---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: billsum
      type: billsum
      config: default
      split: ca_test
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.2033
---

<!-- 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. -->

# my_awesome_billsum_model

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6638
- Rouge1: 0.2033
- Rouge2: 0.1149
- Rougel: 0.1762
- Rougelsum: 0.1759
- 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: 4
- eval_batch_size: 8
- 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   | 248  | 1.9584          | 0.1999 | 0.1073 | 0.1716 | 0.1717    | 19.0    |
| No log        | 2.0   | 496  | 1.8621          | 0.195  | 0.1045 | 0.1685 | 0.1682    | 19.0    |
| 2.2512        | 3.0   | 744  | 1.8095          | 0.1973 | 0.1109 | 0.1728 | 0.1727    | 19.0    |
| 2.2512        | 4.0   | 992  | 1.7797          | 0.1989 | 0.1102 | 0.1724 | 0.1724    | 19.0    |
| 1.8144        | 5.0   | 1240 | 1.7505          | 0.1997 | 0.112  | 0.1735 | 0.1736    | 19.0    |
| 1.8144        | 6.0   | 1488 | 1.7308          | 0.2003 | 0.1134 | 0.1746 | 0.1744    | 19.0    |
| 1.6898        | 7.0   | 1736 | 1.7145          | 0.199  | 0.1114 | 0.1732 | 0.173     | 19.0    |
| 1.6898        | 8.0   | 1984 | 1.7083          | 0.1977 | 0.1106 | 0.1718 | 0.1716    | 19.0    |
| 1.5997        | 9.0   | 2232 | 1.6983          | 0.2014 | 0.1127 | 0.175  | 0.175     | 19.0    |
| 1.5997        | 10.0  | 2480 | 1.6923          | 0.2014 | 0.1153 | 0.1754 | 0.1753    | 19.0    |
| 1.5403        | 11.0  | 2728 | 1.6826          | 0.2009 | 0.1134 | 0.1752 | 0.1751    | 19.0    |
| 1.5403        | 12.0  | 2976 | 1.6768          | 0.2003 | 0.1125 | 0.1745 | 0.1744    | 19.0    |
| 1.491         | 13.0  | 3224 | 1.6722          | 0.2016 | 0.1146 | 0.1756 | 0.1755    | 19.0    |
| 1.491         | 14.0  | 3472 | 1.6750          | 0.2039 | 0.1164 | 0.1773 | 0.177     | 19.0    |
| 1.4496        | 15.0  | 3720 | 1.6679          | 0.2023 | 0.1147 | 0.1765 | 0.1763    | 19.0    |
| 1.4496        | 16.0  | 3968 | 1.6677          | 0.2032 | 0.1148 | 0.177  | 0.1768    | 19.0    |
| 1.4241        | 17.0  | 4216 | 1.6640          | 0.2021 | 0.1135 | 0.1752 | 0.175     | 19.0    |
| 1.4241        | 18.0  | 4464 | 1.6645          | 0.2027 | 0.1155 | 0.1766 | 0.1764    | 19.0    |
| 1.4025        | 19.0  | 4712 | 1.6632          | 0.2028 | 0.1149 | 0.1761 | 0.1757    | 19.0    |
| 1.4025        | 20.0  | 4960 | 1.6638          | 0.2033 | 0.1149 | 0.1762 | 0.1759    | 19.0    |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.1
- Tokenizers 0.13.3