readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wmt16
  model-index:
  - name: t5-turkish-to-english
    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. -->
  
  # t5-turkish-to-english
  
  This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the wmt16 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0282
  
  ## 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
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 0.6168        | 0.02  | 500   | 0.0497          |
  | 0.0832        | 0.04  | 1000  | 0.0448          |
  | 0.0791        | 0.06  | 1500  | 0.0424          |
  | 0.0718        | 0.08  | 2000  | 0.0413          |
  | 0.0661        | 0.1   | 2500  | 0.0406          |
  | 0.0669        | 0.12  | 3000  | 0.0399          |
  | 0.065         | 0.14  | 3500  | 0.0389          |
  | 0.0627        | 0.16  | 4000  | 0.0389          |
  | 0.0637        | 0.17  | 4500  | 0.0396          |
  | 0.0599        | 0.19  | 5000  | 0.0376          |
  | 0.0601        | 0.21  | 5500  | 0.0368          |
  | 0.0594        | 0.23  | 6000  | 0.0379          |
  | 0.0578        | 0.25  | 6500  | 0.0371          |
  | 0.0577        | 0.27  | 7000  | 0.0383          |
  | 0.0566        | 0.29  | 7500  | 0.0377          |
  | 0.0554        | 0.31  | 8000  | 0.0351          |
  | 0.0554        | 0.33  | 8500  | 0.0347          |
  | 0.0546        | 0.35  | 9000  | 0.0351          |
  | 0.0564        | 0.37  | 9500  | 0.0356          |
  | 0.0533        | 0.39  | 10000 | 0.0340          |
  | 0.0515        | 0.41  | 10500 | 0.0339          |
  | 0.0523        | 0.43  | 11000 | 0.0337          |
  | 0.0528        | 0.45  | 11500 | 0.0337          |
  | 0.0536        | 0.47  | 12000 | 0.0332          |
  | 0.0501        | 0.49  | 12500 | 0.0334          |
  | 0.0493        | 0.51  | 13000 | 0.0332          |
  | 0.0504        | 0.52  | 13500 | 0.0331          |
  | 0.0484        | 0.54  | 14000 | 0.0328          |
  | 0.0496        | 0.56  | 14500 | 0.0327          |
  | 0.0469        | 0.58  | 15000 | 0.0331          |
  | 0.0483        | 0.6   | 15500 | 0.0329          |
  | 0.0477        | 0.62  | 16000 | 0.0326          |
  | 0.0492        | 0.64  | 16500 | 0.0326          |
  | 0.0482        | 0.66  | 17000 | 0.0322          |
  | 0.0468        | 0.68  | 17500 | 0.0323          |
  | 0.0474        | 0.7   | 18000 | 0.0320          |
  | 0.0463        | 0.72  | 18500 | 0.0321          |
  | 0.048         | 0.74  | 19000 | 0.0319          |
  | 0.0463        | 0.76  | 19500 | 0.0319          |
  | 0.0467        | 0.78  | 20000 | 0.0316          |
  | 0.0457        | 0.8   | 20500 | 0.0319          |
  | 0.0463        | 0.82  | 21000 | 0.0320          |
  | 0.045         | 0.84  | 21500 | 0.0317          |
  | 0.0442        | 0.86  | 22000 | 0.0314          |
  | 0.0462        | 0.87  | 22500 | 0.0313          |
  | 0.0453        | 0.89  | 23000 | 0.0313          |
  | 0.0455        | 0.91  | 23500 | 0.0316          |
  | 0.0459        | 0.93  | 24000 | 0.0311          |
  | 0.0435        | 0.95  | 24500 | 0.0312          |
  | 0.0451        | 0.97  | 25000 | 0.0310          |
  | 0.043         | 0.99  | 25500 | 0.0310          |
  | 0.0429        | 1.01  | 26000 | 0.0306          |
  | 0.0423        | 1.03  | 26500 | 0.0309          |
  | 0.0418        | 1.05  | 27000 | 0.0309          |
  | 0.0418        | 1.07  | 27500 | 0.0308          |
  | 0.0414        | 1.09  | 28000 | 0.0307          |
  | 0.0426        | 1.11  | 28500 | 0.0308          |
  | 0.0411        | 1.13  | 29000 | 0.0306          |
  | 0.0414        | 1.15  | 29500 | 0.0310          |
  | 0.0411        | 1.17  | 30000 | 0.0305          |
  | 0.0424        | 1.19  | 30500 | 0.0305          |
  | 0.0419        | 1.21  | 31000 | 0.0307          |
  | 0.0415        | 1.22  | 31500 | 0.0304          |
  | 0.0403        | 1.24  | 32000 | 0.0303          |
  | 0.0411        | 1.26  | 32500 | 0.0302          |
  | 0.0414        | 1.28  | 33000 | 0.0304          |
  | 0.0412        | 1.3   | 33500 | 0.0301          |
  | 0.0404        | 1.32  | 34000 | 0.0304          |
  | 0.0403        | 1.34  | 34500 | 0.0304          |
  | 0.0415        | 1.36  | 35000 | 0.0302          |
  | 0.0389        | 1.38  | 35500 | 0.0303          |
  | 0.0401        | 1.4   | 36000 | 0.0300          |
  | 0.0393        | 1.42  | 36500 | 0.0301          |
  | 0.0399        | 1.44  | 37000 | 0.0297          |
  | 0.0404        |