---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-jb-t5
  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-small-finetuned-jb-t5

This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Rouge1: 99.3236
- Rouge2: 99.2849
- Rougel: 99.323
- Rougelsum: 99.3279
- Gen Len: 16.7815

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 235  | 0.0013          | 99.3122 | 99.2729 | 99.3126 | 99.3178   | 16.7831 |
| No log        | 2.0   | 470  | 0.0001          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0599        | 3.0   | 705  | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0599        | 4.0   | 940  | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0017        | 5.0   | 1175 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0017        | 6.0   | 1410 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0009        | 7.0   | 1645 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0009        | 8.0   | 1880 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0007        | 9.0   | 2115 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0007        | 10.0  | 2350 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0007        | 11.0  | 2585 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0007        | 12.0  | 2820 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0006        | 13.0  | 3055 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0006        | 14.0  | 3290 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |
| 0.0006        | 15.0  | 3525 | 0.0000          | 99.3236 | 99.2849 | 99.323  | 99.3279   | 16.7815 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1