German BertForSequenceClassification Cased model (from inovex)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. multi2convai-corona-de-bert is a German model originally trained by inovex.

Predicted Entities

corona.definition, neo.age, corona.infect, corona.event, corona.protect, corona.ibuprofen, corona.travel, neo.hello, neo.joke, corona.test, neo.help, corona.quarantine, corona.leisure, neo.home, corona.rumors, corona.masks, corona.notbetreuung, corona.warn-app, corona.contact, corona.package, corona.vaccine, corona.course, undefined, corona.supplies, corona.traffic, corona.risk, corona.fahrradpruefung, neo.feeling, corona.patients, neo.report, neo.sucks, neo.yes, neo.thanks, corona.illness, neo.wyd, neo.sorry, regio.taxes.help, corona.symptoms, neo.introduce, neo.no, corona.deathRate

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_multi2convai_corona","de") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

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

val documentAssembler = new DocumentAssembler()
      .setInputCols(Array("text"))
      .setOutputCols(Array("document"))

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

val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_multi2convai_corona","de")
    .setInputCols(Array("document", "token"))
    .setOutputCol("class")

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

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

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

import nlu
nlu.load("de.classify.bert.multi2convai.").predict("""PUT YOUR STRING HERE""")

Model Information

References

• https://huggingface.co/inovex/multi2convai-corona-de-bert
• https://multi2convai/en/blog/use-cases
• https://github.com/inovex/multi2convai