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---
license: mit
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-banking77-classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9321428571428572
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 posso pagare con la mia carta di credito
example_title: Card Not Working Example - Italian
---
<!-- 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
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## 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
| 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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