Long Ke T5 Base Translation Aihub Bidirection

🚀 ko2en_bidirection2

This model is a fine - tuned translation model based on the KETI - AIR/long - ke - t5 - base model. It can effectively perform bidirectional translation between Korean and English, providing high - quality translation services.

🚀 Quick Start

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 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: 0.5716
• Bleu: 51.5949
• Gen Len: 28.8348

✨ Features

• Multi - dataset Training: Trained on multiple high - quality translation datasets, including food, science and technology, and spoken language, ensuring comprehensive translation capabilities.
• High - quality Metrics: Achieved a BLEU score of 51.5949 on the evaluation set, indicating excellent translation quality.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

You can use the following text examples to test the model's translation capabilities:

• KO2EN 1: '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.'
• KO2EN 2: '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.'
• EN2KO 1: '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.'
• EN2KO 2: '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.'

📚 Documentation

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

🔧 Technical Details

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

Framework versions

• Transformers 4.25.1
• Pytorch 1.12.0
• Datasets 2.8.0
• Tokenizers 0.13.2

📄 License

This model is licensed under the Apache - 2.0 license.