English BertForTokenClassification Cased model (from pucpr)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. eHelpBERTpt is a English model originally trained by pucpr.

Predicted Entities

DiagnosticProcedure, Chemicals&Drugs, Abbreviation+<>+B, OrganicChemical+<>+I, Hormone+<>+I, PatientorDisabledGroup, DiagnosticProcedure+<>+B, MedicalDevice+<>+B, MedicalDevice, PharmacologicSubstance+<>+I, Abbreviation+<>+I, Abbreviation, MedicalDevice+<>+I, DiagnosticProcedure+<>+I, BodyPart,Organ,orOrganComponent+<>+B, OrganicChemical+<>+B, DiseaseorSyndrome, SignorSymptom, Chemicals&Drugs+<>+B, BodyPart,Organ,orOrganComponent, DiseaseorSyndrome+<>+B, BodyLocationorRegion+<>+B, Chemicals&Drugs+<>+I, BodyLocationorRegion+<>+I, BodyLocationorRegion, X, BodyPart,Organ,orOrganComponent+<>+I, DiseaseorSyndrome+<>+I, TherapeuticorPreventiveProcedure, Hormone+<>+B

Live Demo Open in Colab 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_eHelpBERTpt","en") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("ner")

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

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).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_eHelpBERTpt","en") 
    .setInputCols(Array("sentence", "token")) 
    .setOutputCol("ner")

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

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

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

Model Information

References

• https://huggingface.co/pucpr/eHelpBERTpt