DigitalUmuganda / joeynmt-en-kin

English-to-Kinyarwanda Machine Translation

This model is an English-to-Kinyarwanda machine translation model, it was built and trained using JoeyNMT framework. The translation model uses transformer encoder-decoder based architecture. It was trained on a 47,211 long English-Kinyarwanda bitext dataset prepared by Digital Umuganda.

Model architecture

Encoder && Decoder

Type: Transformer Num_layer: 6 Num_heads: 8 Embedding_dim: 256 ff_size: 1024 Dropout: 0.1 Layer_norm: post Initializer: xavier Total params: 12563968

Pre-processing

Tokenizer_type: subword-nmt
num_merges: 4000
BPE encoding learned on the bitext, separate vocabularies for each language
Pretokenizer: None
No lowercase applied

Training

Optimizer: Adam
Loss: crossentropy
Epochs: 30
Batch_size: 256
Number of GPUs: 1

Evaluation

Evaluation_metrics: Blue_score, chrf
Tokenization: None
Beam_width: 15
Beam_alpha: 1.0

Tools

* joeyNMT 2.0.0
* datasets
* pandas
* numpy
* transformers
* sentencepiece
* pytorch(with cuda)
* sacrebleu
* protobuf>=3.20.1

How to train

Use the following link for more information

Translation

To install joeyNMT run:

$ git clone https://github.com/joeynmt/joeynmt.git $ cd joeynmt $ pip install . -e

Interactive translation(stdin):

$ python -m joeynmt translate args.yaml

File translation:

$ python -m joeynmt translate args.yaml < src_lang.txt > hypothesis_trg_lang.txt

Accuracy measurement

Sacrebleu installation:

$ pip install sacrebleu

Measurement(bleu_score, chrf):

$ sacrebleu reference.tsv -i hypothesis.tsv -m bleu chrf

To-do

• Test the model using differenct datasets including the jw300
• Use the Digital Umuganda dataset on some of the available State Of The Art(SOTA) available models.
• Expand the dataset

Result

The following result were obtained on using the sacrebleu.

English-to-Kinyarwanda:

Blue: 56.5 Chrf: 75.2