Korean ElectraForSequenceClassification Cased model (from searle-j)

Description

Pretrained ElectraForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. kote_for_easygoing_people is a Korean model originally trained by searle-j.

Predicted Entities

깨달음, 놀람, 기쁨, 부담/안_내킴, 우쭐댐/무시함, 공포/무서움, 흐뭇함(귀여움/예쁨), 환영/호의, 부끄러움, 화남/분노, 패배/자기혐오, 귀찮음, 짜증, 불쌍함/연민, 증오/혐오, 기대감, 안심/신뢰, 행복, 재미없음, 절망, 비장함, 어이없음, 지긋지긋, 불평/불만, 고마움, 안타까움/실망, 불안/걱정, 즐거움/신남, 한심함, 뿌듯함, 슬픔, 죄책감, 경악, 없음, 역겨움/징그러움, 힘듦/지침, 신기함/관심, 편안/쾌적, 당황/난처, 의심/불신, 감동/감탄, 아껴주는, 존경, 서러움

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 = BertForSequenceClassification.pretrained("electra_classifier_kote_for_easygoing_people","ko") \
    .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 = BertForSequenceClassification.pretrained("electra_classifier_kote_for_easygoing_people","ko")
    .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)

Model Information

References

• https://huggingface.co/searle-j/kote_for_easygoing_people