English BertForSequenceClassification Cased model (from palakagl)

Description

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

Predicted Entities

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

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("bert_classifier_bert_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 = BertForSequenceClassification.pretrained("bert_classifier_bert_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.bert.by_palakagl").predict("""PUT YOUR STRING HERE""")

Model Information

References

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