readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: speller-t5-9
    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-t5-9
  
  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: 0.1614
  - Rouge1: 14.9554
  - Rouge2: 8.3333
  - Rougel: 14.9554
  - Rougelsum: 14.9554
  - Gen Len: 42.8661
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 1.0873        | 0.04  | 500   | 0.5259          | 13.7946 | 7.1429 | 13.8393 | 13.8393   | 40.7946 |
  | 0.6932        | 0.07  | 1000  | 0.3914          | 14.0625 | 8.3333 | 14.0625 | 14.0625   | 43.5357 |
  | 0.5471        | 0.11  | 1500  | 0.3349          | 13.9633 | 7.9507 | 13.8641 | 13.9633   | 45.0089 |
  | 0.5566        | 0.14  | 2000  | 0.2954          | 14.0625 | 8.3333 | 14.0625 | 14.0625   | 43.1429 |
  | 0.4985        | 0.18  | 2500  | 0.2802          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 44.125  |
  | 0.5175        | 0.22  | 3000  | 0.2631          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 44.4286 |
  | 0.4377        | 0.25  | 3500  | 0.2431          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.5893 |
  | 0.4356        | 0.29  | 4000  | 0.2315          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.9286 |
  | 0.4052        | 0.32  | 4500  | 0.2258          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 43.2232 |
  | 0.3888        | 0.36  | 5000  | 0.2179          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.6607 |
  | 0.3731        | 0.39  | 5500  | 0.2063          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.9196 |
  | 0.436         | 0.43  | 6000  | 0.2075          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.7589 |
  | 0.42          | 0.47  | 6500  | 0.1993          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.5446 |
  | 0.378         | 0.5   | 7000  | 0.2036          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 43.0179 |
  | 0.3431        | 0.54  | 7500  | 0.1914          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.6875 |
  | 0.3574        | 0.57  | 8000  | 0.1852          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.7321 |
  | 0.302         | 0.61  | 8500  | 0.1900          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.7946 |
  | 0.3081        | 0.65  | 9000  | 0.1807          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.7054 |
  | 0.3266        | 0.68  | 9500  | 0.1755          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.5714 |
  | 0.3834        | 0.72  | 10000 | 0.1726          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.8482 |
  | 0.2802        | 0.75  | 10500 | 0.1736          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.8036 |
  | 0.3013        | 0.79  | 11000 | 0.1675          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.7054 |
  | 0.3404        | 0.83  | 11500 | 0.1630          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.6786 |
  | 0.2945        | 0.86  | 12000 | 0.1627          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.6607 |
  | 0.2819        | 0.9   | 12500 | 0.1633          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.7321 |
  | 0.3028        | 0.93  | 13000 | 0.1597          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.6429 |
  | 0.3138        | 0.97  | 13500 | 0.1614          | 14.9554 | 8.3333 | 14.9554 | 14.9554   | 42.8661 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  