---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- indosum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: indosum
      type: indosum
      config: indosum_fold0_source
      split: test
      args: indosum_fold0_source
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.2065
---

<!-- 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 indosum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4806
- Rouge1: 0.2065
- Rouge2: 0.1639
- Rougel: 0.2038
- Rougelsum: 0.2038
- 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.7495        | 1.0   | 892  | 0.5226          | 0.2061 | 0.1635 | 0.2033 | 0.2033    | 19.0    |
| 0.5326        | 2.0   | 1784 | 0.4929          | 0.2063 | 0.1639 | 0.2037 | 0.2037    | 19.0    |
| 0.4982        | 3.0   | 2676 | 0.4840          | 0.2065 | 0.1639 | 0.2038 | 0.2037    | 19.0    |
| 0.4958        | 4.0   | 3568 | 0.4806          | 0.2065 | 0.1639 | 0.2038 | 0.2038    | 19.0    |


### Framework versions

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