Arabic BertForSequenceClassification Cased model (from Ammar-alhaj-ali)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. arabic-MARBERT-poetry-classification is a Arabic model originally trained by Ammar-alhaj-ali.

Predicted Entities

المنسرح, السلسلة, المضارع, موشح, البسيط, السريع, الرمل, المجتث, المتدارك, الطويل, المتقارب, الخفيف, عامي, المواليا, الهزج, الكامل, الوافر, شعر التفعيلة, شعر حر, المقتضب, الدوبيت, المديد, الرجز

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_arabic_marbert_poetry_classification","ar") \
    .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_arabic_marbert_poetry_classification","ar")
    .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)

Model Information

References

• https://huggingface.co/Ammar-alhaj-ali/arabic-MARBERT-poetry-classification
• https://hci-lab.github.io/ArabicPoetry-1-Private/