Japanese Masked Language Model Scoring

Copyright by Pham Quang Nhat Minh (C).

In this package, we implement Masked Language Model Scoring in Pytorch and add Japanese, Vietnamese support.

Installation

Python 3.6+ is required. Clone this repository and install:

pip install -e .

How to use the package

from jmlm.models import get_pretrained
from jmlm.scorers import MLMScorer, LMScorer

# Masked Language Model scroing with cl-tohoku/bert-base-japanese
model, tokenizer = get_pretrained('cl-tohoku/bert-base-japanese', device='cpu')
scorer = MLMScorer(model, tokenizer, device='cpu')
print(scorer.score_sentences(['英語をわかる','英語がわかる']))
# >> [-27.141216278076172, -16.992878675460815]

# Take average score of tokens
print(scorer.score_sentences(['英語をわかる','英語がわかる'], normalize=True))
# >> [-9.047072092692057, -5.6642928918202715]

# Get scores for each token
print(scorer.score_sentences(['英語がわかる'], per_token=True))
# >> [[-6.728611469268799, -2.057461977005005, -8.206805229187012]]

# Masked Language Model scoring for Vietnamese
model, tokenizer = get_pretrained('NlpHUST/vibert4news-base-cased', device='cpu')
scorer = MLMScorer(model, tokenizer, device='cpu')
print(scorer.score_sentences(['Tôi là sinh viên đại học.','Tôi là học sinh đại học']))
## >> [-3.132660969684366, -7.91806149110198]

# Language Model Scoring with rinna/japanese-gpt2-medium
model, tokenizer = get_pretrained('rinna/japanese-gpt2-medium', device='cpu')
scorer = LMScorer(model, tokenizer, device='cpu')
print(scorer.score_sentences(['英語をわかる','英語がわかる']))
## >> [-21.05287978053093, -15.756769746541977]