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# KLUE-BERT-BASE-NER-kluedata

This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2098
- Precision: 0.7925
- Recall: 0.8169
- F1: 0.8045
- Accuracy: 0.9598

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 656
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2252        | 1.0   | 329  | 0.2217          | 0.5880    | 0.6798 | 0.6306 | 0.9262   |
| 0.1414        | 2.0   | 658  | 0.1665          | 0.7082    | 0.7468 | 0.7270 | 0.9476   |
| 0.0993        | 3.0   | 987  | 0.1469          | 0.7405    | 0.7873 | 0.7632 | 0.9542   |
| 0.0617        | 4.0   | 1316 | 0.1522          | 0.7534    | 0.8149 | 0.7830 | 0.9556   |
| 0.0448        | 5.0   | 1645 | 0.1630          | 0.7804    | 0.8042 | 0.7922 | 0.9585   |
| 0.0321        | 6.0   | 1974 | 0.1765          | 0.7811    | 0.8173 | 0.7988 | 0.9586   |
| 0.0227        | 7.0   | 2303 | 0.1810          | 0.7871    | 0.8136 | 0.8001 | 0.9594   |
| 0.017         | 8.0   | 2632 | 0.1929          | 0.7895    | 0.8176 | 0.8033 | 0.9603   |
| 0.0147        | 9.0   | 2961 | 0.1983          | 0.7956    | 0.8196 | 0.8074 | 0.9601   |
| 0.0114        | 10.0  | 3290 | 0.2098          | 0.7925    | 0.8169 | 0.8045 | 0.9598   |


### 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"], "datasets": ["klue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "KLUE-BERT-BASE-NER-kluedata", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "klue", "type": "klue", "config": "ner", "split": "validation", "args": "ner"}, "metrics": [{"type": "precision", "value": 0.7925285792052259, "name": "Precision"}, {"type": "recall", "value": 0.8169320333871081, "name": "Recall"}, {"type": "f1", "value": 0.8045452975512036, "name": "F1"}, {"type": "accuracy", "value": 0.9598270318217097, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:klue license:cc-by-sa-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-klue #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

KLUE-BERT-BASE-NER-kluedata
===========================


This model is a fine-tuned version of klue/bert-base on the klue dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2098
* Precision: 0.7925
* Recall: 0.8169
* F1: 0.8045
* Accuracy: 0.9598


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 656
* 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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 656
* 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 #dataset-klue #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 656
* 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 #dataset-klue #license-cc-by-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 656
* 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