readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: speller-t5-9001
    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-t5-9001
  
  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: 0.1587
  - Rouge1: 17.0762
  - Rouge2: 5.6336
  - Rougel: 17.1181
  - Rougelsum: 17.2316
  - Gen Len: 40.2034
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 1.172         | 0.03  | 500   | 0.5659          | 14.1669 | 4.1265 | 13.7878 | 14.1044   | 42.7458 |
  | 0.7063        | 0.07  | 1000  | 0.4207          | 14.5638 | 4.8305 | 14.4688 | 14.5907   | 43.8898 |
  | 0.6604        | 0.1   | 1500  | 0.3557          | 16.2672 | 4.8685 | 16.2308 | 16.3516   | 43.8644 |
  | 0.5429        | 0.14  | 2000  | 0.3266          | 16.6436 | 5.1161 | 16.6667 | 16.6872   | 43.4831 |
  | 0.5245        | 0.17  | 2500  | 0.2964          | 16.6667 | 5.1963 | 16.6775 | 16.7707   | 42.3983 |
  | 0.5812        | 0.2   | 3000  | 0.2757          | 16.6969 | 5.339  | 16.7331 | 16.8449   | 41.3051 |
  | 0.5019        | 0.24  | 3500  | 0.2626          | 16.686  | 5.4462 | 16.6815 | 16.8733   | 40.7203 |
  | 0.4182        | 0.27  | 4000  | 0.2531          | 16.7142 | 5.5085 | 16.6667 | 16.9373   | 40.6102 |
  | 0.4592        | 0.31  | 4500  | 0.2413          | 16.947  | 5.5404 | 16.9581 | 17.059    | 40.1441 |
  | 0.4626        | 0.34  | 5000  | 0.2299          | 16.9492 | 5.6063 | 16.944  | 17.0235   | 40.3475 |
  | 0.4158        | 0.38  | 5500  | 0.2228          | 16.8653 | 5.5608 | 16.9429 | 17.0407   | 39.5085 |
  | 0.4261        | 0.41  | 6000  | 0.2185          | 16.9293 | 5.5843 | 16.9492 | 17.0365   | 39.8814 |
  | 0.4465        | 0.44  | 6500  | 0.2088          | 16.9492 | 5.5968 | 16.9895 | 17.1106   | 39.4746 |
  | 0.3919        | 0.48  | 7000  | 0.2015          | 16.9492 | 5.5843 | 16.9839 | 17.0937   | 39.6864 |
  | 0.3994        | 0.51  | 7500  | 0.2023          | 17.0836 | 5.6632 | 17.0588 | 17.1895   | 40.5932 |
  | 0.466         | 0.55  | 8000  | 0.1968          | 17.1664 | 5.7257 | 17.1664 | 17.3019   | 40.4153 |
  | 0.419         | 0.58  | 8500  | 0.1899          | 17.0132 | 5.6021 | 17.0625 | 17.1945   | 39.4831 |
  | 0.4047        | 0.61  | 9000  | 0.1877          | 17.0418 | 5.6217 | 16.9895 | 17.1106   | 39.9237 |
  | 0.3728        | 0.65  | 9500  | 0.1798          | 16.9856 | 5.5876 | 16.9947 | 17.1612   | 39.4237 |
  | 0.3685        | 0.68  | 10000 | 0.1768          | 16.9856 | 5.6249 | 16.9492 | 17.1339   | 39.2966 |
  | 0.4241        | 0.72  | 10500 | 0.1739          | 16.9908 | 5.595  | 17.0532 | 17.1845   | 39.3814 |
  | 0.3006        | 0.75  | 11000 | 0.1740          | 16.9492 | 5.5799 | 16.9802 | 17.1525   | 39.5085 |
  | 0.339         | 0.78  | 11500 | 0.1739          | 17.0495 | 5.6497 | 17.047  | 17.1796   | 39.8136 |
  | 0.3387        | 0.82  | 12000 | 0.1711          | 16.9908 | 5.595  | 17.0532 | 17.1845   | 39.4746 |
  | 0.3116        | 0.85  | 12500 | 0.1642          | 16.9492 | 5.5799 | 16.9802 | 17.1525   | 39.161  |
  | 0.3112        | 0.89  | 13000 | 0.1620          | 17.0021 | 5.6076 | 17.0374 | 17.1719   | 39.178  |
  | 0.341         | 0.92  | 13500 | 0.1638          | 17.1664 | 5.7473 | 17.2279 | 17.3384   | 40.1864 |
  | 0.2885        | 0.95  | 14000 | 0.1609          | 17.1664 | 5.7931 | 17.2504 | 17.3565   | 40.1356 |
  | 0.3335        | 0.99  | 14500 | 0.1587          | 17.0762 | 5.6336 | 17.1181 | 17.2316   | 40.2034 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.7.1+cu110
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  