English XLMRobertaForTokenClassification Base Cased model (from moghis)

Description

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

Predicted Entities

PER, LOC, ORG

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_moghis_base_finetuned_panx","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_moghis_base_finetuned_panx","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.xtreme.base_finetuned.by_moghis").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name: xlmroberta_ner_moghis_base_finetuned_panx
Compatibility: Spark NLP 4.1.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [ner]
Language: en
Size: 827.1 MB
Case sensitive: true
Max sentence length: 256

References

  • https://huggingface.co/moghis/xlm-roberta-base-finetuned-panx-en