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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- bleu |
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- rouge |
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model-index: |
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- name: mbart-large-50-English_French_Translation_v2 |
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results: [] |
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language: |
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- en |
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- fr |
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--- |
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# mbart-large-50-English_French_Translation_v2 |
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3902 |
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- Bleu: 35.1914 |
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- Rouge: {'rouge1': 0.641952430267112, 'rouge2': 0.4572909036472911, 'rougeL': 0.607001331434416, 'rougeLsum': 0.6068905123656807} |
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- Meteor: {'meteor': 0.5916610499445853} |
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## Model description |
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This model translates French input text samples to English. |
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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/NLP%20Translation%20Project-EN:FR.ipynb |
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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://www.kaggle.com/datasets/hgultekin/paralel-translation-corpus-in-22-languages |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge | Meteor | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:----------------------------------------------------------------------------------------------------------------------------:|:------------------------------:| |
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| 1.1677 | 1.0 | 750 | 0.3902 | 35.1914 | {'rouge1': 0.6419485887304972, 'rouge2': 0.45727961744986984, 'rougeL': 0.6069956611472951, 'rougeLsum': 0.6068859187671477} | {'meteor': 0.5916768663368279} | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.9.0 |
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- Tokenizers 0.12.1 |
