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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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- scientific_papers |
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metrics: |
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- rouge |
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model-index: |
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- name: bart-base-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: scientific_papers |
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type: scientific_papers |
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args: pubmed |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 9.1984 |
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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-base-finetuned-pubmed |
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This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the scientific_papers dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9804 |
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- Rouge1: 9.1984 |
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- Rouge2: 4.3091 |
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- Rougel: 7.9739 |
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- Rougelsum: 8.6759 |
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- Gen Len: 20.0 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 4 |
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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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| 2.2869 | 1.0 | 29981 | 2.1241 | 9.0852 | 4.1152 | 7.842 | 8.5395 | 20.0 | |
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| 2.1469 | 2.0 | 59962 | 2.0225 | 9.1609 | 4.2437 | 7.9311 | 8.6273 | 20.0 | |
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| 2.113 | 3.0 | 89943 | 1.9959 | 9.3086 | 4.3305 | 8.0363 | 8.7713 | 20.0 | |
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| 2.0632 | 4.0 | 119924 | 1.9804 | 9.1984 | 4.3091 | 7.9739 | 8.6759 | 20.0 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.1+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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