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Reproduced package based on Masked Language Model Scoring (ACL2020).
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Ryutaro-A/mlm-scoring-transformers
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This package is a reproduced implementation ofMasked Language Model Scoring (ACL2020).
The original implementation uses the mxnet library, which does not support Japanese.
Therefore, we are releasing a version that can be used with the Masked Model published on Hugging Face.
We have not tried it on all models, but we believe that most of the pre-trained models can be used.
git clone https://github.com/Ryutaro-A/mlm-scoring-transformers.gitcd mlm-scoring-transformerspip install .
- To calculate scores for Japanese sentences.
importmlmtpretrained_model_name='cl-tohoku/bert-base-japanese-whole-word-masking'scorer=mlmt.MLMScorer(pretrained_model_name,use_cuda=False)japanese_sample_sentences= ['お母さんが行けるなら、わたしは行くのをやめるよ。うちから二人も出ることはないから。','お母さんが行けると、わたしは行くのをやめるよ。うちから二人も出ることはないから。','お母さんが行けたなら、わたしは行くのをやめるよ。うちから二人も出ることはないから。','お母さんが行けるのだったら、わたしは行くのをやめるよ。うちから二人も出ることはないから。','日本酒を飲めば、駅の反対側にある「××酒蔵」が一番だね。とにかく品揃えが抜群だよ。','日本酒を飲むなら、駅の反対側にある「××酒蔵」が一番だね。とにかく品揃えが抜群だよ。','日本酒を飲むんだったら、駅の反対側にある「××酒蔵」が一番だね。とにかく品揃えが抜群だよ。','日本酒を飲むと、駅の反対側にある「××酒蔵」が一番だね。とにかく品揃えが抜群だよ。',]scores=scorer.score_sentences(japanese_sample_sentences)print('input_sentence, score')forsentence,scoreinzip(japanese_sample_sentences,scores):print(sentence,score)# >> input_sentence, score# お母さんが行けるなら、わたしは行くのをやめるよ。うちから二人も出ることはないから。 -72.90809887713657# お母さんが行けると、わたしは行くのをやめるよ。うちから二人も出ることはないから。 -75.87569694537336# お母さんが行けたなら、わたしは行くのをやめるよ。うちから二人も出ることはないから。 -65.31722020490005# お母さんが行けるのだったら、わたしは行くのをやめるよ。うちから二人も出ることはないから。 -86.46473170552028# 日本酒を飲めば、駅の反対側にある「××酒蔵」が一番だね。とにかく品揃えが抜群だよ。 -85.50868926288888# 日本酒を飲むなら、駅の反対側にある「××酒蔵」が一番だね。とにかく品揃えが抜群だよ。 -81.26314979794296# 日本酒を飲むんだったら、駅の反対側にある「××酒蔵」が一番だね。とにかく品揃えが抜群だよ。 -82.7387441759266# 日本酒を飲むと、駅の反対側にある「××酒蔵」が一番だね。とにかく品揃えが抜群だよ。 -92.14111483963103
- To calculate scores for English sentences.
importmlmtpretrained_model_name='bert-base-uncased'scorer=mlmt.MLMScorer(pretrained_model_name,use_cuda=False)english_sample_sentences= ['Due to the rain, our performance in the game was far from perfect.','Due to the rain, our performance in the game was apart from perfect.','Due to the rain, our performance in the game was different from perfect.','Due to the rain, our performance in the game was free from perfect.',]scores=scorer.score_sentences(english_sample_sentences)print('input_sentence, score')forsentence,scoreinzip(english_sample_sentences,scores):print(sentence,score)# >> input_sentence, score# Due to the rain, our performance in the game was far from perfect. -13.874692459549525# Due to the rain, our performance in the game was apart from perfect. -15.486674794020251# Due to the rain, our performance in the game was different from perfect. -16.62563831794064# Due to the rain, our performance in the game was free from perfect. -20.5683701854279
Basically, the config used to pre-train the model is automatically selected, but you can also use your own config.
In that case, setmodel_config
as follows.
config=transformers.BertConfig(hidden_act="gelu",hidden_size=1024,initializer_range=0.02,intermediate_size=4096,layer_norm_eps=1e-12,max_position_embeddings=512,model_type="bert",num_attention_heads=16,num_hidden_layers=24,pad_token_id=0,tokenizer_class="BertJapaneseTokenizer",type_vocab_size=2,vocab_size=32768,hidden_dropout_prob=0.2,attention_probs_dropout_prob=0.37)scorer=mlmt.MLMScorer(pretrained_model_name,model_config=config,use_cuda=False)
2022/12/14 Added option to get per-token log-likelihood as wellSettingget_token_likelihood
toTrue
returns the total score and the per-token score as a dictionary.
scores=my_scorer.score_sentences(sentences=en,get_token_likelihood=True)print('input_sentence, score')forsentence,scoreinzip(en,scores):print(sentence,score["all"])print(score["token"])# Due to the rain, our performance in the game was far from perfect. -13.874687737519245# [-0.00044868520073119083, -0.0002509074244949909, -7.234254390419689, -0.1027699065355511, -0.05655604143014172, -0.04961800099545115, -0.0015554001203739796, -0.004590661092892022, -6.211619135159143, -0.21036846650855923, -0.0017955319970342492, -5.960464655174753e-08, -0.00011099000773481521, -0.00026807801725587353, -0.00048148300554723856]
This software is released under the MIT License, see LICENSE.txt.
Twitter:@ryu1104_m
Mail: ryu1104.as[at]gmail.com
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Reproduced package based on Masked Language Model Scoring (ACL2020).
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