English XLMRobertaForTokenClassification Base Cased model (from AI4Sec)

Description

Pretrained XLMRobertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. cyner-xlm-roberta-base is a English model originally trained by AI4Sec.

Predicted Entities

Vulnerability, Malware, System, Organization, Indicator

Download Copy S3 URI

How to use

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

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

token_classifier = XlmRoBertaForTokenClassification.pretrained("xlmroberta_ner_cyner_base","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("ner")

ner_converter = NerConverter()\
    .setInputCols(["document", "token", "ner"])\
    .setOutputCol("ner_chunk")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, token_classifier, ner_converter])

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 token_classifier = XlmRoBertaForTokenClassification.pretrained("xlmroberta_ner_cyner_base","en")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

val ner_converter = new NerConverter()
    .setInputCols(Array("document", "token', "ner"))
    .setOutputCol("ner_chunk")

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

val data = Seq("PUT YOUR STRING HERE").toDS.toDF("text")

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

import nlu
nlu.load("en.ner.xlmr_roberta.base.by_ai4sec").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name:	xlmroberta_ner_cyner_base
Compatibility:	Spark NLP 4.1.0+
License:	Open Source
Edition:	Official
Input Labels:	[document, token]
Output Labels:	[ner]
Language:	en
Size:	780.3 MB
Case sensitive:	true
Max sentence length:	256

References

https://huggingface.co/AI4Sec/cyner-xlm-roberta-base

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