---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-news_headlines
  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-base-news_headlines

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an valurank/News_headlines dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9975
- Rouge1: 53.7064
- Rouge2: 34.6278
- Rougel: 50.5129
- Rougelsum: 50.5108

## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- weight_decay: 0.01
- lr_scheduler_type: linear
- num_epochs: 7
- max_text_length: 512
- max_target_length: 16

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.9933        | 1.0   | 1531  | 1.4942          | 44.2439 | 22.1239 | 40.5281 | 40.5525   |
| 1.6029        | 2.0   | 3062  | 1.2824          | 46.5726 | 25.1122 | 43.131  | 43.151    |
| 1.409         | 3.0   | 4593  | 1.2358          | 48.3188 | 27.7403 | 44.9576 | 45.0009   |
| 1.2699        | 4.0   | 6124  | 1.1600          | 50.9858 | 30.6655 | 47.775  | 47.8414   |
| 1.1696        | 5.0   | 7655  | 1.0607          | 52.2212 | 32.6952 | 49.0023 | 49.0812   |
| 1.0934        | 6.0   | 9186  | 1.0173          | 53.1629 | 33.9552 | 49.9629 | 50.0118   |
| 1.049         | 7.0   | 10717 | 0.9975          | 53.7064 | 34.6278 | 50.5129 | 50.5108   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3