---
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
---

<!-- 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