mbart-large-50-English_German_Translation

This model is a fine-tuned version of facebook/mbart-large-50 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.2342
• Bleu: 35.5931
• Rouge1: 0.5803386608353808
• Rouge2: 0.3939141514072567
• RougeL: 0.5438629663406402
• RougeLsum: 0.544153348468965
• Meteor: 0.5500546034636025

Model description

Here is the link to the script I created to train this model: https://github.com/DunnBC22/NLP_Projects/blob/main/Machine%20Translation/NLP%20Translation%20Project-EN:DE.ipynb

Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

Training and evaluation data

Here is a the link to the page where I found this dataset: https://www.kaggle.com/datasets/hgultekin/paralel-translation-corpus-in-22-languages

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
1.7738	1.0	900	1.2342	35.7436	0.5806	0.3941	0.5442	0.5444	0.5512

• All values in the chart above are rounded to near ten-thousandth.

Framework versions

• Transformers 4.22.2
• Pytorch 1.12.1
• Datasets 2.5.2
• Tokenizers 0.12.1