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# korean-aihub-learning-math-8batch

This model is a fine-tuned version of [kresnik/wav2vec2-large-xlsr-korean](https://huggingface.co/kresnik/wav2vec2-large-xlsr-korean) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1867
- Wer: 0.5315

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 1.0   | 20   | 33.1529         | 1.0    |
| No log        | 2.0   | 40   | 28.0161         | 1.0    |
| No log        | 3.0   | 60   | 8.7324          | 1.0    |
| No log        | 4.0   | 80   | 4.9786          | 1.0    |
| 21.6269       | 5.0   | 100  | 4.5335          | 1.0    |
| 21.6269       | 6.0   | 120  | 4.4517          | 1.0    |
| 21.6269       | 7.0   | 140  | 4.4068          | 1.0    |
| 21.6269       | 8.0   | 160  | 4.3210          | 1.0    |
| 21.6269       | 9.0   | 180  | 4.0041          | 0.9932 |
| 4.1788        | 10.0  | 200  | 3.0921          | 0.9712 |
| 4.1788        | 11.0  | 220  | 2.1650          | 0.8603 |
| 4.1788        | 12.0  | 240  | 1.6135          | 0.7192 |
| 4.1788        | 13.0  | 260  | 1.3842          | 0.6466 |
| 4.1788        | 14.0  | 280  | 1.2872          | 0.5918 |
| 1.205         | 15.0  | 300  | 1.2234          | 0.5808 |
| 1.205         | 16.0  | 320  | 1.2694          | 0.6    |
| 1.205         | 17.0  | 340  | 1.2287          | 0.5575 |
| 1.205         | 18.0  | 360  | 1.1776          | 0.5877 |
| 1.205         | 19.0  | 380  | 1.2418          | 0.5671 |
| 0.2825        | 20.0  | 400  | 1.2469          | 0.5616 |
| 0.2825        | 21.0  | 420  | 1.2203          | 0.5425 |
| 0.2825        | 22.0  | 440  | 1.2270          | 0.5863 |
| 0.2825        | 23.0  | 460  | 1.1930          | 0.5548 |
| 0.2825        | 24.0  | 480  | 1.1242          | 0.5521 |
| 0.1831        | 25.0  | 500  | 1.2245          | 0.5575 |
| 0.1831        | 26.0  | 520  | 1.2276          | 0.5342 |
| 0.1831        | 27.0  | 540  | 1.1641          | 0.5205 |
| 0.1831        | 28.0  | 560  | 1.1727          | 0.5329 |
| 0.1831        | 29.0  | 580  | 1.1885          | 0.5534 |
| 0.14          | 30.0  | 600  | 1.1867          | 0.5315 |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "korean-aihub-learning-math-8batch", "results": []}]}
transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 

korean-aihub-learning-math-8batch
=================================


This model is a fine-tuned version of kresnik/wav2vec2-large-xlsr-korean on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.1867
* Wer: 0.5315


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: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 16
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 500
* num\_epochs: 30
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.21.0
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1