readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: speller-t5-big-3
    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-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
  