Opus Mt Ko En Korean Parallel Corpora

🚀 opus-mt-ko-en-Korean_Parallel_Corpora

This model is a fine - tuned version of [Helsinki - NLP/opus - mt - ko - en](https://huggingface.co/Helsinki - NLP/opus - mt - ko - en), aiming to provide Korean to English translation capabilities.

🚀 Quick Start

This model is a fine - tuned version of [Helsinki - NLP/opus - mt - ko - en](https://huggingface.co/Helsinki - NLP/opus - mt - ko - en).

📚 Documentation

Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Machine%20Translation/Korean%20to%20English%20(Korean%20Parallel%20Corpora)/Korean_Parallel_Corpora_OPUS_Translation_Project.ipynb

💡 Usage Tip

I apologize in advance if any of the generated text is less than stellar. I am well intentioned, but sometimes the technology can generate some strange outputs.

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Dataset Source: https://huggingface.co/datasets/Moo/korean - parallel - corpora

Histogram of Korean Input Word Counts

Histogram of English Input Word Counts

🔧 Technical Details

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e - 05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: linear
• num_epochs: 6

Training results

• eval_loss: 2.6620
• eval_bleu: 14.3395
• eval_rouge
  • rouge1: 0.4391
  • rouge2: 0.2022
  • rougeL: 0.3671
  • rougeLsum: 0.3671

💡 Usage Tip

The training results values are rounded to the nearest ten - thousandth.

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3

📄 License

This project is under the Apache - 2.0 license.

📋 Information Table