metadata

language:
  - ko
  - en
license: apache-2.0
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: KO2EN 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: KO2EN 2
  - text: >-
      translate_en2ko: The Seoul Metropolitan Government said Wednesday that it
      would develop an AI-based congestion monitoring system to provide better
      information to passengers about crowd density at each subway station.
    example_title: EN2KO 1
  - text: >-
      translate_en2ko: According to Seoul Metro, the operator of the subway
      service in Seoul, the new service will help analyze the real-time flow of
      passengers and crowd levels in subway compartments, improving operational
      efficiency.
    example_title: EN2KO 2
pipeline_tag: translation
base_model: KETI-AIR/long-ke-t5-base

ko2en_bidirection

This model is a fine-tuned version of KETI-AIR/long-ke-t5-base on the csv_dataset.py dataset. It achieves the following results on the evaluation set:

Loss: 0.6808
Bleu: 52.2152
Gen Len: 396.0215

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: 8
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
0.5962	1.0	750093	0.6808	0.0	18.369

Framework versions

Transformers 4.28.1
Pytorch 1.13.0
Datasets 2.9.0
Tokenizers 0.13.2