Swedish BertForTokenClassification Cased model (from Nonzerophilip)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-finetuned-ner_swedish_test is a Swedish model originally trained by Nonzerophilip.

Predicted Entities

LOC, PER, ORG, MISC

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_bert_finetuned_ner_swedish_test","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_bert_finetuned_ner_swedish_test","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)
import nlu
nlu.load("sv.ner.bert.finetuned").predict("""Jag älskar Spark NLP""")

Model Information

Model Name: bert_ner_bert_finetuned_ner_swedish_test
Compatibility: Spark NLP 4.1.0+
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/Nonzerophilip/bert-finetuned-ner_swedish_test