---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-sk-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-finetuned-sk-news

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.7815
- Rouge1: 4.2262
- Rouge2: 0.4191
- Rougel: 3.9794
- Rougelsum: 4.0106

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 24.5955       | 1.0   | 13   | 14.1001         | 3.1611 | 0.5122 | 2.9982 | 3.0234    |
| 21.452        | 2.0   | 26   | 12.4275         | 3.4559 | 0.5141 | 3.2461 | 3.256     |
| 19.6974       | 3.0   | 39   | 11.2369         | 3.5799 | 0.5058 | 3.4025 | 3.4185    |
| 18.2054       | 4.0   | 52   | 10.0774         | 4.0976 | 0.5831 | 3.8641 | 3.8412    |
| 15.314        | 5.0   | 65   | 9.5101          | 3.9583 | 0.4948 | 3.7593 | 3.7337    |
| 14.3465       | 6.0   | 78   | 9.0434          | 4.1681 | 0.437  | 3.8628 | 3.8584    |
| 15.0109       | 7.0   | 91   | 8.8574          | 4.3665 | 0.5    | 4.086  | 4.1242    |
| 15.2609       | 8.0   | 104  | 8.7815          | 4.2262 | 0.4191 | 3.9794 | 4.0106    |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2