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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: 2.1132
- Rouge1: 0.1715
- Rouge2: 0.0919
- Rougel: 0.1417
- Rougelsum: 0.1611
- Gen Len: 19.0
## 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 | 2.3037 | 0.1617 | 0.0691 | 0.1287 | 0.1467 | 19.0 |
| No log | 2.0 | 140 | 2.2106 | 0.1722 | 0.082 | 0.1362 | 0.1585 | 19.0 |
| 2.3539 | 3.0 | 210 | 2.1604 | 0.1738 | 0.0854 | 0.1387 | 0.1604 | 19.0 |
| 2.3539 | 4.0 | 280 | 2.1325 | 0.1727 | 0.0868 | 0.1407 | 0.1632 | 19.0 |
| 2.3539 | 5.0 | 350 | 2.1117 | 0.1707 | 0.0875 | 0.137 | 0.1603 | 19.0 |
| 2.0032 | 6.0 | 420 | 2.0957 | 0.1723 | 0.0907 | 0.1415 | 0.1607 | 19.0 |
| 2.0032 | 7.0 | 490 | 2.0848 | 0.1722 | 0.09 | 0.1414 | 0.1617 | 19.0 |
| 2.0032 | 8.0 | 560 | 2.0790 | 0.1757 | 0.0918 | 0.1429 | 0.164 | 19.0 |
| 1.8158 | 9.0 | 630 | 2.0800 | 0.1723 | 0.0929 | 0.1421 | 0.1614 | 19.0 |
| 1.8158 | 10.0 | 700 | 2.0736 | 0.1733 | 0.0923 | 0.1428 | 0.1617 | 19.0 |
| 1.8158 | 11.0 | 770 | 2.0721 | 0.1755 | 0.0955 | 0.1454 | 0.1631 | 19.0 |
| 1.6764 | 12.0 | 840 | 2.0784 | 0.1763 | 0.0973 | 0.1459 | 0.1637 | 19.0 |
| 1.6764 | 13.0 | 910 | 2.0761 | 0.1752 | 0.094 | 0.1456 | 0.1638 | 19.0 |
| 1.6764 | 14.0 | 980 | 2.0802 | 0.1745 | 0.0951 | 0.145 | 0.1631 | 19.0 |
| 1.5616 | 15.0 | 1050 | 2.0790 | 0.1745 | 0.0952 | 0.1458 | 0.1632 | 19.0 |
| 1.5616 | 16.0 | 1120 | 2.0841 | 0.1735 | 0.0946 | 0.1447 | 0.1629 | 19.0 |
| 1.5616 | 17.0 | 1190 | 2.0904 | 0.1731 | 0.0943 | 0.1444 | 0.1622 | 19.0 |
| 1.4821 | 18.0 | 1260 | 2.0909 | 0.1727 | 0.0934 | 0.1433 | 0.1613 | 19.0 |
| 1.4821 | 19.0 | 1330 | 2.0934 | 0.1738 | 0.0948 | 0.1448 | 0.1632 | 19.0 |
| 1.4256 | 20.0 | 1400 | 2.0948 | 0.1726 | 0.0935 | 0.1434 | 0.1621 | 19.0 |
| 1.4256 | 21.0 | 1470 | 2.0981 | 0.173 | 0.0942 | 0.1435 | 0.1621 | 19.0 |
| 1.4256 | 22.0 | 1540 | 2.1023 | 0.1734 | 0.0945 | 0.1445 | 0.1631 | 19.0 |
| 1.3691 | 23.0 | 1610 | 2.1048 | 0.1726 | 0.0941 | 0.1436 | 0.1616 | 19.0 |
| 1.3691 | 24.0 | 1680 | 2.1058 | 0.1721 | 0.0948 | 0.1435 | 0.1619 | 19.0 |
| 1.3691 | 25.0 | 1750 | 2.1095 | 0.1721 | 0.0945 | 0.1435 | 0.1619 | 19.0 |
| 1.3444 | 26.0 | 1820 | 2.1103 | 0.1721 | 0.0948 | 0.1436 | 0.1624 | 19.0 |
| 1.3444 | 27.0 | 1890 | 2.1113 | 0.1715 | 0.0923 | 0.1417 | 0.1611 | 19.0 |
| 1.3444 | 28.0 | 1960 | 2.1121 | 0.1715 | 0.0919 | 0.1417 | 0.1611 | 19.0 |
| 1.3145 | 29.0 | 2030 | 2.1130 | 0.172 | 0.093 | 0.1425 | 0.1619 | 19.0 |
| 1.3145 | 30.0 | 2100 | 2.1132 | 0.1715 | 0.0919 | 0.1417 | 0.1611 | 19.0 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
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