Description
Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-han-chinese-ws-shanggu
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_shanggu","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_shanggu","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_shanggu |
Compatibility: | Spark NLP 4.3.1+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [ner] |
Language: | zh |
Size: | 396.0 MB |
Case sensitive: | true |
Max sentence length: | 128 |
References
- https://huggingface.co/ckiplab/bert-base-han-chinese-ws-shanggu
- https://github.com/ckiplab/han-transformers