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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-t5/t5-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: MTSUSpring2025SoftwareEngineering |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# MTSUSpring2025SoftwareEngineering |
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This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5063 |
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- Rouge1: 0.0879 |
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- Rouge2: 0.0695 |
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- Rougel: 0.0847 |
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- Rougelsum: 0.0847 |
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- Gen Len: 7.2123 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 1.8535 | 1.0 | 14778 | 1.6426 | 0.0806 | 0.063 | 0.0776 | 0.0776 | 6.8024 | |
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| 1.7555 | 2.0 | 29556 | 1.5664 | 0.0839 | 0.0658 | 0.0808 | 0.0808 | 7.0022 | |
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| 1.7044 | 3.0 | 44334 | 1.5297 | 0.086 | 0.0676 | 0.0828 | 0.0828 | 7.1049 | |
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| 1.7096 | 4.0 | 59112 | 1.5119 | 0.0875 | 0.0692 | 0.0843 | 0.0843 | 7.186 | |
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| 1.6789 | 5.0 | 73890 | 1.5063 | 0.0879 | 0.0695 | 0.0847 | 0.0847 | 7.2123 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.1 |
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