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# KLUE-BERT-BASE-NER-data60

This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2994
- Precision: 0.9092
- Recall: 0.9252
- F1: 0.9171
- Accuracy: 0.9571

## 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
- lr_scheduler_warmup_steps: 3394
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2185        | 1.0   | 1698  | 0.1995          | 0.8549    | 0.8951 | 0.8745 | 0.9416   |
| 0.1782        | 2.0   | 3396  | 0.1777          | 0.8786    | 0.9120 | 0.8950 | 0.9489   |
| 0.1133        | 3.0   | 5094  | 0.1831          | 0.8990    | 0.9155 | 0.9072 | 0.9528   |
| 0.062         | 4.0   | 6792  | 0.2053          | 0.8971    | 0.9214 | 0.9091 | 0.9530   |
| 0.0396        | 5.0   | 8490  | 0.2135          | 0.9019    | 0.9207 | 0.9112 | 0.9535   |
| 0.0255        | 6.0   | 10188 | 0.2346          | 0.9049    | 0.9229 | 0.9138 | 0.9551   |
| 0.0159        | 7.0   | 11886 | 0.2536          | 0.9092    | 0.9232 | 0.9161 | 0.9563   |
| 0.0081        | 8.0   | 13584 | 0.2791          | 0.9100    | 0.9249 | 0.9174 | 0.9567   |
| 0.0037        | 9.0   | 15282 | 0.3040          | 0.9095    | 0.9252 | 0.9173 | 0.9565   |
| 0.0023        | 10.0  | 16980 | 0.2994          | 0.9092    | 0.9252 | 0.9171 | 0.9571   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "KLUE-BERT-BASE-NER-data60", "results": []}]}
transformers pytorch bert token-classification generated_from_trainer license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 

KLUE-BERT-BASE-NER-data60
=========================


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


* Loss: 0.2994
* Precision: 0.9092
* Recall: 0.9252
* F1: 0.9171
* Accuracy: 0.9571


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
* lr\_scheduler\_warmup\_steps: 3394
* num\_epochs: 10
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### 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
* lr\_scheduler\_warmup\_steps: 3394
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #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
* lr\_scheduler\_warmup\_steps: 3394
* num\_epochs: 10
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #license-cc-by-sa-4.0 #autotrain_compatible #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
* lr\_scheduler\_warmup\_steps: 3394
* num\_epochs: 10
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2