---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-teste-full-length
  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-teste-full-length

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5996
- Rouge1: 0.5083
- Rouge2: 0.2820
- Rougel: 0.4095
- Rougelsum: 0.4108

## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 90
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 9.1442        | 0.16  | 100  | 9.7852          | 0.0531 | 0.0    | 0.0524 | 0.0       |
| 1.0643        | 0.33  | 200  | 0.9089          | 0.3623 | 0.1853 | 0.3252 | 0.3261    |
| 0.8283        | 0.49  | 300  | 0.8361          | 0.4184 | 0.2112 | 0.3535 | 0.3548    |
| 0.7754        | 0.65  | 400  | 0.7522          | 0.4407 | 0.2575 | 0.3802 | 0.3828    |
| 0.8012        | 0.82  | 500  | 0.7226          | 0.4643 | 0.2638 | 0.3866 | 0.3866    |
| 0.7758        | 0.98  | 600  | 0.7265          | 0.4624 | 0.2458 | 0.3840 | 0.3847    |
| 0.6744        | 1.15  | 700  | 0.7018          | 0.4477 | 0.2469 | 0.3732 | 0.3741    |
| 0.6636        | 1.31  | 800  | 0.6955          | 0.4786 | 0.2632 | 0.4027 | 0.4038    |
| 0.6839        | 1.47  | 900  | 0.6737          | 0.4773 | 0.2689 | 0.3909 | 0.3898    |
| 0.6264        | 1.64  | 1000 | 0.6504          | 0.4457 | 0.2533 | 0.3747 | 0.3767    |
| 0.6641        | 1.8   | 1100 | 0.6442          | 0.4582 | 0.2428 | 0.3661 | 0.3659    |
| 0.6492        | 1.96  | 1200 | 0.6500          | 0.5004 | 0.2751 | 0.3984 | 0.3993    |
| 0.5823        | 2.13  | 1300 | 0.6344          | 0.4917 | 0.2743 | 0.4000 | 0.4016    |
| 0.5585        | 2.29  | 1400 | 0.6373          | 0.4749 | 0.2490 | 0.3834 | 0.3849    |
| 0.5748        | 2.45  | 1500 | 0.6168          | 0.5036 | 0.2915 | 0.4128 | 0.4145    |
| 0.5452        | 2.62  | 1600 | 0.6135          | 0.5004 | 0.2864 | 0.4038 | 0.4044    |
| 0.5735        | 2.78  | 1700 | 0.6164          | 0.4904 | 0.2689 | 0.4001 | 0.3993    |
| 0.5394        | 2.95  | 1800 | 0.6153          | 0.4864 | 0.2884 | 0.4091 | 0.4089    |
| 0.4816        | 3.11  | 1900 | 0.6070          | 0.5027 | 0.2765 | 0.4042 | 0.4031    |
| 0.5328        | 3.27  | 2000 | 0.6095          | 0.4896 | 0.2783 | 0.4026 | 0.4031    |
| 0.5157        | 3.44  | 2100 | 0.6021          | 0.5165 | 0.2853 | 0.4137 | 0.4145    |
| 0.5295        | 3.6   | 2200 | 0.6063          | 0.4926 | 0.2721 | 0.3965 | 0.3980    |
| 0.5027        | 3.76  | 2300 | 0.6004          | 0.5120 | 0.2885 | 0.4092 | 0.4103    |
| 0.4943        | 3.93  | 2400 | 0.5996          | 0.5083 | 0.2820 | 0.4095 | 0.4108    |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2