Chinese BertForTokenClassification Base Cased model (from ckiplab)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-chinese-ner is a Chinese model originally trained by ckiplab.

Predicted Entities

S-WORK_OF_ART, S-TIME, E-FAC, S-PERCENT, S-PRODUCT, E-LANGUAGE, S-NORP, S-QUANTITY, S-PERSON, E-DATE, S-LOC, S-CARDINAL, E-QUANTITY, S-GPE, S-FAC, MONEY, S-ORG, E-NORP, E-GPE, E-TIME, EVENT, DATE, CARDINAL, FAC, E-PERCENT, E-PERSON, S-ORDINAL, NORP, LOC, E-ORG, E-MONEY, S-LAW, LAW, E-LOC, S-EVENT, ORG, TIME, ORDINAL, E-WORK_OF_ART, LANGUAGE, S-MONEY, E-ORDINAL, PERCENT, E-EVENT, S-LANGUAGE, E-PRODUCT, QUANTITY, WORK_OF_ART, E-LAW, S-DATE, PRODUCT, E-CARDINAL, PERSON, GPE

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_base_chinese_ner","zh") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("ner")

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

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 tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_base_chinese_ner","zh")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

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

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

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

Model Information

References

• https://huggingface.co/ckiplab/bert-base-chinese-ner
• https://github.com/ckiplab/ckip-transformers
• https://muyang.pro
• https://ckip.iis.sinica.edu.tw
• https://github.com/ckiplab/ckip-transformers
• https://github.com/ckiplab/ckip-transformers