Chinese BertForMaskedLM Large Cased model (from hfl)

Description

Pretrained BertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. chinese-macbert-large is a Chinese model originally trained by hfl.

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How to use

documentAssembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")

tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")

bert_loaded = BertEmbeddings.pretrained("bert_embeddings_chinese_mac_large","zh") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("embeddings") \
    .setCaseSensitive(True)

pipeline = Pipeline(stages=[documentAssembler, tokenizer, bert_loaded])

data = spark.createDataFrame([["I love Spark NLP"]]).toDF("text")

result = pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

val tokenizer = new Tokenizer()
    .setInputCols("document")
    .setOutputCol("token")

val bert_loaded = BertEmbeddings.pretrained("bert_embeddings_chinese_mac_large","zh")
    .setInputCols(Array("document", "token"))
    .setOutputCol("embeddings")
    .setCaseSensitive(True)

val pipeline = new Pipeline().setStages(Array(documentAssembler, tokenizer, bert_loaded))

val data = Seq("I love Spark NLP").toDS.toDF("text")

val result = pipeline.fit(data).transform(data)

import nlu
nlu.load("zh.embed.bert.large.by_hfl").predict("""I love Spark NLP""")

Model Information

References

• https://huggingface.co/hfl/chinese-macbert-large
• https://github.com/ymcui/MacBERT/blob/master/LICENSE
• https://2020.emnlp.org
• https://arxiv.org/abs/2004.13922
• https://arxiv.org/abs/2004.13922
• https://github.com/ymcui/Chinese-BERT-wwm
• https://github.com/ymcui/Chinese-ELECTRA
• https://github.com/ymcui/Chinese-XLNet
• https://github.com/airaria/TextBrewer
• https://github.com/ymcui/HFL-Anthology
• https://github.com/chatopera/Synonyms
• https://arxiv.org/abs/2004.13922
• https://arxiv.org/abs/2004.13922