Chinese BertForMaskedLM Cased model (from uer)

Description

Pretrained BertForMaskedLM model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. chinese_roberta_L-10_H-256 is a Chinese model originally trained by uer.

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

bert_loaded = BertEmbeddings.pretrained("bert_embeddings_chinese_roberta_l_10_h_256","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_roberta_l_10_h_256","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.10l_256d_256d").predict("""I love Spark NLP""")

Model Information

References

• https://huggingface.co/uer/chinese_roberta_L-10_H-256
• https://github.com/dbiir/UER-py/
• https://arxiv.org/abs/1909.05658
• https://arxiv.org/abs/1908.08962
• https://github.com/dbiir/UER-py/wiki/Modelzoo
• https://github.com/CLUEbenchmark/CLUECorpus2020/
• https://github.com/dbiir/UER-py/
• https://cloud.tencent.com/