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# kobert-finetuned-klue-v2

This model is a fine-tuned version of [monologg/kobert](https://huggingface.co/monologg/kobert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.2678

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.6289        | 0.54  | 500  | 5.3024          |
| 5.3083        | 1.08  | 1000 | 5.3707          |
| 5.3518        | 1.62  | 1500 | 5.3039          |
| 5.2912        | 2.16  | 2000 | 5.2800          |
| 5.2282        | 2.7   | 2500 | 5.2301          |
| 5.1498        | 3.24  | 3000 | 5.2435          |
| 5.079         | 3.78  | 3500 | 5.1997          |
| 4.8886        | 4.32  | 4000 | 5.1350          |
| 4.8166        | 4.86  | 4500 | 5.1441          |
| 4.5615        | 5.4   | 5000 | 5.1485          |
| 4.4183        | 5.94  | 5500 | 5.0775          |
| 4.1282        | 6.48  | 6000 | 5.0402          |
| 4.1214        | 7.02  | 6500 | 5.1331          |
| 3.7792        | 7.56  | 7000 | 5.1314          |
| 3.7455        | 8.1   | 7500 | 5.1936          |
| 3.5369        | 8.64  | 8000 | 5.1931          |
| 3.4832        | 9.18  | 8500 | 5.2678          |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"tags": ["generated_from_trainer"], "model-index": [{"name": "kobert-finetuned-klue-v2", "results": []}]}
transformers pytorch bert question-answering generated_from_trainer endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us 

kobert-finetuned-klue-v2
========================


This model is a fine-tuned version of monologg/kobert on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 5.2678


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.10.0+cu111
* Datasets 2.0.0
* Tokenizers 0.11.6