English RobertaForSequenceClassification Base Cased model (from bhadresh-savani)

Description

Pretrained RobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-base-emotion is a English model originally trained by bhadresh-savani.

Predicted Entities

surprise, joy, anger, fear, love, sadness

Download Copy S3 URI

How to use

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

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

seq_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_base_emotion","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

pipeline = Pipeline(stages=[documentAssembler, tokenizer, seq_classifier])

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 seq_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_base_emotion","en")
    .setInputCols(Array("document", "token"))
    .setOutputCol("class")

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

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

val result = pipeline.fit(data).transform(data)
import nlu
nlu.load("en.classify.roberta.emotion.base").predict("""PUT YOUR STRING HERE""")

Model Information

Model Name: roberta_classifier_base_emotion
Compatibility: Spark NLP 5.2.0+
License: Open Source
Edition: Official
Input Labels: [document, token]
Output Labels: [class]
Language: en
Size: 443.3 MB
Case sensitive: true
Max sentence length: 256

References

References

  • https://huggingface.co/bhadresh-savani/roberta-base-emotion
  • https://arxiv.org/abs/1907.11692
  • https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb
  • https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/
  • https://paperswithcode.com/sota?task=Text+Classification&dataset=emotion