Description
Pretrained Named Entity Recognition model, uploaded to Hugging Face, adapted and imported into Spark NLP. arabic-ner is a Arabic model orginally trained by hatmimoha.
Predicted Entities
DATE, PRICE, PRODUCT, COMPETITION, ORGANIZATION, LOCATION, DISEASE, PERSON, EVENT
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_arabic_ner","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_arabic_ner","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)
Model Information
| Model Name: | bert_ner_arabic_ner |
| Compatibility: | Spark NLP 3.4.2+ |
| License: | Open Source |
| Edition: | Official |
| Input Labels: | [document, token] |
| Output Labels: | [ner] |
| Language: | ar |
| Size: | 412.6 MB |
| Case sensitive: | true |
| Max sentence length: | 128 |
References
- https://huggingface.co/hatmimoha/arabic-ner
- https://github.com/hatmimoha/arabic-ner