| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - generated_from_trainer |
| - biology |
| - medical |
| metrics: |
| - bleu |
| - rouge |
| - meteor |
| model-index: |
| - name: mbart-large-50-Biomedical_Dataset |
| results: [] |
| language: |
| - en |
| - it |
| pipeline_tag: translation |
| --- |
| |
| # mbart-large-50-Biomedical_Dataset |
| |
| This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50). |
| |
| It achieves the following results on the evaluation set: |
| - Training Loss: 1.0165 |
| - Epoch: 1.0 |
| - Step: 2636 |
| - Validation Loss: 0.9425 |
| - Bleu: 38.9893 |
| - Rouge Metrics: |
| - Rouge1: 0.6826259612196924 |
| - Rouge2: 0.473675987811788 |
| - RougeL: 0.6586445010303293 |
| - RougeLsum: 0.6585487473231793 |
| - Meteor: 0.6299677745833094 |
| - Prediction lengths: 24.362727392855568 |
| |
| ## 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/Biomedical%20Translation%20(EN%20to%20IT)/Biomedical%20-%20Translation%20Project.ipynb |
|
|
| ## 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/paolo-ruggirello/biomedical-dataset |
|
|
| ### Histogram of English Input Word Counts |
|
|
| /Images/Histogram%20of%20English%20Lengths.png) |
|
|
| ### Histogram of Italian Input Word Counts |
|
|
| /Images/Histogram%20of%20Italian%20Inputs.png) |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 1 |
|
|
| ### Training results* |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Bleu | Rouge1 | Rouge2 | RougeL | RougeLsum | Meteor | Prediction Lengths | |
| | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | |
| | 1.0165 | 1.0 | 2636 | 0.9425 | 38.9893 | 0.6826 | 0.4737 | 0.6586 | 0.6585 | 0.6270 | 24.3627 | |
|
|
| Footnotes: |
|
|
| *: All results in this table are rounded to the nearest ten-thousandths of the decimal. |
| |
| ### Framework versions |
| |
| - Transformers 4.26.1 |
| - Pytorch 2.0.1 |
| - Datasets 2.13.1 |
| - Tokenizers 0.13.3 |
| |
| |
| ## License Notice |
| This model is a fine-tuned derivative of a pretrained model. |
| Users must comply with the original model license. |
| |
| |
| ## Dataset Notice |
| This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions. |
