English RoBertaForSequenceClassification Large Cased model (from philschmid)

Description

Pretrained RoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. roberta-large-finetuned-clinc is a English model originally trained by philschmid.

Predicted Entities

todo_list, card_declined, cook_time, pto_request_status, calendar, spending_history, next_holiday, tell_joke, ingredients_list, change_language, restaurant_suggestion, min_payment, pin_change, whisper_mode, date, international_visa, plug_type, w2, translate, pto_used, thank_you, alarm, shopping_list_update, flight_status, change_volume, bill_due, find_phone, carry_on, reminder_update, apr, user_name, uber, calories, report_lost_card, change_accent, payday, timezone, reminder, roll_dice, text, current_location, cancel, change_ai_name, weather, directions, jump_start, recipe, timer, what_song, income, change_user_name, tire_change, sync_device, application_status, lost_luggage, meeting_schedule, what_is_your_name, credit_score, gas_type, maybe, order_checks, do_you_have_pets, oil_change_when, schedule_meeting, interest_rate, rollover_401k, how_old_are_you, last_maintenance, smart_home, book_hotel, freeze_account, nutrition_info, bill_balance, improve_credit_score, pto_balance, replacement_card_duration, travel_suggestion, calendar_update, transfer, vaccines, update_playlist, mpg, schedule_maintenance, confirm_reservation, repeat, restaurant_reservation, meaning_of_life, gas, cancel_reservation, international_fees, routing, meal_suggestion, time, change_speed, new_card, redeem_rewards, insurance_change, insurance, play_music, credit_limit, balance, goodbye, are_you_a_bot, restaurant_reviews, todo_list_update, rewards_balance, no, spelling, what_can_i_ask_you, order, reset_settings, shopping_list, order_status, ingredient_substitution, food_last, transactions, make_call, travel_notification, who_made_you, share_location, damaged_card, next_song, oil_change_how, taxes, direct_deposit, who_do_you_work_for, yes, exchange_rate, definition, what_are_your_hobbies, expiration_date, car_rental, tire_pressure, accept_reservations, calculator, account_blocked, how_busy, distance, book_flight, credit_limit_change, report_fraud, pay_bill, measurement_conversion, where_are_you_from, pto_request, travel_alert, flip_coin, fun_fact, traffic, greeting, oos

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_philschmid_large_finetuned_clinc","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_philschmid_large_finetuned_clinc","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.large_finetuned.by_philschmid").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/philschmid/roberta-large-finetuned-clinc
• https://paperswithcode.com/sota?task=Text+Classification&dataset=clinc_oos