
# FuguMT

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

* source language: en
* 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
fugu_translator = pipeline('translation', model='staka/fugumt-en-ja')
fugu_translator('This is a cat.')
```

If you want to translate multiple sentences, we recommend using [pySBD](https://github.com/nipunsadvilkar/pySBD).
```python
!pip install transformers sentencepiece pysbd

import pysbd
seg_en = pysbd.Segmenter(language="en", clean=False)

from transformers import pipeline
fugu_translator = pipeline('translation', model='staka/fugumt-en-ja')
txt = 'This is a cat. It is very cute.'
print(fugu_translator(seg_en.segment(txt)))
```


### Eval results

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

|source |target |BLEU(*1)| 
|-------|-------|--------|
|en     |ja     |32.7    |

(*1) sacrebleu --tokenize ja-mecab
{"language": ["en", "ja"], "license": "cc-by-sa-4.0", "tags": ["translation"]}
transformers pytorch marian text2text-generation translation en ja license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

FuguMT
======


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


* source language: en
* target language: ja


### How to use


This model uses transformers and sentencepiece.


You can use this model directly with a pipeline:


If you want to translate multiple sentences, we recommend using pySBD.


### Eval results


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


source: en, target: ja, BLEU(\*1): 32.7


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



### How to use


This model uses transformers and sentencepiece.


You can use this model directly with a pipeline:


If you want to translate multiple sentences, we recommend using pySBD. ### Eval results


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


source: en, target: ja, BLEU(\*1): 32.7


(\*1) sacrebleu --tokenize ja-mecab
TAGS
#transformers #pytorch #marian #text2text-generation #translation #en #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### How to use


This model uses transformers and sentencepiece.


You can use this model directly with a pipeline:


If you want to translate multiple sentences, we recommend using pySBD. ### Eval results


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


source: en, target: ja, BLEU(\*1): 32.7


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


This model uses transformers and sentencepiece.


You can use this model directly with a pipeline:


If you want to translate multiple sentences, we recommend using pySBD.### Eval results


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


source: en, target: ja, BLEU(\*1): 32.7


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