German BertForSequenceClassification Cased model (from cm-mueller)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. BACnet-Klassifizierung-Raumlufttechnik is a German model originally trained by cm-mueller.

Predicted Entities

Wärmerückgewinnung, Zuluftuftfilter, Ventilator, Abluft, Vorerhitzer, Andere, Raum, Kühler, Erhitzer, Befeuchter, Zuluftventilator, Nacherhitzer, Aussenluftklappe, Brandschutz, Zuluft, Abluftventilator, RLT_Anlage, Brandschutzklappe, Abluftfilter

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bacnet_klassifizierung_raumlufttechnik","de") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

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

val documentAssembler = new DocumentAssembler()
      .setInputCols(Array("text"))
      .setOutputCols(Array("document"))

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

val seq_classifier = BertForSequenceClassification.pretrained("bert_classifier_bacnet_klassifizierung_raumlufttechnik","de")
    .setInputCols(Array("document", "token"))
    .setOutputCol("class")

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

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

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

import nlu
nlu.load("de.classify.raumlufttechnik.bert.by_cm_mueller").predict("""PUT YOUR STRING HERE""")

Model Information

References

• https://huggingface.co/cm-mueller/BACnet-Klassifizierung-Raumlufttechnik