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: 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
English Input Text Lengths (in Words)
French Input Text Lengths (in Words)
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 |
|---|---|---|---|---|---|---|---|---|---|
| 1.1677 | 1.0 | 750 | 0.3902 | 35.1914 | 0.6419 | 0.4573 | 0.6070 | 0.6069 | 0.5917 |
- All values in the chart above are rounded to the nearest ten-thousandths.
Framework versions
- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1