---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-dataset2
  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-dataset2

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: 1.5680
- Rouge1: 0.556
- Rouge2: 0.2002
- Rougel: 0.5256
- Rougelsum: 0.5248

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.4723        | 1.0   | 500  | 2.1274          | 0.3988 | 0.082  | 0.373  | 0.3729    |
| 2.2471        | 2.0   | 1000 | 1.8718          | 0.4855 | 0.1556 | 0.4623 | 0.4617    |
| 2.0198        | 3.0   | 1500 | 1.7499          | 0.5365 | 0.1946 | 0.5122 | 0.5112    |
| 1.8858        | 4.0   | 2000 | 1.6731          | 0.5431 | 0.1957 | 0.5179 | 0.5174    |
| 1.803         | 5.0   | 2500 | 1.6180          | 0.5562 | 0.2093 | 0.5296 | 0.5294    |
| 1.7344        | 6.0   | 3000 | 1.5948          | 0.5561 | 0.2008 | 0.5254 | 0.5249    |
| 1.7034        | 7.0   | 3500 | 1.5644          | 0.5608 | 0.2069 | 0.5314 | 0.5305    |
| 1.6794        | 8.0   | 4000 | 1.5680          | 0.556  | 0.2002 | 0.5256 | 0.5248    |


### Framework versions

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