Chinese BertForSequenceClassification Base Cased model (from uer)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-base-finetuned-jd-full-chinese is a Chinese model originally trained by uer.

Predicted Entities

star 4, star 5, star 1, star 2, star 3

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_roberta_base_finetuned_jd_full_chinese","zh") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

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

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).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 sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_roberta_base_finetuned_jd_full_chinese","zh")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

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

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

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

Model Information

References

References

• https://huggingface.co/uer/roberta-base-finetuned-jd-full-chinese
• https://arxiv.org/abs/1909.05658
• https://github.com/dbiir/UER-py/wiki/Modelzoo
• https://github.com/zhangxiangxiao/glyph
• https://arxiv.org/abs/1708.02657
• https://github.com/dbiir/UER-py/
• https://cloud.tencent.com/