---
license: mit
tags:
- text-classification
- generated_from_trainer
datasets:
- paws-x
metrics:
- accuracy
model-index:
- name: paws_x_xlm_r_only_ko
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: paws-x
      type: paws-x
      config: ko
      split: train
      args: ko
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8455
---

<!-- 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. -->

# paws_x_xlm_r_only_ko

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the paws-x dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5742
- Accuracy: 0.8455

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5604        | 1.0   | 386  | 0.5049          | 0.779    |
| 0.356         | 2.0   | 772  | 0.4272          | 0.8235   |
| 0.2846        | 3.0   | 1158 | 0.3901          | 0.835    |
| 0.2375        | 4.0   | 1544 | 0.4433          | 0.8395   |
| 0.2           | 5.0   | 1930 | 0.4665          | 0.8375   |
| 0.1717        | 6.0   | 2316 | 0.4815          | 0.8385   |
| 0.1487        | 7.0   | 2702 | 0.4973          | 0.8445   |
| 0.1288        | 8.0   | 3088 | 0.5234          | 0.8365   |
| 0.1158        | 9.0   | 3474 | 0.5528          | 0.844    |
| 0.1061        | 10.0  | 3860 | 0.5742          | 0.8455   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1