readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: speller-t5-4
    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-t5-4
  
  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: 0.1871
  - Rouge1: 17.2619
  - Rouge2: 7.5893
  - Rougel: 17.5595
  - Rougelsum: 17.5595
  - Gen Len: 42.25
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 0.9773        | 0.04  | 500   | 0.5651          | 14.7321 | 5.2264 | 14.7863 | 14.8471   | 47.2321 |
  | 0.8463        | 0.07  | 1000  | 0.4230          | 16.3628 | 5.6052 | 16.3158 | 16.4325   | 47.9018 |
  | 0.6458        | 0.11  | 1500  | 0.3528          | 16.2099 | 5.5195 | 16.2034 | 16.3225   | 47.5179 |
  | 0.6147        | 0.14  | 2000  | 0.3269          | 16.313  | 5.7216 | 16.313  | 16.4242   | 47.2232 |
  | 0.5102        | 0.18  | 2500  | 0.3012          | 16.6071 | 6.0119 | 16.6239 | 16.5792   | 43.1696 |
  | 0.4585        | 0.21  | 3000  | 0.2823          | 16.6295 | 6.0714 | 16.6741 | 16.6071   | 47.25   |
  | 0.4801        | 0.25  | 3500  | 0.2748          | 16.8779 | 6.3885 | 16.8779 | 16.8779   | 44.5268 |
  | 0.4721        | 0.29  | 4000  | 0.2605          | 17.1947 | 7.4353 | 17.3867 | 17.3867   | 42.7054 |
  | 0.4132        | 0.32  | 4500  | 0.2530          | 17.2619 | 7.5605 | 17.5054 | 17.5054   | 42.9286 |
  | 0.4255        | 0.36  | 5000  | 0.2495          | 17.1503 | 7.4107 | 17.3363 | 17.3363   | 42.5625 |
  | 0.3952        | 0.39  | 5500  | 0.2424          | 17.2619 | 7.4702 | 17.4479 | 17.4479   | 42.5089 |
  | 0.3229        | 0.43  | 6000  | 0.2354          | 17.2619 | 7.5605 | 17.5054 | 17.5054   | 44.0268 |
  | 0.4474        | 0.47  | 6500  | 0.2310          | 17.2619 | 7.5335 | 17.4545 | 17.4545   | 42.5625 |
  | 0.3736        | 0.5   | 7000  | 0.2300          | 17.2619 | 7.5335 | 17.4545 | 17.4545   | 42.4286 |
  | 0.332         | 0.54  | 7500  | 0.2133          | 17.2619 | 7.5622 | 17.5085 | 17.5085   | 42.4732 |
  | 0.3347        | 0.57  | 8000  | 0.2148          | 17.2619 | 7.5605 | 17.5054 | 17.5054   | 42.5    |
  | 0.4257        | 0.61  | 8500  | 0.2093          | 17.2619 | 7.5605 | 17.5054 | 17.5054   | 42.3482 |
  | 0.3072        | 0.64  | 9000  | 0.2009          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.3661 |
  | 0.3184        | 0.68  | 9500  | 0.2028          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.4464 |
  | 0.3013        | 0.72  | 10000 | 0.2083          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.2589 |
  | 0.3202        | 0.75  | 10500 | 0.2056          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.4911 |
  | 0.2689        | 0.79  | 11000 | 0.2020          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.8304 |
  | 0.4168        | 0.82  | 11500 | 0.1962          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.2054 |
  | 0.287         | 0.86  | 12000 | 0.1930          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.1875 |
  | 0.3515        | 0.9   | 12500 | 0.1899          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.1875 |
  | 0.2713        | 0.93  | 13000 | 0.1868          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.3304 |
  | 0.2914        | 0.97  | 13500 | 0.1871          | 17.2619 | 7.5893 | 17.5595 | 17.5595   | 42.25   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  