German BertForSequenceClassification Base Cased model (from BramVanroy)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. gbert-base-finetuned-cefr is a German model originally trained by BramVanroy.

Predicted Entities

A2, B2, B1, C1, A1

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_gbert_base_finetuned_cefr","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_gbert_base_finetuned_cefr","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.base_finetuned").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name:	bert_classifier_gbert_base_finetuned_cefr
Compatibility:	Spark NLP 4.1.0+
License:	Open Source
Edition:	Official
Input Labels:	[document, token]
Output Labels:	[class]
Language:	de
Size:	412.7 MB
Case sensitive:	true
Max sentence length:	256

References

https://huggingface.co/BramVanroy/gbert-base-finetuned-cefr

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