transformers pytorch tensorboard marian text2text-generation translation generated_from_trainer base_model:Helsinki-NLP/opus-mt-en-rw base_model:finetune:Helsinki-NLP/opus-mt-en-rw license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: Helsinki-NLP/opus-mt-en-rw
license: apache-2.0
metrics:
- bleu
tags:
- translation
- generated_from_trainer
model-index:
- name: marian-finetuned-kde4-en-to-kin
  results: []
---

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# marian-finetuned-multi-en-to-kin

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-rw](https://huggingface.co/Helsinki-NLP/opus-mt-en-rw) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0842
- Bleu: 28.1477

## Model Description

The model has been fine-tuned to perform machine translation from English to Kinyarwanda.

## Intended Uses & Limitations

The primary intended use of this model is for research purposes.

## Training and Evaluation Data

The model was fine-tuned using a combination of datasets from the following sources:
- [Digital Umuganda](https://huggingface.co/datasets/DigitalUmuganda/kinyarwanda-english-machine-translation-dataset/tree/main)
- [Masakhane](https://huggingface.co/datasets/masakhane/mafand/viewer/en-kin/validation)
- [Muennighoff](https://huggingface.co/datasets/Muennighoff/flores200)

For the training of the machine translation model, the dataset underwent the following preprocessing steps:
- Text was converted to lowercase
- Digits were removed

The combined dataset was divided into training and validation sets, with a split of 90% for training and 10% for validation.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results



### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
