---
license: apache-2.0
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: 0.1432
---

<!-- 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: 0.3540
- Rouge1: 0.1432
- Rouge2: 0.041
- Rougel: 0.1423
- Rougelsum: 0.142
- Gen Len: 18.933

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.4271        | 1.0   | 2500  | 0.3976          | 0.1306 | 0.0372 | 0.1302 | 0.1299    | 18.9665 |
| 2.1992        | 2.0   | 5000  | 0.3720          | 0.1392 | 0.0376 | 0.1382 | 0.1384    | 18.922  |
| 2.1687        | 3.0   | 7500  | 0.3599          | 0.1401 | 0.0391 | 0.1394 | 0.1389    | 18.9215 |
| 2.1096        | 4.0   | 10000 | 0.3540          | 0.1432 | 0.041  | 0.1423 | 0.142     | 18.933  |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3