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---
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: 3.5993
- Rouge1: 0.6403
- Rouge2: 0.0
- Rougel: 0.5176
- Rougelsum: 0.5002

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.3726        | 1.0   | 179  | 3.6312          | 0.4669 | 0.1401 | 0.5602 | 0.5602    |
| 4.1516        | 2.0   | 358  | 3.6174          | 0.6225 | 0.0    | 0.4869 | 0.5002    |
| 4.0           | 3.0   | 537  | 3.6233          | 0.6225 | 0.0    | 0.4869 | 0.5002    |
| 3.874         | 4.0   | 716  | 3.6360          | 0.6403 | 0.0    | 0.5176 | 0.5002    |
| 3.8063        | 5.0   | 895  | 3.6360          | 0.6225 | 0.0    | 0.4869 | 0.5002    |
| 4.0795        | 6.0   | 1074 | 3.6032          | 0.6225 | 0.0    | 0.5135 | 0.5135    |
| 4.0419        | 7.0   | 1253 | 3.6022          | 0.6403 | 0.0    | 0.5176 | 0.5002    |
| 4.0311        | 8.0   | 1432 | 3.5993          | 0.6403 | 0.0    | 0.5176 | 0.5002    |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1