mbart-large-50-Biomedical_Dataset

This model is a fine-tuned version of 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

Histogram of Italian Input Word Counts

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