English 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-en-bert is a English 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

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How to use

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

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

seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_multi2convai_corona","en") \
    .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","en")
    .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("en.classify.bert.multi2convai.").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name:	bert_classifier_multi2convai_corona
Compatibility:	Spark NLP 4.1.0+
License:	Open Source
Edition:	Official
Input Labels:	[document, token]
Output Labels:	[class]
Language:	en
Size:	406.7 MB
Case sensitive:	true
Max sentence length:	256

References

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

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