metadata

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

mt5_small_wmt16_de_en

This model is a fine-tuned version of 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