--- language: - en - de license: mit tags: - generated_from_trainer metrics: - bleu - rouge base_model: facebook/mbart-large-50 model-index: - name: mbart-large-50-English_German_Translation results: [] --- # mbart-large-50-English_German_Translation This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/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