Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-arabic-camelbert-ca-sentiment is a Arabic model originally trained by CAMeL-Lab.
Predicted Entities
negative, neutral, positive
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_ca_sentiment","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_ca_sentiment","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)
import nlu
nlu.load("ar.classify.bert.sentiment.base").predict("""PUT YOUR STRING HERE""")
Model Information
| Model Name: | bert_classifier_bert_base_arabic_camelbert_ca_sentiment | 
| Compatibility: | Spark NLP 4.1.0+ | 
| License: | Open Source | 
| Edition: | Official | 
| Input Labels: | [document, token] | 
| Output Labels: | [class] | 
| Language: | ar | 
| Size: | 409.5 MB | 
| Case sensitive: | true | 
| Max sentence length: | 256 | 
References
- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca-sentiment
- https://aclanthology.org/D15-1299.pdf
- http://lrec-conf.org/workshops/lrec2018/W30/pdf/22_W30.pdf
- https://aclanthology.org/S17-2088.pdf
- https://arxiv.org/abs/2103.06678
- https://github.com/CAMeL-Lab/CAMeLBERT
- https://github.com/CAMeL-Lab/camel_tools