readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: t5-small-entailement-Writer-T5-small
    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-small-entailement-Writer-T5-small
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5628
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | No log        | 1.0   | 83   | 1.2943          |
  | No log        | 2.0   | 166  | 0.9323          |
  | No log        | 3.0   | 249  | 0.8443          |
  | No log        | 4.0   | 332  | 0.7884          |
  | No log        | 5.0   | 415  | 0.7582          |
  | No log        | 6.0   | 498  | 0.7355          |
  | 1.2761        | 7.0   | 581  | 0.7178          |
  | 1.2761        | 8.0   | 664  | 0.7105          |
  | 1.2761        | 9.0   | 747  | 0.6972          |
  | 1.2761        | 10.0  | 830  | 0.6847          |
  | 1.2761        | 11.0  | 913  | 0.6774          |
  | 1.2761        | 12.0  | 996  | 0.6708          |
  | 0.7765        | 13.0  | 1079 | 0.6609          |
  | 0.7765        | 14.0  | 1162 | 0.6566          |
  | 0.7765        | 15.0  | 1245 | 0.6507          |
  | 0.7765        | 16.0  | 1328 | 0.6454          |
  | 0.7765        | 17.0  | 1411 | 0.6438          |
  | 0.7765        | 18.0  | 1494 | 0.6384          |
  | 0.693         | 19.0  | 1577 | 0.6347          |
  | 0.693         | 20.0  | 1660 | 0.6321          |
  | 0.693         | 21.0  | 1743 | 0.6254          |
  | 0.693         | 22.0  | 1826 | 0.6237          |
  | 0.693         | 23.0  | 1909 | 0.6215          |
  | 0.693         | 24.0  | 1992 | 0.6167          |
  | 0.6504        | 25.0  | 2075 | 0.6167          |
  | 0.6504        | 26.0  | 2158 | 0.6131          |
  | 0.6504        | 27.0  | 2241 | 0.6120          |
  | 0.6504        | 28.0  | 2324 | 0.6091          |
  | 0.6504        | 29.0  | 2407 | 0.6076          |
  | 0.6504        | 30.0  | 2490 | 0.6058          |
  | 0.615         | 31.0  | 2573 | 0.6031          |
  | 0.615         | 32.0  | 2656 | 0.6015          |
  | 0.615         | 33.0  | 2739 | 0.6015          |
  | 0.615         | 34.0  | 2822 | 0.6000          |
  | 0.615         | 35.0  | 2905 | 0.5998          |
  | 0.615         | 36.0  | 2988 | 0.5969          |
  | 0.586         | 37.0  | 3071 | 0.5959          |
  | 0.586         | 38.0  | 3154 | 0.5941          |
  | 0.586         | 39.0  | 3237 | 0.5923          |
  | 0.586         | 40.0  | 3320 | 0.5936          |
  | 0.586         | 41.0  | 3403 | 0.5929          |
  | 0.586         | 42.0  | 3486 | 0.5922          |
  | 0.5618        | 43.0  | 3569 | 0.5910          |
  | 0.5618        | 44.0  | 3652 | 0.5885          |
  | 0.5618        | 45.0  | 3735 | 0.5879          |
  | 0.5618        | 46.0  | 3818 | 0.5873          |
  | 0.5618        | 47.0  | 3901 | 0.5877          |
  | 0.5618        | 48.0  | 3984 | 0.5878          |
  | 0.5418        | 49.0  | 4067 | 0.5881          |
  | 0.5418        | 50.0  | 4150 | 0.5858          |
  | 0.5418        | 51.0  | 4233 | 0.5847          |
  | 0.5418        | 52.0  | 4316 | 0.5839          |
  | 0.5418        | 53.0  | 4399 | 0.5843          |
  | 0.5418        | 54.0  | 4482 | 0.5826          |
  | 0.5283        | 55.0  | 4565 | 0.5843          |
  | 0.5283        | 56.0  | 4648 | 0.5833          |
  | 0.5283        | 57.0  | 4731 | 0.5825          |
  | 0.5283        | 58.0  | 4814 | 0.5827          |
  | 0.5283        | 59.0  | 4897 | 0.5830          |
  | 0.5283        | 60.0  | 4980 | 0.5806          |
  | 0.5135        | 61.0  | 5063 | 0.5808          |
  | 0.5135        | 62.0  | 5146 | 0.5806          |
  | 0.5135        | 63.0  | 5229 | 0.5807          |
  | 0.5135        | 64.0  | 5312 | 0.5823          |
  | 0.5135        | 65.0  | 5395 | 0.5801          |
  | 0.5135        | 66.0  | 5478 | 0.5799          |
  | 0.5053        | 67.0  | 5561 | 0.5808          |
  | 0.5053        | 68.0  | 5644 | 0.5796          |
  | 0.5053        | 69.0  | 5727 | 0.5793          |
  | 0.5053        | 70.0  | 5810 | 0.5785          |
  | 0.5053        | 71.0  | 5893 | 0.5790          |
  | 0.5053        | 72.0  | 5976 | 0.5775          |
  | 0.4985        | 73.0  | 6059 | 0.5770          |
  | 0.4985        | 74.0  | 6142 | 0.5777          |
  | 0.4985        | 75.0  | 6225 | 0.5780          |
  | 0.4985        | 7