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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
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