Spanish RobertaForSequenceClassification Cased model (from edumunozsala)

Description

Pretrained RobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta_bne_sentiment_analysis_es is a Spanish model originally trained by edumunozsala.

Predicted Entities

Positivo, Negativo

Live Demo Open in Colab 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_bne_sentiment_analysis","es") \
    .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_bne_sentiment_analysis","es")
    .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("es.classify.roberta.sentiment.").predict("""PUT YOUR STRING HERE""")

Model Information

References

• https://huggingface.co/edumunozsala/roberta_bne_sentiment_analysis_es
• http://www.bne.es/en/Inicio/index.html
• https://arxiv.org/abs/2107.07253
• https://github.com/edumunozsala
• https://paperswithcode.com/sota?task=Sentiment+Analysis&dataset=IMDb+Reviews+in+Spanish