---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-samsum-en
  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-samsum-en

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: 2.4304
- Rouge1: 21.9966
- Rouge2: 9.1451
- Rougel: 19.532
- Rougelsum: 20.6359

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| No log        | 1.0   | 125  | 4.0396          | 8.9392  | 1.5339 | 8.1146  | 8.538     |
| No log        | 2.0   | 250  | 3.0166          | 17.0822 | 6.0564 | 15.1854 | 16.2353   |
| No log        | 3.0   | 375  | 2.7375          | 18.9169 | 7.0912 | 16.8087 | 17.7473   |
| No log        | 4.0   | 500  | 2.5996          | 20.5929 | 7.8755 | 18.2074 | 19.3914   |
| No log        | 5.0   | 625  | 2.5095          | 21.1958 | 8.7027 | 18.8919 | 19.9921   |
| No log        | 6.0   | 750  | 2.4641          | 21.2479 | 8.8452 | 18.9289 | 19.9557   |
| No log        | 7.0   | 875  | 2.4341          | 22.1418 | 9.1294 | 19.6073 | 20.7666   |
| No log        | 8.0   | 1000 | 2.4304          | 21.9966 | 9.1451 | 19.532  | 20.6359   |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1