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

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_multi2convai_corona_en_bert","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

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

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

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

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

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

val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_multi2convai_corona_en_bert","en")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

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

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

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

Model Information

References

• https://huggingface.co/inovex/multi2convai-corona-en-bert
• https://multi2conv.ai
• https://multi2convai/en/blog/use-cases
• https://multi2convai/en/blog/use-cases
• https://multi2conv.ai
• https://github.com/inovex/multi2convai