# 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](https://github.com/joeynmt/joeynmt)

## 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


	
	



			






{}
doi:10.57967/hf/0054 region:us
TAGS
#doi-10.57967/hf/0054 #region-us 

# 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 URL
$ cd joeynmt
$ pip install . -e

Interactive translation(stdin):
>$ python -m joeynmt translate URL

File translation:
>$ python -m joeynmt translate URL < src_lang.txt > hypothesis_trg_lang.txt

## Accuracy measurement
Sacrebleu installation:
> $ pip install sacrebleu

Measurement(bleu_score, chrf):
>  $ sacrebleu URL -i URL -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


	
	



			






# 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 URL
$ cd joeynmt
$ pip install . -e

Interactive translation(stdin):
>$ python -m joeynmt translate URL

File translation:
>$ python -m joeynmt translate URL < src_lang.txt > hypothesis_trg_lang.txt ## Accuracy measurement
Sacrebleu installation:
> $ pip install sacrebleu

Measurement(bleu_score, chrf):
>  $ sacrebleu URL -i URL -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
TAGS
#doi-10.57967/hf/0054 #region-us 
 # 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 URL
$ cd joeynmt
$ pip install . -e

Interactive translation(stdin):
>$ python -m joeynmt translate URL

File translation:
>$ python -m joeynmt translate URL < src_lang.txt > hypothesis_trg_lang.txt ## Accuracy measurement
Sacrebleu installation:
> $ pip install sacrebleu

Measurement(bleu_score, chrf):
>  $ sacrebleu URL -i URL -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
passage: TAGS
#doi-10.57967/hf/0054 #region-us 
# 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 URL
$ cd joeynmt
$ pip install . -e

Interactive translation(stdin):
>$ python -m joeynmt translate URL

File translation:
>$ python -m joeynmt translate URL < src_lang.txt > hypothesis_trg_lang.txt## Accuracy measurement
Sacrebleu installation:
> $ pip install sacrebleu

Measurement(bleu_score, chrf):
>  $ sacrebleu URL -i URL -m bleu chrf