---
license: mit
language:
- ja
tags:
- generated_from_trainer
- ja_qu_ad
- bert
datasets: SkelterLabsInc/JaQuAD
widget:
- text: どこへ出かけた？
  context: 2015年9月1日、私は横浜へ車で出かけました。映画を観た後に中華街まで電車で行き、昼ご飯は重慶飯店で中華フルコースを食べました。
model-index:
- name: xlm-roberta-base-finetuned-JaQuAD
  results: []
---

<!-- 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-finetuned-JaQuAD

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the ja_qu_ad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7495

## Model description

More information needed

## Intended uses & limitations

```python
import torch
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
```

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8661        | 1.0   | 1985 | 0.8036          |
| 0.5348        | 2.0   | 3970 | 0.7495          |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.13.1
- Tokenizers 0.13.3