Procedures Named Entity Recognition Spanish (Base, ONNX)

Description

This is a RoBERTa-based NER model for Spanish medical text. It identifies medical procedures in clinical sentences and labels them using B-PROCEDURE, I-PROCEDURE, or O. The model is intended for extracting procedure names from notes, reports, and clinical records, and is provided in ONNX format for efficient inference.

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How to use

from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline

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

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

tokenClassifier = RoBertaForTokenClassification \
    .pretrained("roberta_procedure_ner_es_onnx", "es", "clinical/models") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("ner")

converter = NerConverter() \
    .setInputCols(["document", "token", "ner"]) \
    .setOutputCol("ner_chunk")

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

data = spark.createDataFrame([["El paciente se sometió a una resonancia magnética y una biopsia de hígado."]]).toDF("text")
result = pipeline.fit(data).transform(data)

result.selectExpr("explode(ner_chunk) as chunk").selectExpr(
    "chunk.result as text",
    "chunk.metadata['entity'] as entity"
).show(truncate=False)

from johnsnowlabs import nlp, medical

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

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

tokenClassifier = nlp.RoBertaForTokenClassification \
    .pretrained("roberta_procedure_ner_es_onnx", "es", "clinical/models") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("ner")

converter = nlp.NerConverter() \
    .setInputCols(["document", "token", "ner"]) \
    .setOutputCol("ner_chunk")

pipeline = nlp.Pipeline(stages=[
    documentAssembler,
    tokenizer,
    tokenClassifier,
    converter
])

data = spark.createDataFrame([["El paciente se sometió a una resonancia magnética y una biopsia de hígado."]]).toDF("text")
result = pipeline.fit(data).transform(data)

result.selectExpr("explode(ner_chunk) as chunk").selectExpr(
    "chunk.result as text",
    "chunk.metadata['entity'] as entity"
).show(truncate=False)

import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotators._
import org.apache.spark.sql.functions._
import org.apache.spark.ml.Pipeline

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

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

val tokenClassifier = RoBertaForTokenClassification
  .pretrained("roberta_procedure_ner_es_onnx", "es", "clinical/models")
  .setInputCols(Array("document", "token"))
  .setOutputCol("ner")

val converter = new NerConverter()
  .setInputCols(Array("document", "token", "ner"))
  .setOutputCol("ner_chunk")

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

val data = Seq("El paciente se sometió a una resonancia magnética y una biopsia de hígado.").toDF("text")
val result = pipeline.fit(data).transform(data)

result.selectExpr("explode(ner_chunk) as chunk")
  .selectExpr("chunk.result as text", "chunk.metadata['entity'] as entity")
  .show(false)

Results

+--------------------+---------+
|text                |entity   |
+--------------------+---------+
|resonancia magnética|PROCEDURE|
|biopsia de          |PROCEDURE|
+--------------------+---------+

Model Information

Model Name:	roberta_procedure_ner_es_onnx
Compatibility:	Healthcare NLP 6.2.0+
License:	Licensed
Edition:	Official
Input Labels:	[token, sentence]
Output Labels:	[ner]
Language:	es
Size:	469.7 MB
Case sensitive:	true
Max sentence length:	128

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