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

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

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_poetry","ar") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

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

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

result = pipeline.fit(data).transform(data)

val documentAssembler = new DocumentAssembler()
    .setInputCol("text")
    .setOutputCol("document")

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

val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_base_arabic_camel_mix_poetry","ar")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

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

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