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---
license: mit
base_model: arthurmluz/ptt5-xlsumm-30epochs
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ptt5-xlsumm-gptextsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 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
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