| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-t5/t5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: MTSUFall2024SoftwareEngineering |
| 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. --> |
|
|
| # MTSUFall2024SoftwareEngineering |
|
|
| 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: 2.0628 |
| - Rouge1: 0.2406 |
| - Rouge2: 0.187 |
| - Rougel: 0.2337 |
| - Rougelsum: 0.2336 |
| - Gen Len: 18.9969 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 2.8688 | 1.0 | 748 | 2.1863 | 0.2392 | 0.1855 | 0.232 | 0.2319 | 18.9985 | |
| | 2.4032 | 2.0 | 1496 | 2.1045 | 0.2387 | 0.1869 | 0.232 | 0.232 | 18.9973 | |
| | 2.3034 | 3.0 | 2244 | 2.0720 | 0.239 | 0.1869 | 0.2324 | 0.2323 | 18.9969 | |
| | 2.2666 | 4.0 | 2992 | 2.0628 | 0.2406 | 0.187 | 0.2337 | 0.2336 | 18.9969 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.44.2 |
| - Pytorch 2.4.0+cu121 |
| - Datasets 3.0.0 |
| - Tokenizers 0.19.1 |
|
|
