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--- |
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language: |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- finance |
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- intent-classification |
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datasets: |
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- banking77 |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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base_model: distilbert-base-uncased |
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model-index: |
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- name: banking-intent-distilbert-classifier |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# banking-intent-distilbert-classifier |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the banking77 dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.2885 |
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- eval_accuracy: 0.9244 |
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- eval_runtime: 1.9357 |
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- eval_samples_per_second: 1591.148 |
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- eval_steps_per_second: 99.705 |
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- epoch: 10.0 |
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- step: 3130 |
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_Note: This is just a simple example of fine-tuning a DistilBERT model for |
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multi-class classification task to see how much it costs to train this |
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model on Google Cloud (using a T4 GPU). It costs me about 1.07 SGD and |
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takes less than 20 mins to complete the training. Although my intention was just |
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to test it out on Google Cloud, the model has been appropriately trained |
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and is now ready to be used. Hopefully, it is what you're looking for._ |
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## Inference example |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("lxyuan/banking-intent-distilbert-classifier") |
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model = AutoModelForSequenceClassification.from_pretrained("lxyuan/banking-intent-distilbert-classifier") |
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banking_intend_classifier = TextClassificationPipeline( |
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model=model, |
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tokenizer=tokenizer, |
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device=0 |
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) |
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banking_intend_classifier("How to report lost card?") |
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>>> [{'label': 'lost_or_stolen_card', 'score': 0.9518502950668335}] |
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``` |
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## Training and evaluation data |
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The BANKING77 dataset consists of online banking queries labeled with their corresponding intents, |
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offering a comprehensive collection of 77 finely categorized intents within the banking domain. |
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With a total of 13,083 customer service queries, it specifically emphasizes precise intent detection |
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within a single domain. |
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## Training procedure |
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- To reproduce the result, please refer to this [notebook](https://github.com/LxYuan0420/nlp/blob/main/notebooks/distillbert-intent-classification-banking.ipynb) |
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- To run the evaluation, please refer to this [evaluation notebook](https://github.com/LxYuan0420/nlp/blob/main/notebooks/Evaluator_from_Huggingface.ipynb) |
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### Evaluation |
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<details> |
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<summary>Evaluation result</summary> |
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Classification Report: |
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precision recall f1-score support |
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activate_my_card 1.0000 0.9750 0.9873 40 |
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age_limit 0.9756 1.0000 0.9877 40 |
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apple_pay_or_google_pay 1.0000 1.0000 1.0000 40 |
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atm_support 0.9750 0.9750 0.9750 40 |
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automatic_top_up 1.0000 0.9000 0.9474 40 |
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balance_not_updated_after_bank_transfer 0.8205 0.8000 0.8101 40 |
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balance_not_updated_after_cheque_or_cash_deposit 1.0000 0.9750 0.9873 40 |
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beneficiary_not_allowed 0.9250 0.9250 0.9250 40 |
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cancel_transfer 1.0000 0.9750 0.9873 40 |
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card_about_to_expire 0.9756 1.0000 0.9877 40 |
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card_acceptance 0.9189 0.8500 0.8831 40 |
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card_arrival 0.9459 0.8750 0.9091 40 |
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card_delivery_estimate 0.8605 0.9250 0.8916 40 |
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card_linking 0.9302 1.0000 0.9639 40 |
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card_not_working 0.8478 0.9750 0.9070 40 |
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card_payment_fee_charged 0.7917 0.9500 0.8636 40 |
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card_payment_not_recognised 0.9231 0.9000 0.9114 40 |
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card_payment_wrong_exchange_rate 0.9048 0.9500 0.9268 40 |
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card_swallowed 1.0000 0.8750 0.9333 40 |
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cash_withdrawal_charge 0.9744 0.9500 0.9620 40 |
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cash_withdrawal_not_recognised 0.8667 0.9750 0.9176 40 |
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change_pin 0.9302 1.0000 0.9639 40 |
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compromised_card 0.8889 0.8000 0.8421 40 |
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contactless_not_working 1.0000 0.9000 0.9474 40 |
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country_support 0.9512 0.9750 0.9630 40 |
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declined_card_payment 0.8125 0.9750 0.8864 40 |
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declined_cash_withdrawal 0.7843 1.0000 0.8791 40 |
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declined_transfer 0.9667 0.7250 0.8286 40 |
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direct_debit_payment_not_recognised 0.9444 0.8500 0.8947 40 |
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disposable_card_limits 0.8974 0.8750 0.8861 40 |
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edit_personal_details 0.9302 1.0000 0.9639 40 |
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exchange_charge 0.9722 0.8750 0.9211 40 |
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exchange_rate 0.9091 1.0000 0.9524 40 |
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exchange_via_app 0.8085 0.9500 0.8736 40 |
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extra_charge_on_statement 1.0000 0.9500 0.9744 40 |
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failed_transfer 0.8333 0.8750 0.8537 40 |
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fiat_currency_support 0.8718 0.8500 0.8608 40 |
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get_disposable_virtual_card 0.9722 0.8750 0.9211 40 |
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get_physical_card 0.9756 1.0000 0.9877 40 |
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getting_spare_card 0.9500 0.9500 0.9500 40 |
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getting_virtual_card 0.8667 0.9750 0.9176 40 |
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lost_or_stolen_card 0.8261 0.9500 0.8837 40 |
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lost_or_stolen_phone 0.9750 0.9750 0.9750 40 |
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order_physical_card 0.9231 0.9000 0.9114 40 |
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passcode_forgotten 1.0000 1.0000 1.0000 40 |
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pending_card_payment 0.9500 0.9500 0.9500 40 |
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pending_cash_withdrawal 1.0000 0.9500 0.9744 40 |
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pending_top_up 0.9268 0.9500 0.9383 40 |
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pending_transfer 0.8611 0.7750 0.8158 40 |
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pin_blocked 0.9714 0.8500 0.9067 40 |
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receiving_money 1.0000 0.9250 0.9610 40 |
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Refund_not_showing_up 1.0000 0.9250 0.9610 40 |
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request_refund 0.9512 0.9750 0.9630 40 |
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reverted_card_payment? 0.9286 0.9750 0.9512 40 |
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supported_cards_and_currencies 0.9744 0.9500 0.9620 40 |
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terminate_account 0.9302 1.0000 0.9639 40 |
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top_up_by_bank_transfer_charge 1.0000 0.8250 0.9041 40 |
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top_up_by_card_charge 0.9286 0.9750 0.9512 40 |
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top_up_by_cash_or_cheque 0.8810 0.9250 0.9024 40 |
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top_up_failed 0.9024 0.9250 0.9136 40 |
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top_up_limits 0.9286 0.9750 0.9512 40 |
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top_up_reverted 0.9706 0.8250 0.8919 40 |
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topping_up_by_card 0.8421 0.8000 0.8205 40 |
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transaction_charged_twice 0.9302 1.0000 0.9639 40 |
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transfer_fee_charged 0.9024 0.9250 0.9136 40 |
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transfer_into_account 0.9167 0.8250 0.8684 40 |
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transfer_not_received_by_recipient 0.7778 0.8750 0.8235 40 |
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transfer_timing 0.8372 0.9000 0.8675 40 |
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unable_to_verify_identity 0.9250 0.9250 0.9250 40 |
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verify_my_identity 0.7955 0.8750 0.8333 40 |
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verify_source_of_funds 0.9524 1.0000 0.9756 40 |
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verify_top_up 1.0000 1.0000 1.0000 40 |
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virtual_card_not_working 1.0000 0.9250 0.9610 40 |
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visa_or_mastercard 0.9737 0.9250 0.9487 40 |
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why_verify_identity 0.9118 0.7750 0.8378 40 |
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wrong_amount_of_cash_received 1.0000 0.8750 0.9333 40 |
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wrong_exchange_rate_for_cash_withdrawal 0.9730 0.9000 0.9351 40 |
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accuracy 0.9244 3080 |
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macro avg 0.9282 0.9244 0.9243 3080 |
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weighted avg 0.9282 0.9244 0.9243 3080 |
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</details> |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 1.9.0+cu111 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
