---
library_name: transformers
license: cc-by-sa-4.0
base_model: cl-tohoku/bert-base-japanese-whole-word-masking
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: test_trainer
  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. -->

# test_trainer

This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2071
- Accuracy: 0.7405

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.0935 | 200  | 0.2734          | 0.5675   |
| No log        | 0.1871 | 400  | 0.2632          | 0.5931   |
| 0.3013        | 0.2806 | 600  | 0.2381          | 0.6531   |
| 0.3013        | 0.3742 | 800  | 0.2324          | 0.6814   |
| 0.2601        | 0.4677 | 1000 | 0.2241          | 0.7087   |
| 0.2601        | 0.5613 | 1200 | 0.2163          | 0.7229   |
| 0.2601        | 0.6548 | 1400 | 0.2173          | 0.7299   |
| 0.2511        | 0.7484 | 1600 | 0.2115          | 0.7343   |
| 0.2511        | 0.8419 | 1800 | 0.2073          | 0.7387   |
| 0.2369        | 0.9355 | 2000 | 0.2071          | 0.7405   |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0