readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  widget:
  - text: "translate to SQL: How many models with BERT architecture are in the HuggingFace Hub?"
  - text: "translate to English: SELECT COUNT Model FROM table WHERE Architecture = RoBERTa AND creator = Manuel Romero"
  
  metrics:
  - bleu
  model-index:
  - name: t5-small-finetuned-wikisql-sql-nl-nl-sql
    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-sql-nl-nl-sql
  
  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: 0.1932
  - Bleu: 41.8787
  - Gen Len: 16.6251
  
  ## 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 | Bleu    | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
  | 0.2655        | 1.0   | 8097  | 0.2252          | 39.7999 | 16.6893 |
  | 0.2401        | 2.0   | 16194 | 0.2066          | 40.9456 | 16.6712 |
  | 0.2236        | 3.0   | 24291 | 0.1985          | 41.3509 | 16.5884 |
  | 0.2158        | 4.0   | 32388 | 0.1944          | 41.6988 | 16.6165 |
  | 0.2122        | 5.0   | 40485 | 0.1932          | 41.8787 | 16.6251 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  