metadata
license: mit
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-banking77-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9321428571428572
widget:
- text: 'Can I track the card you sent to me? '
example_title: Card Arrival Example - English
- text: 'Posso tracciare la carta che mi avete spedito? '
example_title: Card Arrival Example - Italian
- text: Can you explain your exchange rate policy to me?
example_title: Exchange Rate Example - English
- text: Potete spiegarmi la vostra politica dei tassi di cambio?
example_title: Exchange Rate Example - Italian
- text: I can't pay by my credit card
example_title: Card Not Working Example - English
- text: Non riesco a pagare con la mia carta di credito
example_title: Card Not Working Example - Italian
xlm-roberta-base-banking77-classification
This model is a fine-tuned version of xlm-roberta-base on the banking77 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3034
- Accuracy: 0.9321
- F1 Score: 0.9321
Model description
Experiment on a cross-language model to assess how accurate the classification is by using for fine tuning an English dataset but later querying the model in Italian.
Intended uses & limitations
The model can be used on text classification. In particular is fine tuned on banking domain for multilingual task.
Training and evaluation data
The dataset used is banking77
The 77 labels are:
| label | intent |
|---|---|
| 0 | activate_my_card |
| 1 | age_limit |
| 2 | apple_pay_or_google_pay |
| 3 | atm_support |
| 4 | automatic_top_up |
| 5 | balance_not_updated_after_bank_transfer |
| 6 | balance_not_updated_after_cheque_or_cash_deposit |
| 7 | beneficiary_not_allowed |
| 8 | cancel_transfer |
| 9 | card_about_to_expire |
| 10 | card_acceptance |
| 11 | card_arrival |
| 12 | card_delivery_estimate |
| 13 | card_linking |
| 14 | card_not_working |
| 15 | card_payment_fee_charged |
| 16 | card_payment_not_recognised |
| 17 | card_payment_wrong_exchange_rate |
| 18 | card_swallowed |
| 19 | cash_withdrawal_charge |
| 20 | cash_withdrawal_not_recognised |
| 21 | change_pin |
| 22 | compromised_card |
| 23 | contactless_not_working |
| 24 | country_support |
| 25 | declined_card_payment |
| 26 | declined_cash_withdrawal |
| 27 | declined_transfer |
| 28 | direct_debit_payment_not_recognised |
| 29 | disposable_card_limits |
| 30 | edit_personal_details |
| 31 | exchange_charge |
| 32 | exchange_rate |
| 33 | exchange_via_app |
| 34 | extra_charge_on_statement |
| 35 | failed_transfer |
| 36 | fiat_currency_support |
| 37 | get_disposable_virtual_card |
| 38 | get_physical_card |
| 39 | getting_spare_card |
| 40 | getting_virtual_card |
| 41 | lost_or_stolen_card |
| 42 | lost_or_stolen_phone |
| 43 | order_physical_card |
| 44 | passcode_forgotten |
| 45 | pending_card_payment |
| 46 | pending_cash_withdrawal |
| 47 | pending_top_up |
| 48 | pending_transfer |
| 49 | pin_blocked |
| 50 | receiving_money |
| 51 | Refund_not_showing_up |
| 52 | request_refund |
| 53 | reverted_card_payment? |
| 54 | supported_cards_and_currencies |
| 55 | terminate_account |
| 56 | top_up_by_bank_transfer_charge |
| 57 | top_up_by_card_charge |
| 58 | top_up_by_cash_or_cheque |
| 59 | top_up_failed |
| 60 | top_up_limits |
| 61 | top_up_reverted |
| 62 | topping_up_by_card |
| 63 | transaction_charged_twice |
| 64 | transfer_fee_charged |
| 65 | transfer_into_account |
| 66 | transfer_not_received_by_recipient |
| 67 | transfer_timing |
| 68 | unable_to_verify_identity |
| 69 | verify_my_identity |
| 70 | verify_source_of_funds |
| 71 | verify_top_up |
| 72 | virtual_card_not_working |
| 73 | visa_or_mastercard |
| 74 | why_verify_identity |
| 75 | wrong_amount_of_cash_received |
| 76 | wrong_exchange_rate_for_cash_withdrawal |
Training procedure
from transformers import pipeline
pipe = pipeline("text-classification", model="nickprock/xlm-roberta-base-banking77-classification")
pipe("Non riesco a pagare con la carta di credito")
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|---|---|---|---|---|---|
| 3.8002 | 1.0 | 157 | 2.7771 | 0.5159 | 0.4483 |
| 2.4006 | 2.0 | 314 | 1.6937 | 0.7140 | 0.6720 |
| 1.4633 | 3.0 | 471 | 1.0385 | 0.8308 | 0.8153 |
| 0.9234 | 4.0 | 628 | 0.7008 | 0.8789 | 0.8761 |
| 0.6163 | 5.0 | 785 | 0.5029 | 0.9068 | 0.9063 |
| 0.4282 | 6.0 | 942 | 0.4084 | 0.9123 | 0.9125 |
| 0.3203 | 7.0 | 1099 | 0.3515 | 0.9253 | 0.9253 |
| 0.245 | 8.0 | 1256 | 0.3295 | 0.9227 | 0.9225 |
| 0.1863 | 9.0 | 1413 | 0.3092 | 0.9269 | 0.9269 |
| 0.1518 | 10.0 | 1570 | 0.2901 | 0.9338 | 0.9338 |
| 0.1179 | 11.0 | 1727 | 0.2938 | 0.9318 | 0.9319 |
| 0.0969 | 12.0 | 1884 | 0.2906 | 0.9328 | 0.9328 |
| 0.0805 | 13.0 | 2041 | 0.2963 | 0.9295 | 0.9295 |
| 0.063 | 14.0 | 2198 | 0.2998 | 0.9289 | 0.9288 |
| 0.0554 | 15.0 | 2355 | 0.2933 | 0.9351 | 0.9349 |
| 0.046 | 16.0 | 2512 | 0.2960 | 0.9328 | 0.9326 |
| 0.04 | 17.0 | 2669 | 0.3032 | 0.9318 | 0.9318 |
| 0.035 | 18.0 | 2826 | 0.3061 | 0.9312 | 0.9312 |
| 0.0317 | 19.0 | 2983 | 0.3030 | 0.9331 | 0.9330 |
| 0.0315 | 20.0 | 3140 | 0.3034 | 0.9321 | 0.9321 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1