English BertForSequenceClassification Cased model (from philschmid)

Description

Pretrained BertForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. BERT-Banking77 is a English model originally trained by philschmid.

Predicted Entities

request_refund, automatic_top_up, terminate_account, cancel_transfer, top_up_limits, top_up_failed, supported_cards_and_currencies, receiving_money, get_physical_card, exchange_charge, lost_or_stolen_card, topping_up_by_card, pending_cash_withdrawal, transfer_timing, pending_top_up, card_about_to_expire, pending_transfer, card_arrival, cash_withdrawal_charge, passcode_forgotten, card_linking, change_pin, direct_debit_payment_not_recognised, transfer_into_account, card_payment_fee_charged, verify_source_of_funds, failed_transfer, extra_charge_on_statement, exchange_rate, card_acceptance, verify_top_up, edit_personal_details, card_swallowed, transfer_not_received_by_recipient, declined_card_payment, reverted_card_payment?, card_delivery_estimate, Refund_not_showing_up, wrong_amount_of_cash_received, card_payment_wrong_exchange_rate, visa_or_mastercard, order_physical_card, apple_pay_or_google_pay, contactless_not_working, verify_my_identity, declined_cash_withdrawal, getting_spare_card, why_verify_identity, top_up_reverted, compromised_card, get_disposable_virtual_card, disposable_card_limits, country_support, top_up_by_bank_transfer_charge, activate_my_card, pin_blocked, transfer_fee_charged, unable_to_verify_identity, transaction_charged_twice, age_limit, cash_withdrawal_not_recognised, top_up_by_cash_or_cheque, lost_or_stolen_phone, fiat_currency_support, beneficiary_not_allowed, exchange_via_app, atm_support, virtual_card_not_working, balance_not_updated_after_bank_transfer, getting_virtual_card, pending_card_payment, card_not_working, wrong_exchange_rate_for_cash_withdrawal, declined_transfer, card_payment_not_recognised, top_up_by_card_charge, balance_not_updated_after_cheque_or_cash_deposit

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_banking77","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_banking77","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.banking.").predict("""PUT YOUR STRING HERE""")

Model Information

References

• https://huggingface.co/philschmid/BERT-Banking77
• https://paperswithcode.com/sota?task=Text+Classification&dataset=BANKING77