metadata

license: mit
tags:
  - generated_from_trainer
metrics:
  - bleu
  - rouge
model-index:
  - name: mbart-large-50-English_French_Translation_v2
    results: []
language:
  - en
  - fr

mbart-large-50-English_French_Translation_v2

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: 0.3902
Bleu: 35.1914
Rouge: {'rouge1': 0.641952430267112, 'rouge2': 0.4572909036472911, 'rougeL': 0.607001331434416, 'rougeLsum': 0.6068905123656807}
Meteor: {'meteor': 0.5916610499445853}

Model description

This model translates French input text samples to English.

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Machine%20Translation/NLP%20Translation%20Project-EN:FR.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://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	Rouge	Meteor
1.1677	1.0	750	0.3902	35.1914	{'rouge1': 0.6419485887304972, 'rouge2': 0.45727961744986984, 'rougeL': 0.6069956611472951, 'rougeLsum': 0.6068859187671477}	{'meteor': 0.5916768663368279}

Framework versions

Transformers 4.26.1
Pytorch 1.12.1
Datasets 2.9.0
Tokenizers 0.12.1