---
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-rinnna-jp-jdrtsp-fw07sp-14
  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. -->

# hubert-rinnna-jp-jdrtsp-fw07sp-14

This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1685
- Wer: 0.2927
- Cer: 0.1710

## 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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 4.4197        | 1.0   | 404   | 4.0767          | 0.9928 | 0.9960 |
| 2.8984        | 2.0   | 808   | 2.7950          | 0.9928 | 0.9960 |
| 2.1179        | 3.0   | 1212  | 1.9178          | 0.9928 | 0.9960 |
| 1.4282        | 4.0   | 1616  | 1.0854          | 0.6262 | 0.4791 |
| 1.0793        | 5.0   | 2020  | 0.7672          | 0.4996 | 0.2944 |
| 0.9064        | 6.0   | 2424  | 0.6212          | 0.4573 | 0.2737 |
| 0.8366        | 7.0   | 2828  | 0.5247          | 0.4132 | 0.2450 |
| 0.7425        | 8.0   | 3232  | 0.4502          | 0.3786 | 0.2257 |
| 0.7017        | 9.0   | 3636  | 0.3912          | 0.3509 | 0.2082 |
| 0.6275        | 10.0  | 4040  | 0.3407          | 0.3328 | 0.1979 |
| 0.5853        | 11.0  | 4444  | 0.3045          | 0.3226 | 0.1920 |
| 0.5551        | 12.0  | 4848  | 0.2657          | 0.3139 | 0.1865 |
| 0.5105        | 13.0  | 5252  | 0.2455          | 0.3086 | 0.1827 |
| 0.5073        | 14.0  | 5656  | 0.2389          | 0.3092 | 0.1832 |
| 0.4722        | 15.0  | 6060  | 0.2170          | 0.3030 | 0.1781 |
| 0.481         | 16.0  | 6464  | 0.2089          | 0.3023 | 0.1783 |
| 0.4738        | 17.0  | 6868  | 0.2002          | 0.3004 | 0.1763 |
| 0.4518        | 18.0  | 7272  | 0.1990          | 0.3006 | 0.1765 |
| 0.4402        | 19.0  | 7676  | 0.1900          | 0.2999 | 0.1764 |
| 0.4387        | 20.0  | 8080  | 0.1826          | 0.2970 | 0.1740 |
| 0.4212        | 21.0  | 8484  | 0.1767          | 0.2955 | 0.1733 |
| 0.3893        | 22.0  | 8888  | 0.1707          | 0.2937 | 0.1719 |
| 0.4055        | 23.0  | 9292  | 0.1704          | 0.2943 | 0.1723 |
| 0.394         | 24.0  | 9696  | 0.1684          | 0.2934 | 0.1716 |
| 0.3997        | 25.0  | 10100 | 0.1685          | 0.2927 | 0.1710 |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3