.9911  | 27.0863 | 26.7372   | 39.2432 |
| 0.3439        | 0.58  | 8000  | 0.2102          | 26.8839 | 10.0139 | 27.0715 | 26.7186   | 39.3694 |
| 0.3644        | 0.61  | 8500  | 0.2050          | 26.9076 | 10.0704 | 27.1328 | 26.8411   | 39.2252 |
| 0.3161        | 0.65  | 9000  | 0.2008          | 26.9219 | 10.1927 | 27.1542 | 26.8697   | 38.7928 |
| 0.3273        | 0.69  | 9500  | 0.2018          | 26.8221 | 9.9879  | 27.0473 | 26.7137   | 39.1892 |
| 0.3423        | 0.72  | 10000 | 0.1992          | 26.8572 | 10.0937 | 27.0701 | 26.7469   | 39.2342 |
| 0.3129        | 0.76  | 10500 | 0.1964          | 26.9076 | 10.0704 | 27.1328 | 26.8411   | 39.1712 |
| 0.2841        | 0.79  | 11000 | 0.1937          | 27.4202 | 10.9493 | 27.5146 | 27.2724   | 39.1261 |
| 0.2865        | 0.83  | 11500 | 0.1901          | 27.4559 | 11.0314 | 27.5146 | 27.3022   | 39.2072 |
| 0.2747        | 0.87  | 12000 | 0.1862          | 27.4127 | 10.9878 | 27.5146 | 27.2611   | 38.9459 |
| 0.2766        | 0.9   | 12500 | 0.1905          | 27.4616 | 11.1083 | 27.5146 | 27.3079   | 39.0991 |
| 0.3           | 0.94  | 13000 | 0.1866          | 27.4616 | 11.1083 | 27.5146 | 27.3079   | 39.0541 |
| 0.2729        | 0.98  | 13500 | 0.1829          | 27.4616 | 11.1083 | 27.5146 | 27.3079   | 39.1171 |


### 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-t5-big-3", "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-big-3
================


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: 0.1829
* Rouge1: 27.4616
* Rouge2: 11.1083
* Rougel: 27.5146
* Rougelsum: 27.3079
* Gen Len: 39.1171


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