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---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- bills-summarization
metrics:
- rouge
model-index:
- name: ft-t5-with-dill-sum
results:
- task:
name: Summarization
type: summarization
dataset:
name: billsum
type: bills-summarization
metrics:
- name: Rouge1
type: rouge
value: 0.1886
---
<!-- 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. -->
# ft-t5-with-dill-sum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3109
- Rouge1: 0.1886
- Rouge2: 0.104
- Rougel: 0.166
- Rougelsum: 0.1659
- 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.5462 | 1.0 | 31 | 2.4185 | 0.187 | 0.1023 | 0.1637 | 0.1639 | 19.0 |
| 2.5478 | 2.0 | 62 | 2.4166 | 0.187 | 0.1018 | 0.1637 | 0.1639 | 19.0 |
| 2.5729 | 3.0 | 93 | 2.4114 | 0.1868 | 0.1015 | 0.1637 | 0.1638 | 19.0 |
| 2.5806 | 4.0 | 124 | 2.4072 | 0.1855 | 0.1006 | 0.1626 | 0.1627 | 19.0 |
| 2.5231 | 5.0 | 155 | 2.4025 | 0.1877 | 0.1042 | 0.165 | 0.165 | 19.0 |
| 2.5245 | 6.0 | 186 | 2.3948 | 0.1869 | 0.1024 | 0.1642 | 0.1642 | 19.0 |
| 2.5273 | 7.0 | 217 | 2.3860 | 0.1886 | 0.1032 | 0.1652 | 0.1653 | 19.0 |
| 2.4941 | 8.0 | 248 | 2.3765 | 0.188 | 0.1033 | 0.1649 | 0.165 | 19.0 |
| 2.4612 | 9.0 | 279 | 2.3698 | 0.19 | 0.1057 | 0.1671 | 0.1671 | 19.0 |
| 2.463 | 10.0 | 310 | 2.3578 | 0.1882 | 0.1039 | 0.1662 | 0.1663 | 19.0 |
| 2.4539 | 11.0 | 341 | 2.3491 | 0.1898 | 0.1057 | 0.1667 | 0.1667 | 19.0 |
| 2.441 | 12.0 | 372 | 2.3392 | 0.1901 | 0.1055 | 0.1669 | 0.1668 | 19.0 |
| 2.4389 | 13.0 | 403 | 2.3292 | 0.1893 | 0.1053 | 0.1666 | 0.1665 | 19.0 |
| 2.3945 | 14.0 | 434 | 2.3203 | 0.1903 | 0.1051 | 0.1676 | 0.1675 | 19.0 |
| 2.4148 | 15.0 | 465 | 2.3109 | 0.1886 | 0.104 | 0.166 | 0.1659 | 19.0 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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