Description
Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-arabic-camelbert-msa-did-madar-twitter5
is a Arabic model originally trained by CAMeL-Lab
.
Predicted Entities
Palestine
, Egypt
, United_Arab_Emirates
, Somalia
, Djibouti
, Libya
, Tunisia
, Bahrain
, Jordan
, Sudan
, Morocco
, Lebanon
, Saudi_Arabia
, Kuwait
, Mauritania
, Yemen
, Qatar
, Iraq
, Syria
, Algeria
, Oman
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_msa_did_madar_twitter5","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_msa_did_madar_twitter5","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.twitter.base").predict("""PUT YOUR STRING HERE""")
Model Information
Model Name: | bert_classifier_bert_base_arabic_camelbert_msa_did_madar_twitter5 |
Compatibility: | Spark NLP 5.1.4+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [class] |
Language: | ar |
Size: | 408.6 MB |
Case sensitive: | true |
Max sentence length: | 256 |
References
References
- https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-did-madar-twitter5
- https://camel.abudhabi.nyu.edu/madar-shared-task-2019/
- https://arxiv.org/abs/2103.06678
- https://github.com/CAMeL-Lab/CAMeLBERT
- https://github.com/CAMeL-Lab/camel_tools