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base_model: weny22/sum_model_t5_saved
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: extract_long_text_unbalanced_smaller
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. -->
# extract_long_text_unbalanced_smaller
This model is a fine-tuned version of [weny22/sum_model_t5_saved](https://huggingface.co/weny22/sum_model_t5_saved) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4822
- Rouge1: 0.1997
- Rouge2: 0.0696
- Rougel: 0.1604
- Rougelsum: 0.1602
- Gen Len: 18.9893
## 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: 0.002
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 72 | 2.6068 | 0.1714 | 0.0484 | 0.1369 | 0.1367 | 18.988 |
| No log | 2.0 | 144 | 2.3827 | 0.1801 | 0.0547 | 0.1414 | 0.1412 | 18.994 |
| No log | 3.0 | 216 | 2.2953 | 0.1858 | 0.0568 | 0.1457 | 0.1456 | 19.0 |
| No log | 4.0 | 288 | 2.2509 | 0.188 | 0.0599 | 0.1479 | 0.1478 | 18.9953 |
| No log | 5.0 | 360 | 2.2338 | 0.1834 | 0.057 | 0.1449 | 0.1447 | 18.9967 |
| No log | 6.0 | 432 | 2.2428 | 0.1871 | 0.0608 | 0.1483 | 0.1482 | 18.9953 |
| 3.0458 | 7.0 | 504 | 2.2195 | 0.1926 | 0.0626 | 0.1538 | 0.1537 | 18.9867 |
| 3.0458 | 8.0 | 576 | 2.2549 | 0.1932 | 0.0619 | 0.1521 | 0.152 | 18.9967 |
| 3.0458 | 9.0 | 648 | 2.2675 | 0.1955 | 0.0642 | 0.156 | 0.1558 | 18.9607 |
| 3.0458 | 10.0 | 720 | 2.2858 | 0.1981 | 0.0665 | 0.1573 | 0.1572 | 18.9807 |
| 3.0458 | 11.0 | 792 | 2.2980 | 0.1942 | 0.0653 | 0.1557 | 0.1554 | 18.972 |
| 3.0458 | 12.0 | 864 | 2.3413 | 0.1999 | 0.0682 | 0.1597 | 0.1595 | 18.9807 |
| 3.0458 | 13.0 | 936 | 2.3324 | 0.1987 | 0.0676 | 0.1585 | 0.1585 | 18.9733 |
| 1.907 | 14.0 | 1008 | 2.3481 | 0.2002 | 0.0688 | 0.1599 | 0.1597 | 18.9913 |
| 1.907 | 15.0 | 1080 | 2.4027 | 0.2023 | 0.0704 | 0.1617 | 0.1617 | 18.9887 |
| 1.907 | 16.0 | 1152 | 2.4132 | 0.2032 | 0.0728 | 0.1634 | 0.1634 | 18.9833 |
| 1.907 | 17.0 | 1224 | 2.4393 | 0.1988 | 0.0682 | 0.1586 | 0.1584 | 18.9853 |
| 1.907 | 18.0 | 1296 | 2.4435 | 0.1991 | 0.0698 | 0.1594 | 0.1591 | 18.9867 |
| 1.907 | 19.0 | 1368 | 2.4703 | 0.2014 | 0.0703 | 0.1608 | 0.1608 | 18.9873 |
| 1.907 | 20.0 | 1440 | 2.4822 | 0.1997 | 0.0696 | 0.1604 | 0.1602 | 18.9893 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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