
# FuguMT

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

* source language: ja
* target language: en 

### 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-ja-en')
fugu_translator('猫はかわいいです。')
```

### Eval results

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

|source |target |BLEU(*1)| 
|-------|-------|--------|
|ja     |en     |39.1    |

(*1) sacrebleu
{"language": ["en", "ja"], "license": "cc-by-sa-4.0", "tags": ["translation"], "widget": [{"text": "\u732b\u306f\u304b\u308f\u3044\u3044\u3067\u3059\u3002"}]}
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: ja
* target language: en


### 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: ja, target: en, BLEU(\*1): 39.1


(\*1) sacrebleu



### 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: ja, target: en, BLEU(\*1): 39.1


(\*1) sacrebleu
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: ### Eval results


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


source: ja, target: en, BLEU(\*1): 39.1


(\*1) sacrebleu
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:### Eval results


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


source: ja, target: en, BLEU(\*1): 39.1


(\*1) sacrebleu