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