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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- pub_med_summarization_dataset |
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metrics: |
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- rouge |
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model-index: |
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- name: wikihow-t5-small-finetuned-pubmed |
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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: pub_med_summarization_dataset |
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type: pub_med_summarization_dataset |
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args: document |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 8.9619 |
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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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# wikihow-t5-small-finetuned-pubmed |
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This model is a fine-tuned version of [deep-learning-analytics/wikihow-t5-small](https://huggingface.co/deep-learning-analytics/wikihow-t5-small) on the pub_med_summarization_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2702 |
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- Rouge1: 8.9619 |
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- Rouge2: 3.2719 |
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- Rougel: 8.1558 |
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- Rougelsum: 8.5714 |
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- Gen Len: 19.0 |
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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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More information needed |
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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: 2 |
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- eval_batch_size: 2 |
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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: 5 |
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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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| 2.5984 | 1.0 | 4000 | 2.3696 | 10.237 | 3.8609 | 8.9776 | 9.677 | 19.0 | |
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| 2.5677 | 2.0 | 8000 | 2.3132 | 9.302 | 3.4499 | 8.3816 | 8.8831 | 19.0 | |
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| 2.5038 | 3.0 | 12000 | 2.2884 | 9.0578 | 3.3103 | 8.23 | 8.6723 | 19.0 | |
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| 2.4762 | 4.0 | 16000 | 2.2758 | 9.0001 | 3.2882 | 8.1845 | 8.6084 | 19.0 | |
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| 2.4393 | 5.0 | 20000 | 2.2702 | 8.9619 | 3.2719 | 8.1558 | 8.5714 | 19.0 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 1.18.3 |
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- Tokenizers 0.11.6 |
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