transformers tensorboard safetensors t5 text2text-generation summarization generated_from_trainer base_model:google-t5/t5-small base_model:finetune:google-t5/t5-small license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: google-t5/t5-small
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: t5-small-finetuned-xsum
  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-xsum

This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9389
- Rouge1: 0.2199
- Rouge2: 0.0413
- Rougel: 0.1739
- Rougelsum: 0.1836

## 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: 0.0002
- 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
- training_steps: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.9818        | 1.0   | 1    | 3.5789          | 0.1857 | 0.0245 | 0.1420 | 0.1556    |
| 3.5098        | 2.0   | 2    | 3.4107          | 0.1863 | 0.0245 | 0.1391 | 0.1564    |
| 3.1669        | 3.0   | 3    | 3.2830          | 0.2008 | 0.0254 | 0.1466 | 0.1703    |
| 2.8568        | 4.0   | 4    | 3.1945          | 0.1980 | 0.0222 | 0.1411 | 0.1622    |
| 2.7102        | 5.0   | 5    | 3.1215          | 0.2019 | 0.0222 | 0.1472 | 0.1609    |
| 2.4563        | 6.0   | 6    | 3.0798          | 0.2167 | 0.0189 | 0.1533 | 0.1737    |
| 2.3367        | 7.0   | 7    | 3.0364          | 0.2050 | 0.0139 | 0.1420 | 0.1577    |
| 2.269         | 8.0   | 8    | 3.0071          | 0.2041 | 0.0139 | 0.1435 | 0.1561    |
| 2.0398        | 9.0   | 9    | 2.9865          | 0.2246 | 0.0139 | 0.1510 | 0.1721    |
| 1.9314        | 10.0  | 10   | 2.9783          | 0.2076 | 0.0139 | 0.1542 | 0.1681    |
| 1.9148        | 11.0  | 11   | 2.9684          | 0.2076 | 0.0139 | 0.1542 | 0.1681    |
| 1.8131        | 12.0  | 12   | 2.9598          | 0.2076 | 0.0139 | 0.1542 | 0.1681    |
| 1.7866        | 13.0  | 13   | 2.9497          | 0.2195 | 0.0184 | 0.1501 | 0.1722    |
| 1.689         | 14.0  | 14   | 2.9451          | 0.2067 | 0.0203 | 0.1453 | 0.1621    |
| 1.7257        | 15.0  | 15   | 2.9405          | 0.2155 | 0.0321 | 0.1599 | 0.1777    |
| 1.6441        | 16.0  | 16   | 2.9405          | 0.2155 | 0.0321 | 0.1599 | 0.1777    |
| 1.574         | 17.0  | 17   | 2.9409          | 0.2155 | 0.0321 | 0.1599 | 0.1777    |
| 1.587         | 18.0  | 18   | 2.9393          | 0.2260 | 0.0388 | 0.1678 | 0.1860    |
| 1.5362        | 19.0  | 19   | 2.9387          | 0.2199 | 0.0413 | 0.1739 | 0.1836    |
| 1.5133        | 20.0  | 20   | 2.9389          | 0.2199 | 0.0413 | 0.1739 | 0.1836    |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
