---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: t5_summarize
  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. -->

# t5_summarize

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: 2.6492
- Evaluation Runtime: 28.4792
- Rounded Rouge Scores: {'rouge1': 0.174, 'rouge2': 0.0607, 'rougeL': 0.1367, 'rougeLsum': 0.1369}

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Evaluation Runtime | Rounded Rouge Scores                                                        |
|:-------------:|:-----:|:----:|:---------------:|:------------------:|:---------------------------------------------------------------------------:|
| 2.7245        | 1.0   | 500  | 2.6814          | 29.2864            | {'rouge1': 0.1697, 'rouge2': 0.0584, 'rougeL': 0.1344, 'rougeLsum': 0.1345} |
| 2.7318        | 2.0   | 1000 | 2.6707          | 27.6464            | {'rouge1': 0.1735, 'rouge2': 0.0597, 'rougeL': 0.1372, 'rougeLsum': 0.1373} |
| 2.7164        | 3.0   | 1500 | 2.6646          | 27.3926            | {'rouge1': 0.1734, 'rouge2': 0.06, 'rougeL': 0.1371, 'rougeLsum': 0.1372}   |
| 2.7054        | 4.0   | 2000 | 2.6600          | 27.3819            | {'rouge1': 0.1739, 'rouge2': 0.0599, 'rougeL': 0.1367, 'rougeLsum': 0.1368} |
| 2.6955        | 5.0   | 2500 | 2.6581          | 27.9933            | {'rouge1': 0.1731, 'rouge2': 0.0601, 'rougeL': 0.1361, 'rougeLsum': 0.1361} |
| 2.6865        | 6.0   | 3000 | 2.6535          | 28.2157            | {'rouge1': 0.1733, 'rouge2': 0.0603, 'rougeL': 0.1363, 'rougeLsum': 0.1364} |
| 2.6821        | 7.0   | 3500 | 2.6521          | 29.0758            | {'rouge1': 0.174, 'rouge2': 0.0606, 'rougeL': 0.1366, 'rougeLsum': 0.1369}  |
| 2.681         | 8.0   | 4000 | 2.6508          | 31.2621            | {'rouge1': 0.1743, 'rouge2': 0.0609, 'rougeL': 0.1367, 'rougeLsum': 0.1369} |
| 2.6771        | 9.0   | 4500 | 2.6499          | 30.4251            | {'rouge1': 0.1735, 'rouge2': 0.0605, 'rougeL': 0.1364, 'rougeLsum': 0.1365} |
| 2.6751        | 10.0  | 5000 | 2.6492          | 28.4792            | {'rouge1': 0.174, 'rouge2': 0.0607, 'rougeL': 0.1367, 'rougeLsum': 0.1369}  |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2