---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: T5_summ_gen_v1
  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: 0.1986
---

<!-- 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. -->

# T5_summ_gen_v1

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: 2.0950
- Rouge1: 0.1986
- Rouge2: 0.1044
- Rougel: 0.1726
- Rougelsum: 0.1727
- 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: 16
- eval_batch_size: 16
- 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   | 62   | 2.2294          | 0.1988 | 0.1023 | 0.1715 | 0.1714    | 19.0    |
| No log        | 2.0   | 124  | 2.2038          | 0.1998 | 0.1024 | 0.1727 | 0.1725    | 19.0    |
| No log        | 3.0   | 186  | 2.1890          | 0.2011 | 0.1049 | 0.1744 | 0.1746    | 19.0    |
| No log        | 4.0   | 248  | 2.1767          | 0.2002 | 0.1059 | 0.1736 | 0.1737    | 19.0    |
| No log        | 5.0   | 310  | 2.1593          | 0.2015 | 0.1064 | 0.1739 | 0.1741    | 19.0    |
| No log        | 6.0   | 372  | 2.1522          | 0.2022 | 0.1059 | 0.1747 | 0.175     | 19.0    |
| No log        | 7.0   | 434  | 2.1404          | 0.2028 | 0.1078 | 0.1746 | 0.1748    | 19.0    |
| No log        | 8.0   | 496  | 2.1369          | 0.2015 | 0.1061 | 0.1735 | 0.1737    | 19.0    |
| 2.382         | 9.0   | 558  | 2.1299          | 0.1999 | 0.1053 | 0.1723 | 0.1725    | 19.0    |
| 2.382         | 10.0  | 620  | 2.1205          | 0.2003 | 0.1058 | 0.173  | 0.1729    | 19.0    |
| 2.382         | 11.0  | 682  | 2.1170          | 0.1998 | 0.105  | 0.1727 | 0.1727    | 19.0    |
| 2.382         | 12.0  | 744  | 2.1122          | 0.2003 | 0.1057 | 0.1734 | 0.1734    | 19.0    |
| 2.382         | 13.0  | 806  | 2.1084          | 0.1993 | 0.1042 | 0.1725 | 0.1726    | 19.0    |
| 2.382         | 14.0  | 868  | 2.1046          | 0.1988 | 0.1037 | 0.1723 | 0.1725    | 19.0    |
| 2.382         | 15.0  | 930  | 2.1023          | 0.1992 | 0.1047 | 0.1727 | 0.1729    | 19.0    |
| 2.382         | 16.0  | 992  | 2.1006          | 0.1992 | 0.1047 | 0.1727 | 0.1729    | 19.0    |
| 2.2855        | 17.0  | 1054 | 2.0979          | 0.1983 | 0.1034 | 0.1722 | 0.1723    | 19.0    |
| 2.2855        | 18.0  | 1116 | 2.0961          | 0.1988 | 0.1046 | 0.1729 | 0.173     | 19.0    |
| 2.2855        | 19.0  | 1178 | 2.0953          | 0.1986 | 0.1044 | 0.1725 | 0.1726    | 19.0    |
| 2.2855        | 20.0  | 1240 | 2.0950          | 0.1986 | 0.1044 | 0.1726 | 0.1727    | 19.0    |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1