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--- |
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license: apache-2.0 |
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base_model: Helsinki-NLP/opus-mt-ko-en |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: opus-mt-ko-en-Korean_Parallel_Corpora |
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results: [] |
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datasets: |
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- Moo/korean-parallel-corpora |
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language: |
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- ko |
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- en |
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metrics: |
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- bleu |
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- rouge |
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pipeline_tag: translation |
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--- |
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# opus-mt-ko-en-Korean_Parallel_Corpora |
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This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en). |
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### Model description |
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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 |
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* 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. |
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### Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. |
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### Training and evaluation data |
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Dataset Source: https://huggingface.co/datasets/Moo/korean-parallel-corpora |
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### Histogram of Korean Input Word Counts |
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/Images/Histogram%20of%20Korean%20Word%20Counts.png) |
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### Histogram of English Input Word Counts |
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/Images/Histogram%20of%20English%20Word%20Counts.png) |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 6 |
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### Training results |
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- eval_loss: 2.6620 |
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- eval_bleu: 14.3395 |
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- eval_rouge |
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- rouge1: 0.4391 |
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- rouge2: 0.2022 |
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- rougeL: 0.3671 |
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- rougeLsum: 0.3671 |
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* The training results values are rounded to the nearest ten-thousandth. |
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### Framework versions |
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- Transformers 4.31.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
