---
license: apache-2.0
base_model: t5-small
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: 0.2001
---

<!-- 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-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1970
- Rouge1: 0.2001
- Rouge2: 0.1053
- Rougel: 0.1716
- Rougelsum: 0.1717
- 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 124  | 2.5355          | 0.1414 | 0.0544 | 0.1183 | 0.1182    | 19.0    |
| No log        | 2.0   | 248  | 2.3807          | 0.1674 | 0.0738 | 0.1416 | 0.1412    | 19.0    |
| No log        | 3.0   | 372  | 2.3128          | 0.1977 | 0.1007 | 0.1695 | 0.1697    | 19.0    |
| No log        | 4.0   | 496  | 2.2729          | 0.1987 | 0.1008 | 0.1695 | 0.1694    | 19.0    |
| 2.8078        | 5.0   | 620  | 2.2460          | 0.1997 | 0.1025 | 0.1707 | 0.1707    | 19.0    |
| 2.8078        | 6.0   | 744  | 2.2251          | 0.2011 | 0.1034 | 0.1715 | 0.1714    | 19.0    |
| 2.8078        | 7.0   | 868  | 2.2133          | 0.2016 | 0.1049 | 0.172  | 0.172     | 19.0    |
| 2.8078        | 8.0   | 992  | 2.2035          | 0.2018 | 0.1062 | 0.1723 | 0.1725    | 19.0    |
| 2.4762        | 9.0   | 1116 | 2.1985          | 0.2008 | 0.1059 | 0.172  | 0.1723    | 19.0    |
| 2.4762        | 10.0  | 1240 | 2.1970          | 0.2001 | 0.1053 | 0.1716 | 0.1717    | 19.0    |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1