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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