mbart-large-50-English_Spanish_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: 1.0290
• Bleu: 41.4437
• Rouge:
  • Rouge1: 0.6751402780531002
  • Rouge2: 0.49769602014143044
  • RougeL: 0.6371513427059108
  • RougeLsum: 0.6376403149816605
• Meteor: 0.6479226630466496

Model description

This project translates Spanish text inputs into English.

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:ES.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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Rouge1	Rouge2	RougeL	RougeLsum	Meteor
1.5608	1.0	900	1.0899	39.9184	0.6645	0.4846	0.6254	0.6259	0.6376
0.9734	2.0	1800	1.0290	41.4436	0.6751	0.4977	0.6371	0.6376	0.6479

Framework versions

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