English BertForSequenceClassification Cased model (from vaariis)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. extreme-go-emotion is a English model originally trained by vaariis.

Predicted Entities

optimism 🤞, disappointment 😞, amusement 😂, desire 😍, nervousness 😬, sadness 😞, surprise 😲, love ❤️, approval 👍, confusion 😕, embarrassment 😳, curiosity 🤔, grief 😢, disapproval 👎, gratitude 🙏, disgust 🤮, excitement 🤩, realization 💡, anger 😡, remorse 😞, admiration 👏, caring 🤗, fear 😨, annoyance 😒, joy 😃, neutral 😐, relief 😅, pride 😌

Live Demo Open in Colab Download Copy S3 URI

How to use

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

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

sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_extreme_go_emotion","en") \
    .setInputCols(["document", "token"]) \
    .setOutputCol("class")

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

data = spark.createDataFrame([["PUT YOUR STRING HERE"]]).toDF("text")

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

val documentAssembler = new DocumentAssembler() 
          .setInputCol("text") 
          .setOutputCol("document")

val tokenizer = new Tokenizer() 
    .setInputCols(Array("document"))
    .setOutputCol("token")

val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_extreme_go_emotion","en") 
    .setInputCols(Array("document", "token")) 
    .setOutputCol("class")

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

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

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

Model Information

References

References

• https://huggingface.co/vaariis/extreme-go-emotion