Description
Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-han-chinese-ws is a Chinese model originally trained by ckiplab.
Predicted Entities
B, I
How to use
documentAssembler = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")
tokenizer = Tokenizer() \
    .setInputCols("document") \
    .setOutputCol("token")
tokenClassifier = BertForTokenClassification.pretrained("bert_token_classifier_base_han_chinese_ws","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_han_chinese_ws","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
| Model Name: | bert_token_classifier_base_han_chinese_ws | 
| Compatibility: | Spark NLP 5.2.0+ | 
| License: | Open Source | 
| Edition: | Official | 
| Input Labels: | [document, token] | 
| Output Labels: | [ner] | 
| Language: | zh | 
| Size: | 395.4 MB | 
| Case sensitive: | true | 
| Max sentence length: | 128 | 
References
References
- https://huggingface.co/ckiplab/bert-base-han-chinese-ws
- https://github.com/ckiplab/han-transformers
- http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/akiwi/kiwi.sh
- http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/dkiwi/kiwi.sh
- http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/pkiwi/kiwi.sh
- http://asbc.iis.sinica.edu.tw
- https://ckip.iis.sinica.edu.tw/