| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-t5/t5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: TokenizerTestingMTSUFall2024SoftwareEngineering |
| 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. --> |
|
|
| # TokenizerTestingMTSUFall2024SoftwareEngineering |
|
|
| 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: 1.5834 |
| - Rouge1: 0.2719 |
| - Rouge2: 0.2175 |
| - Rougel: 0.2629 |
| - Rougelsum: 0.2629 |
| - Gen Len: 18.9753 |
|
|
| ## 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: 4 |
| - eval_batch_size: 4 |
| - 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 | |
| |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 1.8415 | 1.0 | 12975 | 1.6850 | 0.2704 | 0.2117 | 0.2604 | 0.2604 | 18.9826 | |
| | 1.7849 | 2.0 | 25950 | 1.6190 | 0.2713 | 0.2159 | 0.2621 | 0.262 | 18.9747 | |
| | 1.7692 | 3.0 | 38925 | 1.5916 | 0.2718 | 0.2172 | 0.2628 | 0.2627 | 18.9762 | |
| | 1.74 | 4.0 | 51900 | 1.5834 | 0.2719 | 0.2175 | 0.2629 | 0.2629 | 18.9753 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.44.2 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 3.0.1 |
| - Tokenizers 0.19.1 |
|
|
