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---
license: cc-by-sa-4.0
base_model: klue/bert-base
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: klue_ner_bert_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: klue
type: klue
config: ner
split: validation
args: ner
metrics:
- name: Precision
type: precision
value: 0.883861132284665
- name: Recall
type: recall
value: 0.8966608084358524
- name: F1
type: f1
value: 0.890214963707426
- name: Accuracy
type: accuracy
value: 0.9781297871646948
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# klue_ner_bert_model
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: 0.0843
- Precision: 0.8839
- Recall: 0.8967
- F1: 0.8902
- Accuracy: 0.9781
## Model description
KLUE BERT base is a pre-trained BERT Model on Korean Language. The developers of KLUE BERT base developed the model in the context of the development of the [Korean Language Understanding Evaluation (KLUE) Benchmark](https://arxiv.org/pdf/2105.09680.pdf).
## Intended uses & limitations
## How to Get Started With the Model
```python
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("klue/bert-base")
tokenizer = AutoTokenizer.from_pretrained("klue/bert-base")
```
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0638 | 1.0 | 2626 | 0.0807 | 0.8623 | 0.8702 | 0.8662 | 0.9747 |
| 0.0402 | 2.0 | 5252 | 0.0780 | 0.8756 | 0.8896 | 0.8825 | 0.9770 |
| 0.025 | 3.0 | 7878 | 0.0843 | 0.8839 | 0.8967 | 0.8902 | 0.9781 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.0
- Tokenizers 0.13.3
|