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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google-bert/bert-base-uncased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: BERT-BankingClassifier |
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results: [] |
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datasets: |
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- legacy-datasets/banking77 |
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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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# BERT-BankingClassifier |
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This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2867 |
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- Accuracy: 0.9326 |
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## Model description |
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This model is a BERT (bert-base-uncased) model fine-tuned using the Banking77 dataset for text classification tasks. Banking77 is a dataset designed to categorize customer support messages in the banking and finance domain into 77 distinct categories. This model is specifically tailored for classifying customer complaints and inquiries in the banking sector, helping to assign messages to the correct categories effectively. |
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Fine-Tuning Process : https://github.com/saribasmetehan/bank_administrative_assistant/blob/main/BERT_BankingClassifier.ipynb |
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| Label Index | Intent Category | |
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|-------------|--------------------------------------------------| |
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| 0 | activate_my_card | |
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| 1 | age_limit | |
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| 2 | apple_pay_or_google_pay | |
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| 3 | atm_support | |
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| 4 | automatic_top_up | |
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| 5 | balance_not_updated_after_bank_transfer | |
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| 6 | balance_not_updated_after_cheque_or_cash_deposit| |
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| 7 | beneficiary_not_allowed | |
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| 8 | cancel_transfer | |
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| 9 | card_about_to_expire | |
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| 10 | card_acceptance | |
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| 11 | card_arrival | |
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| 12 | card_delivery_estimate | |
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| 13 | card_linking | |
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| 14 | card_not_working | |
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| 15 | card_payment_fee_charged | |
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| 16 | card_payment_not_recognised | |
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| 17 | card_payment_wrong_exchange_rate | |
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| 18 | card_swallowed | |
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| 19 | cash_withdrawal_charge | |
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| 20 | cash_withdrawal_not_recognised | |
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| 21 | change_pin | |
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| 22 | compromised_card | |
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| 23 | contactless_not_working | |
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| 24 | country_support | |
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| 25 | declined_card_payment | |
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| 26 | declined_cash_withdrawal | |
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| 27 | declined_transfer | |
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| 28 | direct_debit_payment_not_recognised | |
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| 29 | disposable_card_limits | |
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| 30 | edit_personal_details | |
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| 31 | exchange_charge | |
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| 32 | exchange_rate | |
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| 33 | exchange_via_app | |
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| 34 | extra_charge_on_statement | |
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| 35 | failed_transfer | |
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| 36 | fiat_currency_support | |
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| 37 | get_disposable_virtual_card | |
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| 38 | get_physical_card | |
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| 39 | getting_spare_card | |
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| 40 | getting_virtual_card | |
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| 41 | lost_or_stolen_card | |
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| 42 | lost_or_stolen_phone | |
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| 43 | order_physical_card | |
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| 44 | passcode_forgotten | |
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| 45 | pending_card_payment | |
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| 46 | pending_cash_withdrawal | |
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| 47 | pending_top_up | |
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| 48 | pending_transfer | |
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| 49 | pin_blocked | |
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| 50 | receiving_money | |
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| 51 | refund_not_showing_up | |
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| 52 | request_refund | |
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| 53 | reverted_card_payment | |
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| 54 | supported_cards_and_currencies | |
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| 55 | terminate_account | |
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| 56 | top_up_by_bank_transfer_charge | |
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| 57 | top_up_by_card_charge | |
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| 58 | top_up_by_cash_or_cheque | |
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| 59 | top_up_failed | |
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| 60 | top_up_limits | |
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| 61 | top_up_reverted | |
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| 62 | topping_up_by_card | |
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| 63 | transaction_charged_twice | |
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| 64 | transfer_fee_charged | |
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| 65 | transfer_into_account | |
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| 66 | transfer_not_received_by_recipient | |
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| 67 | transfer_timing | |
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| 68 | unable_to_verify_identity | |
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| 69 | verify_my_identity | |
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| 70 | verify_source_of_funds | |
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| 71 | verify_top_up | |
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| 72 | virtual_card_not_working | |
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| 73 | visa_or_mastercard | |
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| 74 | why_verify_identity | |
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| 75 | wrong_amount_of_cash_received | |
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| 76 | wrong_exchange_rate_for_cash_withdrawal | |
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## Example |
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```markdown |
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from transformers import pipeline, AutoTokenizer |
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text = "i forgot my PIN" |
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model_id = "saribasmetehan/BERT-BankingClassifier" |
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classifier = pipeline("text-classification", model=model_id) |
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preds = classifier(text) |
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print(preds) |
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#[{'label': 'LABEL_27', 'score': 0.9552194476127625}] |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-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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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.5368 | 1.0 | 732 | 0.5945 | 0.8862 | |
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| 0.6013 | 2.0 | 1464 | 0.3156 | 0.9283 | |
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| 0.1507 | 3.0 | 2196 | 0.2895 | 0.9290 | |
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| 0.089 | 4.0 | 2928 | 0.2867 | 0.9326 | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
