Arabic BertForSequenceClassification Base Cased model (from CAMeL-Lab)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-arabic-camelbert-mix-poetry is a Arabic model originally trained by CAMeL-Lab.

Predicted Entities

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

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_bert_base_arabic_camelbert_mix_poetry","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_bert_base_arabic_camelbert_mix_poetry","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/CAMeL-Lab/bert-base-arabic-camelbert-mix-poetry
• https://arxiv.org/pdf/1905.05700.pdf
• https://arxiv.org/abs/2103.06678
• https://github.com/CAMeL-Lab/CAMeLBERT
• https://github.com/CAMeL-Lab/camel_tools