---
license: apache-2.0
base_model: google-t5/t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-finetuned-billsum
  results: []
datasets:
- FiscalNote/billsum
---

<!-- 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-base-finetuned-billsum

This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an **FiscalNote/billsum** dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1725
- Rouge1: 54.1481
- Rouge2: 33.3953
- Rougel: 42.8337
- Rougelsum: 47.5287
- Gen Len: 116.8581

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.5944        | 0.4219 | 500  | 1.2582          | 50.6899 | 31.6418 | 40.2325 | 44.2687   | 111.7541 |
| 1.3588        | 0.8439 | 1000 | 1.1591          | 55.865  | 35.992  | 44.7636 | 49.2805   | 114.3552 |
| 1.275         | 1.2658 | 1500 | 1.1214          | 56.3449 | 37.0781 | 45.604  | 49.9711   | 110.7724 |
| 1.3266        | 1.6878 | 2000 | 1.1791          | 54.4797 | 33.8689 | 43.1813 | 47.8507   | 114.8278 |
| 1.3591        | 2.1097 | 2500 | 1.1725          | 54.243  | 33.5179 | 42.9187 | 47.6231   | 116.4601 |
| 1.3484        | 2.5316 | 3000 | 1.1724          | 54.1433 | 33.3914 | 42.8348 | 47.5267   | 116.7736 |
| 1.3467        | 2.9536 | 3500 | 1.1724          | 54.1359 | 33.3794 | 42.8167 | 47.5153   | 116.7819 |
| 1.3483        | 3.3755 | 4000 | 1.1724          | 54.1446 | 33.3947 | 42.8274 | 47.5313   | 116.8529 |
| 1.342         | 3.7975 | 4500 | 1.1724          | 54.1341 | 33.3888 | 42.8239 | 47.5291   | 116.7957 |
| 1.3475        | 4.2194 | 5000 | 1.1725          | 54.1411 | 33.3931 | 42.8224 | 47.5218   | 116.8229 |
| 1.3542        | 4.6414 | 5500 | 1.1725          | 54.1481 | 33.3953 | 42.8337 | 47.5287   | 116.8581 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1