| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-t5/t5-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: T5_base_fine_tuned |
| 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. --> |
|
|
| # T5_base_fine_tuned |
| |
| This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the BillSum dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.1618 |
| - Rouge1: 0.1578 |
| - Rouge2: 0.0635 |
| - Rougel: 0.1288 |
| - Rougelsum: 0.1288 |
| - 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: 8 |
| - eval_batch_size: 8 |
| - 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 |
| - num_epochs: 4 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 1.487 | 1.0 | 1000 | 2.1837 | 0.1507 | 0.0586 | 0.1233 | 0.1233 | 19.0 | |
| | 1.4014 | 2.0 | 2000 | 2.1688 | 0.1571 | 0.0639 | 0.1282 | 0.1282 | 19.0 | |
| | 1.3501 | 3.0 | 3000 | 2.1673 | 0.1564 | 0.063 | 0.1283 | 0.1283 | 19.0 | |
| | 1.3332 | 4.0 | 4000 | 2.1618 | 0.1578 | 0.0635 | 0.1288 | 0.1288 | 19.0 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.46.2 |
| - Pytorch 2.5.1+cu121 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.3 |
| |
