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--- |
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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: Swin-Bert_Mimic |
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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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# Swin-Bert_Mimic |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1025 |
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- Rouge1: 35.8104 |
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- Rouge2: 22.5915 |
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- Rougel: 34.3056 |
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- Rougelsum: 35.1416 |
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- Gen Len: 21.289 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 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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| 0.0677 | 1.0 | 7500 | 0.0742 | 34.0952 | 25.4639 | 34.0546 | 34.0407 | 14.412 | |
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| 0.0621 | 2.0 | 15000 | 0.0686 | 37.767 | 26.9356 | 37.0596 | 37.4647 | 18.921 | |
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| 0.0595 | 3.0 | 22500 | 0.0670 | 38.07 | 26.9203 | 37.1384 | 37.7633 | 22.422 | |
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| 0.0536 | 4.0 | 30000 | 0.0655 | 38.064 | 27.0799 | 37.3483 | 37.7981 | 18.476 | |
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| 0.0484 | 5.0 | 37500 | 0.0655 | 38.8419 | 27.551 | 37.992 | 38.573 | 19.552 | |
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| 0.0436 | 6.0 | 45000 | 0.0672 | 39.2556 | 27.3445 | 38.1583 | 38.9199 | 19.699 | |
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| 0.0394 | 7.0 | 52500 | 0.0680 | 38.6881 | 27.1077 | 37.6518 | 38.3678 | 19.322 | |
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| 0.0355 | 8.0 | 60000 | 0.0697 | 39.2775 | 27.1638 | 38.1169 | 38.786 | 20.125 | |
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| 0.0318 | 9.0 | 67500 | 0.0719 | 38.8973 | 27.0819 | 37.8138 | 38.4725 | 20.237 | |
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| 0.0265 | 10.0 | 75000 | 0.0746 | 38.2854 | 26.3015 | 37.0627 | 37.8955 | 20.799 | |
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| 0.0241 | 11.0 | 82500 | 0.0769 | 37.7814 | 25.9821 | 36.6626 | 37.3682 | 20.437 | |
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| 0.0204 | 12.0 | 90000 | 0.0810 | 37.7945 | 26.012 | 36.5089 | 37.3188 | 20.945 | |
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| 0.0172 | 13.0 | 97500 | 0.0846 | 37.5296 | 25.3082 | 36.2752 | 36.9433 | 20.397 | |
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| 0.0147 | 14.0 | 105000 | 0.0876 | 36.6675 | 24.5001 | 35.264 | 36.034 | 22.044 | |
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| 0.012 | 15.0 | 112500 | 0.0907 | 35.8928 | 23.4706 | 34.3812 | 35.2234 | 21.344 | |
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| 0.0103 | 16.0 | 120000 | 0.0947 | 35.6648 | 22.8131 | 34.1013 | 35.0637 | 22.095 | |
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| 0.0084 | 17.0 | 127500 | 0.0971 | 35.7702 | 22.9984 | 34.2882 | 35.1362 | 21.501 | |
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| 0.0068 | 18.0 | 135000 | 0.0996 | 35.4212 | 22.3513 | 33.9646 | 34.8255 | 22.152 | |
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| 0.0058 | 19.0 | 142500 | 0.1019 | 35.9704 | 23.1195 | 34.4672 | 35.3553 | 21.404 | |
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| 0.0048 | 20.0 | 150000 | 0.1025 | 35.8104 | 22.5915 | 34.3056 | 35.1416 | 21.289 | |
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### Framework versions |
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- Transformers 4.37.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.1 |
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