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# KR-Medium-NER-kluedata

This model is a fine-tuned version of [snunlp/KR-Medium](https://huggingface.co/snunlp/KR-Medium) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1809
- Precision: 0.7771
- Recall: 0.8131
- F1: 0.7947
- Accuracy: 0.9606

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 328
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2442        | 1.0   | 165  | 0.2390          | 0.5542    | 0.6426 | 0.5952 | 0.9211   |
| 0.1419        | 2.0   | 330  | 0.1694          | 0.7017    | 0.7143 | 0.7080 | 0.9461   |
| 0.1067        | 3.0   | 495  | 0.1402          | 0.7220    | 0.7894 | 0.7542 | 0.9550   |
| 0.0682        | 4.0   | 660  | 0.1431          | 0.7468    | 0.7990 | 0.7721 | 0.9573   |
| 0.0544        | 5.0   | 825  | 0.1440          | 0.7679    | 0.8082 | 0.7876 | 0.9596   |
| 0.0408        | 6.0   | 990  | 0.1525          | 0.7706    | 0.8014 | 0.7857 | 0.9594   |
| 0.0292        | 7.0   | 1155 | 0.1608          | 0.7703    | 0.8148 | 0.7919 | 0.9603   |
| 0.0241        | 8.0   | 1320 | 0.1706          | 0.7767    | 0.8099 | 0.7930 | 0.9604   |
| 0.0206        | 9.0   | 1485 | 0.1769          | 0.7805    | 0.8174 | 0.7985 | 0.9611   |
| 0.0177        | 10.0  | 1650 | 0.1809          | 0.7771    | 0.8131 | 0.7947 | 0.9606   |


### Framework versions

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

{"tags": ["generated_from_trainer"], "datasets": ["klue"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "KR-Medium-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": 0.7770999530736743, "name": "Precision"}, {"type": "recall", "value": 0.8130742793013958, "name": "Recall"}, {"type": "f1", "value": 0.7946801946939055, "name": "F1"}, {"type": "accuracy", "value": 0.9606297815722364, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:klue model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-klue #model-index #autotrain_compatible #endpoints_compatible #region-us 

KR-Medium-NER-kluedata
======================


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


* Loss: 0.1809
* Precision: 0.7771
* Recall: 0.8131
* F1: 0.7947
* Accuracy: 0.9606


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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 328
* 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: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 328
* 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 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 328
* 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 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 128
* eval\_batch\_size: 128
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 328
* 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