
# TakoMT

This is a translation model using Marian-NMT.
For more details, please see [my repository](https://github.com/s-taka/fugumt).

In addition to the data listed in the repository I also used [ParaCrawl](https://paracrawl.eu/).

* source languages: de, en, es, fr, it, ru, uk 
* target language: ja 


### How to use

This model uses transformers and sentencepiece.
```python
!pip install transformers sentencepiece
```

You can use this model directly with a pipeline:

```python
from transformers import pipeline
tako_translator = pipeline('translation', model='staka/takomt')
tako_translator('This is a cat.')
```

### Eval results

The results of the evaluation using [tatoeba](https://tatoeba.org/ja)(randomly selected 500 sentences) are as follows:

|source |target |BLEU(*1)| 
|-------|-------|--------|
|de     |ja     |27.8    |
|en     |ja     |28.4    |
|es     |ja     |32.0    |
|fr     |ja     |27.9    |
|it     |ja     |24.3    |
|ru     |ja     |27.3    |
|uk     |ja     |29.8    |


(*1) sacrebleu --tokenize ja-mecab

{"language": ["de", "en", "es", "fr", "it", "ja", "ru", "uk", "multilingual"], "license": "cc-by-sa-4.0", "tags": ["translation"]}
transformers pytorch marian text2text-generation translation de en es fr it ja ru uk multilingual license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #marian #text2text-generation #translation #de #en #es #fr #it #ja #ru #uk #multilingual #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

TakoMT
======


This is a translation model using Marian-NMT.
For more details, please see my repository.


In addition to the data listed in the repository I also used ParaCrawl.


* source languages: de, en, es, fr, it, ru, uk
* target language: ja


### How to use


This model uses transformers and sentencepiece.


You can use this model directly with a pipeline:


### Eval results


The results of the evaluation using tatoeba(randomly selected 500 sentences) are as follows:


source: de, target: ja, BLEU(\*1): 27.8
source: en, target: ja, BLEU(\*1): 28.4
source: es, target: ja, BLEU(\*1): 32.0
source: fr, target: ja, BLEU(\*1): 27.9
source: it, target: ja, BLEU(\*1): 24.3
source: ru, target: ja, BLEU(\*1): 27.3
source: uk, target: ja, BLEU(\*1): 29.8


(\*1) sacrebleu --tokenize ja-mecab



### How to use


This model uses transformers and sentencepiece.


You can use this model directly with a pipeline: ### Eval results


The results of the evaluation using tatoeba(randomly selected 500 sentences) are as follows:


source: de, target: ja, BLEU(\*1): 27.8
source: en, target: ja, BLEU(\*1): 28.4
source: es, target: ja, BLEU(\*1): 32.0
source: fr, target: ja, BLEU(\*1): 27.9
source: it, target: ja, BLEU(\*1): 24.3
source: ru, target: ja, BLEU(\*1): 27.3
source: uk, target: ja, BLEU(\*1): 29.8


(\*1) sacrebleu --tokenize ja-mecab
TAGS
#transformers #pytorch #marian #text2text-generation #translation #de #en #es #fr #it #ja #ru #uk #multilingual #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### How to use


This model uses transformers and sentencepiece.


You can use this model directly with a pipeline: ### Eval results


The results of the evaluation using tatoeba(randomly selected 500 sentences) are as follows:


source: de, target: ja, BLEU(\*1): 27.8
source: en, target: ja, BLEU(\*1): 28.4
source: es, target: ja, BLEU(\*1): 32.0
source: fr, target: ja, BLEU(\*1): 27.9
source: it, target: ja, BLEU(\*1): 24.3
source: ru, target: ja, BLEU(\*1): 27.3
source: uk, target: ja, BLEU(\*1): 29.8


(\*1) sacrebleu --tokenize ja-mecab
passage: TAGS
#transformers #pytorch #marian #text2text-generation #translation #de #en #es #fr #it #ja #ru #uk #multilingual #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
### How to use


This model uses transformers and sentencepiece.


You can use this model directly with a pipeline:### Eval results


The results of the evaluation using tatoeba(randomly selected 500 sentences) are as follows:


source: de, target: ja, BLEU(\*1): 27.8
source: en, target: ja, BLEU(\*1): 28.4
source: es, target: ja, BLEU(\*1): 32.0
source: fr, target: ja, BLEU(\*1): 27.9
source: it, target: ja, BLEU(\*1): 24.3
source: ru, target: ja, BLEU(\*1): 27.3
source: uk, target: ja, BLEU(\*1): 29.8


(\*1) sacrebleu --tokenize ja-mecab