readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: t5-small-finetuned-wikisql
    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-finetuned-wikisql
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1354
  - Rouge2 Precision: 0.8107
  - Rouge2 Recall: 0.7197
  - Rouge2 Fmeasure: 0.7553
  
  ## 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: 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
  |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
  | 0.2176        | 1.0   | 3569  | 0.1725          | 0.783            | 0.6963        | 0.7301          |
  | 0.1807        | 2.0   | 7138  | 0.1505          | 0.7994           | 0.7094        | 0.7446          |
  | 0.1667        | 3.0   | 10707 | 0.1414          | 0.8069           | 0.7162        | 0.7517          |
  | 0.1546        | 4.0   | 14276 | 0.1367          | 0.8094           | 0.7184        | 0.7541          |
  | 0.1535        | 5.0   | 17845 | 0.1354          | 0.8107           | 0.7197        | 0.7553          |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  