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---
license: mit
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
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
base_model: xlm-roberta-base
model-index:
- name: xlm-roberta-base-banking77-classification
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: banking77
type: banking77
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.9321428571428572
name: Accuracy
- task:
type: text-classification
name: Text Classification
dataset:
name: banking77
type: banking77
config: default
split: test
metrics:
- type: accuracy
value: 0.9321428571428572
name: Accuracy
verified: true
- type: precision
value: 0.9339627666926148
name: Precision Macro
verified: true
- type: precision
value: 0.9321428571428572
name: Precision Micro
verified: true
- type: precision
value: 0.9339627666926148
name: Precision Weighted
verified: true
- type: recall
value: 0.9321428571428572
name: Recall Macro
verified: true
- type: recall
value: 0.9321428571428572
name: Recall Micro
verified: true
- type: recall
value: 0.9321428571428572
name: Recall Weighted
verified: true
- type: f1
value: 0.9320514513719953
name: F1 Macro
verified: true
- type: f1
value: 0.9321428571428572
name: F1 Micro
verified: true
- type: f1
value: 0.9320514513719956
name: F1 Weighted
verified: true
- type: loss
value: 0.30337899923324585
name: loss
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-banking77-classification
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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](https://huggingface.co/datasets/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
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