transformers safetensors t5 text2text-generation generated_from_trainer base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google-t5/t5-small
library_name: transformers
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: Final_Tuning
  results: []
---


# Final_Tuning

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on our [Congressional bill and summarization dataset](https://huggingface.co/datasets/cheaptrix/billsum-US_congress_and_house).

It achieves the following results on the evaluation set:
- Loss: 1.4253
- Rouge1: 0.2818
- Rouge2: 0.2299
- Rougel: 0.2729
- Rougelsum: 0.2729
- Gen Len: 18.9624

## Model description

MTSU SoftwareEngineering 2024: Fine-Tuned model for whatsinthebill.ai, a server hosted federal bill summarization model.

Hyperparameters found using Optuna grid search.

## Intended uses & limitations

To be used on congressional bills, acts, amendments, etc. as a summarization pipeline.

## Training and evaluation data

Trained on the dataset of Congressional bills and summaries my team and I cleaned and collated. 

Can be found at https://huggingface.co/datasets/cheaptrix/billsum-US_congress_and_house

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 3.28e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.7639        | 1.0   | 12429 | 1.5726          | 0.2754 | 0.2199 | 0.266  | 0.266     | 18.9728 |
| 1.658         | 2.0   | 24858 | 1.4926          | 0.2783 | 0.2246 | 0.2691 | 0.269     | 18.9693 |
| 1.6068        | 3.0   | 37287 | 1.4537          | 0.2819 | 0.2292 | 0.2728 | 0.2728    | 18.9616 |
| 1.5955        | 4.0   | 49716 | 1.4304          | 0.2812 | 0.2293 | 0.2723 | 0.2723    | 18.9615 |
| 1.5633        | 5.0   | 62145 | 1.4253          | 0.2818 | 0.2299 | 0.2729 | 0.2729    | 18.9624 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
