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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-baseV_38821 |
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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-baseV_38821 |
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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: 2.2026 |
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- Rouge1: 34.9358 |
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- Rouge2: 11.6813 |
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- Rougel: 21.4935 |
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- Rougelsum: 27.4979 |
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- Gen Len: 126.3262 |
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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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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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: 20 |
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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 | 232 | 2.4684 | 33.3966 | 9.9982 | 19.6472 | 27.3865 | 126.8712 | |
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| No log | 2.0 | 465 | 2.3905 | 34.2228 | 10.5192 | 20.3584 | 27.4209 | 126.8712 | |
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| 2.8064 | 3.0 | 697 | 2.3486 | 34.5949 | 11.0682 | 20.8844 | 27.3403 | 126.6738 | |
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| 2.8064 | 4.0 | 930 | 2.3193 | 34.6865 | 11.0996 | 20.9574 | 27.337 | 126.2318 | |
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| 2.5767 | 5.0 | 1162 | 2.2963 | 34.3101 | 11.0183 | 20.8461 | 27.155 | 126.721 | |
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| 2.5767 | 6.0 | 1395 | 2.2774 | 34.9299 | 11.5927 | 21.3549 | 27.7805 | 126.4249 | |
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| 2.483 | 7.0 | 1627 | 2.2646 | 34.4741 | 11.1383 | 21.2722 | 27.3822 | 126.3004 | |
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| 2.483 | 8.0 | 1860 | 2.2521 | 34.9384 | 11.2651 | 21.3153 | 27.5792 | 126.9828 | |
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| 2.4134 | 9.0 | 2092 | 2.2410 | 34.9546 | 11.424 | 21.1427 | 27.6608 | 126.7854 | |
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| 2.4134 | 10.0 | 2325 | 2.2326 | 34.7566 | 11.5721 | 21.4418 | 27.5167 | 126.7425 | |
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| 2.3576 | 11.0 | 2557 | 2.2263 | 34.5968 | 11.623 | 21.2384 | 27.365 | 126.4506 | |
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| 2.3576 | 12.0 | 2790 | 2.2194 | 34.7363 | 11.5612 | 21.47 | 27.6572 | 126.5665 | |
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| 2.3288 | 13.0 | 3022 | 2.2142 | 34.971 | 11.7203 | 21.49 | 27.7418 | 126.5665 | |
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| 2.3288 | 14.0 | 3255 | 2.2114 | 34.761 | 11.6621 | 21.3963 | 27.568 | 126.6266 | |
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| 2.3288 | 15.0 | 3487 | 2.2064 | 34.9197 | 11.5475 | 21.4017 | 27.6388 | 126.3305 | |
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| 2.2951 | 16.0 | 3720 | 2.2067 | 34.8124 | 11.615 | 21.5177 | 27.605 | 126.3605 | |
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| 2.2951 | 17.0 | 3952 | 2.2042 | 34.7608 | 11.4738 | 21.3464 | 27.379 | 126.4034 | |
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| 2.2832 | 18.0 | 4185 | 2.2032 | 34.7593 | 11.6239 | 21.4029 | 27.4669 | 126.2489 | |
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| 2.2832 | 19.0 | 4417 | 2.2029 | 34.8386 | 11.5919 | 21.4719 | 27.5147 | 126.2318 | |
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| 2.2571 | 19.96 | 4640 | 2.2026 | 34.9358 | 11.6813 | 21.4935 | 27.4979 | 126.3262 | |
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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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