README.md

---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- rouge
- sacrebleu
model-index:
- name: mt5_small_wmt16_de_en
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: wmt16
      type: wmt16
      config: de-en
      split: validation
      args: de-en
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3666
    - name: Sacrebleu
      type: sacrebleu
      value: 6.4622
---

<!-- 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_wmt16_de_en

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4612
- Rouge1: 0.3666
- Rouge2: 0.147
- Rougel: 0.3362
- Sacrebleu: 6.4622

## Model description

Multilingual T5 (mT5) is a massively multilingual pretrained text-to-text transformer model, 
trained following a similar recipe as T5. 

## Intended uses & limitations

This is tried to be familiarized with the mt5 model in order to use it for the translation of English to Korean.

## Training and evaluation data

This work was done as an exercise for English-Korean translation, 
so I trained by selecting only very small  part of a very large original dataset.
Therefore, the quality is not expected to be very good.
이 일은 영어 한국어 번역을 위한 연습으로 한 것이기 때문에 매우 큰 원 dataset에서 아주 작은 크기만의 글뭉치만 선택을 해서 훈련을 했다. 
따라서 질은 그리 좋지 않을 것으로 예상된다.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.3059        | 1.6   | 500  | 2.5597          | 0.3398 | 0.1261 | 0.3068 | 5.5524    |
| 2.4093        | 3.2   | 1000 | 2.4996          | 0.3609 | 0.144  | 0.3304 | 6.2002    |
| 2.2322        | 4.8   | 1500 | 2.4612          | 0.3666 | 0.147  | 0.3362 | 6.4622    |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3