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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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- biology |
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- medical |
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metrics: |
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- bleu |
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- rouge |
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- meteor |
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model-index: |
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- name: mbart-large-50-Biomedical_Dataset |
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results: [] |
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language: |
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- en |
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- it |
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pipeline_tag: translation |
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--- |
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# mbart-large-50-Biomedical_Dataset |
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This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50). |
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It achieves the following results on the evaluation set: |
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- Training Loss: 1.0165 |
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- Epoch: 1.0 |
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- Step: 2636 |
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- Validation Loss: 0.9425 |
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- Bleu: 38.9893 |
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- Rouge Metrics: |
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- Rouge1: 0.6826259612196924 |
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- Rouge2: 0.473675987811788 |
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- RougeL: 0.6586445010303293 |
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- RougeLsum: 0.6585487473231793 |
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- Meteor: 0.6299677745833094 |
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- Prediction lengths: 24.362727392855568 |
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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/Biomedical%20Translation%20(EN%20to%20IT)/Biomedical%20-%20Translation%20Project.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://huggingface.co/datasets/paolo-ruggirello/biomedical-dataset |
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### Histogram of English Input Word Counts |
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/Images/Histogram%20of%20English%20Lengths.png) |
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### Histogram of Italian Input Word Counts |
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/Images/Histogram%20of%20Italian%20Inputs.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: 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 | Rouge1 | Rouge2 | RougeL | RougeLsum | Meteor | Prediction Lengths | |
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| :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | |
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| 1.0165 | 1.0 | 2636 | 0.9425 | 38.9893 | 0.6826 | 0.4737 | 0.6586 | 0.6585 | 0.6270 | 24.3627 | |
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Footnotes: |
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*: All results in this table are rounded to the nearest ten-thousandths of the decimal. |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
