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  • John Snow Labs
  • Sep 12, 2025

German Public Health Mention Sequence Classifier (BERT-base) ONNX

  • public_health
  • de
  • licensed
  • sequence_classification
  • health_mention
  • onnx

Description

This model is a bert-base-german based sequence classification model that can classify public health mentions in German social media text.

Predicted Entities

non-health, health-related

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How to use

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

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

sequence_classifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_health_mentions_bert_onnx", "en", "clinical/models")\
  .setInputCols(["document", "token"])\
  .setOutputCol("class")

pipeline = Pipeline(stages=[
    document_assembler, 
    tokenizer,
    sequence_classifier    
])

data = spark.createDataFrame([
      ["Die Temperaturen klettern am Wochenende."],
      ["Zu den Symptomen gehört u.a. eine verringerte Greifkraft."]
    ]).toDF("text")

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

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

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

sequenceClassifier = medical.BertForSequenceClassification.pretrained("bert_sequence_classifier_health_mentions_bert_onnx", "en", "clinical/models")\
    .setInputCols(["document","token"])\
    .setOutputCol("classes")

pipeline = nlp.Pipeline(stages=[
    document_assembler,
    tokenizer,
    sequenceClassifier
])

data = spark.createDataFrame([
      ["Die Temperaturen klettern am Wochenende."],
      ["Zu den Symptomen gehört u.a. eine verringerte Greifkraft."]
    ]).toDF("text")

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


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

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

val sequenceClassifier = MedicalBertForSequenceClassification.pretrained("bert_sequence_classifier_health_mentions_bert_onnx", "en", "clinical/models")
  .setInputCols(Array("document","token"))
  .setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))

val data = Seq(Array("Die Temperaturen klettern am Wochenende.",
                     "Zu den Symptomen gehört u.a. eine verringerte Greifkraft.")).toDF("text")

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

Results


+---------------------------------------------------------+----------------+
|text                                                     |result          |
+---------------------------------------------------------+----------------+
|Die Temperaturen klettern am Wochenende.                 |[non-health]    |
|Zu den Symptomen gehört u.a. eine verringerte Greifkraft.|[health-related]|
+---------------------------------------------------------+----------------+

Model Information

Model Name: bert_sequence_classifier_health_mentions_bert_onnx
Compatibility: Healthcare NLP 6.1.1+
License: Licensed
Edition: Official
Input Labels: [document, token]
Output Labels: [label]
Language: de
Size: 440.8 MB
Case sensitive: true