metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - bleu
  - rouge
model-index:
  - name: mbart-large-50-English_German_Translation
    results: []
language:
  - en
  - de

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