Edit on GitHub
  • John Snow Labs
  • Jul 20, 2022

Swedish BertForTokenClassification Base Cased model (from KBLab)

  • bert
  • ner
  • open_source
  • sv

Description

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

Predicted Entities

LOC, ORG, MSR, OBJ, ORG/PRS, LOC/PRS, EVN, TME, PRS/WRK, LOC/ORG, WRK, PER, OBJ/ORG

Download Copy S3 URI

How to use

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

sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
       .setInputCols(["document"])\
       .setOutputCol("sentence")

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

tokenClassifier = BertForTokenClassification.pretrained("bert_ner_KBLab_bert_base_swedish_cased_ner","sv") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("ner")

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

data = spark.createDataFrame([["Jag älskar Spark NLP"]]).toDF("text")

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

val documentAssembler = new DocumentAssembler() 
          .setInputCol("text") 
          .setOutputCol("document")

val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
       .setInputCols(Array("document"))
       .setOutputCol("sentence")

val tokenizer = new Tokenizer() 
    .setInputCols(Array("sentence"))
    .setOutputCol("token")

val tokenClassifier = BertForTokenClassification.pretrained("bert_ner_KBLab_bert_base_swedish_cased_ner","sv") 
    .setInputCols(Array("sentence", "token")) 
    .setOutputCol("ner")

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

val data = Seq("Jag älskar Spark NLP").toDF("text")

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

Model Information

Model Name: bert_ner_KBLab_bert_base_swedish_cased_ner
Compatibility: Spark NLP 4.0.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [ner]
Language: sv
Size: 465.8 MB
Case sensitive: true
Max sentence length: 128

References

  • https://huggingface.co/KBLab/bert-base-swedish-cased-ner