English BertForSequenceClassification Base Uncased model (from transformersbook)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. bert-base-uncased-finetuned-clinc is a English model originally trained by transformersbook.

Predicted Entities

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

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

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

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

val sequenceClassifier_loaded = BertForSequenceClassification.pretrained("bert_classifier_base_uncased_finetuned_clinc","en") 
    .setInputCols(Array("document", "token")) 
    .setOutputCol("class")

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

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

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

import nlu
nlu.load("en.classify.bert.uncased_base_finetuned.by_transformersbook").predict("""PUT YOUR STRING HERE""")

Model Information

References

• https://huggingface.co/transformersbook/bert-base-uncased-finetuned-clinc
• https://arxiv.org/abs/1909.02027
• https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/
• https://github.com/nlp-with-transformers/notebooks/blob/main/08_model-compression.ipynb