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# KoT5_Translate_ko_jp

This model is a fine-tuned version of [KETI-AIR/ke-t5-base](https://huggingface.co/KETI-AIR/ke-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3331
- Bleu: 44.5463

## Model description

한국어-일본어 번역기 모델을 위해서 만들었습니다.
KETI-AIR님이 공유해주신 ke-t5-base에 Text2Text Task로 한국어-일본어 Translate를 위해서 Fine-Tuning 진행한 모델입니다.

## Training and evaluation data

[noahkim/Kor_Jpn_Translation_Dataset](https://huggingface.co/datasets/noahkim/Kor_Jpn_Translation_Dataset)
제가 AIHub에서 다운 받아 허깅페이스에 공유한 한국어-일본어 문화 분야 이중 말뭉치를 Fine-Tuning 데이터셋으로 활용했습니다.

## Supported Tasks and Leaderboards

Translation

## Languages

Kor
Jpan


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 3.8739        | 0.08  | 500  | 1.7216          | 3.3261  |
| 1.2621        | 0.15  | 1000 | 0.6792          | 28.6184 |
| 0.7413        | 0.23  | 1500 | 0.5153          | 35.9355 |
| 0.635         | 0.3   | 2000 | 0.4807          | 38.4874 |
| 0.5643        | 0.38  | 2500 | 0.4322          | 40.7997 |
| 0.5137        | 0.46  | 3000 | 0.4027          | 41.9025 |
| 0.4806        | 0.53  | 3500 | 0.3862          | 42.5947 |
| 0.4552        | 0.61  | 4000 | 0.3721          | 42.9976 |
| 0.4395        | 0.69  | 4500 | 0.3585          | 43.5369 |
| 0.4213        | 0.76  | 5000 | 0.3487          | 44.0028 |
| 0.411         | 0.84  | 5500 | 0.3418          | 44.1845 |
| 0.3992        | 0.91  | 6000 | 0.3348          | 44.3701 |
| 0.3966        | 0.99  | 6500 | 0.3331          | 44.5463 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "T5_Translation_ko_jp", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

KoT5\_Translate\_ko\_jp
=======================


This model is a fine-tuned version of KETI-AIR/ke-t5-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3331
* Bleu: 44.5463


Model description
-----------------


한국어-일본어 번역기 모델을 위해서 만들었습니다.
KETI-AIR님이 공유해주신 ke-t5-base에 Text2Text Task로 한국어-일본어 Translate를 위해서 Fine-Tuning 진행한 모델입니다.


Training and evaluation data
----------------------------


noahkim/Kor\_Jpn\_Translation\_Dataset
제가 AIHub에서 다운 받아 허깅페이스에 공유한 한국어-일본어 문화 분야 이중 말뭉치를 Fine-Tuning 데이터셋으로 활용했습니다.


Supported Tasks and Leaderboards
--------------------------------


Translation


Languages
---------


Kor
Jpan


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0005
* train\_batch\_size: 64
* eval\_batch\_size: 128
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 128
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2