metadata

language:
  - ko
  - en
tags:
  - generated_from_trainer
datasets:
  - >-
    KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation
metrics:
  - bleu
model-index:
  - name: ko2en
    results:
      - task:
          name: Translation
          type: translation
        dataset:
          name: >-
            KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation
            koen,none,none,none,none
          type: >-
            KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation
          args: koen,none,none,none,none
        metrics:
          - name: Bleu
            type: bleu
            value: 58.7008
license: apache-2.0
pipeline_tag: translation
widget:
  - text: >-
      translate_ko2en: IBM 왓슨X는 AI 및 데이터 플랫폼이다. 신뢰할 수 있는 데이터, 속도, 거버넌스를 갖고 파운데이션
      모델 및 머신 러닝 기능을 포함한 AI 모델을 학습시키고, 조정해, 조직 전체에서 활용하기 위한 전 과정을 아우르는 기술과 서비스를
      제공한다.
    example_title: Sample 1
  - text: >-
      translate_ko2en: 이용자는 신뢰할 수 있고 개방된 환경에서 자신의 데이터에 대해 자체적인 AI를 구축하거나, 시장에
      출시된 AI 모델을 정교하게 조정할 수 있다. 대규모로 활용하기 위한 도구 세트, 기술, 인프라 및 전문 컨설팅 서비스를 활용할 수
      있다.
    example_title: Sample 2

ko2en

This model is a fine-tuned version of KETI-AIR/long-ke-t5-base on the KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation koen,none,none,none,none dataset. It achieves the following results on the evaluation set:

Loss: 0.5186
Bleu: 58.7008
Gen Len: 27.0073

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: 0.001
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 128
total_eval_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
0.6234	1.0	93762	0.5843	33.9843	17.5378
0.5334	2.0	187524	0.5369	35.3271	17.5388
0.4704	3.0	281286	0.5186	36.0533	17.5335

Framework versions

Transformers 4.25.1
Pytorch 1.12.0
Datasets 2.8.0
Tokenizers 0.13.2