---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: senate_bills_summary_model
  results: []
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/cheaptrix/MTSUFall2024SoftwareEngineering/runs/rigak20g)
# senate_bills_summary_model

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co./google-t5/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9849
- Rouge1: 0.2462
- Rouge2: 0.1934
- Rougel: 0.2389
- Rougelsum: 0.2389
- Gen Len: 18.9981

## 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: 14
- eval_batch_size: 14
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.7477        | 1.0   | 749  | 2.1012          | 0.2474 | 0.192  | 0.2397 | 0.2396    | 18.9992 |
| 2.3215        | 2.0   | 1498 | 2.0257          | 0.2464 | 0.1931 | 0.2391 | 0.239     | 18.9992 |
| 2.2304        | 3.0   | 2247 | 1.9963          | 0.2459 | 0.1933 | 0.2386 | 0.2386    | 18.9989 |
| 2.194         | 4.0   | 2996 | 1.9849          | 0.2462 | 0.1934 | 0.2389 | 0.2389    | 18.9981 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1