readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: speller-t5-900
    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-t5-900
  
  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
  