metadata
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: Final_Tuning
results: []
Final_Tuning
This model is a fine-tuned version of google-t5/t5-small on our Congressional bill and summarization dataset.
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