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  • John Snow Labs
  • Sep 07, 2022

German BertForSequenceClassification Base Uncased model (from Tobias)

  • de
  • open_source
  • bert
  • sequence_classification
  • classification

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-uncased_German_MultiLable_classification is a German model originally trained by Tobias.

Predicted Entities

GeneralUtilitys, Location, HotelOrganisation, ReasonForStay, Food, Unknown, Room, Staff

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_bert_base_uncased_german_multilable_classification","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_bert_base_uncased_german_multilable_classification","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.bert.uncased_base").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name: bert_classifier_bert_base_uncased_german_multilable_classification
Compatibility: Spark NLP 4.1.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [class]
Language: de
Size: 409.8 MB
Case sensitive: false
Max sentence length: 256

References

  • https://huggingface.co/Tobias/bert-base-uncased_German_MultiLable_classification