English RobertaForTokenClassification Base Cased model (from abhishek-shrm)

Description

Pretrained RobertaForTokenClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-base-finetuned-beer-ner is a English model originally trained by abhishek-shrm.

Predicted Entities

CARBONATION, BREWING PROCESS, PACKAGING, FLAVOR, ALCOHOL CONTENT, AFTERTASTE, BEER STYLE, PRICE, TEXTURE, ACIDITY, TEMPERATURE STATE, NUTRITIONAL CONTENT, PALATE, INGREDIENTS, APPEARANCE, AROMA

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("roberta_ner_roberta_base_finetuned_beer_ner","en") \
    .setInputCols(["sentence", "token"]) \
    .setOutputCol("ner")

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

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).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("roberta_ner_roberta_base_finetuned_beer_ner","en") 
    .setInputCols(Array("sentence", "token")) 
    .setOutputCol("ner")

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

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

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

import nlu
nlu.load("en.ner.roberta.base_finetuned.by_abhishek_shrm").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name:	roberta_ner_roberta_base_finetuned_beer_ner
Compatibility:	Spark NLP 4.1.0+
License:	Open Source
Edition:	Official
Input Labels:	[document, token]
Output Labels:	[ner]
Language:	en
Size:	420.2 MB
Case sensitive:	true
Max sentence length:	128

References

https://huggingface.co/abhishek-shrm/roberta-base-finetuned-beer-ner

PREVIOUSEnglish RobertaForTokenClassification Base Cased model (from surrey-nlp)

NEXTMongolian RobertaForTokenClassification Base Cased model (from bayartsogt)