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t5-small-mathT5-finetune_qatoexp

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metadata
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

t5-small-mathT5-finetune_qatoexp

This model is a fine-tuned version of 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