---
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: 1.4609
- Rouge1: 0.2922
- Rouge2: 0.2362
- Rougel: 0.282
- Rougelsum: 0.282
- Gen Len: 19.951

## 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: 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: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.8149        | 1.0   | 12429 | 1.6198          | 0.2838 | 0.2222 | 0.2726 | 0.2726    | 19.9612 |
| 1.7131        | 2.0   | 24858 | 1.5410          | 0.2874 | 0.2291 | 0.2768 | 0.2767    | 19.9596 |
| 1.6671        | 3.0   | 37287 | 1.5011          | 0.2892 | 0.2316 | 0.2787 | 0.2787    | 19.9572 |
| 1.6542        | 4.0   | 49716 | 1.4750          | 0.291  | 0.2349 | 0.2809 | 0.2808    | 19.9496 |
| 1.6116        | 5.0   | 62145 | 1.4636          | 0.292  | 0.236  | 0.2818 | 0.2818    | 19.9516 |
| 1.6247        | 6.0   | 74574 | 1.4609          | 0.2922 | 0.2362 | 0.282  | 0.282     | 19.951  |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0