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

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

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

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

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("document")
    .setOutputCol("token")

val sequenceClassifier = BertForSequenceClassification.pretrained("bert_sequence_classifier_textclassification","en")
    .setInputCols(Array("document", "token"))
    .setOutputCol("ner")

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

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

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

Model Information

References

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