English BertForSequenceClassification Cased model (from idrimadrid)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. autonlp-creator_classifications-4021083 is a English model originally trained by idrimadrid.

Predicted Entities

NBC - Heroes, Mattel, Icon Comics, South Park, Dreamworks, Shueisha, Star Trek, Disney, Microsoft, Image Comics, SyFy, Hanna-Barbera, Lego, George Lucas, Capcom, Ubisoft, HarperCollins, Namco, Hasbro, IDW Publishing, Cartoon Network, George R. R. Martin, Universal Studios, Sony Pictures, Marvel Comics, Sega, Wildstorm, Nintendo, DC Comics, Clive Barker, Matt Groening, ABC Studios, J. R. R. Tolkien, Stephen King, Ian Fleming, Konami, J. K. Rowling, Mortal Kombat, Dark Horse Comics, Blizzard Entertainment, Team Epic TV

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_autonlp_creator_classifications_4021083","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_autonlp_creator_classifications_4021083","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

References

• https://huggingface.co/idrimadrid/autonlp-creator_classifications-4021083