--- 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](https://huggingface.co/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