readme: ---
  license: mit
  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 [unicamp-dl/ptt5-base-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-base-t5-vocab) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.1636
  - Rouge2 Precision: 0.0429
  - Rouge2 Recall: 0.0139
  - Rouge2 Fmeasure: 0.021
  
  ## 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
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
  |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
  | No log        | 1.0   | 2    | 10.5445         | 0.0429           | 0.0139        | 0.021           |
  | No log        | 2.0   | 4    | 7.0508          | 0.0429           | 0.0139        | 0.021           |
  | No log        | 3.0   | 6    | 5.0775          | 0.0429           | 0.0139        | 0.021           |
  | No log        | 4.0   | 8    | 3.9094          | 0.0429           | 0.0139        | 0.021           |
  | No log        | 5.0   | 10   | 3.1636          | 0.0429           | 0.0139        | 0.021           |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  