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  • John Snow Labs
  • May 09, 2022

German Named Entity Recognition (from severinsimmler)

  • bert
  • ner
  • token_classification
  • de
  • open_source

Description

Pretrained Named Entity Recognition model, uploaded to Hugging Face, adapted and imported into Spark NLP. literary-german-bert is a German model orginally trained by severinsimmler.

Predicted Entities

PER

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_literary_german_bert","de") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("ner")

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

data = spark.createDataFrame([["Ich liebe 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_literary_german_bert","de") 
    .setInputCols(Array("sentence", "token")) 
    .setOutputCol("ner")

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

val data = Seq("Ich liebe Spark NLP").toDF("text")

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

import nlu
nlu.load("de.ner.literary.bert.by_severinsimmler").predict("""Ich liebe Spark NLP""")

Model Information

Model Name: bert_ner_literary_german_bert
Compatibility: Spark NLP 3.4.2+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [ner]
Language: de
Size: 410.4 MB
Case sensitive: true
Max sentence length: 128

References

  • https://huggingface.co/severinsimmler/literary-german-bert
  • https://figshare.com/articles/Corpus_of_German-Language_Fiction_txt_/4524680/1
  • https://gitlab2.informatik.uni-wuerzburg.de/kallimachos/DROC-Release
  • https://figshare.com/articles/Corpus_of_German-Language_Fiction_txt_/4524680/1
  • https://opus.bibliothek.uni-wuerzburg.de/opus4-wuerzburg/frontdoor/deliver/index/docId/14333/file/Jannidis_Figurenerkennung_Roman.pdf
  • http://webdoc.sub.gwdg.de/pub/mon/dariah-de/dwp-2018-27.pdf
  • https://opus.bibliothek.uni-wuerzburg.de/opus4-wuerzburg/frontdoor/deliver/index/docId/14333/file/Jannidis_Figurenerkennung_Roman.pdf