---
license: mit
base_model: rinna/japanese-roberta-base
tags:
- generated_from_trainer
datasets:
- ja_qu_ad
model-index:
- name: rinna-roberta-qa-ja
  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. -->

# rinna-roberta-qa-ja

This model is a fine-tuned version of [rinna/japanese-roberta-base](https://huggingface.co/rinna/japanese-roberta-base) on the ja_qu_ad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6226

## 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: 7e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 9

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1602        | 0.47  | 150  | 0.9559          |
| 1.1212        | 0.95  | 300  | 0.7738          |
| 0.6935        | 1.42  | 450  | 0.8850          |
| 0.6329        | 1.9   | 600  | 0.5839          |
| 0.3988        | 2.37  | 750  | 1.0007          |
| 0.3905        | 2.85  | 900  | 0.9119          |
| 0.2245        | 3.32  | 1050 | 1.2299          |
| 0.2331        | 3.79  | 1200 | 1.2494          |
| 0.1556        | 4.27  | 1350 | 1.3691          |
| 0.1537        | 4.74  | 1500 | 1.2580          |
| 0.0811        | 5.22  | 1650 | 1.3939          |
| 0.1139        | 5.69  | 1800 | 1.1321          |
| 0.0623        | 6.17  | 1950 | 1.3723          |
| 0.1096        | 6.64  | 2100 | 1.1039          |
| 0.0622        | 7.11  | 2250 | 1.4657          |
| 0.0731        | 7.59  | 2400 | 1.5731          |
| 0.0482        | 8.06  | 2550 | 1.7190          |
| 0.0518        | 8.54  | 2700 | 1.6226          |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3