---
license: mit
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: {I will update this once I post the code on github.}

## 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

## 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 [^1]

| 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 |

<br />
Footnotes:

[^1]: 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