readme: ---
  tags:
  - translation
  - japanese
  
  language:
  - ja
  - en
  
  license: mit
  
  widget:
  - text: "今日もご安全に"
  
  ---
  ## mbart-ja-en
  このモデルは[facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)をベースに[JESC dataset](https://nlp.stanford.edu/projects/jesc/index_ja.html)でファインチューニングしたものです。  
  This model is based on [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) and fine-tuned with [JESC dataset](https://nlp.stanford.edu/projects/jesc/index_ja.html).
  
  ## How to use
  ```py
  from transformers import (
      MBartForConditionalGeneration, MBartTokenizer
  )
  
  tokenizer = MBartTokenizer.from_pretrained("ken11/mbart-ja-en")
  model = MBartForConditionalGeneration.from_pretrained("ken11/mbart-ja-en")
  
  inputs = tokenizer("こんにちは", return_tensors="pt")
  translated_tokens = model.generate(**inputs, decoder_start_token_id=tokenizer.lang_code_to_id["en_XX"], early_stopping=True, max_length=48)
  pred = tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
  print(pred)
  ```
  
  ## Training Data
  I used the [JESC dataset](https://nlp.stanford.edu/projects/jesc/index_ja.html) for training.  
  Thank you for publishing such a large dataset.  
  
  ## Tokenizer
  The tokenizer uses the [sentencepiece](https://github.com/google/sentencepiece) trained on the JESC dataset.
  
  ## Note
  The result of evaluating the sacrebleu score for [JEC Basic Sentence Data of Kyoto University](https://nlp.ist.i.kyoto-u.ac.jp/EN/?JEC+Basic+Sentence+Data#i0163896) was `18.18` .
  
  ## Licenese
  [The MIT license](https://opensource.org/licenses/MIT)
  