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# 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

{"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}]}]}]}
transformers pytorch tensorboard safetensors xlm-roberta text-classification generated_from_trainer dataset:banking77 base_model:xlm-roberta-base license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #dataset-banking77 #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

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:



Training procedure
------------------


### 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



### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #dataset-banking77 #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #dataset-banking77 #base_model-xlm-roberta-base #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.21.1
* Pytorch 1.12.1+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1