Description
Pretrained RobertaForSequenceClassification model, adapted from Hugging Face and curated to provide scalability and production-readiness using Spark NLP. RoBERTa-Banking77
is a English model originally trained by philschmid
.
Predicted Entities
card_payment_not_recognised
, activate_my_card
, exchange_charge
, getting_virtual_card
, wrong_amount_of_cash_received
, card_delivery_estimate
, unable_to_verify_identity
, cash_withdrawal_charge
, get_physical_card
, wrong_exchange_rate_for_cash_withdrawal
, declined_cash_withdrawal
, top_up_by_card_charge
, card_not_working
, card_swallowed
, card_payment_wrong_exchange_rate
, atm_support
, getting_spare_card
, card_acceptance
, card_linking
, request_refund
, reverted_card_payment?
, top_up_failed
, verify_my_identity
, exchange_rate
, virtual_card_not_working
, country_support
, disposable_card_limits
, card_arrival
, supported_cards_and_currencies
, top_up_reverted
, apple_pay_or_google_pay
, transaction_charged_twice
, Refund_not_showing_up
, balance_not_updated_after_cheque_or_cash_deposit
, lost_or_stolen_phone
, order_physical_card
, declined_card_payment
, cash_withdrawal_not_recognised
, edit_personal_details
, contactless_not_working
, change_pin
, cancel_transfer
, extra_charge_on_statement
, balance_not_updated_after_bank_transfer
, lost_or_stolen_card
, failed_transfer
, verify_source_of_funds
, verify_top_up
, pending_card_payment
, transfer_timing
, why_verify_identity
, card_about_to_expire
, compromised_card
, direct_debit_payment_not_recognised
, transfer_into_account
, pending_top_up
, top_up_limits
, top_up_by_cash_or_cheque
, pin_blocked
, visa_or_mastercard
, declined_transfer
, get_disposable_virtual_card
, automatic_top_up
, top_up_by_bank_transfer_charge
, terminate_account
, passcode_forgotten
, beneficiary_not_allowed
, receiving_money
, fiat_currency_support
, topping_up_by_card
, pending_transfer
, exchange_via_app
, transfer_fee_charged
, pending_cash_withdrawal
, transfer_not_received_by_recipient
, age_limit
, card_payment_fee_charged
How to use
documentAssembler = DocumentAssembler() \
.setInputCol("text") \
.setOutputCol("document")
tokenizer = Tokenizer() \
.setInputCols("document") \
.setOutputCol("token")
seq_classifier = RoBertaForSequenceClassification.pretrained("roberta_classifier_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 = RoBertaForSequenceClassification.pretrained("roberta_classifier_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.roberta.banking.").predict("""PUT YOUR STRING HERE""")
Model Information
Model Name: | roberta_classifier_banking77 |
Compatibility: | Spark NLP 4.1.0+ |
License: | Open Source |
Edition: | Official |
Input Labels: | [document, token] |
Output Labels: | [class] |
Language: | en |
Size: | 309.2 MB |
Case sensitive: | true |
Max sentence length: | 256 |
References
- https://huggingface.co/philschmid/RoBERTa-Banking77
- https://paperswithcode.com/sota?task=Text+Classification&dataset=BANKING77