---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-News
  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. -->

# mt5-small-News

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3419
- Rouge1: 6.9313
- Rouge2: 1.9587
- Rougel: 6.8503
- Rougelsum: 6.9385

## 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
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.4281        | 1.0   | 1250  | 2.5899          | 7.0481 | 2.0747 | 6.9849 | 7.0179    |
| 3.2368        | 2.0   | 2500  | 2.4568          | 6.7532 | 1.7462 | 6.6934 | 6.7462    |
| 3.0526        | 3.0   | 3750  | 2.4315          | 6.6106 | 1.9088 | 6.5307 | 6.5784    |
| 2.9412        | 4.0   | 5000  | 2.3882          | 7.0644 | 1.9283 | 6.9687 | 7.0399    |
| 2.8711        | 5.0   | 6250  | 2.3700          | 7.2808 | 1.9358 | 7.2006 | 7.2603    |
| 2.8193        | 6.0   | 7500  | 2.3604          | 7.0911 | 1.9737 | 6.9918 | 7.0491    |
| 2.7866        | 7.0   | 8750  | 2.3479          | 6.9948 | 2.0044 | 6.8824 | 6.9737    |
| 2.7699        | 8.0   | 10000 | 2.3419          | 6.9313 | 1.9587 | 6.8503 | 6.9385    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2