English RoBertaForSequenceClassification Mini Cased model (from optimum)

Description

Pretrained RoBertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. MiniLMv2-L12-H384-finetuned-clinc is a English model originally trained by optimum.

Predicted Entities

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

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_minilmv2_l12_h384_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_minilmv2_l12_h384_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.v2_mini_lm_mini_finetuned").predict("""PUT YOUR STRING HERE""")

Model Information

References

References

• https://huggingface.co/optimum/MiniLMv2-L12-H384-finetuned-clinc
• https://paperswithcode.com/sota?task=Text+Classification&dataset=clinc_oos