--- license: mit base_model: arthurmluz/ptt5-xlsumm-30epochs tags: - generated_from_trainer metrics: - rouge model-index: - name: ptt5-xlsumm-gptextsum results: [] --- # ptt5-xlsumm-gptextsum This model is a fine-tuned version of [arthurmluz/ptt5-xlsumm-30epochs](https://huggingface.co/arthurmluz/ptt5-xlsumm-30epochs) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2170 - Rouge1: 0.4041 - Rouge2: 0.207 - Rougel: 0.3276 - Rougelsum: 0.3632 - Gen Len: 18.875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 70 | 2.3635 | 0.318 | 0.1516 | 0.2478 | 0.2858 | 18.675 | | No log | 2.0 | 140 | 2.1986 | 0.3333 | 0.1619 | 0.268 | 0.3003 | 18.7 | | 2.3332 | 3.0 | 210 | 2.1209 | 0.3244 | 0.1429 | 0.2551 | 0.293 | 18.5 | | 2.3332 | 4.0 | 280 | 2.0820 | 0.3448 | 0.1618 | 0.2703 | 0.3098 | 18.5 | | 2.3332 | 5.0 | 350 | 2.0565 | 0.3502 | 0.1769 | 0.2756 | 0.3135 | 18.75 | | 1.785 | 6.0 | 420 | 2.0446 | 0.3541 | 0.1808 | 0.2734 | 0.3169 | 18.825 | | 1.785 | 7.0 | 490 | 2.0435 | 0.3628 | 0.1867 | 0.2843 | 0.3228 | 18.75 | | 1.785 | 8.0 | 560 | 2.0482 | 0.3679 | 0.1886 | 0.2918 | 0.3314 | 18.725 | | 1.4946 | 9.0 | 630 | 2.0481 | 0.3741 | 0.1869 | 0.2949 | 0.3336 | 18.85 | | 1.4946 | 10.0 | 700 | 2.0494 | 0.379 | 0.1979 | 0.3012 | 0.338 | 18.8 | | 1.4946 | 11.0 | 770 | 2.0602 | 0.3786 | 0.196 | 0.3045 | 0.3342 | 18.8 | | 1.2836 | 12.0 | 840 | 2.0691 | 0.3749 | 0.1971 | 0.3012 | 0.3352 | 18.85 | | 1.2836 | 13.0 | 910 | 2.0795 | 0.3789 | 0.1953 | 0.3082 | 0.3371 | 18.8 | | 1.2836 | 14.0 | 980 | 2.0898 | 0.3809 | 0.196 | 0.3052 | 0.3378 | 18.825 | | 1.1472 | 15.0 | 1050 | 2.1076 | 0.3904 | 0.1979 | 0.3137 | 0.3462 | 18.875 | | 1.1472 | 16.0 | 1120 | 2.1109 | 0.3905 | 0.1929 | 0.3101 | 0.3436 | 18.875 | | 1.1472 | 17.0 | 1190 | 2.1253 | 0.4046 | 0.2029 | 0.3242 | 0.3594 | 18.875 | | 1.0175 | 18.0 | 1260 | 2.1418 | 0.4093 | 0.2074 | 0.3255 | 0.3614 | 18.875 | | 1.0175 | 19.0 | 1330 | 2.1578 | 0.4065 | 0.213 | 0.3289 | 0.363 | 18.875 | | 0.9398 | 20.0 | 1400 | 2.1593 | 0.4125 | 0.2197 | 0.333 | 0.3716 | 18.875 | | 0.9398 | 21.0 | 1470 | 2.1663 | 0.4091 | 0.2111 | 0.3282 | 0.368 | 18.875 | | 0.9398 | 22.0 | 1540 | 2.1775 | 0.4081 | 0.2111 | 0.3301 | 0.366 | 18.875 | | 0.8644 | 23.0 | 1610 | 2.1889 | 0.4015 | 0.206 | 0.3274 | 0.3613 | 18.875 | | 0.8644 | 24.0 | 1680 | 2.1932 | 0.4015 | 0.206 | 0.3274 | 0.3613 | 18.875 | | 0.8644 | 25.0 | 1750 | 2.2016 | 0.4075 | 0.2089 | 0.331 | 0.3672 | 18.875 | | 0.8385 | 26.0 | 1820 | 2.2058 | 0.4082 | 0.2087 | 0.33 | 0.3688 | 18.875 | | 0.8385 | 27.0 | 1890 | 2.2089 | 0.4075 | 0.2089 | 0.331 | 0.3672 | 18.875 | | 0.8385 | 28.0 | 1960 | 2.2131 | 0.4061 | 0.2079 | 0.3286 | 0.3651 | 18.875 | | 0.7999 | 29.0 | 2030 | 2.2161 | 0.4061 | 0.2079 | 0.3286 | 0.3651 | 18.875 | | 0.7999 | 30.0 | 2100 | 2.2170 | 0.4041 | 0.207 | 0.3276 | 0.3632 | 18.875 | ### Framework versions - Transformers 4.34.0 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.14.1