| --- |
| 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 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # 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 |
