transformers safetensors t5 text2text-generation generated_from_trainer dataset:bills-summarization base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 model-index autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: t5-small
datasets:
- bills-summarization
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: ft-t5-with-dill-sum
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: billsum
      type: bills-summarization
    metrics:
    - type: rouge
      value: 0.1886
      name: Rouge1
---

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# 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
