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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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- cnn_dailymail |
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metrics: |
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- rouge |
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model-index: |
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- name: t5-small-finetuned-cnndm_3epoch |
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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: cnn_dailymail |
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type: cnn_dailymail |
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args: 3.0.0 |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 24.5435 |
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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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# t5-small-finetuned-cnndm_3epoch |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6622 |
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- Rouge1: 24.5435 |
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- Rouge2: 11.7919 |
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- Rougel: 20.2929 |
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- Rougelsum: 23.1661 |
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- Gen Len: 18.9996 |
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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: 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: 3 |
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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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| 1.9113 | 0.14 | 5000 | 1.7162 | 24.4374 | 11.6932 | 20.1741 | 23.0427 | 18.9997 | |
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| 1.8772 | 0.28 | 10000 | 1.7008 | 24.3715 | 11.6699 | 20.1387 | 22.9772 | 18.9997 | |
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| 1.8609 | 0.42 | 15000 | 1.6911 | 24.4174 | 11.6986 | 20.1756 | 23.0205 | 18.9997 | |
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| 1.8564 | 0.56 | 20000 | 1.6871 | 24.4374 | 11.6801 | 20.1663 | 23.0366 | 18.9995 | |
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| 1.8495 | 0.7 | 25000 | 1.6796 | 24.4019 | 11.6901 | 20.177 | 23.034 | 18.999 | |
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| 1.8448 | 0.84 | 30000 | 1.6787 | 24.4813 | 11.7227 | 20.1985 | 23.0847 | 18.999 | |
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| 1.8427 | 0.98 | 35000 | 1.6762 | 24.4905 | 11.7591 | 20.2548 | 23.1006 | 18.9993 | |
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| 1.8341 | 1.11 | 40000 | 1.6747 | 24.4743 | 11.7124 | 20.1782 | 23.0726 | 18.9996 | |
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| 1.822 | 1.25 | 45000 | 1.6753 | 24.4797 | 11.7292 | 20.2319 | 23.0816 | 18.9993 | |
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| 1.8262 | 1.39 | 50000 | 1.6713 | 24.4865 | 11.7079 | 20.2214 | 23.0919 | 18.9986 | |
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| 1.8281 | 1.53 | 55000 | 1.6702 | 24.5095 | 11.7364 | 20.2534 | 23.1264 | 18.9991 | |
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| 1.8228 | 1.67 | 60000 | 1.6678 | 24.5153 | 11.7595 | 20.2544 | 23.1138 | 18.9993 | |
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| 1.824 | 1.81 | 65000 | 1.6662 | 24.5324 | 11.7804 | 20.2671 | 23.1498 | 18.9997 | |
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| 1.8265 | 1.95 | 70000 | 1.6648 | 24.5795 | 11.7917 | 20.2935 | 23.1855 | 18.9992 | |
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| 1.8179 | 2.09 | 75000 | 1.6658 | 24.5426 | 11.804 | 20.2861 | 23.1586 | 18.9996 | |
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| 1.8147 | 2.23 | 80000 | 1.6646 | 24.5429 | 11.7914 | 20.2889 | 23.1542 | 18.9993 | |
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| 1.8026 | 2.37 | 85000 | 1.6632 | 24.5451 | 11.8045 | 20.2781 | 23.1555 | 18.9996 | |
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| 1.8141 | 2.51 | 90000 | 1.6643 | 24.5078 | 11.7781 | 20.2631 | 23.121 | 18.9996 | |
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| 1.8124 | 2.65 | 95000 | 1.6628 | 24.5728 | 11.7958 | 20.2875 | 23.178 | 18.9996 | |
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| 1.8098 | 2.79 | 100000 | 1.6635 | 24.5534 | 11.7998 | 20.2979 | 23.169 | 18.9996 | |
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| 1.8153 | 2.93 | 105000 | 1.6622 | 24.5435 | 11.7919 | 20.2929 | 23.1661 | 18.9996 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.0.0 |
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- Tokenizers 0.11.6 |
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