---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: custom_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: 0.1968
---

<!-- 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. -->

# custom_billsum_model

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: 2.2150
- Rouge1: 0.1968
- Rouge2: 0.0981
- Rougel: 0.1672
- Rougelsum: 0.167
- 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   | 2.4551          | 0.1626 | 0.0663 | 0.135  | 0.135     | 19.0    |
| No log        | 2.0   | 124  | 2.3987          | 0.1882 | 0.0866 | 0.1577 | 0.1577    | 19.0    |
| No log        | 3.0   | 186  | 2.3639          | 0.1964 | 0.0937 | 0.1652 | 0.165     | 19.0    |
| No log        | 4.0   | 248  | 2.3370          | 0.1943 | 0.0931 | 0.164  | 0.1638    | 19.0    |
| No log        | 5.0   | 310  | 2.3135          | 0.1942 | 0.0938 | 0.1646 | 0.1643    | 19.0    |
| No log        | 6.0   | 372  | 2.2949          | 0.195  | 0.0938 | 0.1648 | 0.1648    | 19.0    |
| No log        | 7.0   | 434  | 2.2809          | 0.1937 | 0.0944 | 0.1643 | 0.1642    | 19.0    |
| No log        | 8.0   | 496  | 2.2676          | 0.1949 | 0.0957 | 0.1664 | 0.166     | 19.0    |
| 2.5047        | 9.0   | 558  | 2.2582          | 0.1954 | 0.097  | 0.1665 | 0.1662    | 19.0    |
| 2.5047        | 10.0  | 620  | 2.2510          | 0.1951 | 0.0966 | 0.1661 | 0.166     | 19.0    |
| 2.5047        | 11.0  | 682  | 2.2416          | 0.1962 | 0.0979 | 0.1673 | 0.1671    | 19.0    |
| 2.5047        | 12.0  | 744  | 2.2360          | 0.196  | 0.0975 | 0.1664 | 0.1663    | 19.0    |
| 2.5047        | 13.0  | 806  | 2.2302          | 0.1965 | 0.098  | 0.1667 | 0.1666    | 19.0    |
| 2.5047        | 14.0  | 868  | 2.2262          | 0.1973 | 0.0985 | 0.1673 | 0.1671    | 19.0    |
| 2.5047        | 15.0  | 930  | 2.2224          | 0.197  | 0.0976 | 0.1668 | 0.1667    | 19.0    |
| 2.5047        | 16.0  | 992  | 2.2200          | 0.1973 | 0.0984 | 0.1673 | 0.1671    | 19.0    |
| 2.3391        | 17.0  | 1054 | 2.2183          | 0.1967 | 0.0974 | 0.1669 | 0.1666    | 19.0    |
| 2.3391        | 18.0  | 1116 | 2.2164          | 0.1968 | 0.0974 | 0.1669 | 0.1666    | 19.0    |
| 2.3391        | 19.0  | 1178 | 2.2152          | 0.1969 | 0.0982 | 0.1673 | 0.1671    | 19.0    |
| 2.3391        | 20.0  | 1240 | 2.2150          | 0.1968 | 0.0981 | 0.1672 | 0.167     | 19.0    |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3