---
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: 1.5063
- Rouge1: 0.0879
- Rouge2: 0.0695
- Rougel: 0.0847
- Rougelsum: 0.0847
- Gen Len: 7.2123

## 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: 1e-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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.8535        | 1.0   | 14778 | 1.6426          | 0.0806 | 0.063  | 0.0776 | 0.0776    | 6.8024  |
| 1.7555        | 2.0   | 29556 | 1.5664          | 0.0839 | 0.0658 | 0.0808 | 0.0808    | 7.0022  |
| 1.7044        | 3.0   | 44334 | 1.5297          | 0.086  | 0.0676 | 0.0828 | 0.0828    | 7.1049  |
| 1.7096        | 4.0   | 59112 | 1.5119          | 0.0875 | 0.0692 | 0.0843 | 0.0843    | 7.186   |
| 1.6789        | 5.0   | 73890 | 1.5063          | 0.0879 | 0.0695 | 0.0847 | 0.0847    | 7.2123  |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1