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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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metrics: |
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- rouge |
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model-index: |
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- name: bart-large-cnn-finetuned-pubmed-finetuned-roundup-e16 |
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results: [] |
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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-large-cnn-finetuned-pubmed-finetuned-roundup-e16 |
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This model is a fine-tuned version of [theojolliffe/bart-large-cnn-finetuned-pubmed](https://huggingface.co/theojolliffe/bart-large-cnn-finetuned-pubmed) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6815 |
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- Rouge1: 48.7608 |
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- Rouge2: 29.554 |
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- Rougel: 30.5554 |
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- Rougelsum: 46.4001 |
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- Gen Len: 142.0 |
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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: 16 |
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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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| No log | 1.0 | 25 | 1.4287 | 46.5701 | 28.6267 | 34.7827 | 45.0622 | 142.0 | |
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| No log | 2.0 | 50 | 1.4419 | 46.6171 | 27.4276 | 31.0085 | 43.1797 | 142.0 | |
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| No log | 3.0 | 75 | 1.5418 | 50.1144 | 29.3433 | 32.0144 | 46.9217 | 142.0 | |
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| No log | 4.0 | 100 | 1.7125 | 49.1395 | 28.611 | 30.9759 | 46.8346 | 142.0 | |
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| No log | 5.0 | 125 | 1.8978 | 43.9629 | 24.1224 | 26.0032 | 41.2272 | 142.0 | |
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| No log | 6.0 | 150 | 2.0990 | 49.0579 | 29.5182 | 31.5829 | 46.0207 | 142.0 | |
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| No log | 7.0 | 175 | 2.2380 | 48.8754 | 27.7691 | 28.8597 | 45.3281 | 142.0 | |
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| No log | 8.0 | 200 | 2.2922 | 48.311 | 29.2517 | 33.8241 | 46.6099 | 142.0 | |
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| No log | 9.0 | 225 | 2.3820 | 45.4663 | 23.9904 | 27.5497 | 41.9446 | 142.0 | |
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| No log | 10.0 | 250 | 2.4856 | 48.2224 | 27.7455 | 28.159 | 45.4726 | 142.0 | |
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| No log | 11.0 | 275 | 2.4731 | 46.1799 | 22.1941 | 26.8254 | 43.9986 | 142.0 | |
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| No log | 12.0 | 300 | 2.5278 | 47.8623 | 27.6514 | 26.6377 | 42.9255 | 142.0 | |
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| No log | 13.0 | 325 | 2.6229 | 45.573 | 25.4966 | 27.7158 | 42.2306 | 142.0 | |
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| No log | 14.0 | 350 | 2.6032 | 48.1972 | 27.0387 | 28.336 | 45.0293 | 142.0 | |
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| No log | 15.0 | 375 | 2.6600 | 47.7301 | 27.3567 | 29.3389 | 44.3516 | 142.0 | |
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| No log | 16.0 | 400 | 2.6815 | 48.7608 | 29.554 | 30.5554 | 46.4001 | 142.0 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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