mbart-ja-en

このモデルはfacebook/mbart-large-cc25をベースにJESC dataset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 and fine-tuned with JESC dataset.

How to use

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 for training.
Thank you for publishing such a large dataset.

Tokenizer

The tokenizer uses the sentencepiece trained on the JESC dataset.

Note

The result of evaluating the sacrebleu score for JEC Basic Sentence Data of Kyoto University was 18.18 .

Licenese

The MIT license

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