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  • John Snow Labs
  • May 04, 2022

Arabic Named Entity Recognition (from Davlan)

  • bert
  • ner
  • token_classification
  • ar
  • open_source

Description

Pretrained Named Entity Recognition model, uploaded to Hugging Face, adapted and imported into Spark NLP. bert-base-multilingual-cased-ner-hrl is a Arabic model orginally trained by Davlan.

Predicted Entities

DATE, LOC, ORG, PER

Download Copy S3 URI

How to use

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

sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")\
.setInputCols(["document"])\
.setOutputCol("sentence")

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

tokenClassifier = BertForTokenClassification.pretrained("bert_ner_bert_base_multilingual_cased_ner_hrl","ar") \
.setInputCols(["sentence", "token"]) \
.setOutputCol("ner")

pipeline = Pipeline(stages=[documentAssembler, sentenceDetector, tokenizer, tokenClassifier])

data = spark.createDataFrame([["أنا أحب الشرارة NLP"]]).toDF("text")

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

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

val sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl", "xx")
.setInputCols(Array("document"))
.setOutputCol("sentence")

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

val tokenClassifier = BertForTokenClassification.pretrained("bert_ner_bert_base_multilingual_cased_ner_hrl","ar") 
.setInputCols(Array("sentence", "token")) 
.setOutputCol("ner")

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

val data = Seq("أنا أحب الشرارة NLP").toDF("text")

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

import nlu
nlu.load("ar.ner.multilingual_cased_ner_hrl").predict("""أنا أحب الشرارة NLP""")

Model Information

Model Name: bert_ner_bert_base_multilingual_cased_ner_hrl
Compatibility: Spark NLP 3.4.2+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [ner]
Language: ar
Size: 665.8 MB
Case sensitive: true
Max sentence length: 128

References

  • https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl
  • https://camel.abudhabi.nyu.edu/anercorp/
  • https://www.clips.uantwerpen.be/conll2003/ner/
  • https://www.clips.uantwerpen.be/conll2003/ner/
  • https://www.clips.uantwerpen.be/conll2002/ner/
  • https://github.com/EuropeanaNewspapers/ner-corpora/tree/master/enp_FR.bnf.bio
  • https://ontotext.fbk.eu/icab.html
  • https://github.com/LUMII-AILab/FullStack/tree/master/NamedEntities
  • https://www.clips.uantwerpen.be/conll2002/ner/
  • https://github.com/davidsbatista/NER-datasets/tree/master/Portuguese