Turkish BertForSequenceClassification Uncased model (from sfurkan)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. LexBERT-textclassification-turkish-uncased is a Turkish model originally trained by sfurkan.

Predicted Entities

Genelge, Tüzük, Kanun Hükmünde Kararname, Yönetmelik, Özelge, Cumhurbaşkanlığı Kararnamesi, Kanun, Komisyon Raporu, Tebliğ, Resmi Gazete

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How to use

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

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

sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_lex_textclassification_turkish_uncased","tr") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

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

data = spark.createDataFrame([["Spark NLP'yi seviyorum"]]).toDF("text")

result = pipeline.fit(data).transform(data)
val documentAssembler = new DocumentAssembler() 
          .setInputCol("text") 
          .setOutputCol("document")

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

val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_lex_textclassification_turkish_uncased","tr") 
    .setInputCols(Array("document", "token")) 
    .setOutputCol("class")

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

val data = Seq("Spark NLP'yi seviyorum").toDF("text")

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("tr.classify.bert.uncased").predict("""Spark NLP'yi seviyorum""")

Model Information

Model Name: bert_classifier_lex_textclassification_turkish_uncased
Compatibility: Spark NLP 5.1.4+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [class]
Language: tr
Size: 414.7 MB
Case sensitive: false
Max sentence length: 256

References

References

  • https://huggingface.co/sfurkan/LexBERT-textclassification-turkish-uncased