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---
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
/Images/Histogram%20of%20Korean%20Word%20Counts.png)
### Histogram of English Input Word Counts
/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 |