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base_model: weny22/sum_model_t5_saved
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: extract_long_text_balanced_data
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_balanced_data
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: 2.3331
- Rouge1: 0.2094
- Rouge2: 0.0794
- Rougel: 0.1697
- Rougelsum: 0.1696
- Gen Len: 18.9853
## 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 | 119 | 2.3318 | 0.18 | 0.0552 | 0.1439 | 0.1439 | 18.97 |
| No log | 2.0 | 238 | 2.1980 | 0.1899 | 0.06 | 0.1503 | 0.1503 | 18.972 |
| No log | 3.0 | 357 | 2.1448 | 0.1952 | 0.0646 | 0.1542 | 0.1541 | 18.9993 |
| No log | 4.0 | 476 | 2.1372 | 0.1983 | 0.0683 | 0.1574 | 0.1574 | 18.9453 |
| 2.8015 | 5.0 | 595 | 2.1142 | 0.2 | 0.0725 | 0.1611 | 0.1611 | 18.9933 |
| 2.8015 | 6.0 | 714 | 2.0970 | 0.2027 | 0.0757 | 0.1629 | 0.1629 | 18.9987 |
| 2.8015 | 7.0 | 833 | 2.1187 | 0.2027 | 0.0755 | 0.1637 | 0.1634 | 18.968 |
| 2.8015 | 8.0 | 952 | 2.1222 | 0.2013 | 0.0737 | 0.1619 | 0.1618 | 18.9753 |
| 2.02 | 9.0 | 1071 | 2.1316 | 0.2021 | 0.0764 | 0.1648 | 0.1647 | 18.9667 |
| 2.02 | 10.0 | 1190 | 2.1455 | 0.2109 | 0.0784 | 0.169 | 0.1689 | 18.982 |
| 2.02 | 11.0 | 1309 | 2.1580 | 0.2065 | 0.0781 | 0.167 | 0.1669 | 18.968 |
| 2.02 | 12.0 | 1428 | 2.1792 | 0.2088 | 0.0788 | 0.1693 | 0.169 | 18.9767 |
| 1.683 | 13.0 | 1547 | 2.1958 | 0.2085 | 0.0781 | 0.1689 | 0.1689 | 18.9913 |
| 1.683 | 14.0 | 1666 | 2.2436 | 0.2082 | 0.0785 | 0.1693 | 0.1692 | 18.978 |
| 1.683 | 15.0 | 1785 | 2.2480 | 0.2075 | 0.0797 | 0.1678 | 0.1678 | 18.9853 |
| 1.683 | 16.0 | 1904 | 2.2714 | 0.208 | 0.0789 | 0.1686 | 0.1685 | 18.9887 |
| 1.45 | 17.0 | 2023 | 2.2771 | 0.2091 | 0.0787 | 0.1693 | 0.1691 | 18.98 |
| 1.45 | 18.0 | 2142 | 2.2913 | 0.2103 | 0.0792 | 0.17 | 0.1698 | 18.9873 |
| 1.45 | 19.0 | 2261 | 2.3163 | 0.2094 | 0.0792 | 0.1699 | 0.1697 | 18.9893 |
| 1.45 | 20.0 | 2380 | 2.3331 | 0.2094 | 0.0794 | 0.1697 | 0.1696 | 18.9853 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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