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