transformers pytorch xlm-roberta text-classification generated_from_trainer dataset:paws-x license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- paws-x
license: mit
metrics:
- accuracy
tags:
- text-classification
- generated_from_trainer
model-index:
- name: paws_x_xlm_r_only_ja
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: paws-x
      type: paws-x
      config: ja
      split: train
      args: ja
    metrics:
    - type: accuracy
      value: 0.8395
      name: Accuracy
---

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# paws_x_xlm_r_only_ja

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: 0.6163
- Accuracy: 0.8395

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.515         | 1.0   | 386  | 0.4769          | 0.794    |
| 0.3088        | 2.0   | 772  | 0.4256          | 0.8385   |
| 0.2416        | 3.0   | 1158 | 0.4412          | 0.8265   |
| 0.204         | 4.0   | 1544 | 0.4471          | 0.838    |
| 0.1689        | 5.0   | 1930 | 0.4369          | 0.8405   |
| 0.1424        | 6.0   | 2316 | 0.5206          | 0.838    |
| 0.121         | 7.0   | 2702 | 0.5247          | 0.8425   |
| 0.1061        | 8.0   | 3088 | 0.5708          | 0.843    |
| 0.092         | 9.0   | 3474 | 0.5840          | 0.838    |
| 0.0846        | 10.0  | 3860 | 0.6163          | 0.8395   |


### Framework versions

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