transformers pytorch mbart text2text-generation generated_from_trainer en de license:mit autotrain_compatible endpoints_compatible region:us
---
language:
- en
- de
license: mit
metrics:
- bleu
- rouge
tags:
- generated_from_trainer
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