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# korean-aihub-learning-math-1-test

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: 1.2537
- Wer: 0.4765

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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   | 35   | 29.8031         | 1.0    |
| No log        | 2.0   | 70   | 5.7158          | 1.0    |
| 19.8789       | 3.0   | 105  | 4.5005          | 1.0    |
| 19.8789       | 4.0   | 140  | 4.3677          | 0.9984 |
| 19.8789       | 5.0   | 175  | 3.8013          | 0.9882 |
| 3.9785        | 6.0   | 210  | 2.4132          | 0.8730 |
| 3.9785        | 7.0   | 245  | 1.5867          | 0.7045 |
| 3.9785        | 8.0   | 280  | 1.3179          | 0.6082 |
| 1.2266        | 9.0   | 315  | 1.2431          | 0.6066 |
| 1.2266        | 10.0  | 350  | 1.1791          | 0.5384 |
| 1.2266        | 11.0  | 385  | 1.0994          | 0.5298 |
| 0.3916        | 12.0  | 420  | 1.1552          | 0.5196 |
| 0.3916        | 13.0  | 455  | 1.1495          | 0.5486 |
| 0.3916        | 14.0  | 490  | 1.1340          | 0.5290 |
| 0.2488        | 15.0  | 525  | 1.2208          | 0.5525 |
| 0.2488        | 16.0  | 560  | 1.1682          | 0.5024 |
| 0.2488        | 17.0  | 595  | 1.1479          | 0.5008 |
| 0.1907        | 18.0  | 630  | 1.1735          | 0.4882 |
| 0.1907        | 19.0  | 665  | 1.2302          | 0.4914 |
| 0.1461        | 20.0  | 700  | 1.2497          | 0.4890 |
| 0.1461        | 21.0  | 735  | 1.2434          | 0.4914 |
| 0.1461        | 22.0  | 770  | 1.2031          | 0.5031 |
| 0.1147        | 23.0  | 805  | 1.2451          | 0.4976 |
| 0.1147        | 24.0  | 840  | 1.2746          | 0.4937 |
| 0.1147        | 25.0  | 875  | 1.2405          | 0.4828 |
| 0.0892        | 26.0  | 910  | 1.2228          | 0.4929 |
| 0.0892        | 27.0  | 945  | 1.2642          | 0.4898 |
| 0.0892        | 28.0  | 980  | 1.2586          | 0.4843 |
| 0.0709        | 29.0  | 1015 | 1.2518          | 0.4788 |
| 0.0709        | 30.0  | 1050 | 1.2537          | 0.4765 |


### Framework versions

- Transformers 4.20.1
- 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-1-test", "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-1-test
=================================


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: 1.2537
* Wer: 0.4765


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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* 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 4.20.1
* 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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* 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 4.20.1
* 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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* 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 4.20.1
* 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: 4
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 8
* 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 4.20.1
* Pytorch 1.12.0+cu113
* Datasets 2.4.0
* Tokenizers 0.12.1