| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-t5/t5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: MTSUSpring2025SoftwareEngineering |
| 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. --> |
|
|
| # MTSUSpring2025SoftwareEngineering |
|
|
| 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: 1.4293 |
| - Rouge1: 0.0827 |
| - Rouge2: 0.0661 |
| - Rougel: 0.0799 |
| - Rougelsum: 0.0799 |
| - Gen Len: 6.8285 |
|
|
| ## 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: 3e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.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: 3 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 1.7038 | 1.0 | 14778 | 1.5115 | 0.0881 | 0.0694 | 0.0848 | 0.0849 | 7.1571 | |
| | 1.6169 | 2.0 | 29556 | 1.4481 | 0.0853 | 0.0679 | 0.0823 | 0.0824 | 6.984 | |
| | 1.5833 | 3.0 | 44334 | 1.4293 | 0.0827 | 0.0661 | 0.0799 | 0.0799 | 6.8285 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.49.0 |
| - Pytorch 2.6.0+cu124 |
| - Datasets 3.4.1 |
| - Tokenizers 0.21.1 |
|
|
