# Kinyarwanda-to-English Machine Translation

This model is a Kinyarwanda-to-English 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 different datasets including the jw300
>* Use the Digital Umuganda dataset on some available State Of The Art(SOTA) models.
>* Expand the dataset

## Result
The following result was obtained using sacrebleu.


Kinyarwanda-to-English:
```
Blue: 79.87
Chrf: 84.40
```
	
	



			






{"language": "rw", "library_name": "JoeyNMT", "tags": ["JoeyNMT", "Machine-translation"], "datasets": ["DigitalUmuganda/kinyarwanda-english-machine-translation-dataset"], "task": "Machine-translation", "widget": [{"text": "Muraho neza, murakaza neza mu Rwanda.", "example_title": "Muraho neza, murakaza neza mu Rwanda."}]}
JoeyNMT Machine-translation rw dataset:DigitalUmuganda/kinyarwanda-english-machine-translation-dataset region:us
TAGS
#JoeyNMT #Machine-translation #rw #dataset-DigitalUmuganda/kinyarwanda-english-machine-translation-dataset #region-us 

# Kinyarwanda-to-English Machine Translation

This model is a Kinyarwanda-to-English 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:


Interactive translation(stdin):


File translation:


## Accuracy measurement
Sacrebleu installation:


Measurement(bleu_score, chrf):


## To-do

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

## Result
The following result was obtained using sacrebleu.


Kinyarwanda-to-English:

	
	



			






# Kinyarwanda-to-English Machine Translation

This model is a Kinyarwanda-to-English 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:


Interactive translation(stdin):


File translation: ## Accuracy measurement
Sacrebleu installation:


Measurement(bleu_score, chrf): ## To-do

>* Test the model using different datasets including the jw300
>* Use the Digital Umuganda dataset on some available State Of The Art(SOTA) models.
>* Expand the dataset ## Result
The following result was obtained using sacrebleu.


Kinyarwanda-to-English:
TAGS
#JoeyNMT #Machine-translation #rw #dataset-DigitalUmuganda/kinyarwanda-english-machine-translation-dataset #region-us 
 # Kinyarwanda-to-English Machine Translation

This model is a Kinyarwanda-to-English 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:


Interactive translation(stdin):


File translation: ## Accuracy measurement
Sacrebleu installation:


Measurement(bleu_score, chrf): ## To-do

>* Test the model using different datasets including the jw300
>* Use the Digital Umuganda dataset on some available State Of The Art(SOTA) models.
>* Expand the dataset ## Result
The following result was obtained using sacrebleu.


Kinyarwanda-to-English:
TAGS
#JoeyNMT #Machine-translation #rw #dataset-DigitalUmuganda/kinyarwanda-english-machine-translation-dataset #region-us 
# Kinyarwanda-to-English Machine Translation

This model is a Kinyarwanda-to-English 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:


Interactive translation(stdin):


File translation:## Accuracy measurement
Sacrebleu installation:


Measurement(bleu_score, chrf):## To-do

>* Test the model using different datasets including the jw300
>* Use the Digital Umuganda dataset on some available State Of The Art(SOTA) models.
>* Expand the dataset## Result
The following result was obtained using sacrebleu.


Kinyarwanda-to-English: