---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ko-en
tags:
- generated_from_trainer
model-index:
- name: opus-mt-ko-en-Korean_Parallel_Corpora
  results: []
datasets:
- Moo/korean-parallel-corpora
language:
- ko
- en
metrics:
- bleu
- rouge
pipeline_tag: translation
---

# 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).

### 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

* 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
![German Word Count of Input Text](https://github.com/DunnBC22/NLP_Projects/raw/main/Machine%20Translation/Korean%20to%20English%20(Korean%20Parallel%20Corpora)/Images/Histogram%20of%20Korean%20Word%20Counts.png)

### Histogram of English Input Word Counts
![English Word Count of Input Text](https://github.com/DunnBC22/NLP_Projects/raw/main/Machine%20Translation/Korean%20to%20English%20(Korean%20Parallel%20Corpora)/Images/Histogram%20of%20English%20Word%20Counts.png)

## 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

* 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