readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: speller-t5-8
    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-t5-8
  
  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: 0.1626
  - Rouge1: 14.5345
  - Rouge2: 9.6847
  - Rougel: 14.4144
  - Rougelsum: 14.3544
  - Gen Len: 37.7748
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 0.9929        | 0.04  | 500   | 0.5644          | 12.8915 | 6.982  | 12.7499 | 12.6126   | 39.3604 |
  | 1.2778        | 0.07  | 1000  | 0.4272          | 13.8323 | 8.1081 | 13.7538 | 13.6059   | 38.8288 |
  | 0.6384        | 0.11  | 1500  | 0.3654          | 14.1341 | 8.5586 | 14.034  | 13.9139   | 38.4865 |
  | 0.5993        | 0.15  | 2000  | 0.3318          | 14.009  | 8.3655 | 13.9339 | 13.7688   | 38.8018 |
  | 0.5327        | 0.18  | 2500  | 0.2969          | 14.2342 | 8.7838 | 14.1141 | 13.9339   | 37.7928 |
  | 0.4831        | 0.22  | 3000  | 0.2761          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 38.9369 |
  | 0.4582        | 0.25  | 3500  | 0.2562          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 39.2162 |
  | 0.3983        | 0.29  | 4000  | 0.2449          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 38.9459 |
  | 0.4459        | 0.33  | 4500  | 0.2422          | 14.4717 | 9.5045 | 14.4144 | 14.2206   | 38.8378 |
  | 0.4073        | 0.36  | 5000  | 0.2375          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 38.3964 |
  | 0.4047        | 0.4   | 5500  | 0.2263          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 39.2703 |
  | 0.3423        | 0.44  | 6000  | 0.2208          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 38.5405 |
  | 0.3348        | 0.47  | 6500  | 0.2109          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 37.7568 |
  | 0.3421        | 0.51  | 7000  | 0.2053          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 37.7117 |
  | 0.3319        | 0.54  | 7500  | 0.2025          | 14.2342 | 8.7838 | 14.1141 | 14.0541   | 37.5586 |
  | 0.3239        | 0.58  | 8000  | 0.1991          | 14.2342 | 8.7838 | 14.1141 | 14.0541   | 38.0541 |
  | 0.2963        | 0.62  | 8500  | 0.1959          | 14.2342 | 8.7838 | 14.1141 | 14.0541   | 38.0    |
  | 0.3117        | 0.65  | 9000  | 0.1899          | 14.2342 | 8.7838 | 14.1141 | 14.0541   | 37.7117 |
  | 0.2737        | 0.69  | 9500  | 0.1898          | 14.2342 | 8.7838 | 14.1141 | 14.0541   | 37.5135 |
  | 0.3425        | 0.73  | 10000 | 0.1830          | 14.2342 | 8.7838 | 14.1141 | 14.0541   | 37.6306 |
  | 0.2986        | 0.76  | 10500 | 0.1831          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 37.7658 |
  | 0.3312        | 0.8   | 11000 | 0.1734          | 14.2342 | 8.7838 | 14.1141 | 14.0541   | 37.973  |
  | 0.3461        | 0.83  | 11500 | 0.1753          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 37.6847 |
  | 0.2786        | 0.87  | 12000 | 0.1740          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 37.7748 |
  | 0.2911        | 0.91  | 12500 | 0.1672          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 37.7387 |
  | 0.2618        | 0.94  | 13000 | 0.1691          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 37.5135 |
  | 0.2844        | 0.98  | 13500 | 0.1626          | 14.5345 | 9.6847 | 14.4144 | 14.3544   | 37.7748 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  