---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
datasets:
- thaisum
metrics:
- rouge
model-index:
- name: mt5_thaisum_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: thaisum
      type: thaisum
      config: thaisum
      split: validation
      args: thaisum
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.2017
---

<!-- 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_thaisum_model

This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the thaisum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3039
- Rouge1: 0.2017
- Rouge2: 0.0806
- Rougel: 0.2016
- Rougelsum: 0.2017
- Gen Len: 18.9995

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.0742        | 1.0   | 5000  | 0.3272          | 0.1713 | 0.055  | 0.1703 | 0.1716    | 18.9945 |
| 1.7874        | 2.0   | 10000 | 0.3073          | 0.194  | 0.0742 | 0.1942 | 0.194     | 18.997  |
| 1.6341        | 3.0   | 15000 | 0.3035          | 0.2002 | 0.0804 | 0.1999 | 0.2002    | 19.0    |
| 1.4501        | 4.0   | 20000 | 0.3039          | 0.2017 | 0.0806 | 0.2016 | 0.2017    | 18.9995 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3