English RobertaForSequenceClassification Cased model (from palakagl)

Description

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

Predicted Entities

transport_ticket, general_commandstop, iot_cleaning, general_praise, music_settings, general_quirky, recommendation_locations, iot_hue_lightoff, audio_volume_mute, calendar_set, iot_coffee, datetime_convert, general_explain, cooking_recipe, qa_definition, news_query, music_likeness, recommendation_movies, general_dontcare, general_affirm, recommendation_events, alarm_set, qa_maths, qa_factoid, play_podcasts, takeaway_query, email_sendemail, email_addcontact, transport_traffic, iot_wemo_off, general_negate, iot_hue_lightdim, audio_volume_up, general_repeat, iot_wemo_on, alarm_query, lists_createoradd, music_query, weather_query, transport_query, alarm_remove, takeaway_order, social_post, general_confirm, calendar_query, iot_hue_lightup, general_joke, calendar_remove, email_querycontact, iot_hue_lightchange, iot_hue_lighton, play_radio, social_query, lists_query, transport_taxi, lists_remove, email_query, datetime_query, play_music, qa_stock, audio_volume_down, qa_currency, play_game, play_audiobook

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_multiclass_textclassification","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_multiclass_textclassification","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.by_palakagl").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/palakagl/Roberta_Multiclass_TextClassification