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--- |
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license: apache-2.0 |
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base_model: Danish-summarisation/DanSumT5-base |
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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: DanSumT5-base-finetuned-test_6887 |
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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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# DanSumT5-base-finetuned-test_6887 |
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This model is a fine-tuned version of [Danish-summarisation/DanSumT5-base](https://huggingface.co/Danish-summarisation/DanSumT5-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5277 |
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- Rouge1: 31.3188 |
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- Rouge2: 7.8236 |
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- Rougel: 17.8296 |
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- Rougelsum: 28.6162 |
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- Gen Len: 127.0 |
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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: 3 |
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- eval_batch_size: 3 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 12 |
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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: 15 |
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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 | 0.99 | 66 | 2.7401 | 29.9221 | 6.3861 | 16.5877 | 27.0611 | 125.09 | |
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| No log | 1.99 | 133 | 2.6815 | 30.6874 | 6.9413 | 16.9609 | 27.8851 | 125.98 | |
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| No log | 3.0 | 200 | 2.6440 | 31.2045 | 7.4012 | 17.7421 | 28.3497 | 126.63 | |
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| No log | 4.0 | 267 | 2.6199 | 31.3329 | 7.4574 | 17.8549 | 28.643 | 126.98 | |
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| No log | 4.99 | 333 | 2.5984 | 31.5184 | 7.7763 | 17.9153 | 29.0627 | 127.0 | |
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| No log | 5.99 | 400 | 2.5822 | 31.8839 | 7.9755 | 18.0572 | 29.2282 | 126.65 | |
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| No log | 7.0 | 467 | 2.5677 | 31.5939 | 7.9515 | 17.865 | 29.2019 | 126.45 | |
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| 2.8684 | 8.0 | 534 | 2.5587 | 31.4931 | 7.6042 | 17.6853 | 28.8366 | 126.79 | |
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| 2.8684 | 8.99 | 600 | 2.5496 | 31.105 | 7.6714 | 17.5128 | 28.5242 | 126.78 | |
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| 2.8684 | 9.99 | 667 | 2.5423 | 31.6087 | 8.0358 | 17.9956 | 28.9514 | 126.78 | |
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| 2.8684 | 11.0 | 734 | 2.5364 | 31.411 | 7.9534 | 17.895 | 28.7595 | 127.0 | |
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| 2.8684 | 12.0 | 801 | 2.5326 | 31.4648 | 7.9777 | 17.9589 | 28.8168 | 127.0 | |
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| 2.8684 | 12.99 | 867 | 2.5296 | 31.374 | 7.8341 | 17.8341 | 28.8146 | 127.0 | |
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| 2.8684 | 13.99 | 934 | 2.5278 | 31.2822 | 7.7789 | 17.7983 | 28.5903 | 127.0 | |
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| 2.8684 | 14.83 | 990 | 2.5277 | 31.3188 | 7.8236 | 17.8296 | 28.6162 | 127.0 | |
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### Framework versions |
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- Transformers 4.32.1 |
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- Pytorch 2.1.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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