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  • John Snow Labs
  • Jan 04, 2026

Medication Named Entity Recognition Romanian (Base, ONNX)

  • medical
  • clinical
  • xlmroberta
  • ner
  • ro
  • licensed
  • onnx

Description

This is an XLM-RoBERTa-based NER model for Romanian medical text. It identifies medication mentions in clinical sentences and labels them using B-MEDICATION, I-MEDICATION, or O. The model is intended for extracting drug names from notes, prescriptions, and reports, 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 = XlmRoBertaForTokenClassification \
    .pretrained("xlmroberta_medical_ner_ro_onnx", "ro", "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([["Pacientul a primit metformin 500 mg zilnic pentru diabet."]]).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.XlmRoBertaForTokenClassification \
    .pretrained("xlmroberta_medical_ner_ro_onnx", "ro", "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([["Pacientul a primit metformin 500 mg zilnic pentru diabet."]]).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 = XlmRoBertaForTokenClassification
  .pretrained("xlmroberta_medical_ner_ro_onnx", "ro", "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("Pacientul a primit metformin 500 mg zilnic pentru diabet.").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    |
+---------+----------+
|metformin|MEDICATION|
+---------+----------+

Model Information

Model Name: xlmroberta_medical_ner_ro_onnx
Compatibility: Healthcare NLP 6.2.0+
License: Licensed
Edition: Official
Input Labels: [token, sentence]
Output Labels: [ner]
Language: ro
Size: 1.0 GB
Case sensitive: true
Max sentence length: 128