readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: speller-t5-big
    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-big
  
  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: 0.1571
  - Rouge1: 23.6607
  - Rouge2: 10.9056
  - Rougel: 23.8095
  - Rougelsum: 23.8095
  - Gen Len: 45.0357
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 1.1176        | 0.04  | 500   | 0.5179          | 20.9642 | 10.0469 | 20.7738 | 20.9865   | 53.2232 |
  | 0.8525        | 0.07  | 1000  | 0.3809          | 20.4241 | 9.7577  | 20.5357 | 20.6659   | 46.9196 |
  | 0.677         | 0.11  | 1500  | 0.3261          | 21.131  | 9.6429  | 21.1607 | 21.4583   | 47.125  |
  | 0.5804        | 0.14  | 2000  | 0.3008          | 22.7307 | 10.9056 | 22.8051 | 22.9167   | 46.7321 |
  | 0.5251        | 0.18  | 2500  | 0.2805          | 22.3214 | 10.6737 | 22.4702 | 22.619    | 47.1339 |
  | 0.5026        | 0.21  | 3000  | 0.2676          | 22.5298 | 10.7639 | 22.6488 | 22.6786   | 47.4464 |
  | 0.433         | 0.25  | 3500  | 0.2515          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 46.8929 |
  | 0.5218        | 0.29  | 4000  | 0.2383          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 46.6071 |
  | 0.45          | 0.32  | 4500  | 0.2300          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 46.8304 |
  | 0.3818        | 0.36  | 5000  | 0.2270          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 46.5625 |
  | 0.4404        | 0.39  | 5500  | 0.2192          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 45.3304 |
  | 0.3476        | 0.43  | 6000  | 0.2099          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 45.2857 |
  | 0.3802        | 0.47  | 6500  | 0.2066          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 47.0    |
  | 0.3423        | 0.5   | 7000  | 0.2003          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 45.3839 |
  | 0.3288        | 0.54  | 7500  | 0.1924          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 45.7768 |
  | 0.3788        | 0.57  | 8000  | 0.1897          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 46.3125 |
  | 0.3475        | 0.61  | 8500  | 0.1905          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 45.8571 |
  | 0.3229        | 0.64  | 9000  | 0.1829          | 22.9167 | 10.2679 | 22.9167 | 22.9167   | 46.6161 |
  | 0.2918        | 0.68  | 9500  | 0.1840          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 46.0714 |
  | 0.3683        | 0.72  | 10000 | 0.1834          | 22.9167 | 10.2679 | 22.9167 | 22.9167   | 46.8393 |
  | 0.3074        | 0.75  | 10500 | 0.1758          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 45.875  |
  | 0.329         | 0.79  | 11000 | 0.1686          | 22.9167 | 10.2679 | 22.9167 | 22.9167   | 46.7589 |
  | 0.2692        | 0.82  | 11500 | 0.1699          | 22.9167 | 10.2679 | 22.9167 | 22.9167   | 46.6696 |
  | 0.2775        | 0.86  | 12000 | 0.1732          | 23.4375 | 10.523  | 23.4375 | 23.5119   | 46.2232 |
  | 0.2754        | 0.9   | 12500 | 0.1643          | 22.9167 | 10.2679 | 22.9167 | 22.9167   | 46.3125 |
  | 0.3348        | 0.93  | 13000 | 0.1611          | 22.9167 | 10.2679 | 22.9167 | 22.9167   | 46.2411 |
  | 0.2875        | 0.97  | 13500 | 0.1571          | 23.6607 | 10.9056 | 23.8095 | 23.8095   | 45.0357 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  