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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- pubmed-summarization |
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metrics: |
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- rouge |
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model-index: |
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- name: bart-finetuned-summarization-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: pubmed-summarization |
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type: pubmed-summarization |
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config: section |
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split: validation |
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args: section |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 43.1219 |
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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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# bart-finetuned-summarization-pubmed |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the pubmed-summarization dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.7193 |
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- Rouge1: 43.1219 |
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- Rouge2: 18.7311 |
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- Rougel: 28.1006 |
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- Rougelsum: 38.0914 |
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- Gen Len: 128.6263 |
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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: 10 |
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- eval_batch_size: 10 |
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- seed: 42 |
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- gradient_accumulation_steps: 5 |
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- total_train_batch_size: 50 |
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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: 2 |
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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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| 1.8564 | 1.0 | 2398 | 1.7437 | 43.2294 | 18.867 | 28.2156 | 38.1868 | 128.4766 | |
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| 1.75 | 2.0 | 4796 | 1.7193 | 43.1219 | 18.7311 | 28.1006 | 38.0914 | 128.6263 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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