---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-task1-dataset1
  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-task1-dataset1

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: 0.4728
- Rouge1: 0.0
- Rouge2: 0.0
- Rougel: 0.0
- Rougelsum: 0.0

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.2448        | 1.0   | 500  | 0.8911          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.8138        | 2.0   | 1000 | 0.6679          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.5751        | 3.0   | 1500 | 0.5918          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.4767        | 4.0   | 2000 | 0.5153          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.4308        | 5.0   | 2500 | 0.5080          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.393         | 6.0   | 3000 | 0.4732          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.3691        | 7.0   | 3500 | 0.4692          | 0.0    | 0.0    | 0.0    | 0.0       |
| 0.3682        | 8.0   | 4000 | 0.4728          | 0.0    | 0.0    | 0.0    | 0.0       |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0