readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - math_qa
  metrics:
  - rouge
  model-index:
  - name: t5-small-mathT5-finetune_qatoexp
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: math_qa
        type: math_qa
        config: default
        split: train
        args: default
      metrics:
      - name: Rouge1
        type: rouge
        value: 21.9174
  ---
  
  <!-- 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-mathT5-finetune_qatoexp
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the math_qa dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.8677
  - Rouge1: 21.9174
  - Rouge2: 8.4401
  - Rougel: 19.1645
  - Rougelsum: 19.8239
  - Gen Len: 18.9765
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  We have trained T5-small on MathQA dataset for sequence to sequence generation of explanations from given math problem.
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 10
  - eval_batch_size: 10
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 2.4496        | 1.0   | 2984  | 2.2096          | 19.6477 | 6.508  | 16.9295 | 17.5212   | 18.9064 |
  | 2.2893        | 2.0   | 5968  | 2.0837          | 20.4879 | 7.2528 | 17.7778 | 18.4085   | 18.968  |
  | 2.1869        | 3.0   | 8952  | 2.0125          | 20.8462 | 7.6105 | 18.1516 | 18.8343   | 18.9837 |
  | 2.1456        | 4.0   | 11936 | 1.9633          | 20.7623 | 7.7113 | 18.1274 | 18.783    | 18.9886 |
  | 2.1171        | 5.0   | 14920 | 1.9321          | 21.0648 | 7.8897 | 18.4162 | 19.0551   | 18.9844 |
  | 2.0854        | 6.0   | 17904 | 1.9061          | 21.4445 | 8.0883 | 18.8038 | 19.4176   | 18.9812 |
  | 2.0592        | 7.0   | 20888 | 1.8902          | 21.5714 | 8.2751 | 18.8864 | 19.537    | 18.9772 |
  | 2.0609        | 8.0   | 23872 | 1.8770          | 21.7737 | 8.3297 | 19.022  | 19.6897   | 18.9763 |
  | 2.0285        | 9.0   | 26856 | 1.8701          | 21.964  | 8.4358 | 19.1701 | 19.845    | 18.9747 |
  | 2.0165        | 10.0  | 29840 | 1.8677          | 21.9174 | 8.4401 | 19.1645 | 19.8239   | 18.9765 |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  