---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-text-sum-2
  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-text-sum-2

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: 2.3612
- Rouge1: 21.38
- Rouge2: 6.57
- Rougel: 21.08

## 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: 0.0001
- train_batch_size: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|
| 4.7204        | 1.45  | 500   | 2.6053          | 16.9   | 4.9    | 16.73  |
| 3.1289        | 2.9   | 1000  | 2.4878          | 17.96  | 5.26   | 17.82  |
| 2.8862        | 4.35  | 1500  | 2.4109          | 17.4   | 5.08   | 17.14  |
| 2.7669        | 5.8   | 2000  | 2.4006          | 18.53  | 5.29   | 18.21  |
| 2.6433        | 7.25  | 2500  | 2.4017          | 18.69  | 5.71   | 18.53  |
| 2.5514        | 8.7   | 3000  | 2.3917          | 19.32  | 5.89   | 19.12  |
| 2.4947        | 10.14 | 3500  | 2.3994          | 20.56  | 6.08   | 20.19  |
| 2.3995        | 11.59 | 4000  | 2.3608          | 20.11  | 6.52   | 19.75  |
| 2.3798        | 13.04 | 4500  | 2.3251          | 19.98  | 6.26   | 19.76  |
| 2.3029        | 14.49 | 5000  | 2.3387          | 19.71  | 6.11   | 19.42  |
| 2.2563        | 15.94 | 5500  | 2.3372          | 20.18  | 6.34   | 19.8   |
| 2.2109        | 17.39 | 6000  | 2.3410          | 20.58  | 6.35   | 20.14  |
| 2.166         | 18.84 | 6500  | 2.3432          | 20.93  | 6.5    | 20.63  |
| 2.1283        | 20.29 | 7000  | 2.3404          | 21.0   | 6.5    | 20.73  |
| 2.1054        | 21.74 | 7500  | 2.3563          | 20.95  | 6.54   | 20.48  |
| 2.0658        | 23.19 | 8000  | 2.3575          | 19.73  | 6.18   | 19.54  |
| 2.0461        | 24.64 | 8500  | 2.3382          | 20.78  | 6.42   | 20.52  |
| 2.0135        | 26.09 | 9000  | 2.3628          | 20.94  | 6.55   | 20.66  |
| 2.0122        | 27.54 | 9500  | 2.3725          | 21.1   | 6.87   | 20.96  |
| 1.9623        | 28.99 | 10000 | 2.3612          | 21.38  | 6.57   | 21.08  |
| 1.9518        | 30.43 | 10500 | 2.3619          | 20.12  | 6.25   | 19.8   |
| 1.9327        | 31.88 | 11000 | 2.3642          | 20.9   | 6.6    | 20.55  |
| 1.9147        | 33.33 | 11500 | 2.3703          | 21.0   | 6.37   | 20.59  |
| 1.9145        | 34.78 | 12000 | 2.3823          | 21.24  | 6.84   | 20.92  |
| 1.9065        | 36.23 | 12500 | 2.3686          | 20.16  | 6.41   | 19.87  |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2