Spanish BertForTokenClassification Cased model (from rjuez00)

Description

Pretrained BertForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. meddocan-beto-ner is a Spanish model originally trained by rjuez00.

Predicted Entities

CALLE, NUMERO_FAX, FECHAS, CENTRO_SALUD, INSTITUCION, PROFESION, ID_EMPLEO_PERSONAL_SANITARIO, SEXO_SUJETO_ASISTENCIA, PAIS, FAMILIARES_SUJETO_ASISTENCIA, EDAD_SUJETO_ASISTENCIA, CORREO_ELECTRONICO, NUMERO_TELEFONO, HOSPITAL, ID_CONTACTO_ASISTENCIAL, ID_ASEGURAMIENTO, OTROS_SUJETO_ASISTENCIA, NOMBRE_SUJETO_ASISTENCIA, ID_SUJETO_ASISTENCIA, NOMBRE_PERSONAL_SANITARIO, ID_TITULACION_PERSONAL_SANITARIO, TERRITORIO

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_meddocan_beto_ner","es") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("ner")

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

data = spark.createDataFrame([["Amo Spark NLP"]]).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_meddocan_beto_ner","es") 
    .setInputCols(Array("sentence", "token")) 
    .setOutputCol("ner")

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

val data = Seq("Amo Spark NLP").toDF("text")

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

import nlu
nlu.load("es.ner.beto_bert").predict("""Amo Spark NLP""")

Model Information

References

• https://huggingface.co/rjuez00/meddocan-beto-ner