How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
# Warning: Pipeline type "translation" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# pip install "transformers<5.0.0"
from transformers import pipeline

pipe = pipeline("translation", model="staka/fugumt-en-ja")
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("staka/fugumt-en-ja")
model = AutoModelForSeq2SeqLM.from_pretrained("staka/fugumt-en-ja", device_map="auto")
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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.

!pip install transformers sentencepiece

You can use this model directly with a pipeline:

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.

!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(randomly selected 500 sentences) are as follows:

source target BLEU(*1)
en ja 32.7

(*1) sacrebleu --tokenize ja-mecab

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